diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..ff01c6627fe26d214b02b8fb7ba0746e22ffcac5 Binary files /dev/null and b/.DS_Store differ diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..9460417ec47d252f8c66da445b20a52a027c5b3e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,25 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +simulation_output/8801c5f6d6194430_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/1f6f5cd4cdf143a9_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/d6f7b9a72c6d4fe6_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/9e29ed31e4bc4f24_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/bd1ac9bace5e44fe_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/f3fade75331f4fa4_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/7dc162efcc9e4d70_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/221396834e1143f5_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/7fcfb634a7a94875_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/ef69fd6843084d2f_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/f605de6cfac343ca_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/20b69cdb1c674c50_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/992d922e19844b91_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/75c69a8f3f734305_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/9d584066592f464d_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/df33d9806b2f42ff_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/20c1bf21f50f47d4_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/3efc336d759a4cd1_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/ce52bc34661f4b71_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/41cba5875d8f4d46_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/c972e4d8af854198_simulation_output.nc filter=lfs diff=lfs merge=lfs -text +simulation_output/a0c8ebc0401040be_simulation_output.nc filter=lfs diff=lfs merge=lfs -text diff --git a/level1a_part0.json b/level1a_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..8fc714cf9469af7ec111c3da02b062dfc4277c4f --- /dev/null +++ b/level1a_part0.json @@ -0,0 +1,3474 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35272:35281:1'} The data starts from February 22 00:00 and ends on February 24 00:00. Based on the above data, answer the following question:", + "question": "What is the average Surface temperature in English Channel?", + "response": "Based on the provided data, the average Surface temperature at English Channel is 279 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "English Channel", + "operation": "average", + "variable": "2m_temperature", + "true_value": "278.97344768290026", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3dbee68c2a292eac", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35272:35281:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34837:34861:1'} The data starts from November 05 06:00 and ends on November 11 00:00. Based on the above data, answer the following question:", + "question": "Which fjord has the lowest recorded 10-meter U component of wind?", + "response": "Based on the provided data, Boknafjorden has the lowest recorded 10-meter U component of wind among the fjords with the lowest recorded 10-meter U component of wind of -13.66 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Boknafjorden", + "true_value": "-13.657798767089844", + "geofeature": "fjord", + "upper_level_location": "fjord", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0c93069b9b4b5207", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34837:34861:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47160:47162:1'} The data starts from April 13 00:00 and ends on April 13 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 500 hPa in Gulf of Kamchatka?", + "response": "Based on the provided data, the minimum U (zonal) component of wind at 500 hPa at Gulf of Kamchatka is 2.23 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Gulf of Kamchatka", + "operation": "minimum", + "variable": "u_component_of_wind_500", + "true_value": "2.2300827503204346", + "target_variable": "u_component_of_wind_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d5b123a959975a5d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47160:47162:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67039:67054:1'} The data starts from November 19 18:00 and ends on November 23 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum Temperature at 300 hPa in Nauru?", + "response": "Based on the provided data, the minimum Temperature at 300 hPa at Nauru is 242 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Nauru", + "operation": "minimum", + "variable": "temperature_300", + "true_value": "241.9889678955078", + "target_variable": "temperature_300", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bb44ab06aa846626", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67039:67054:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40545:40569:1'} The data starts from October 02 06:00 and ends on October 08 00:00. Based on the above data, answer the following question:", + "question": "Which state in Libya has the highest recorded V (meridional) component of wind at 50 hPa?", + "response": "Based on the provided data, Ajdabiya has the highest recorded V (meridional) component of wind at 50 hPa among the states in Libya with the highest recorded V (meridional) component of wind at 50 hPa of 8.933 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Ajdabiya, Libya", + "true_value": "8.933426856994629", + "geofeature": "state", + "upper_level_location": "Libya", + "target_variable": "v_component_of_wind_50", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "a54e33ea350931de", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40545:40569:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62521:62548:1'} The data starts from October 17 06:00 and ends on October 23 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface temperature in South America?", + "response": "Based on the provided data, the maximum Surface temperature at South America is 311.5 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "South America", + "operation": "maximum", + "variable": "2m_temperature", + "true_value": "311.45892333984375", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a5e88a7ce7335fd4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62521:62548:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30737:30744:1'} The data starts from January 15 06:00 and ends on January 16 18:00. Based on the above data, answer the following question:", + "question": "Which fjord has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Storfjorden has the lowest recorded Mean sea level pressure among the fjords with the lowest recorded Mean sea level pressure of 9.868e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Storfjorden", + "true_value": "98682.59375", + "geofeature": "fjord", + "upper_level_location": "fjord", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "018c410b53b1a2cd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30737:30744:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60651:60670:1'} The data starts from July 06 18:00 and ends on July 11 06:00. Based on the above data, answer the following question:", + "question": "Which state in Mozambique has the highest recorded V (meridional) component of wind at 250 hPa?", + "response": "Based on the provided data, Zambezia has the highest recorded V (meridional) component of wind at 250 hPa among the states in Mozambique with the highest recorded V (meridional) component of wind at 250 hPa of 16.77 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Zambezia, Mozambique", + "true_value": "16.771371841430664", + "geofeature": "state", + "upper_level_location": "Mozambique", + "target_variable": "v_component_of_wind_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "b9b51b09f0d61a9a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60651:60670:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67718:67721:1'} The data starts from May 08 12:00 and ends on May 09 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface pressure in Gulf of Ob?", + "response": "Based on the provided data, the maximum Surface pressure at Gulf of Ob is 1.029e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Gulf of Ob", + "operation": "maximum", + "variable": "surface_pressure", + "true_value": "102910.046875", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "25152c66ec016134", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67718:67721:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72150:72170:1'} The data starts from May 20 12:00 and ends on May 25 06:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind in Kaberamaido, Uganda?", + "response": "Based on the provided data, the median 10-meter V component of wind at Kaberamaido, Uganda is 1.202 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Kaberamaido, Uganda", + "operation": "median", + "variable": "10m_v_component_of_wind", + "true_value": "1.202087640762329", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "62a12b8df027b1fc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72150:72170:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41138:41161:1'} The data starts from February 27 12:00 and ends on March 05 00:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 500 hPa in Gambela Peoples, Ethiopia?", + "response": "Based on the provided data, the average V (meridional) component of wind at 500 hPa at Gambela Peoples, Ethiopia is 1.206 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Gambela Peoples, Ethiopia", + "operation": "average", + "variable": "v_component_of_wind_500", + "true_value": "1.2062357927962355", + "target_variable": "v_component_of_wind_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e45ec81d09ab7ba7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41138:41161:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56582:56593:1'} The data starts from September 23 12:00 and ends on September 26 00:00. Based on the above data, answer the following question:", + "question": "What is the average Specific humidity at 925 hPa in Eritrea?", + "response": "Based on the provided data, the average Specific humidity at 925 hPa at Eritrea is 0.01062 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Eritrea", + "operation": "average", + "variable": "specific_humidity_925", + "true_value": "0.01061784986207199", + "target_variable": "specific_humidity_925", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ef85964718a511d7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56582:56593:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75201:75220:1'} The data starts from June 22 06:00 and ends on June 26 18:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 600 hPa in Ionian Sea?", + "response": "Based on the provided data, the average V (meridional) component of wind at 600 hPa at Ionian Sea is -6.548 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Ionian Sea", + "operation": "average", + "variable": "v_component_of_wind_600", + "true_value": "-6.5475905251579976", + "target_variable": "v_component_of_wind_600", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8f7116b17ccfed61", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75201:75220:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81611:81619:1'} The data starts from November 10 18:00 and ends on November 12 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Mean sea level pressure?", + "response": "Based on the provided data, Antarctica experienced the lowest average Mean sea level pressure over the specified time-period, with an average Mean sea level pressure of 1.006e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "100574.36824923946", + "geofeature": "continent", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "79fb71dfe2990b7f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81611:81619:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48352:48369:1'} The data starts from February 05 00:00 and ends on February 09 00:00. Based on the above data, answer the following question:", + "question": "Which state in Trinidad and Tobago has the highest recorded Mean sea level pressure?", + "response": "Based on the provided data, Eastern Tobago has the highest recorded Mean sea level pressure among the states in Trinidad and Tobago with the highest recorded Mean sea level pressure of 1.014e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Eastern Tobago, Trinidad and Tobago", + "true_value": "101416.9140625", + "geofeature": "state", + "upper_level_location": "Trinidad and Tobago", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ad235a0d10d2e32a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48352:48369:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59330:59338:1'} The data starts from August 11 12:00 and ends on August 13 06:00. Based on the above data, answer the following question:", + "question": "Which country in Africa has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Mauritania has the lowest recorded Mean sea level pressure among the countries in Africa with the lowest recorded Mean sea level pressure of 1.002e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Mauritania", + "true_value": "100175.921875", + "geofeature": "country", + "upper_level_location": "Africa", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5dbebd610764762a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59330:59338:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34100:34110:1'} The data starts from May 05 00:00 and ends on May 07 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Kangertittivaq experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 9.177e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Kangertittivaq", + "true_value": "91765.86642497269", + "geofeature": "water_body", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "9c32dbc4fb0cb79f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34100:34110:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53636:53655:1'} The data starts from September 18 00:00 and ends on September 22 12:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Specific humidity at 250 hPa?", + "response": "Based on the provided data, South Georgia and the Islands experienced the lowest average Specific humidity at 250 hPa over the specified time-period, with an average Specific humidity at 250 hPa of 9.66e-06 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "South Georgia and the Islands", + "true_value": "9.66003683786764e-06", + "geofeature": "country", + "target_variable": "specific_humidity_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "4d6688058a3472b7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53636:53655:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51307:51324:1'} The data starts from February 12 18:00 and ends on February 16 18:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Surface temperature?", + "response": "Based on the provided data, Greenland experienced the lowest average Surface temperature over the specified time-period, with an average Surface temperature of 241.4 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Greenland", + "true_value": "241.3562090603574", + "geofeature": "country", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "a7f132fd2047b44f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51307:51324:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83954:83964:1'} The data starts from June 18 12:00 and ends on June 20 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Surface temperature?", + "response": "Based on the provided data, Antarctica experienced the lowest average Surface temperature over the specified time-period, with an average Surface temperature of 227.1 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "227.09315219020903", + "geofeature": "continent", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "113c3f1596413272", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83954:83964:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92410:92433:1'} The data starts from April 02 12:00 and ends on April 08 00:00. Based on the above data, answer the following question:", + "question": "Which gulf has the highest recorded Temperature at 200 hPa?", + "response": "Based on the provided data, Amundsen Gulf has the highest recorded Temperature at 200 hPa among the gulfs with the highest recorded Temperature at 200 hPa of 236.5 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Amundsen Gulf", + "true_value": "236.45875549316406", + "geofeature": "gulf", + "upper_level_location": "gulf", + "target_variable": "temperature_200", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "908ca87977ecfde5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92410:92433:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64930:64944:1'} The data starts from June 11 12:00 and ends on June 14 18:00. Based on the above data, answer the following question:", + "question": "Which state in Ghana has the lowest recorded Specific humidity at 850 hPa?", + "response": "Based on the provided data, Western has the lowest recorded Specific humidity at 850 hPa among the states in Ghana with the lowest recorded Specific humidity at 850 hPa of 0.009853 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Western, Ghana", + "true_value": "0.009852507151663303", + "geofeature": "state", + "upper_level_location": "Ghana", + "target_variable": "specific_humidity_850", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "160341b4136d0257", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64930:64944:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60306:60316:1'} The data starts from April 11 12:00 and ends on April 13 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Antarctica experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 6.842e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "68421.35441300635", + "geofeature": "continent", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "dad3b6ab70e93f2a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60306:60316:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31353:31372:1'} The data starts from June 17 06:00 and ends on June 21 18:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the lowest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Norway has the lowest recorded 10-meter V component of wind among the countries in Europe with the lowest recorded 10-meter V component of wind of -15.66 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Norway", + "true_value": "-15.66464614868164", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "8fefcb7ba5764b24", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31353:31372:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31023:31040:1'} The data starts from March 26 18:00 and ends on March 30 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average 10-meter U component of wind?", + "response": "Based on the provided data, Oceania experienced the lowest average 10-meter U component of wind over the specified time-period, with an average 10-meter U component of wind of -1.059 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "-1.0590915666943204", + "geofeature": "continent", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "9ef762f390ecc5c7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31023:31040:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41107:41129:1'} The data starts from February 19 18:00 and ends on February 25 00:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average Specific humidity at 250 hPa?", + "response": "Based on the provided data, Joseph Bonaparte Gulf experienced the highest average Specific humidity at 250 hPa over the specified time-period, with an average Specific humidity at 250 hPa of 0.0003149 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Joseph Bonaparte Gulf", + "true_value": "0.00031494372887749567", + "geofeature": "water_body", + "target_variable": "specific_humidity_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "610a31bb3903c0ba", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41107:41129:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43529:43552:1'} The data starts from October 17 06:00 and ends on October 22 18:00. Based on the above data, answer the following question:", + "question": "What is the average Surface pressure in Europe?", + "response": "Based on the provided data, the average Surface pressure at Europe is 9.722e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Europe", + "operation": "average", + "variable": "surface_pressure", + "true_value": "97216.28142974769", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8eb9281acfa310f7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43529:43552:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66767:66773:1'} The data starts from September 12 18:00 and ends on September 14 00:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Siachen Glacier experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 5.72e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Siachen Glacier", + "true_value": "57204.370066502495", + "geofeature": "country", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "65200f3f3a46c75b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66767:66773:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35953:35970:1'} The data starts from August 11 06:00 and ends on August 15 06:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Antarctica experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 6.737e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "67374.80356175404", + "geofeature": "continent", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "3a9cfc9f58a75390", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35953:35970:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68347:68348:1'} The data corresponds to corresponds to a snapshot on October 12 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Surface pressure?", + "response": "Based on the provided data, Oceania experienced the highest average Surface pressure over the specified time-period, with an average Surface pressure of 9.814e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "98140.5543024372", + "geofeature": "continent", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "cb1f3290bdd1e568", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68347:68348:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84956:84981:1'} The data starts from February 24 00:00 and ends on March 02 00:00. Based on the above data, answer the following question:", + "question": "Which sea has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Greenland Sea has the lowest recorded Mean sea level pressure among the seas with the lowest recorded Mean sea level pressure of 9.479e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Greenland Sea", + "true_value": "94790.7890625", + "geofeature": "sea", + "upper_level_location": "sea", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "89aa22c4ff2b751a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84956:84981:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50167:50188:1'} The data starts from May 03 18:00 and ends on May 08 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 400 hPa in Asia?", + "response": "Based on the provided data, the minimum V (meridional) component of wind at 400 hPa at Asia is -49.87 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Asia", + "operation": "minimum", + "variable": "v_component_of_wind_400", + "true_value": "-49.87015151977539", + "target_variable": "v_component_of_wind_400", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3f237be95848396d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50167:50188:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46646:46656:1'} The data starts from December 05 12:00 and ends on December 07 18:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Siachen Glacier experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 5.659e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Siachen Glacier", + "true_value": "56594.68706550612", + "geofeature": "country", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "06161c734a0b067c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46646:46656:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53826:53830:1'} The data starts from November 04 12:00 and ends on November 05 06:00. Based on the above data, answer the following question:", + "question": "Which channel has the lowest recorded Geopotential at 600 hPa?", + "response": "Based on the provided data, Ronne Entrance has the lowest recorded Geopotential at 600 hPa among the channels with the lowest recorded Geopotential at 600 hPa of 3.521e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Ronne Entrance", + "true_value": "35205.93359375", + "geofeature": "channel", + "upper_level_location": "channel", + "target_variable": "geopotential_600", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "15135eb1f56c795e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53826:53830:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91360:91380:1'} The data starts from July 14 00:00 and ends on July 18 18:00. Based on the above data, answer the following question:", + "question": "Which channel has the highest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Mozambique Channel has the highest recorded 10-meter V component of wind among the channels with the highest recorded 10-meter V component of wind of 12.7 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Mozambique Channel", + "true_value": "12.703763008117676", + "geofeature": "channel", + "upper_level_location": "channel", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "98f5a7952ae22f1f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91360:91380:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54656:54670:1'} The data starts from May 30 00:00 and ends on June 02 06:00. Based on the above data, answer the following question:", + "question": "What is the median Surface pressure in Amundsen Sea?", + "response": "Based on the provided data, the median Surface pressure at Amundsen Sea is 9.719e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Amundsen Sea", + "operation": "median", + "variable": "surface_pressure", + "true_value": "97194.4609375", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "51ba0549e0270536", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54656:54670:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82390:82414:1'} The data starts from May 24 12:00 and ends on May 30 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average U (zonal) component of wind at 300 hPa?", + "response": "Based on the provided data, James Bay experienced the highest average U (zonal) component of wind at 300 hPa over the specified time-period, with an average U (zonal) component of wind at 300 hPa of 38.82 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "James Bay", + "true_value": "38.82438364115072", + "geofeature": "water_body", + "target_variable": "u_component_of_wind_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "3e1543dc59b1c3d6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82390:82414:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40631:40647:1'} The data starts from October 23 18:00 and ends on October 27 12:00. Based on the above data, answer the following question:", + "question": "Which country in North America has the lowest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Canada has the lowest recorded 10-meter V component of wind among the countries in North America with the lowest recorded 10-meter V component of wind of -17.58 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Canada", + "true_value": "-17.58201789855957", + "geofeature": "country", + "upper_level_location": "North America", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5e214de74719244e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40631:40647:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44505:44528:1'} The data starts from June 18 06:00 and ends on June 23 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average 10-meter V component of wind?", + "response": "Based on the provided data, Oceania experienced the highest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of 2.246 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "2.2461849990752327", + "geofeature": "continent", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "71f4408857fd41c7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44505:44528:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67007:67012:1'} The data starts from November 11 18:00 and ends on November 12 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average V (meridional) component of wind at 500 hPa?", + "response": "Based on the provided data, Africa experienced the highest average V (meridional) component of wind at 500 hPa over the specified time-period, with an average V (meridional) component of wind at 500 hPa of 0.3769 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Africa", + "true_value": "0.37685618936814563", + "geofeature": "continent", + "target_variable": "v_component_of_wind_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "72ef3d9b91b77232", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67007:67012:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67396:67424:1'} The data starts from February 17 00:00 and ends on February 23 18:00. Based on the above data, answer the following question:", + "question": "What is the average Surface temperature in Nicaragua?", + "response": "Based on the provided data, the average Surface temperature at Nicaragua is 298 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Nicaragua", + "operation": "average", + "variable": "2m_temperature", + "true_value": "297.9703432150163", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8829386912f6f088", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67396:67424:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65187:65195:1'} The data starts from August 14 18:00 and ends on August 16 12:00. Based on the above data, answer the following question:", + "question": "Which country in South America has the highest recorded V (meridional) component of wind at 925 hPa?", + "response": "Based on the provided data, Chile has the highest recorded V (meridional) component of wind at 925 hPa among the countries in South America with the highest recorded V (meridional) component of wind at 925 hPa of 17.3 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Chile", + "true_value": "17.30115509033203", + "geofeature": "country", + "upper_level_location": "South America", + "target_variable": "v_component_of_wind_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "401be45ae6c529e4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65187:65195:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82740:82745:1'} The data starts from August 20 00:00 and ends on August 21 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 500 hPa in Saône-et-Loire, France?", + "response": "Based on the provided data, the maximum Geopotential at 500 hPa at Saône-et-Loire, France is 5.717e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Saône-et-Loire, France", + "operation": "maximum", + "variable": "geopotential_500", + "true_value": "57171.8984375", + "target_variable": "geopotential_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e8abc8bdfa979fdb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82740:82745:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58898:58925:1'} The data starts from April 25 12:00 and ends on May 02 00:00. Based on the above data, answer the following question:", + "question": "Which state in Oman has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Al Dhahira has the lowest recorded Mean sea level pressure among the states in Oman with the lowest recorded Mean sea level pressure of 1.002e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Al Dhahira, Oman", + "true_value": "100200.5703125", + "geofeature": "state", + "upper_level_location": "Oman", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5c20f53e6ea675e9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58898:58925:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43947:43955:1'} The data starts from January 29 18:00 and ends on January 31 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Specific humidity at 250 hPa?", + "response": "Based on the provided data, South America experienced the highest average Specific humidity at 250 hPa over the specified time-period, with an average Specific humidity at 250 hPa of 0.0001471 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "South America", + "true_value": "0.00014712338176831835", + "geofeature": "continent", + "target_variable": "specific_humidity_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c63af8c05d7b86f4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43947:43955:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50437:50456:1'} The data starts from July 10 06:00 and ends on July 14 18:00. Based on the above data, answer the following question:", + "question": "Which sea has the highest recorded U (zonal) component of wind at 500 hPa?", + "response": "Based on the provided data, Scotia Sea has the highest recorded U (zonal) component of wind at 500 hPa among the seas with the highest recorded U (zonal) component of wind at 500 hPa of 49.3 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Scotia Sea", + "true_value": "49.2972412109375", + "geofeature": "sea", + "upper_level_location": "sea", + "target_variable": "u_component_of_wind_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "258782dfcf6c18c9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50437:50456:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62873:62896:1'} The data starts from January 13 06:00 and ends on January 18 18:00. Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure in Antarctica?", + "response": "Based on the provided data, the average Mean sea level pressure at Antarctica is 1.005e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Antarctica", + "operation": "average", + "variable": "mean_sea_level_pressure", + "true_value": "100490.51359472213", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "faa83e10a6a09444", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62873:62896:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86172:86176:1'} The data starts from December 25 00:00 and ends on December 25 18:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter U component of wind in Oceania?", + "response": "Based on the provided data, the median 10-meter U component of wind at Oceania is -1.319 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Oceania", + "operation": "median", + "variable": "10m_u_component_of_wind", + "true_value": "-1.3187583684921265", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d2a96730d9860937", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86172:86176:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41997:42021:1'} The data starts from September 30 06:00 and ends on October 06 00:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average Geopotential at 200 hPa?", + "response": "Based on the provided data, Belize experienced the highest average Geopotential at 200 hPa over the specified time-period, with an average Geopotential at 200 hPa of 1.223e+05 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Belize", + "true_value": "122312.44478956512", + "geofeature": "country", + "target_variable": "geopotential_200", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "52cd07315bcda07d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41997:42021:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57314:57319:1'} The data starts from March 25 12:00 and ends on March 26 12:00. Based on the above data, answer the following question:", + "question": "Which state in Montserrat has the lowest recorded U (zonal) component of wind at 600 hPa?", + "response": "Based on the provided data, Saint Peter has the lowest recorded U (zonal) component of wind at 600 hPa among the states in Montserrat with the lowest recorded U (zonal) component of wind at 600 hPa of -10.05 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Saint Peter, Montserrat", + "true_value": "-10.049066543579102", + "geofeature": "state", + "upper_level_location": "Montserrat", + "target_variable": "u_component_of_wind_600", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "e4ede1104da9d0cf", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57314:57319:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33601:33613:1'} The data starts from December 31 06:00 and ends on January 03 00:00 (1 year later). Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind in North America?", + "response": "Based on the provided data, the maximum 10-meter U component of wind at North America is 19.38 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "North America", + "operation": "maximum", + "variable": "10m_u_component_of_wind", + "true_value": "19.37788200378418", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "67cce36b0a867fc3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33601:33613:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78103:78110:1'} The data starts from June 16 18:00 and ends on June 18 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Temperature at 400 hPa?", + "response": "Based on the provided data, Lützow-Holm Bay experienced the lowest average Temperature at 400 hPa over the specified time-period, with an average Temperature at 400 hPa of 221.7 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Lützow-Holm Bay", + "true_value": "221.66242374683654", + "geofeature": "water_body", + "target_variable": "temperature_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "2071bbe3bf6b1436", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78103:78110:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86514:86531:1'} The data starts from March 20 12:00 and ends on March 24 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind in Savu Sea?", + "response": "Based on the provided data, the maximum 10-meter U component of wind at Savu Sea is 5.12 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Savu Sea", + "operation": "maximum", + "variable": "10m_u_component_of_wind", + "true_value": "5.119524002075195", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dca78bf39f55a66c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86514:86531:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70845:70854:1'} The data starts from June 29 06:00 and ends on July 01 06:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature in United States Minor Outlying Islands?", + "response": "Based on the provided data, the median Surface temperature at United States Minor Outlying Islands is 300.3 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "United States Minor Outlying Islands", + "operation": "median", + "variable": "2m_temperature", + "true_value": "300.28887939453125", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8c831e99624f2124", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70845:70854:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31727:31751:1'} The data starts from September 18 18:00 and ends on September 24 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Temperature at 150 hPa in Grenada?", + "response": "Based on the provided data, the maximum Temperature at 150 hPa at Grenada is 205.8 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Grenada", + "operation": "maximum", + "variable": "temperature_150", + "true_value": "205.78262329101562", + "target_variable": "temperature_150", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6c692a2af6184920", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31727:31751:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80089:80101:1'} The data starts from October 26 06:00 and ends on October 29 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 925 hPa in Ross Sea?", + "response": "Based on the provided data, the maximum Geopotential at 925 hPa at Ross Sea is 5597 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Ross Sea", + "operation": "maximum", + "variable": "geopotential_925", + "true_value": "5596.97265625", + "target_variable": "geopotential_925", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d88304d8baa72bda", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80089:80101:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79548:79563:1'} The data starts from June 13 00:00 and ends on June 16 12:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average 10-meter V component of wind?", + "response": "Based on the provided data, Vincennes Bay experienced the highest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of 7.137 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Vincennes Bay", + "true_value": "7.137289123293093", + "geofeature": "water_body", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ccb665168d3c2390", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79548:79563:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37713:37716:1'} The data starts from October 24 06:00 and ends on October 24 18:00. Based on the above data, answer the following question:", + "question": "Which state in Jamaica has the lowest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Saint Thomas has the lowest recorded 10-meter V component of wind among the states in Jamaica with the lowest recorded 10-meter V component of wind of -4.269 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Saint Thomas, Jamaica", + "true_value": "-4.26938009262085", + "geofeature": "state", + "upper_level_location": "Jamaica", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "f5df568f32153082", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37713:37716:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82979:83000:1'} The data starts from October 18 18:00 and ends on October 23 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 500 hPa in Northern Cyprus?", + "response": "Based on the provided data, the minimum U (zonal) component of wind at 500 hPa at Northern Cyprus is 5.699 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Northern Cyprus", + "operation": "minimum", + "variable": "u_component_of_wind_500", + "true_value": "5.698971271514893", + "target_variable": "u_component_of_wind_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "70be71bf8f57acd7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82979:83000:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55191:55200:1'} The data starts from October 10 18:00 and ends on October 12 18:00. Based on the above data, answer the following question:", + "question": "Which state in Vietnam has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Kiên Giang has the lowest recorded Mean sea level pressure among the states in Vietnam with the lowest recorded Mean sea level pressure of 1.008e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Kiên Giang, Vietnam", + "true_value": "100773.7265625", + "geofeature": "state", + "upper_level_location": "Vietnam", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0cebc0fdea68e296", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55191:55200:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34666:34681:1'} The data starts from September 23 12:00 and ends on September 27 00:00. Based on the above data, answer the following question:", + "question": "What is the average Temperature at 600 hPa in Asia?", + "response": "Based on the provided data, the average Temperature at 600 hPa at Asia is 271.1 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Asia", + "operation": "average", + "variable": "temperature_600", + "true_value": "271.12208752182255", + "target_variable": "temperature_600", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "584fc5761de0acde", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34666:34681:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88162:88178:1'} The data starts from May 06 12:00 and ends on May 10 06:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the lowest recorded Geopotential at 300 hPa?", + "response": "Based on the provided data, Russia has the lowest recorded Geopotential at 300 hPa among the countries in Europe with the lowest recorded Geopotential at 300 hPa of 8.435e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Russia", + "true_value": "84346.8984375", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "geopotential_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "17c93af7471f2a74", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88162:88178:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34920:34930:1'} The data starts from November 26 00:00 and ends on November 28 06:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 250 hPa in North America?", + "response": "Based on the provided data, the median Temperature at 250 hPa at North America is 216.6 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "North America", + "operation": "median", + "variable": "temperature_250", + "true_value": "216.59423828125", + "target_variable": "temperature_250", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dfa0e9e2d65ee6bf", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34920:34930:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35260:35262:1'} The data starts from February 19 00:00 and ends on February 19 06:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 600 hPa in Tai Po, Hong Kong S.A.R.?", + "response": "Based on the provided data, the median Specific humidity at 600 hPa at Tai Po, Hong Kong S.A.R. is 0.002255 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Tai Po, Hong Kong S.A.R.", + "operation": "median", + "variable": "specific_humidity_600", + "true_value": "0.002255318220704794", + "target_variable": "specific_humidity_600", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "82cb49abb2897da2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35260:35262:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88573:88582:1'} The data starts from August 17 06:00 and ends on August 19 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter V component of wind in Armenia?", + "response": "Based on the provided data, the minimum 10-meter V component of wind at Armenia is -1.725 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Armenia", + "operation": "minimum", + "variable": "10m_v_component_of_wind", + "true_value": "-1.7246288061141968", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0ec2a4ae3cfc6f50", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88573:88582:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59563:59591:1'} The data starts from October 08 18:00 and ends on October 15 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 400 hPa in Asia?", + "response": "Based on the provided data, the minimum Geopotential at 400 hPa at Asia is 6.584e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Asia", + "operation": "minimum", + "variable": "geopotential_400", + "true_value": "65844.296875", + "target_variable": "geopotential_400", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7018de54b4ac6bc7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59563:59591:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60325:60339:1'} The data starts from April 16 06:00 and ends on April 19 12:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average 10-meter U component of wind?", + "response": "Based on the provided data, Barbados experienced the lowest average 10-meter U component of wind over the specified time-period, with an average 10-meter U component of wind of -9.796 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Barbados", + "true_value": "-9.795656204223633", + "geofeature": "country", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "caf194cfd029bcc4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60325:60339:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74254:74257:1'} The data starts from October 28 12:00 and ends on October 29 00:00. Based on the above data, answer the following question:", + "question": "Which country in Asia has the lowest recorded U (zonal) component of wind at 600 hPa?", + "response": "Based on the provided data, India has the lowest recorded U (zonal) component of wind at 600 hPa among the countries in Asia with the lowest recorded U (zonal) component of wind at 600 hPa of -14.09 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "India", + "true_value": "-14.085894584655762", + "geofeature": "country", + "upper_level_location": "Asia", + "target_variable": "u_component_of_wind_600", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "b2ed3b9c3063c96d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74254:74257:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59364:59391:1'} The data starts from August 20 00:00 and ends on August 26 12:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the lowest recorded Surface temperature?", + "response": "Based on the provided data, Russia has the lowest recorded Surface temperature among the countries in Europe with the lowest recorded Surface temperature of 265.8 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Russia", + "true_value": "265.8380126953125", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "3bb15fc524512654", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59364:59391:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31263:31286:1'} The data starts from May 25 18:00 and ends on May 31 06:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average 10-meter V component of wind?", + "response": "Based on the provided data, Europe experienced the lowest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of -0.3263 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Europe", + "true_value": "-0.3263159355343913", + "geofeature": "continent", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "fe2c250862340d21", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31263:31286:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36331:36333:1'} The data starts from November 13 18:00 and ends on November 14 00:00. Based on the above data, answer the following question:", + "question": "Which state in Poland has the lowest recorded U (zonal) component of wind at 50 hPa?", + "response": "Based on the provided data, Pomeranian has the lowest recorded U (zonal) component of wind at 50 hPa among the states in Poland with the lowest recorded U (zonal) component of wind at 50 hPa of 11.53 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Pomeranian, Poland", + "true_value": "11.52730655670166", + "geofeature": "state", + "upper_level_location": "Poland", + "target_variable": "u_component_of_wind_50", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "61b511cec42c1b30", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36331:36333:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79586:79597:1'} The data starts from June 22 12:00 and ends on June 25 00:00. Based on the above data, answer the following question:", + "question": "Which country in Oceania has the highest recorded 10-meter U component of wind?", + "response": "Based on the provided data, New Zealand has the highest recorded 10-meter U component of wind among the countries in Oceania with the highest recorded 10-meter U component of wind of 16.29 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "New Zealand", + "true_value": "16.287466049194336", + "geofeature": "country", + "upper_level_location": "Oceania", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "51dd242147f01ea3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79586:79597:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93480:93501:1'} The data starts from December 26 00:00 and ends on December 31 00:00. Based on the above data, answer the following question:", + "question": "Which country in Oceania has the highest recorded V (meridional) component of wind at 925 hPa?", + "response": "Based on the provided data, Australia has the highest recorded V (meridional) component of wind at 925 hPa among the countries in Oceania with the highest recorded V (meridional) component of wind at 925 hPa of 21.88 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Australia", + "true_value": "21.880542755126953", + "geofeature": "country", + "upper_level_location": "Oceania", + "target_variable": "v_component_of_wind_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "44d3d0dd5f0a9b32", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93480:93501:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69566:69584:1'} The data starts from August 13 12:00 and ends on August 17 18:00. Based on the above data, answer the following question:", + "question": "Which state in Uganda has the lowest recorded V (meridional) component of wind at 925 hPa?", + "response": "Based on the provided data, Buvuma has the lowest recorded V (meridional) component of wind at 925 hPa among the states in Uganda with the lowest recorded V (meridional) component of wind at 925 hPa of -3.088 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Buvuma, Uganda", + "true_value": "-3.0880110263824463", + "geofeature": "state", + "upper_level_location": "Uganda", + "target_variable": "v_component_of_wind_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "02b9bda534057c0c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69566:69584:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29400:29419:1'} The data starts from February 15 00:00 and ends on February 19 12:00. Based on the above data, answer the following question:", + "question": "Which ocean has the highest recorded Surface pressure?", + "response": "Based on the provided data, North Atlantic Ocean has the highest recorded Surface pressure among the oceans with the highest recorded Surface pressure of 1.044e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "North Atlantic Ocean", + "true_value": "104361.984375", + "geofeature": "ocean", + "upper_level_location": "ocean", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "6f85834047626fb9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29400:29419:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48379:48381:1'} The data starts from February 11 18:00 and ends on February 12 00:00. Based on the above data, answer the following question:", + "question": "Which state in Malaysia has the lowest recorded Geopotential at 400 hPa?", + "response": "Based on the provided data, Negeri Sembilan has the lowest recorded Geopotential at 400 hPa among the states in Malaysia with the lowest recorded Geopotential at 400 hPa of 7.416e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Negeri Sembilan, Malaysia", + "true_value": "74162.5390625", + "geofeature": "state", + "upper_level_location": "Malaysia", + "target_variable": "geopotential_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "fb9022480cc039c4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48379:48381:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48659:48679:1'} The data starts from April 21 18:00 and ends on April 26 12:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average V (meridional) component of wind at 400 hPa?", + "response": "Based on the provided data, Moldova experienced the lowest average V (meridional) component of wind at 400 hPa over the specified time-period, with an average V (meridional) component of wind at 400 hPa of -19.04 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Moldova", + "true_value": "-19.038454827348403", + "geofeature": "country", + "target_variable": "v_component_of_wind_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "41f399831163417a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48659:48679:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53431:53440:1'} The data starts from July 28 18:00 and ends on July 30 18:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 300 hPa in Uchiura Bay?", + "response": "Based on the provided data, the median Specific humidity at 300 hPa at Uchiura Bay is 0.0006883 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Uchiura Bay", + "operation": "median", + "variable": "specific_humidity_300", + "true_value": "0.0006883080932311714", + "target_variable": "specific_humidity_300", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "10e342f48af4e305", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53431:53440:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65025:65043:1'} The data starts from July 05 06:00 and ends on July 09 12:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Kangertittivaq experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 9.255e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Kangertittivaq", + "true_value": "92553.18915452591", + "geofeature": "water_body", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "42713c6268bef075", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65025:65043:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82333:82356:1'} The data starts from May 10 06:00 and ends on May 15 18:00. Based on the above data, answer the following question:", + "question": "Which sea has the highest recorded Surface pressure?", + "response": "Based on the provided data, Tasman Sea has the highest recorded Surface pressure among the seas with the highest recorded Surface pressure of 1.035e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Tasman Sea", + "true_value": "103548.7421875", + "geofeature": "sea", + "upper_level_location": "sea", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1eb3a835c1f0eb89", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82333:82356:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86482:86498:1'} The data starts from March 12 12:00 and ends on March 16 06:00. Based on the above data, answer the following question:", + "question": "Which state in Gabon has the lowest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Ngounié has the lowest recorded 10-meter V component of wind among the states in Gabon with the lowest recorded 10-meter V component of wind of -0.952 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Ngounié, Gabon", + "true_value": "-0.9519556760787964", + "geofeature": "state", + "upper_level_location": "Gabon", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "08a44f95982e6188", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86482:86498:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81965:81988:1'} The data starts from February 07 06:00 and ends on February 12 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 300 hPa in Nibok, Nauru?", + "response": "Based on the provided data, the maximum U (zonal) component of wind at 300 hPa at Nibok, Nauru is -3.078 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Nibok, Nauru", + "operation": "maximum", + "variable": "u_component_of_wind_300", + "true_value": "-3.077650785446167", + "target_variable": "u_component_of_wind_300", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "63b523229a742791", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81965:81988:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76083:76101:1'} The data starts from January 28 18:00 and ends on February 02 00:00. Based on the above data, answer the following question:", + "question": "Which ocean has the highest recorded 10-meter V component of wind?", + "response": "Based on the provided data, North Atlantic Ocean has the highest recorded 10-meter V component of wind among the oceans with the highest recorded 10-meter V component of wind of 21.1 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "North Atlantic Ocean", + "true_value": "21.10432243347168", + "geofeature": "ocean", + "upper_level_location": "ocean", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "f9e1cd08a50b93d9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76083:76101:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88085:88103:1'} The data starts from April 17 06:00 and ends on April 21 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter V component of wind in Islands, Hong Kong S.A.R.?", + "response": "Based on the provided data, the maximum 10-meter V component of wind at Islands, Hong Kong S.A.R. is 3.359 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Islands, Hong Kong S.A.R.", + "operation": "maximum", + "variable": "10m_v_component_of_wind", + "true_value": "3.3588764667510986", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d68a5fef06c52637", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88085:88103:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68689:68697:1'} The data starts from January 06 06:00 and ends on January 08 00:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Surface pressure?", + "response": "Based on the provided data, Europe experienced the highest average Surface pressure over the specified time-period, with an average Surface pressure of 9.801e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Europe", + "true_value": "98012.10573203105", + "geofeature": "continent", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "19a0cfc870c30db5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68689:68697:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36276:36279:1'} The data starts from October 31 00:00 and ends on October 31 12:00. Based on the above data, answer the following question:", + "question": "Which state in Greenland has the highest recorded V (meridional) component of wind at 700 hPa?", + "response": "Based on the provided data, Qaasuitsup Kommunia has the highest recorded V (meridional) component of wind at 700 hPa among the states in Greenland with the highest recorded V (meridional) component of wind at 700 hPa of 21.07 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Qaasuitsup Kommunia, Greenland", + "true_value": "21.073158264160156", + "geofeature": "state", + "upper_level_location": "Greenland", + "target_variable": "v_component_of_wind_700", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "04195178ac56aa9a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36276:36279:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60172:60188:1'} The data starts from March 09 00:00 and ends on March 12 18:00. Based on the above data, answer the following question:", + "question": "Which state in Kenya has the highest recorded Mean sea level pressure?", + "response": "Based on the provided data, Rift Valley has the highest recorded Mean sea level pressure among the states in Kenya with the highest recorded Mean sea level pressure of 1.017e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Rift Valley, Kenya", + "true_value": "101689.125", + "geofeature": "state", + "upper_level_location": "Kenya", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "7db3f87043068d8b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60172:60188:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69363:69377:1'} The data starts from June 23 18:00 and ends on June 27 00:00. Based on the above data, answer the following question:", + "question": "Which river has the lowest recorded Surface temperature?", + "response": "Based on the provided data, Columbia River has the lowest recorded Surface temperature among the rivers with the lowest recorded Surface temperature of 286.5 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Columbia River", + "true_value": "286.5429992675781", + "geofeature": "river", + "upper_level_location": "river", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "e7f874957c75d0d8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69363:69377:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36913:36922:1'} The data starts from April 07 06:00 and ends on April 09 06:00. Based on the above data, answer the following question:", + "question": "Which strait has the highest recorded Specific humidity at 925 hPa?", + "response": "Based on the provided data, Strait of Malacca has the highest recorded Specific humidity at 925 hPa among the straits with the highest recorded Specific humidity at 925 hPa of 0.01693 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Strait of Malacca", + "true_value": "0.016926435753703117", + "geofeature": "strait", + "upper_level_location": "strait", + "target_variable": "specific_humidity_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "62f685927ff75c46", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36913:36922:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89673:89679:1'} The data starts from May 18 06:00 and ends on May 19 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure in Korçë, Albania?", + "response": "Based on the provided data, the maximum Mean sea level pressure at Korçë, Albania is 1.019e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Korçë, Albania", + "operation": "maximum", + "variable": "mean_sea_level_pressure", + "true_value": "101888.7421875", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "274d7d7f75ec0ddc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89673:89679:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49576:49592:1'} The data starts from December 07 00:00 and ends on December 10 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Surface temperature?", + "response": "Based on the provided data, Antarctica experienced the lowest average Surface temperature over the specified time-period, with an average Surface temperature of 241.6 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "241.64775223775837", + "geofeature": "continent", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "429f986d4ac069a6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49576:49592:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74057:74071:1'} The data starts from September 09 06:00 and ends on September 12 12:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average 10-meter V component of wind?", + "response": "Based on the provided data, Dixon Entrance experienced the highest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of 5.799 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Dixon Entrance", + "true_value": "5.798543463617988", + "geofeature": "water_body", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ced340ff18b03efe", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74057:74071:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39831:39842:1'} The data starts from April 06 18:00 and ends on April 09 06:00. Based on the above data, answer the following question:", + "question": "Which river has the highest recorded U (zonal) component of wind at 50 hPa?", + "response": "Based on the provided data, Amazon River has the highest recorded U (zonal) component of wind at 50 hPa among the rivers with the highest recorded U (zonal) component of wind at 50 hPa of 19.97 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Amazon River", + "true_value": "19.96878433227539", + "geofeature": "river", + "upper_level_location": "river", + "target_variable": "u_component_of_wind_50", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c27e560b02fa4441", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39831:39842:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91393:91409:1'} The data starts from July 22 06:00 and ends on July 26 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind in Asia?", + "response": "Based on the provided data, the maximum 10-meter U component of wind at Asia is 18.55 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Asia", + "operation": "maximum", + "variable": "10m_u_component_of_wind", + "true_value": "18.551284790039062", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d7d64c6987bd5058", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91393:91409:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85978:85999:1'} The data starts from November 06 12:00 and ends on November 11 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average 10-meter V component of wind?", + "response": "Based on the provided data, Europe experienced the highest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of 0.4826 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Europe", + "true_value": "0.4826368964306168", + "geofeature": "continent", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c7215ccb797e7fed", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85978:85999:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92652:92667:1'} The data starts from June 02 00:00 and ends on June 05 12:00. Based on the above data, answer the following question:", + "question": "What is the average Surface temperature in Oceania?", + "response": "Based on the provided data, the average Surface temperature at Oceania is 289.7 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Oceania", + "operation": "average", + "variable": "2m_temperature", + "true_value": "289.67758794965124", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e4095bf9a68c1227", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92652:92667:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54034:54039:1'} The data starts from December 26 12:00 and ends on December 27 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Specific humidity at 400 hPa?", + "response": "Based on the provided data, Antarctica experienced the lowest average Specific humidity at 400 hPa over the specified time-period, with an average Specific humidity at 400 hPa of 0.0001009 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "0.00010085497711112773", + "geofeature": "continent", + "target_variable": "specific_humidity_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "fcb6c3c60e034f75", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54034:54039:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55774:55795:1'} The data starts from March 05 12:00 and ends on March 10 12:00. Based on the above data, answer the following question:", + "question": "Which state in Nicaragua has the highest recorded Specific humidity at 600 hPa?", + "response": "Based on the provided data, Rio San Juan has the highest recorded Specific humidity at 600 hPa among the states in Nicaragua with the highest recorded Specific humidity at 600 hPa of 0.002897 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Rio San Juan, Nicaragua", + "true_value": "0.002896759659051895", + "geofeature": "state", + "upper_level_location": "Nicaragua", + "target_variable": "specific_humidity_600", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c66443c9af15c128", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55774:55795:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84617:84640:1'} The data starts from December 01 06:00 and ends on December 06 18:00. Based on the above data, answer the following question:", + "question": "Which strait has the highest recorded Surface temperature?", + "response": "Based on the provided data, Strait of Malacca has the highest recorded Surface temperature among the straits with the highest recorded Surface temperature of 306.6 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Strait of Malacca", + "true_value": "306.6111755371094", + "geofeature": "strait", + "upper_level_location": "strait", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "88273db86d877457", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84617:84640:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35280:35300:1'} The data starts from February 24 00:00 and ends on February 28 18:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter V component of wind in South America?", + "response": "Based on the provided data, the average 10-meter V component of wind at South America is -0.193 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "South America", + "operation": "average", + "variable": "10m_v_component_of_wind", + "true_value": "-0.19295867407844325", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3610b7cb8c85a9b1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35280:35300:1" + } + } +] \ No newline at end of file diff --git a/level1a_part1.json b/level1a_part1.json new file mode 100644 index 0000000000000000000000000000000000000000..8e2321717ba859a918206a5958bfabaecf26c68c --- /dev/null +++ b/level1a_part1.json @@ -0,0 +1,3466 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91279:91299:1'} The data starts from June 23 18:00 and ends on June 28 12:00. Based on the above data, answer the following question:", + "question": "Which lagoon has the lowest recorded U (zonal) component of wind at 850 hPa?", + "response": "Based on the provided data, Lake Pontchartrain has the lowest recorded U (zonal) component of wind at 850 hPa among the lagoons with the lowest recorded U (zonal) component of wind at 850 hPa of -9.421 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Lake Pontchartrain", + "true_value": "-9.420738220214844", + "geofeature": "lagoon", + "upper_level_location": "lagoon", + "target_variable": "u_component_of_wind_850", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1f8ce2cdc69d567f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91279:91299:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65671:65691:1'} The data starts from December 13 18:00 and ends on December 18 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure in Bosporus?", + "response": "Based on the provided data, the minimum Surface pressure at Bosporus is 9.75e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Bosporus", + "operation": "minimum", + "variable": "surface_pressure", + "true_value": "97497.78125", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1ee6e8e85a2ed208", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65671:65691:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32333:32360:1'} The data starts from February 17 06:00 and ends on February 23 18:00. Based on the above data, answer the following question:", + "question": "Which lagoon has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Husky Lakes has the lowest recorded Mean sea level pressure among the lagoons with the lowest recorded Mean sea level pressure of 1.005e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Husky Lakes", + "true_value": "100510.5546875", + "geofeature": "lagoon", + "upper_level_location": "lagoon", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "832aeec768e032e6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32333:32360:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60707:60735:1'} The data starts from July 20 18:00 and ends on July 27 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Geopotential at 250 hPa?", + "response": "Based on the provided data, Antarctica experienced the lowest average Geopotential at 250 hPa over the specified time-period, with an average Geopotential at 250 hPa of 9.135e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "91354.77835261337", + "geofeature": "continent", + "target_variable": "geopotential_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "a6403ba0e4706634", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60707:60735:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37748:37752:1'} The data starts from November 02 00:00 and ends on November 02 18:00. Based on the above data, answer the following question:", + "question": "Which sea has the highest recorded Specific humidity at 150 hPa?", + "response": "Based on the provided data, South China Sea has the highest recorded Specific humidity at 150 hPa among the seas with the highest recorded Specific humidity at 150 hPa of 1.823e-05 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "South China Sea", + "true_value": "1.823159800551366e-05", + "geofeature": "sea", + "upper_level_location": "sea", + "target_variable": "specific_humidity_150", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "abe63e8e9da083ed", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37748:37752:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54764:54783:1'} The data starts from June 26 00:00 and ends on June 30 12:00. Based on the above data, answer the following question:", + "question": "What is the average Surface pressure in North America?", + "response": "Based on the provided data, the average Surface pressure at North America is 9.325e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "North America", + "operation": "average", + "variable": "surface_pressure", + "true_value": "93252.21069187131", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6d9856e9ccc4cdac", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54764:54783:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79854:79878:1'} The data starts from August 28 12:00 and ends on September 03 06:00. Based on the above data, answer the following question:", + "question": "What is the median Surface pressure in Idlib, Syria?", + "response": "Based on the provided data, the median Surface pressure at Idlib, Syria is 9.802e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Idlib, Syria", + "operation": "median", + "variable": "surface_pressure", + "true_value": "98018.09375", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "041c9f6f2627e4e1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79854:79878:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82164:82176:1'} The data starts from March 29 00:00 and ends on March 31 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Surface temperature?", + "response": "Based on the provided data, Oceania experienced the highest average Surface temperature over the specified time-period, with an average Surface temperature of 298.3 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "298.3354223925323", + "geofeature": "continent", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0a782cd80f8f1cc4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82164:82176:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58428:58430:1'} The data starts from December 29 00:00 and ends on December 29 06:00. Based on the above data, answer the following question:", + "question": "Which state in São Tomé and Principe has the lowest recorded Surface pressure?", + "response": "Based on the provided data, São Tomé has the lowest recorded Surface pressure among the states in São Tomé and Principe with the lowest recorded Surface pressure of 1.01e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "São Tomé, São Tomé and Principe", + "true_value": "101023.3828125", + "geofeature": "state", + "upper_level_location": "São Tomé and Principe", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5813bb29e451d6e5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58428:58430:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37457:37483:1'} The data starts from August 21 06:00 and ends on August 27 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average U (zonal) component of wind at 300 hPa?", + "response": "Based on the provided data, Oceania experienced the highest average U (zonal) component of wind at 300 hPa over the specified time-period, with an average U (zonal) component of wind at 300 hPa of 31.2 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "31.204732258567173", + "geofeature": "continent", + "target_variable": "u_component_of_wind_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "b163fb3d47b8165a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37457:37483:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40935:40945:1'} The data starts from January 07 18:00 and ends on January 10 00:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average V (meridional) component of wind at 850 hPa?", + "response": "Based on the provided data, Cordova Bay experienced the highest average V (meridional) component of wind at 850 hPa over the specified time-period, with an average V (meridional) component of wind at 850 hPa of 25.34 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Cordova Bay", + "true_value": "25.340774497972895", + "geofeature": "water_body", + "target_variable": "v_component_of_wind_850", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0fd9da0f0d45f5f7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40935:40945:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64091:64103:1'} The data starts from November 13 18:00 and ends on November 16 12:00. Based on the above data, answer the following question:", + "question": "Which gulf has the highest recorded Mean sea level pressure?", + "response": "Based on the provided data, Khatanga Gulf has the highest recorded Mean sea level pressure among the gulfs with the highest recorded Mean sea level pressure of 1.042e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Khatanga Gulf", + "true_value": "104200.7109375", + "geofeature": "gulf", + "upper_level_location": "gulf", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "cb2ef9105a8e0d55", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64091:64103:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29335:29343:1'} The data starts from January 29 18:00 and ends on January 31 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 400 hPa in South America?", + "response": "Based on the provided data, the minimum V (meridional) component of wind at 400 hPa at South America is -24.08 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "South America", + "operation": "minimum", + "variable": "v_component_of_wind_400", + "true_value": "-24.079208374023438", + "target_variable": "v_component_of_wind_400", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b69a4682f11057c4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29335:29343:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65128:65150:1'} The data starts from July 31 00:00 and ends on August 05 06:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 150 hPa in Philippines?", + "response": "Based on the provided data, the average V (meridional) component of wind at 150 hPa at Philippines is -6.157 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Philippines", + "operation": "average", + "variable": "v_component_of_wind_150", + "true_value": "-6.1568382056440605", + "target_variable": "v_component_of_wind_150", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9e15a5aeaa595a50", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65128:65150:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70262:70278:1'} The data starts from February 03 12:00 and ends on February 07 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 850 hPa in Asia?", + "response": "Based on the provided data, the minimum V (meridional) component of wind at 850 hPa at Asia is -21.49 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Asia", + "operation": "minimum", + "variable": "v_component_of_wind_850", + "true_value": "-21.487194061279297", + "target_variable": "v_component_of_wind_850", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9e67f3f18793845c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70262:70278:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81243:81270:1'} The data starts from August 10 18:00 and ends on August 17 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Specific humidity at 400 hPa?", + "response": "Based on the provided data, Lützow-Holm Bay experienced the lowest average Specific humidity at 400 hPa over the specified time-period, with an average Specific humidity at 400 hPa of 1.499e-05 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Lützow-Holm Bay", + "true_value": "1.4991225070164026e-05", + "geofeature": "water_body", + "target_variable": "specific_humidity_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "2d945047e235b186", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81243:81270:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76437:76459:1'} The data starts from April 27 06:00 and ends on May 02 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average 10-meter U component of wind?", + "response": "Based on the provided data, Oceania experienced the lowest average 10-meter U component of wind over the specified time-period, with an average 10-meter U component of wind of -2.019 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "-2.0188383142314748", + "geofeature": "continent", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "06b7c5ed1aa2c16f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76437:76459:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90000:90006:1'} The data starts from August 08 00:00 and ends on August 09 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average Mean sea level pressure?", + "response": "Based on the provided data, Pitcairn Islands experienced the highest average Mean sea level pressure over the specified time-period, with an average Mean sea level pressure of 1.025e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Pitcairn Islands", + "true_value": "102525.8145159467", + "geofeature": "country", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "77afefbe4e21685b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90000:90006:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68562:68570:1'} The data starts from December 05 12:00 and ends on December 07 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 850 hPa in Asia?", + "response": "Based on the provided data, the maximum U (zonal) component of wind at 850 hPa at Asia is 20.01 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Asia", + "operation": "maximum", + "variable": "u_component_of_wind_850", + "true_value": "20.01368522644043", + "target_variable": "u_component_of_wind_850", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "eb14db08e32896fd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68562:68570:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31490:31504:1'} The data starts from July 21 12:00 and ends on July 24 18:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average V (meridional) component of wind at 500 hPa?", + "response": "Based on the provided data, Vatican experienced the lowest average V (meridional) component of wind at 500 hPa over the specified time-period, with an average V (meridional) component of wind at 500 hPa of -13.71 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Vatican", + "true_value": "-13.712060928344727", + "geofeature": "country", + "target_variable": "v_component_of_wind_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "9270c67b0b848447", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31490:31504:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79207:79219:1'} The data starts from March 19 18:00 and ends on March 22 12:00. Based on the above data, answer the following question:", + "question": "Which fjord has the lowest recorded Geopotential at 500 hPa?", + "response": "Based on the provided data, Sognefjorden has the lowest recorded Geopotential at 500 hPa among the fjords with the lowest recorded Geopotential at 500 hPa of 5.168e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Sognefjorden", + "true_value": "51683.79296875", + "geofeature": "fjord", + "upper_level_location": "fjord", + "target_variable": "geopotential_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "78a78aca27f23a60", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79207:79219:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76057:76074:1'} The data starts from January 22 06:00 and ends on January 26 06:00. Based on the above data, answer the following question:", + "question": "Which inlet has the lowest recorded Specific humidity at 500 hPa?", + "response": "Based on the provided data, Baird Inlet has the lowest recorded Specific humidity at 500 hPa among the inlets with the lowest recorded Specific humidity at 500 hPa of 9.611e-05 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Baird Inlet", + "true_value": "9.610881534172222e-05", + "geofeature": "inlet", + "upper_level_location": "inlet", + "target_variable": "specific_humidity_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0dbaa06164f7a665", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76057:76074:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54405:54412:1'} The data starts from March 28 06:00 and ends on March 29 18:00. Based on the above data, answer the following question:", + "question": "Which reef has the highest recorded Geopotential at 400 hPa?", + "response": "Based on the provided data, Great Barrier Reef has the highest recorded Geopotential at 400 hPa among the reefs with the highest recorded Geopotential at 400 hPa of 7.445e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Great Barrier Reef", + "true_value": "74447.03125", + "geofeature": "reef", + "upper_level_location": "reef", + "target_variable": "geopotential_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "46b28be36dad42c5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54405:54412:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34786:34790:1'} The data starts from October 23 12:00 and ends on October 24 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average Specific humidity at 700 hPa?", + "response": "Based on the provided data, Golfo de Panamá experienced the highest average Specific humidity at 700 hPa over the specified time-period, with an average Specific humidity at 700 hPa of 0.008863 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Golfo de Panamá", + "true_value": "0.008863389379990984", + "geofeature": "water_body", + "target_variable": "specific_humidity_700", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "fa291015bae9b81e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34786:34790:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62431:62456:1'} The data starts from September 24 18:00 and ends on September 30 18:00. Based on the above data, answer the following question:", + "question": "Which river has the highest recorded Mean sea level pressure?", + "response": "Based on the provided data, Columbia River has the highest recorded Mean sea level pressure among the rivers with the highest recorded Mean sea level pressure of 1.021e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Columbia River", + "true_value": "102064.1328125", + "geofeature": "river", + "upper_level_location": "river", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1e42cb77030e9c3d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62431:62456:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74131:74150:1'} The data starts from September 27 18:00 and ends on October 02 06:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter V component of wind in Long Island Sound?", + "response": "Based on the provided data, the average 10-meter V component of wind at Long Island Sound is 0.4076 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Long Island Sound", + "operation": "average", + "variable": "10m_v_component_of_wind", + "true_value": "0.4076209968295715", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "55f26a336474d60a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74131:74150:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87448:87449:1'} The data corresponds to corresponds to a snapshot on November 09 00:00. Based on the above data, answer the following question:", + "question": "Which country in Asia has the highest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Japan has the highest recorded 10-meter V component of wind among the countries in Asia with the highest recorded 10-meter V component of wind of 10.95 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Japan", + "true_value": "10.945016860961914", + "geofeature": "country", + "upper_level_location": "Asia", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5dd7cbf4bb06524f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87448:87449:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92893:92907:1'} The data starts from August 01 06:00 and ends on August 04 12:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Temperature at 250 hPa?", + "response": "Based on the provided data, South Georgia and the Islands experienced the lowest average Temperature at 250 hPa over the specified time-period, with an average Temperature at 250 hPa of 210.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "South Georgia and the Islands", + "true_value": "210.91224723348418", + "geofeature": "country", + "target_variable": "temperature_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "9fc04b50c364a5fa", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92893:92907:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74069:74071:1'} The data starts from September 12 06:00 and ends on September 12 12:00. Based on the above data, answer the following question:", + "question": "Which ocean has the lowest recorded V (meridional) component of wind at 1000 hPa?", + "response": "Based on the provided data, South Pacific Ocean has the lowest recorded V (meridional) component of wind at 1000 hPa among the oceans with the lowest recorded V (meridional) component of wind at 1000 hPa of -17.8 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "South Pacific Ocean", + "true_value": "-17.80132484436035", + "geofeature": "ocean", + "upper_level_location": "ocean", + "target_variable": "v_component_of_wind_1000", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "addc729c7de033e0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74069:74071:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38320:38329:1'} The data starts from March 25 00:00 and ends on March 27 00:00. Based on the above data, answer the following question:", + "question": "Which ocean has the highest recorded 10-meter V component of wind?", + "response": "Based on the provided data, North Pacific Ocean has the highest recorded 10-meter V component of wind among the oceans with the highest recorded 10-meter V component of wind of 21.15 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "North Pacific Ocean", + "true_value": "21.15056610107422", + "geofeature": "ocean", + "upper_level_location": "ocean", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5a1ad6d393527f9d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38320:38329:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76209:76224:1'} The data starts from March 01 06:00 and ends on March 04 18:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Kangertittivaq experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 9.097e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Kangertittivaq", + "true_value": "90967.92039339166", + "geofeature": "water_body", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "389a4ffb3574fc8d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76209:76224:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61303:61314:1'} The data starts from December 16 18:00 and ends on December 19 06:00. Based on the above data, answer the following question:", + "question": "Which state in Barbados has the highest recorded Geopotential at 925 hPa?", + "response": "Based on the provided data, Saint Joseph has the highest recorded Geopotential at 925 hPa among the states in Barbados with the highest recorded Geopotential at 925 hPa of 8003 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Saint Joseph, Barbados", + "true_value": "8002.8994140625", + "geofeature": "state", + "upper_level_location": "Barbados", + "target_variable": "geopotential_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "a108989193b745cd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61303:61314:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42105:42107:1'} The data starts from October 27 06:00 and ends on October 27 12:00. Based on the above data, answer the following question:", + "question": "Which state in Belize has the highest recorded Mean sea level pressure?", + "response": "Based on the provided data, Cayo has the highest recorded Mean sea level pressure among the states in Belize with the highest recorded Mean sea level pressure of 1.014e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Cayo, Belize", + "true_value": "101419.609375", + "geofeature": "state", + "upper_level_location": "Belize", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "69edc0f18d704bf6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42105:42107:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72895:72898:1'} The data starts from November 22 18:00 and ends on November 23 06:00. Based on the above data, answer the following question:", + "question": "Which channel has the highest recorded Geopotential at 500 hPa?", + "response": "Based on the provided data, Yucatan Channel has the highest recorded Geopotential at 500 hPa among the channels with the highest recorded Geopotential at 500 hPa of 5.775e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Yucatan Channel", + "true_value": "57750.52734375", + "geofeature": "channel", + "upper_level_location": "channel", + "target_variable": "geopotential_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "e46b54b54f120dc7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72895:72898:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75944:75962:1'} The data starts from December 25 00:00 and ends on December 29 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average Surface temperature?", + "response": "Based on the provided data, Joseph Bonaparte Gulf experienced the highest average Surface temperature over the specified time-period, with an average Surface temperature of 301.5 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Joseph Bonaparte Gulf", + "true_value": "301.50096927848523", + "geofeature": "water_body", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "2a10805a13ce3acd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75944:75962:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43859:43864:1'} The data starts from January 07 18:00 and ends on January 08 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure in Latvia?", + "response": "Based on the provided data, the maximum Mean sea level pressure at Latvia is 1.011e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Latvia", + "operation": "maximum", + "variable": "mean_sea_level_pressure", + "true_value": "101073.3046875", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "001fb701df8c0fdb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43859:43864:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36057:36078:1'} The data starts from September 06 06:00 and ends on September 11 06:00. Based on the above data, answer the following question:", + "question": "Which state in Uruguay has the lowest recorded Specific humidity at 400 hPa?", + "response": "Based on the provided data, San José has the lowest recorded Specific humidity at 400 hPa among the states in Uruguay with the lowest recorded Specific humidity at 400 hPa of 1.923e-06 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "San José, Uruguay", + "true_value": "1.923470790643478e-06", + "geofeature": "state", + "upper_level_location": "Uruguay", + "target_variable": "specific_humidity_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "fa69ab54bc4b12ab", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36057:36078:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82411:82424:1'} The data starts from May 29 18:00 and ends on June 01 18:00. Based on the above data, answer the following question:", + "question": "Which state in United Republic of Tanzania has the lowest recorded Surface pressure?", + "response": "Based on the provided data, Arusha has the lowest recorded Surface pressure among the states in United Republic of Tanzania with the lowest recorded Surface pressure of 8.303e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Arusha, United Republic of Tanzania", + "true_value": "83030.25", + "geofeature": "state", + "upper_level_location": "United Republic of Tanzania", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ec71a3b976eb341a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82411:82424:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34869:34886:1'} The data starts from November 13 06:00 and ends on November 17 06:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Geopotential at 300 hPa?", + "response": "Based on the provided data, Antarctica experienced the lowest average Geopotential at 300 hPa over the specified time-period, with an average Geopotential at 300 hPa of 8.405e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "84051.08361829924", + "geofeature": "continent", + "target_variable": "geopotential_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "182935809570a2e4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34869:34886:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49938:49965:1'} The data starts from March 07 12:00 and ends on March 14 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 50 hPa in Bolama, Guinea-Bissau?", + "response": "Based on the provided data, the minimum Geopotential at 50 hPa at Bolama, Guinea-Bissau is 2.007e+05 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Bolama, Guinea-Bissau", + "operation": "minimum", + "variable": "geopotential_50", + "true_value": "200684.859375", + "target_variable": "geopotential_50", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c2c7fcb2850dcb5a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49938:49965:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50707:50731:1'} The data starts from September 15 18:00 and ends on September 21 12:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average Temperature at 400 hPa?", + "response": "Based on the provided data, Bhutan experienced the highest average Temperature at 400 hPa over the specified time-period, with an average Temperature at 400 hPa of 259.8 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Bhutan", + "true_value": "259.75408562722157", + "geofeature": "country", + "target_variable": "temperature_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "79db57331effb9d5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50707:50731:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40781:40786:1'} The data starts from November 30 06:00 and ends on December 01 06:00. Based on the above data, answer the following question:", + "question": "Which state in Montserrat has the lowest recorded Specific humidity at 400 hPa?", + "response": "Based on the provided data, Saint Peter has the lowest recorded Specific humidity at 400 hPa among the states in Montserrat with the lowest recorded Specific humidity at 400 hPa of 0.0001025 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Saint Peter, Montserrat", + "true_value": "0.00010251916683046147", + "geofeature": "state", + "upper_level_location": "Montserrat", + "target_variable": "specific_humidity_400", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1377c705cdd6984b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40781:40786:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56747:56753:1'} The data starts from November 03 18:00 and ends on November 05 00:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average 10-meter V component of wind?", + "response": "Based on the provided data, Vincennes Bay experienced the highest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of 8.995 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Vincennes Bay", + "true_value": "8.994708370072791", + "geofeature": "water_body", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "e4fa5175ceb4e880", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56747:56753:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69226:69234:1'} The data starts from May 20 12:00 and ends on May 22 06:00. Based on the above data, answer the following question:", + "question": "Which country in Asia has the highest recorded Mean sea level pressure?", + "response": "Based on the provided data, China has the highest recorded Mean sea level pressure among the countries in Asia with the highest recorded Mean sea level pressure of 1.028e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "China", + "true_value": "102804.1640625", + "geofeature": "country", + "upper_level_location": "Asia", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0db6cdec222b6e0a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69226:69234:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44968:44978:1'} The data starts from October 12 00:00 and ends on October 14 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Siachen Glacier experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 5.644e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Siachen Glacier", + "true_value": "56437.60542789261", + "geofeature": "country", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1658748b1dfdc16b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44968:44978:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88975:88992:1'} The data starts from November 25 18:00 and ends on November 29 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average 10-meter U component of wind?", + "response": "Based on the provided data, Africa experienced the lowest average 10-meter U component of wind over the specified time-period, with an average 10-meter U component of wind of -0.8244 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Africa", + "true_value": "-0.8243809246524798", + "geofeature": "continent", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "17b2e497ca6cd55a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88975:88992:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32992:33017:1'} The data starts from August 01 00:00 and ends on August 07 00:00. Based on the above data, answer the following question:", + "question": "Which state in Iraq has the lowest recorded 10-meter U component of wind?", + "response": "Based on the provided data, As-Sulaymaniyah has the lowest recorded 10-meter U component of wind among the states in Iraq with the lowest recorded 10-meter U component of wind of -5.138 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "As-Sulaymaniyah, Iraq", + "true_value": "-5.138203144073486", + "geofeature": "state", + "upper_level_location": "Iraq", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "818f66275af0e153", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32992:33017:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63628:63643:1'} The data starts from July 21 00:00 and ends on July 24 12:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 300 hPa in Antarctica?", + "response": "Based on the provided data, the median Temperature at 300 hPa at Antarctica is 208.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Antarctica", + "operation": "median", + "variable": "temperature_300", + "true_value": "208.90811157226562", + "target_variable": "temperature_300", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "023a2f7eaa932741", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63628:63643:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86197:86212:1'} The data starts from December 31 06:00 and ends on January 03 18:00 (1 year later). Based on the above data, answer the following question:", + "question": "Which state in Tunisia has the lowest recorded Specific humidity at 925 hPa?", + "response": "Based on the provided data, Tozeur has the lowest recorded Specific humidity at 925 hPa among the states in Tunisia with the lowest recorded Specific humidity at 925 hPa of 0.002082 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Tozeur, Tunisia", + "true_value": "0.00208232831209898", + "geofeature": "state", + "upper_level_location": "Tunisia", + "target_variable": "specific_humidity_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "de2b3dd60f77aaf6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86197:86212:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30158:30182:1'} The data starts from August 23 12:00 and ends on August 29 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Temperature at 700 hPa?", + "response": "Based on the provided data, Falkland Islands experienced the lowest average Temperature at 700 hPa over the specified time-period, with an average Temperature at 700 hPa of 259 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Falkland Islands", + "true_value": "258.99937654194537", + "geofeature": "country", + "target_variable": "temperature_700", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "2c74fd69e90bc724", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30158:30182:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65023:65025:1'} The data starts from July 04 18:00 and ends on July 05 00:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter V component of wind in Saint Patrick, Grenada?", + "response": "Based on the provided data, the average 10-meter V component of wind at Saint Patrick, Grenada is -0.5125 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Saint Patrick, Grenada", + "operation": "average", + "variable": "10m_v_component_of_wind", + "true_value": "-0.5125325173139572", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5260a0ee42d2f499", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65023:65025:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88801:88817:1'} The data starts from October 13 06:00 and ends on October 17 00:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the lowest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Russia has the lowest recorded 10-meter V component of wind among the countries in Europe with the lowest recorded 10-meter V component of wind of -13.87 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Russia", + "true_value": "-13.871519088745117", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "da8224edf0eac409", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88801:88817:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88840:88855:1'} The data starts from October 23 00:00 and ends on October 26 12:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the highest recorded V (meridional) component of wind at 150 hPa?", + "response": "Based on the provided data, Portugal has the highest recorded V (meridional) component of wind at 150 hPa among the countries in Europe with the highest recorded V (meridional) component of wind at 150 hPa of 30.59 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Portugal", + "true_value": "30.589962005615234", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "v_component_of_wind_150", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "b838ea04827e8fd6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88840:88855:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60909:60910:1'} The data corresponds to corresponds to a snapshot on September 09 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Mean sea level pressure?", + "response": "Based on the provided data, Heard Island and McDonald Islands experienced the lowest average Mean sea level pressure over the specified time-period, with an average Mean sea level pressure of 9.856e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Heard Island and McDonald Islands", + "true_value": "98557.6211516141", + "geofeature": "country", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1272131d561bd8c9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60909:60910:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34346:34349:1'} The data starts from July 05 12:00 and ends on July 06 00:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind in South Atlantic Ocean?", + "response": "Based on the provided data, the median 10-meter V component of wind at South Atlantic Ocean is 1.457 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "South Atlantic Ocean", + "operation": "median", + "variable": "10m_v_component_of_wind", + "true_value": "1.4572079181671143", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f9811016a6f89400", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34346:34349:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57556:57567:1'} The data starts from May 25 00:00 and ends on May 27 12:00. Based on the above data, answer the following question:", + "question": "Which country in Oceania has the highest recorded Surface temperature?", + "response": "Based on the provided data, Australia has the highest recorded Surface temperature among the countries in Oceania with the highest recorded Surface temperature of 305.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Australia", + "true_value": "305.86602783203125", + "geofeature": "country", + "upper_level_location": "Oceania", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "b68b0a83303210c5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57556:57567:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41375:41389:1'} The data starts from April 27 18:00 and ends on May 01 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Mean sea level pressure in Sichuan, China?", + "response": "Based on the provided data, the minimum Mean sea level pressure at Sichuan, China is 9.967e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Sichuan, China", + "operation": "minimum", + "variable": "mean_sea_level_pressure", + "true_value": "99672.0625", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3187b1cf28706d07", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41375:41389:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42924:42933:1'} The data starts from May 19 00:00 and ends on May 21 00:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Surface temperature?", + "response": "Based on the provided data, Antarctica experienced the lowest average Surface temperature over the specified time-period, with an average Surface temperature of 226.8 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "226.79882286755907", + "geofeature": "continent", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "dc51eb3bacccb8ad", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42924:42933:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57902:57926:1'} The data starts from August 19 12:00 and ends on August 25 06:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Geopotential at 100 hPa?", + "response": "Based on the provided data, Asia experienced the highest average Geopotential at 100 hPa over the specified time-period, with an average Geopotential at 100 hPa of 1.64e+05 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Asia", + "true_value": "163959.11306423863", + "geofeature": "continent", + "target_variable": "geopotential_100", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "cd16b9cba03e0cf4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57902:57926:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85627:85655:1'} The data starts from August 10 18:00 and ends on August 17 12:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind in Criuleni, Moldova?", + "response": "Based on the provided data, the median 10-meter V component of wind at Criuleni, Moldova is -2.45 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Criuleni, Moldova", + "operation": "median", + "variable": "10m_v_component_of_wind", + "true_value": "-2.450089931488037", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4f1933507ab98f98", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85627:85655:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53579:53599:1'} The data starts from September 03 18:00 and ends on September 08 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure in Suriname?", + "response": "Based on the provided data, the minimum Surface pressure at Suriname is 9.764e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Suriname", + "operation": "minimum", + "variable": "surface_pressure", + "true_value": "97640.6796875", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "505e16ea5b128dce", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53579:53599:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76742:76765:1'} The data starts from July 12 12:00 and ends on July 18 00:00. Based on the above data, answer the following question:", + "question": "Which river has the highest recorded U (zonal) component of wind at 925 hPa?", + "response": "Based on the provided data, Columbia River has the highest recorded U (zonal) component of wind at 925 hPa among the rivers with the highest recorded U (zonal) component of wind at 925 hPa of 5.253 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Columbia River", + "true_value": "5.252635955810547", + "geofeature": "river", + "upper_level_location": "river", + "target_variable": "u_component_of_wind_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "b081bf7d6534ed13", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76742:76765:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70387:70412:1'} The data starts from March 06 18:00 and ends on March 12 18:00. Based on the above data, answer the following question:", + "question": "What is the median Geopotential at 150 hPa in Persian Gulf?", + "response": "Based on the provided data, the median Geopotential at 150 hPa at Persian Gulf is 1.371e+05 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Persian Gulf", + "operation": "median", + "variable": "geopotential_150", + "true_value": "137101.6875", + "target_variable": "geopotential_150", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "81a00454b2f60ed6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70387:70412:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58707:58723:1'} The data starts from March 08 18:00 and ends on March 12 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average 10-meter U component of wind?", + "response": "Based on the provided data, Oceania experienced the lowest average 10-meter U component of wind over the specified time-period, with an average 10-meter U component of wind of -2.625 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "-2.6250986542258157", + "geofeature": "continent", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ae811b4c101821d7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58707:58723:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44686:44687:1'} The data corresponds to corresponds to a snapshot on August 02 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Temperature at 150 hPa?", + "response": "Based on the provided data, Antarctica experienced the lowest average Temperature at 150 hPa over the specified time-period, with an average Temperature at 150 hPa of 194.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "194.8526867360857", + "geofeature": "continent", + "target_variable": "temperature_150", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "63acc2b7ff65619d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44686:44687:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57075:57091:1'} The data starts from January 24 18:00 and ends on January 28 12:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Norway has the lowest recorded Mean sea level pressure among the countries in Europe with the lowest recorded Mean sea level pressure of 9.659e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Norway", + "true_value": "96585.734375", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5fac4ffc2f0b5699", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57075:57091:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87284:87298:1'} The data starts from September 29 00:00 and ends on October 02 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average V (meridional) component of wind at 925 hPa?", + "response": "Based on the provided data, South Georgia and the Islands experienced the lowest average V (meridional) component of wind at 925 hPa over the specified time-period, with an average V (meridional) component of wind at 925 hPa of -10.99 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "South Georgia and the Islands", + "true_value": "-10.986001364411718", + "geofeature": "country", + "target_variable": "v_component_of_wind_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "73eb83483fa985cd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87284:87298:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29593:29607:1'} The data starts from April 04 06:00 and ends on April 07 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average V (meridional) component of wind at 300 hPa?", + "response": "Based on the provided data, Europe experienced the highest average V (meridional) component of wind at 300 hPa over the specified time-period, with an average V (meridional) component of wind at 300 hPa of 0.3313 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Europe", + "true_value": "0.33131344009658154", + "geofeature": "continent", + "target_variable": "v_component_of_wind_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "200843a08095c03c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29593:29607:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50277:50283:1'} The data starts from May 31 06:00 and ends on June 01 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average 10-meter U component of wind?", + "response": "Based on the provided data, Asia experienced the highest average 10-meter U component of wind over the specified time-period, with an average 10-meter U component of wind of 0.8112 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Asia", + "true_value": "0.8111795698157761", + "geofeature": "continent", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "fc316bfa6f2412d3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50277:50283:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46195:46199:1'} The data starts from August 14 18:00 and ends on August 15 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 300 hPa in North America?", + "response": "Based on the provided data, the maximum U (zonal) component of wind at 300 hPa at North America is 53.76 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "North America", + "operation": "maximum", + "variable": "u_component_of_wind_300", + "true_value": "53.75646209716797", + "target_variable": "u_component_of_wind_300", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "68bc08cad7aa6a81", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46195:46199:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38440:38462:1'} The data starts from April 24 00:00 and ends on April 29 06:00. Based on the above data, answer the following question:", + "question": "Which channel has the highest recorded V (meridional) component of wind at 300 hPa?", + "response": "Based on the provided data, Drake Passage has the highest recorded V (meridional) component of wind at 300 hPa among the channels with the highest recorded V (meridional) component of wind at 300 hPa of 50.36 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Drake Passage", + "true_value": "50.360721588134766", + "geofeature": "channel", + "upper_level_location": "channel", + "target_variable": "v_component_of_wind_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c0575434ee3d2d27", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38440:38462:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46162:46182:1'} The data starts from August 06 12:00 and ends on August 11 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Kangertittivaq experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 9.145e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Kangertittivaq", + "true_value": "91450.74348326557", + "geofeature": "water_body", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5763f226995725e8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46162:46182:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40154:40171:1'} The data starts from June 26 12:00 and ends on June 30 12:00. Based on the above data, answer the following question:", + "question": "Which country in North America has the highest recorded Temperature at 200 hPa?", + "response": "Based on the provided data, Greenland has the highest recorded Temperature at 200 hPa among the countries in North America with the highest recorded Temperature at 200 hPa of 236.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Greenland", + "true_value": "236.93948364257812", + "geofeature": "country", + "upper_level_location": "North America", + "target_variable": "temperature_200", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "8429523fef0a319c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40154:40171:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41504:41529:1'} The data starts from May 30 00:00 and ends on June 05 00:00. Based on the above data, answer the following question:", + "question": "Which state in Kuwait has the highest recorded 10-meter U component of wind?", + "response": "Based on the provided data, Al Jahrah has the highest recorded 10-meter U component of wind among the states in Kuwait with the highest recorded 10-meter U component of wind of 5.531 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Al Jahrah, Kuwait", + "true_value": "5.531399726867676", + "geofeature": "state", + "upper_level_location": "Kuwait", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "86f109fd3760c4ad", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41504:41529:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67482:67491:1'} The data starts from March 10 12:00 and ends on March 12 12:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Russia has the lowest recorded Mean sea level pressure among the countries in Europe with the lowest recorded Mean sea level pressure of 9.569e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Russia", + "true_value": "95690.75", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "fadb328fb6e007dd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67482:67491:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40223:40246:1'} The data starts from July 13 18:00 and ends on July 19 06:00. Based on the above data, answer the following question:", + "question": "What is the average Specific humidity at 600 hPa in Molucca Sea?", + "response": "Based on the provided data, the average Specific humidity at 600 hPa at Molucca Sea is 0.005978 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Molucca Sea", + "operation": "average", + "variable": "specific_humidity_600", + "true_value": "0.005978030935584358", + "target_variable": "specific_humidity_600", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "52c719380176f427", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40223:40246:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47427:47440:1'} The data starts from June 18 18:00 and ends on June 21 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 400 hPa in Moore's Island, The Bahamas?", + "response": "Based on the provided data, the minimum V (meridional) component of wind at 400 hPa at Moore's Island, The Bahamas is -5.489 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Moore's Island, The Bahamas", + "operation": "minimum", + "variable": "v_component_of_wind_400", + "true_value": "-5.489428997039795", + "target_variable": "v_component_of_wind_400", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6d862c7e71f14265", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47427:47440:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80335:80358:1'} The data starts from December 26 18:00 and ends on January 01 06:00 (1 year later). Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average Specific humidity at 600 hPa?", + "response": "Based on the provided data, Bismarck Sea experienced the highest average Specific humidity at 600 hPa over the specified time-period, with an average Specific humidity at 600 hPa of 0.006781 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Bismarck Sea", + "true_value": "0.006780842644403553", + "geofeature": "water_body", + "target_variable": "specific_humidity_600", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "7b1e696c83767afc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80335:80358:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58147:58175:1'} The data starts from October 19 18:00 and ends on October 26 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 925 hPa in British Indian Ocean Territory?", + "response": "Based on the provided data, the minimum Geopotential at 925 hPa at British Indian Ocean Territory is 7486 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "British Indian Ocean Territory", + "operation": "minimum", + "variable": "geopotential_925", + "true_value": "7486.1875", + "target_variable": "geopotential_925", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b30dc6c6abf11e31", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58147:58175:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40999:41009:1'} The data starts from January 23 18:00 and ends on January 26 00:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average U (zonal) component of wind at 200 hPa?", + "response": "Based on the provided data, Asia experienced the highest average U (zonal) component of wind at 200 hPa over the specified time-period, with an average U (zonal) component of wind at 200 hPa of 32.4 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Asia", + "true_value": "32.40373649657564", + "geofeature": "continent", + "target_variable": "u_component_of_wind_200", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "cb7f2284e9ffb50b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40999:41009:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89942:89943:1'} The data corresponds to corresponds to a snapshot on July 24 12:00. Based on the above data, answer the following question:", + "question": "Which bay has the lowest recorded 10-meter U component of wind?", + "response": "Based on the provided data, Sulzberger Bay has the lowest recorded 10-meter U component of wind among the baies with the lowest recorded 10-meter U component of wind of -20.28 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Sulzberger Bay", + "true_value": "-20.284194946289062", + "geofeature": "bay", + "upper_level_location": "bay", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "84df606964e9ee13", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89942:89943:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36370:36397:1'} The data starts from November 23 12:00 and ends on November 30 00:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind in Gulf of Guinea?", + "response": "Based on the provided data, the median 10-meter V component of wind at Gulf of Guinea is 3.014 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Gulf of Guinea", + "operation": "median", + "variable": "10m_v_component_of_wind", + "true_value": "3.0136606693267822", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a59790b3adfbc890", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36370:36397:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91949:91950:1'} The data corresponds to corresponds to a snapshot on December 08 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average Mean sea level pressure?", + "response": "Based on the provided data, Baykonur Cosmodrome experienced the highest average Mean sea level pressure over the specified time-period, with an average Mean sea level pressure of 1.038e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Baykonur Cosmodrome", + "true_value": "103825.73709439696", + "geofeature": "country", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5c88fa61f30e9f95", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91949:91950:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91519:91546:1'} The data starts from August 22 18:00 and ends on August 29 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average 10-meter V component of wind?", + "response": "Based on the provided data, Waddenzee experienced the lowest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of -6.915 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Waddenzee", + "true_value": "-6.914678707944957", + "geofeature": "water_body", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "b4188bdbcd5e9a1c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91519:91546:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88289:88293:1'} The data starts from June 07 06:00 and ends on June 08 00:00. Based on the above data, answer the following question:", + "question": "Which sound has the lowest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Peacock Sound has the lowest recorded 10-meter V component of wind among the sounds with the lowest recorded 10-meter V component of wind of -8.454 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Peacock Sound", + "true_value": "-8.454312324523926", + "geofeature": "sound", + "upper_level_location": "sound", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "3450bd5ae5425f48", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88289:88293:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62012:62039:1'} The data starts from June 12 00:00 and ends on June 18 12:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature in Bransfield Strait?", + "response": "Based on the provided data, the median Surface temperature at Bransfield Strait is 270.2 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Bransfield Strait", + "operation": "median", + "variable": "2m_temperature", + "true_value": "270.1648864746094", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fa22f7e3b088351b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62012:62039:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41992:42019:1'} The data starts from September 29 00:00 and ends on October 05 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average U (zonal) component of wind at 300 hPa?", + "response": "Based on the provided data, Oceania experienced the highest average U (zonal) component of wind at 300 hPa over the specified time-period, with an average U (zonal) component of wind at 300 hPa of 24.45 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "24.4465593508997", + "geofeature": "continent", + "target_variable": "u_component_of_wind_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "5a89984f22584ca1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41992:42019:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42943:42966:1'} The data starts from May 23 18:00 and ends on May 29 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average Temperature at 200 hPa?", + "response": "Based on the provided data, Ireland experienced the highest average Temperature at 200 hPa over the specified time-period, with an average Temperature at 200 hPa of 230.1 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Ireland", + "true_value": "230.0546322696493", + "geofeature": "country", + "target_variable": "temperature_200", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "34fb9564d45f722e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42943:42966:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32625:32629:1'} The data starts from May 01 06:00 and ends on May 02 00:00. Based on the above data, answer the following question:", + "question": "Which country in Africa has the lowest recorded 10-meter U component of wind?", + "response": "Based on the provided data, Libya has the lowest recorded 10-meter U component of wind among the countries in Africa with the lowest recorded 10-meter U component of wind of -8.365 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Libya", + "true_value": "-8.364717483520508", + "geofeature": "country", + "upper_level_location": "Africa", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "e7ab47881b5530ba", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32625:32629:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65979:66002:1'} The data starts from February 28 18:00 and ends on March 05 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum Temperature at 925 hPa in Bohol Sea?", + "response": "Based on the provided data, the maximum Temperature at 925 hPa at Bohol Sea is 296.7 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Bohol Sea", + "operation": "maximum", + "variable": "temperature_925", + "true_value": "296.71722412109375", + "target_variable": "temperature_925", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7632f50be54438b7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65979:66002:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51249:51255:1'} The data starts from January 29 06:00 and ends on January 30 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 300 hPa in Sweden?", + "response": "Based on the provided data, the minimum U (zonal) component of wind at 300 hPa at Sweden is -0.6527 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Sweden", + "operation": "minimum", + "variable": "u_component_of_wind_300", + "true_value": "-0.6527314186096191", + "target_variable": "u_component_of_wind_300", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "21b85984ff9128f3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51249:51255:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91627:91647:1'} The data starts from September 18 18:00 and ends on September 23 12:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average U (zonal) component of wind at 850 hPa?", + "response": "Based on the provided data, Timor Sea experienced the lowest average U (zonal) component of wind at 850 hPa over the specified time-period, with an average U (zonal) component of wind at 850 hPa of -11.87 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Timor Sea", + "true_value": "-11.867404744465219", + "geofeature": "water_body", + "target_variable": "u_component_of_wind_850", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "2c722b87c23aad8c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91627:91647:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60701:60729:1'} The data starts from July 19 06:00 and ends on July 26 00:00. Based on the above data, answer the following question:", + "question": "Which state in Gibraltar has the lowest recorded 10-meter U component of wind?", + "response": "Based on the provided data, Gibraltar has the lowest recorded 10-meter U component of wind among the states in Gibraltar with the lowest recorded 10-meter U component of wind of -4.729 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Gibraltar, Gibraltar", + "true_value": "-4.729496002197266", + "geofeature": "state", + "upper_level_location": "Gibraltar", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "f7378d153a82a4e4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60701:60729:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72779:72803:1'} The data starts from October 24 18:00 and ends on October 30 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 50 hPa in Bight of Benin?", + "response": "Based on the provided data, the maximum Geopotential at 50 hPa at Bight of Benin is 2.025e+05 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Bight of Benin", + "operation": "maximum", + "variable": "geopotential_50", + "true_value": "202510.78125", + "target_variable": "geopotential_50", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "efd4d653b66668b0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72779:72803:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81878:81906:1'} The data starts from January 16 12:00 and ends on January 23 06:00. Based on the above data, answer the following question:", + "question": "Which country in Oceania has the highest recorded Surface pressure?", + "response": "Based on the provided data, New Zealand has the highest recorded Surface pressure among the countries in Oceania with the highest recorded Surface pressure of 1.027e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "New Zealand", + "true_value": "102734.859375", + "geofeature": "country", + "upper_level_location": "Oceania", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "eb7760adfc26db37", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81878:81906:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53126:53154:1'} The data starts from May 13 12:00 and ends on May 20 06:00. Based on the above data, answer the following question:", + "question": "Which reef has the lowest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Great Barrier Reef has the lowest recorded 10-meter V component of wind among the reefs with the lowest recorded 10-meter V component of wind of -4.476 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Great Barrier Reef", + "true_value": "-4.476067543029785", + "geofeature": "reef", + "upper_level_location": "reef", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c362f0fb9033f676", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53126:53154:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40045:40059:1'} The data starts from May 30 06:00 and ends on June 02 12:00. Based on the above data, answer the following question:", + "question": "What is the average Geopotential at 600 hPa in South America?", + "response": "Based on the provided data, the average Geopotential at 600 hPa at South America is 4.293e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "South America", + "operation": "average", + "variable": "geopotential_600", + "true_value": "42934.10332726908", + "target_variable": "geopotential_600", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b95b81cb9a4e693b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40045:40059:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62875:62896:1'} The data starts from January 13 18:00 and ends on January 18 18:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average Geopotential at 850 hPa?", + "response": "Based on the provided data, Portugal experienced the highest average Geopotential at 850 hPa over the specified time-period, with an average Geopotential at 850 hPa of 1.539e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Portugal", + "true_value": "15389.626866379056", + "geofeature": "country", + "target_variable": "geopotential_850", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ee1ffb2917b10902", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62875:62896:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62895:62898:1'} The data starts from January 18 18:00 and ends on January 19 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average 10-meter V component of wind?", + "response": "Based on the provided data, Faroe Islands experienced the highest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of 8.188 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Faroe Islands", + "true_value": "8.188062894236719", + "geofeature": "country", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "71369819b484ee9e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62895:62898:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36879:36889:1'} The data starts from March 29 18:00 and ends on April 01 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure in Sibuyan Sea?", + "response": "Based on the provided data, the maximum Mean sea level pressure at Sibuyan Sea is 1.014e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Sibuyan Sea", + "operation": "maximum", + "variable": "mean_sea_level_pressure", + "true_value": "101443.5625", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d61e803b017feada", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36879:36889:1" + } + } +] \ No newline at end of file diff --git a/level1a_part2.json b/level1a_part2.json new file mode 100644 index 0000000000000000000000000000000000000000..c5837b7154581a7561657f5eb59a99261615bf7e --- /dev/null +++ b/level1a_part2.json @@ -0,0 +1,3472 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75133:75137:1'} The data starts from June 05 06:00 and ends on June 06 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter U component of wind in Al Bahah, Saudi Arabia?", + "response": "Based on the provided data, the minimum 10-meter U component of wind at Al Bahah, Saudi Arabia is -0.4896 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Al Bahah, Saudi Arabia", + "operation": "minimum", + "variable": "10m_u_component_of_wind", + "true_value": "-0.4895903468132019", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "63c67da4883a2a13", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75133:75137:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62819:62839:1'} The data starts from December 30 18:00 and ends on January 04 12:00 (1 year later). Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind in Serranilla Bank?", + "response": "Based on the provided data, the median 10-meter V component of wind at Serranilla Bank is -1.321 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Serranilla Bank", + "operation": "median", + "variable": "10m_v_component_of_wind", + "true_value": "-1.3209784030914307", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f13be2a84fdf7f70", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62819:62839:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41635:41657:1'} The data starts from July 01 18:00 and ends on July 07 00:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Temperature at 500 hPa?", + "response": "Based on the provided data, Antarctica experienced the lowest average Temperature at 500 hPa over the specified time-period, with an average Temperature at 500 hPa of 229.8 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "229.80336705146524", + "geofeature": "continent", + "target_variable": "temperature_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1bf6305aa7d9ea94", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41635:41657:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42760:42782:1'} The data starts from April 08 00:00 and ends on April 13 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind in Barima-Waini, Guyana?", + "response": "Based on the provided data, the maximum 10-meter U component of wind at Barima-Waini, Guyana is -1.482 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Barima-Waini, Guyana", + "operation": "maximum", + "variable": "10m_u_component_of_wind", + "true_value": "-1.4815149307250977", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3a0173de5915b097", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42760:42782:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40482:40493:1'} The data starts from September 16 12:00 and ends on September 19 00:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average 10-meter V component of wind?", + "response": "Based on the provided data, Antarctica experienced the lowest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of -0.4817 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "-0.481748438435901", + "geofeature": "continent", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "dba6bbc8438494d8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40482:40493:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88625:88630:1'} The data starts from August 30 06:00 and ends on August 31 06:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 500 hPa in Norwegian Sea?", + "response": "Based on the provided data, the average V (meridional) component of wind at 500 hPa at Norwegian Sea is 11.44 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Norwegian Sea", + "operation": "average", + "variable": "v_component_of_wind_500", + "true_value": "11.442809129748268", + "target_variable": "v_component_of_wind_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c45e09c6bc94bde3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88625:88630:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85526:85531:1'} The data starts from July 16 12:00 and ends on July 17 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 600 hPa in Wallis and Futuna?", + "response": "Based on the provided data, the minimum U (zonal) component of wind at 600 hPa at Wallis and Futuna is -5.14 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Wallis and Futuna", + "operation": "minimum", + "variable": "u_component_of_wind_600", + "true_value": "-5.139817714691162", + "target_variable": "u_component_of_wind_600", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "eb2cffb049b62410", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85526:85531:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87784:87808:1'} The data starts from February 01 00:00 and ends on February 06 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average 10-meter V component of wind?", + "response": "Based on the provided data, Africa experienced the lowest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of -0.6651 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Africa", + "true_value": "-0.6650955965190947", + "geofeature": "continent", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "d2d115b9026a8dcc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87784:87808:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30138:30142:1'} The data starts from August 18 12:00 and ends on August 19 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average U (zonal) component of wind at 700 hPa?", + "response": "Based on the provided data, Cumberland Sound experienced the highest average U (zonal) component of wind at 700 hPa over the specified time-period, with an average U (zonal) component of wind at 700 hPa of 20.07 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Cumberland Sound", + "true_value": "20.07164860996349", + "geofeature": "water_body", + "target_variable": "u_component_of_wind_700", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "303f4c5dddbda178", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30138:30142:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54161:54166:1'} The data starts from January 27 06:00 and ends on January 28 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average 10-meter V component of wind?", + "response": "Based on the provided data, Goldsmith Channel experienced the lowest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of -7.575 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Goldsmith Channel", + "true_value": "-7.57521161611811", + "geofeature": "water_body", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "df3274de5e4ef6a1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54161:54166:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69150:69173:1'} The data starts from May 01 12:00 and ends on May 07 00:00. Based on the above data, answer the following question:", + "question": "What is the average Geopotential at 150 hPa in INDIAN OCEAN?", + "response": "Based on the provided data, the average Geopotential at 150 hPa at INDIAN OCEAN is 1.354e+05 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "INDIAN OCEAN", + "operation": "average", + "variable": "geopotential_150", + "true_value": "135397.37389868894", + "target_variable": "geopotential_150", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7342d67e93a3be50", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69150:69173:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86484:86488:1'} The data starts from March 13 00:00 and ends on March 13 18:00. Based on the above data, answer the following question:", + "question": "What is the average Geopotential at 500 hPa in Morocco?", + "response": "Based on the provided data, the average Geopotential at 500 hPa at Morocco is 5.684e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Morocco", + "operation": "average", + "variable": "geopotential_500", + "true_value": "56842.39530317404", + "target_variable": "geopotential_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "744b2c3c9a5187fd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86484:86488:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83459:83468:1'} The data starts from February 15 18:00 and ends on February 17 18:00. Based on the above data, answer the following question:", + "question": "Which inlet has the lowest recorded V (meridional) component of wind at 150 hPa?", + "response": "Based on the provided data, Baird Inlet has the lowest recorded V (meridional) component of wind at 150 hPa among the inlets with the lowest recorded V (meridional) component of wind at 150 hPa of -15.36 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Baird Inlet", + "true_value": "-15.360581398010254", + "geofeature": "inlet", + "upper_level_location": "inlet", + "target_variable": "v_component_of_wind_150", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "901de24534f5b29f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83459:83468:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48461:48482:1'} The data starts from March 03 06:00 and ends on March 08 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind in Poland?", + "response": "Based on the provided data, the maximum 10-meter U component of wind at Poland is 13.27 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Poland", + "operation": "maximum", + "variable": "10m_u_component_of_wind", + "true_value": "13.266761779785156", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "79a9ec9ea8e19395", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48461:48482:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82117:82134:1'} The data starts from March 17 06:00 and ends on March 21 06:00. Based on the above data, answer the following question:", + "question": "Which country in South America has the highest recorded Mean sea level pressure?", + "response": "Based on the provided data, Chile has the highest recorded Mean sea level pressure among the countries in South America with the highest recorded Mean sea level pressure of 1.029e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Chile", + "true_value": "102881.3046875", + "geofeature": "country", + "upper_level_location": "South America", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0c03c4a9336322c9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82117:82134:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58325:58341:1'} The data starts from December 03 06:00 and ends on December 07 00:00. Based on the above data, answer the following question:", + "question": "What is the average Temperature at 500 hPa in Oceania?", + "response": "Based on the provided data, the average Temperature at 500 hPa at Oceania is 264.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Oceania", + "operation": "average", + "variable": "temperature_500", + "true_value": "264.88659573289186", + "target_variable": "temperature_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f7b8409069b5fae5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58325:58341:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91469:91473:1'} The data starts from August 10 06:00 and ends on August 11 00:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Mean sea level pressure?", + "response": "Based on the provided data, French Southern and Antarctic Lands experienced the lowest average Mean sea level pressure over the specified time-period, with an average Mean sea level pressure of 9.866e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "French Southern and Antarctic Lands", + "true_value": "98663.57080869573", + "geofeature": "country", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0b49ba13a4e21300", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91469:91473:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43553:43576:1'} The data starts from October 23 06:00 and ends on October 28 18:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average Geopotential at 700 hPa?", + "response": "Based on the provided data, Persian Gulf experienced the highest average Geopotential at 700 hPa over the specified time-period, with an average Geopotential at 700 hPa of 3.125e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Persian Gulf", + "true_value": "31249.58351505904", + "geofeature": "water_body", + "target_variable": "geopotential_700", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1f5a30f6cf0b818a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43553:43576:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70242:70258:1'} The data starts from January 29 12:00 and ends on February 02 06:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Temperature at 150 hPa?", + "response": "Based on the provided data, Antarctica experienced the highest average Temperature at 150 hPa over the specified time-period, with an average Temperature at 150 hPa of 229.1 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "229.08577653608896", + "geofeature": "continent", + "target_variable": "temperature_150", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c15bbfee1fada9e7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70242:70258:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52233:52234:1'} The data corresponds to corresponds to a snapshot on October 02 06:00. Based on the above data, answer the following question:", + "question": "Which sea has the lowest recorded Geopotential at 925 hPa?", + "response": "Based on the provided data, Davis Sea has the lowest recorded Geopotential at 925 hPa among the seas with the lowest recorded Geopotential at 925 hPa of 1887 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Davis Sea", + "true_value": "1886.6719970703125", + "geofeature": "sea", + "upper_level_location": "sea", + "target_variable": "geopotential_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "8a2375fa4fa4ff23", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52233:52234:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69883:69889:1'} The data starts from October 31 18:00 and ends on November 02 00:00. Based on the above data, answer the following question:", + "question": "Which state in Jordan has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Irbid has the lowest recorded Mean sea level pressure among the states in Jordan with the lowest recorded Mean sea level pressure of 1.01e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Irbid, Jordan", + "true_value": "101025.875", + "geofeature": "state", + "upper_level_location": "Jordan", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "4c20b34331ab141b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69883:69889:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91867:91872:1'} The data starts from November 17 18:00 and ends on November 18 18:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature in Oceania?", + "response": "Based on the provided data, the median Surface temperature at Oceania is 298.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Oceania", + "operation": "median", + "variable": "2m_temperature", + "true_value": "298.86578369140625", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bb99c093faba5c82", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91867:91872:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61805:61822:1'} The data starts from April 21 06:00 and ends on April 25 06:00. Based on the above data, answer the following question:", + "question": "Which state in Spratly Islands has the highest recorded V (meridional) component of wind at 850 hPa?", + "response": "Based on the provided data, Spratly Islands has the highest recorded V (meridional) component of wind at 850 hPa among the states in Spratly Islands with the highest recorded V (meridional) component of wind at 850 hPa of 4.054 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Spratly Islands, Spratly Islands", + "true_value": "4.054388999938965", + "geofeature": "state", + "upper_level_location": "Spratly Islands", + "target_variable": "v_component_of_wind_850", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "73865015353cbcd1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61805:61822:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48595:48620:1'} The data starts from April 05 18:00 and ends on April 11 18:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average V (meridional) component of wind at 100 hPa?", + "response": "Based on the provided data, Portugal experienced the lowest average V (meridional) component of wind at 100 hPa over the specified time-period, with an average V (meridional) component of wind at 100 hPa of -14.38 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Portugal", + "true_value": "-14.376862849458876", + "geofeature": "country", + "target_variable": "v_component_of_wind_100", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "06c7656fc96ec294", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48595:48620:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36484:36491:1'} The data starts from December 22 00:00 and ends on December 23 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface temperature in Antarctica?", + "response": "Based on the provided data, the minimum Surface temperature at Antarctica is 233.5 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Antarctica", + "operation": "minimum", + "variable": "2m_temperature", + "true_value": "233.51712036132812", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7884f8ebad0fdf43", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36484:36491:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37262:37264:1'} The data starts from July 03 12:00 and ends on July 03 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure in Davao Gulf?", + "response": "Based on the provided data, the maximum Mean sea level pressure at Davao Gulf is 1.011e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Davao Gulf", + "operation": "maximum", + "variable": "mean_sea_level_pressure", + "true_value": "101103.765625", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "48565578e5fab7f1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37262:37264:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87167:87173:1'} The data starts from August 30 18:00 and ends on September 01 00:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average U (zonal) component of wind at 200 hPa?", + "response": "Based on the provided data, Norfolk Island experienced the highest average U (zonal) component of wind at 200 hPa over the specified time-period, with an average U (zonal) component of wind at 200 hPa of 64.81 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Norfolk Island", + "true_value": "64.81339263916016", + "geofeature": "country", + "target_variable": "u_component_of_wind_200", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "03a4fb396abfb518", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87167:87173:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63353:63357:1'} The data starts from May 13 06:00 and ends on May 14 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind in Maldives?", + "response": "Based on the provided data, the maximum 10-meter U component of wind at Maldives is 7.44 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Maldives", + "operation": "maximum", + "variable": "10m_u_component_of_wind", + "true_value": "7.439510822296143", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a97ab87a71d52d44", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63353:63357:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88564:88566:1'} The data starts from August 15 00:00 and ends on August 15 06:00. Based on the above data, answer the following question:", + "question": "What is the average Temperature at 925 hPa in Ordu, Turkey?", + "response": "Based on the provided data, the average Temperature at 925 hPa at Ordu, Turkey is 301.6 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Ordu, Turkey", + "operation": "average", + "variable": "temperature_925", + "true_value": "301.597498929393", + "target_variable": "temperature_925", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7b1f08875dc07933", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88564:88566:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87168:87189:1'} The data starts from August 31 00:00 and ends on September 05 00:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind in Viesites, Latvia?", + "response": "Based on the provided data, the median 10-meter V component of wind at Viesites, Latvia is -0.4837 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Viesites, Latvia", + "operation": "median", + "variable": "10m_v_component_of_wind", + "true_value": "-0.4836992621421814", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a23eaa22aee2f3dd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87168:87189:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33725:33743:1'} The data starts from January 31 06:00 and ends on February 04 12:00. Based on the above data, answer the following question:", + "question": "What is the average Surface temperature in North America?", + "response": "Based on the provided data, the average Surface temperature at North America is 260.5 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "North America", + "operation": "average", + "variable": "2m_temperature", + "true_value": "260.4972308310104", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "10cd22fd82c20de8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33725:33743:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55569:55572:1'} The data starts from January 13 06:00 and ends on January 13 18:00. Based on the above data, answer the following question:", + "question": "Which inlet has the highest recorded Surface pressure?", + "response": "Based on the provided data, Baird Inlet has the highest recorded Surface pressure among the inlets with the highest recorded Surface pressure of 9.887e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Baird Inlet", + "true_value": "98874.2890625", + "geofeature": "inlet", + "upper_level_location": "inlet", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c6f7c51aebdb8827", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55569:55572:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52979:53001:1'} The data starts from April 06 18:00 and ends on April 12 00:00. Based on the above data, answer the following question:", + "question": "Which channel has the lowest recorded V (meridional) component of wind at 1000 hPa?", + "response": "Based on the provided data, The North Western Passages has the lowest recorded V (meridional) component of wind at 1000 hPa among the channels with the lowest recorded V (meridional) component of wind at 1000 hPa of -15.65 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "The North Western Passages", + "true_value": "-15.650400161743164", + "geofeature": "channel", + "upper_level_location": "channel", + "target_variable": "v_component_of_wind_1000", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "cf176b679e1c2b92", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52979:53001:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41523:41531:1'} The data starts from June 03 18:00 and ends on June 05 12:00. Based on the above data, answer the following question:", + "question": "What is the average Temperature at 300 hPa in Oceania?", + "response": "Based on the provided data, the average Temperature at 300 hPa at Oceania is 234.3 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Oceania", + "operation": "average", + "variable": "temperature_300", + "true_value": "234.27102430163086", + "target_variable": "temperature_300", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "56faae6da41b106f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41523:41531:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41249:41269:1'} The data starts from March 27 06:00 and ends on April 01 00:00. Based on the above data, answer the following question:", + "question": "Which channel has the highest recorded Surface temperature?", + "response": "Based on the provided data, Yucatan Channel has the highest recorded Surface temperature among the channels with the highest recorded Surface temperature of 307.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Yucatan Channel", + "true_value": "307.89044189453125", + "geofeature": "channel", + "upper_level_location": "channel", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "43350dd3d4d45f93", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41249:41269:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33511:33518:1'} The data starts from December 08 18:00 and ends on December 10 06:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 925 hPa in Croatia?", + "response": "Based on the provided data, the median Specific humidity at 925 hPa at Croatia is 0.005191 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Croatia", + "operation": "median", + "variable": "specific_humidity_925", + "true_value": "0.005191122647374868", + "target_variable": "specific_humidity_925", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c38662e92baf303b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33511:33518:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78715:78716:1'} The data corresponds to corresponds to a snapshot on November 16 18:00. Based on the above data, answer the following question:", + "question": "Which country in North America has the lowest recorded U (zonal) component of wind at 250 hPa?", + "response": "Based on the provided data, United States of America has the lowest recorded U (zonal) component of wind at 250 hPa among the countries in North America with the lowest recorded U (zonal) component of wind at 250 hPa of -12.7 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "United States of America", + "true_value": "-12.701025009155273", + "geofeature": "country", + "upper_level_location": "North America", + "target_variable": "u_component_of_wind_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c40be064b15a6029", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78715:78716:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67838:67858:1'} The data starts from June 07 12:00 and ends on June 12 06:00. Based on the above data, answer the following question:", + "question": "Which reef has the lowest recorded Geopotential at 925 hPa?", + "response": "Based on the provided data, Great Barrier Reef has the lowest recorded Geopotential at 925 hPa among the reefs with the lowest recorded Geopotential at 925 hPa of 7418 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Great Barrier Reef", + "true_value": "7418.02783203125", + "geofeature": "reef", + "upper_level_location": "reef", + "target_variable": "geopotential_925", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "f96cf870c4bc3c73", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67838:67858:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82918:82922:1'} The data starts from October 03 12:00 and ends on October 04 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface pressure in Lesotho?", + "response": "Based on the provided data, the maximum Surface pressure at Lesotho is 8.613e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Lesotho", + "operation": "maximum", + "variable": "surface_pressure", + "true_value": "86125.34375", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9fa5488f8e9661e3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82918:82922:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72884:72901:1'} The data starts from November 20 00:00 and ends on November 24 00:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average 10-meter V component of wind?", + "response": "Based on the provided data, Saint Pierre and Miquelon experienced the highest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of 6.696 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Saint Pierre and Miquelon", + "true_value": "6.695894718170166", + "geofeature": "country", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "6dd46223ff970a52", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72884:72901:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85831:85850:1'} The data starts from September 30 18:00 and ends on October 05 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Geopotential at 1000 hPa?", + "response": "Based on the provided data, Bellingshausen Sea experienced the lowest average Geopotential at 1000 hPa over the specified time-period, with an average Geopotential at 1000 hPa of -2167 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Bellingshausen Sea", + "true_value": "-2167.1269187608464", + "geofeature": "water_body", + "target_variable": "geopotential_1000", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "13071fbe2e47eab8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85831:85850:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68372:68400:1'} The data starts from October 19 00:00 and ends on October 25 18:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Antarctica experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 6.9e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "68995.03306080091", + "geofeature": "continent", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "dcf22aea67a9709e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68372:68400:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34847:34863:1'} The data starts from November 07 18:00 and ends on November 11 12:00. Based on the above data, answer the following question:", + "question": "Which inlet has the lowest recorded Temperature at 700 hPa?", + "response": "Based on the provided data, Baird Inlet has the lowest recorded Temperature at 700 hPa among the inlets with the lowest recorded Temperature at 700 hPa of 253.4 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Baird Inlet", + "true_value": "253.40582275390625", + "geofeature": "inlet", + "upper_level_location": "inlet", + "target_variable": "temperature_700", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ee1b351b20a308bc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34847:34863:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57748:57750:1'} The data starts from July 12 00:00 and ends on July 12 06:00. Based on the above data, answer the following question:", + "question": "What is the median Mean sea level pressure in South Pacific Ocean?", + "response": "Based on the provided data, the median Mean sea level pressure at South Pacific Ocean is 1.014e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "South Pacific Ocean", + "operation": "median", + "variable": "mean_sea_level_pressure", + "true_value": "101441.34375", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1b71e4fc93bec4a9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57748:57750:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59433:59460:1'} The data starts from September 06 06:00 and ends on September 12 18:00. Based on the above data, answer the following question:", + "question": "Which fjord has the lowest recorded 10-meter U component of wind?", + "response": "Based on the provided data, Storfjorden has the lowest recorded 10-meter U component of wind among the fjords with the lowest recorded 10-meter U component of wind of -12.99 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Storfjorden", + "true_value": "-12.992912292480469", + "geofeature": "fjord", + "upper_level_location": "fjord", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "f95f853ab551220a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59433:59460:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86564:86578:1'} The data starts from April 02 00:00 and ends on April 05 06:00. Based on the above data, answer the following question:", + "question": "Which bay has the highest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Bay of Biscay has the highest recorded 10-meter V component of wind among the baies with the highest recorded 10-meter V component of wind of 15.71 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Bay of Biscay", + "true_value": "15.712278366088867", + "geofeature": "bay", + "upper_level_location": "bay", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "7317658424206427", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86564:86578:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29580:29598:1'} The data starts from April 01 00:00 and ends on April 05 06:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Surface pressure?", + "response": "Based on the provided data, Oceania experienced the highest average Surface pressure over the specified time-period, with an average Surface pressure of 9.815e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Oceania", + "true_value": "98151.22560827783", + "geofeature": "continent", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "798d5da52d3f5e01", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29580:29598:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73470:73473:1'} The data starts from April 15 12:00 and ends on April 16 00:00. Based on the above data, answer the following question:", + "question": "Which country in Asia has the highest recorded Specific humidity at 500 hPa?", + "response": "Based on the provided data, Indonesia has the highest recorded Specific humidity at 500 hPa among the countries in Asia with the highest recorded Specific humidity at 500 hPa of 0.005618 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Indonesia", + "true_value": "0.0056175910867750645", + "geofeature": "country", + "upper_level_location": "Asia", + "target_variable": "specific_humidity_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "f2be9e6fd8c24479", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73470:73473:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32783:32809:1'} The data starts from June 09 18:00 and ends on June 16 00:00. Based on the above data, answer the following question:", + "question": "Which channel has the highest recorded V (meridional) component of wind at 300 hPa?", + "response": "Based on the provided data, Ronne Entrance has the highest recorded V (meridional) component of wind at 300 hPa among the channels with the highest recorded V (meridional) component of wind at 300 hPa of 33.17 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Ronne Entrance", + "true_value": "33.172218322753906", + "geofeature": "channel", + "upper_level_location": "channel", + "target_variable": "v_component_of_wind_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "87826469d0e2bee7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32783:32809:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42901:42928:1'} The data starts from May 13 06:00 and ends on May 19 18:00. Based on the above data, answer the following question:", + "question": "Which country in Africa has the lowest recorded 10-meter U component of wind?", + "response": "Based on the provided data, Libya has the lowest recorded 10-meter U component of wind among the countries in Africa with the lowest recorded 10-meter U component of wind of -10.18 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Libya", + "true_value": "-10.18381118774414", + "geofeature": "country", + "upper_level_location": "Africa", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "687bb0a1668ff03b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42901:42928:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83459:83470:1'} The data starts from February 15 18:00 and ends on February 18 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum V (meridional) component of wind at 150 hPa in Europe?", + "response": "Based on the provided data, the maximum V (meridional) component of wind at 150 hPa at Europe is 43.56 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Europe", + "operation": "maximum", + "variable": "v_component_of_wind_150", + "true_value": "43.5638427734375", + "target_variable": "v_component_of_wind_150", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "647aad4ab69d2746", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83459:83470:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68263:68265:1'} The data starts from September 21 18:00 and ends on September 22 00:00. Based on the above data, answer the following question:", + "question": "Which inlet has the highest recorded 10-meter V component of wind?", + "response": "Based on the provided data, Baird Inlet has the highest recorded 10-meter V component of wind among the inlets with the highest recorded 10-meter V component of wind of 14.61 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Baird Inlet", + "true_value": "14.614127159118652", + "geofeature": "inlet", + "upper_level_location": "inlet", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "02c5de2070111124", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68263:68265:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32392:32401:1'} The data starts from March 04 00:00 and ends on March 06 00:00. Based on the above data, answer the following question:", + "question": "Which state in Hong Kong S.A.R. has the highest recorded Surface temperature?", + "response": "Based on the provided data, North has the highest recorded Surface temperature among the states in Hong Kong S.A.R. with the highest recorded Surface temperature of 292.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "North, Hong Kong S.A.R.", + "true_value": "292.9151611328125", + "geofeature": "state", + "upper_level_location": "Hong Kong S.A.R.", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "43da9cf6e13412ae", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32392:32401:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42874:42892:1'} The data starts from May 06 12:00 and ends on May 10 18:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average 10-meter U component of wind?", + "response": "Based on the provided data, Niue experienced the lowest average 10-meter U component of wind over the specified time-period, with an average 10-meter U component of wind of -9.204 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Niue", + "true_value": "-9.20378303527832", + "geofeature": "country", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1a0069bbe05ec2c5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42874:42892:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92489:92507:1'} The data starts from April 22 06:00 and ends on April 26 12:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 500 hPa in Mauritius?", + "response": "Based on the provided data, the median V (meridional) component of wind at 500 hPa at Mauritius is 6.996 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Mauritius", + "operation": "median", + "variable": "v_component_of_wind_500", + "true_value": "6.995573997497559", + "target_variable": "v_component_of_wind_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "36fe73065c1d2643", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92489:92507:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43215:43225:1'} The data starts from July 30 18:00 and ends on August 02 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface pressure in Monaghan, Ireland?", + "response": "Based on the provided data, the maximum Surface pressure at Monaghan, Ireland is 1.009e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Monaghan, Ireland", + "operation": "maximum", + "variable": "surface_pressure", + "true_value": "100934.59375", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "61215f1849b38b14", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43215:43225:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43416:43417:1'} The data corresponds to corresponds to a snapshot on September 19 00:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average 10-meter V component of wind?", + "response": "Based on the provided data, Norfolk Island experienced the lowest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of -6.105 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Norfolk Island", + "true_value": "-6.104948043823242", + "geofeature": "country", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "11c03fc42959b42b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43416:43417:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59858:59879:1'} The data starts from December 21 12:00 and ends on December 26 12:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average 10-meter V component of wind?", + "response": "Based on the provided data, Boknafjorden experienced the highest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of 10.63 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Boknafjorden", + "true_value": "10.628810765781678", + "geofeature": "water_body", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "89983dc53089bd30", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59858:59879:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66990:66999:1'} The data starts from November 07 12:00 and ends on November 09 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average U (zonal) component of wind at 50 hPa?", + "response": "Based on the provided data, North America experienced the highest average U (zonal) component of wind at 50 hPa over the specified time-period, with an average U (zonal) component of wind at 50 hPa of 11.75 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "North America", + "true_value": "11.750529018495206", + "geofeature": "continent", + "target_variable": "u_component_of_wind_50", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "db066d8e7aaa1ee5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66990:66999:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88898:88905:1'} The data starts from November 06 12:00 and ends on November 08 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 100 hPa in Azerbaijan?", + "response": "Based on the provided data, the minimum V (meridional) component of wind at 100 hPa at Azerbaijan is -11.72 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Azerbaijan", + "operation": "minimum", + "variable": "v_component_of_wind_100", + "true_value": "-11.72215747833252", + "target_variable": "v_component_of_wind_100", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3fbcb79a4ae7a9a3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88898:88905:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93148:93171:1'} The data starts from October 04 00:00 and ends on October 09 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Specific humidity at 100 hPa in Chad?", + "response": "Based on the provided data, the maximum Specific humidity at 100 hPa at Chad is 3.896e-06 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Chad", + "operation": "maximum", + "variable": "specific_humidity_100", + "true_value": "3.895861027558567e-06", + "target_variable": "specific_humidity_100", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7222feafef25d945", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93148:93171:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92614:92618:1'} The data starts from May 23 12:00 and ends on May 24 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Surface pressure?", + "response": "Based on the provided data, Kangertittivaq experienced the lowest average Surface pressure over the specified time-period, with an average Surface pressure of 9.055e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Kangertittivaq", + "true_value": "90547.77477483467", + "geofeature": "water_body", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "05a878ff7ca99c6f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92614:92618:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41096:41097:1'} The data corresponds to corresponds to a snapshot on February 17 00:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the lowest recorded Specific humidity at 600 hPa?", + "response": "Based on the provided data, Russia has the lowest recorded Specific humidity at 600 hPa among the countries in Europe with the lowest recorded Specific humidity at 600 hPa of 3.535e-05 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Russia", + "true_value": "3.53467657987494e-05", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "specific_humidity_600", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "81111d76557ec507", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41096:41097:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79465:79470:1'} The data starts from May 23 06:00 and ends on May 24 06:00. Based on the above data, answer the following question:", + "question": "Which state in Peru has the highest recorded Surface pressure?", + "response": "Based on the provided data, Lima has the highest recorded Surface pressure among the states in Peru with the highest recorded Surface pressure of 1.016e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Lima, Peru", + "true_value": "101559.421875", + "geofeature": "state", + "upper_level_location": "Peru", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "f8b2cd894e3e0d99", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79465:79470:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67991:68004:1'} The data starts from July 15 18:00 and ends on July 18 18:00. Based on the above data, answer the following question:", + "question": "What is the median Geopotential at 600 hPa in Holguín, Cuba?", + "response": "Based on the provided data, the median Geopotential at 600 hPa at Holguín, Cuba is 4.368e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Holguín, Cuba", + "operation": "median", + "variable": "geopotential_600", + "true_value": "43675.9609375", + "target_variable": "geopotential_600", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1cd06f2862d483d1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67991:68004:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47016:47023:1'} The data starts from March 08 00:00 and ends on March 09 12:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 200 hPa in Jersey?", + "response": "Based on the provided data, the average U (zonal) component of wind at 200 hPa at Jersey is 3.338 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Jersey", + "operation": "average", + "variable": "u_component_of_wind_200", + "true_value": "3.337537697383335", + "target_variable": "u_component_of_wind_200", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4b604c7674397745", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47016:47023:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40777:40794:1'} The data starts from November 29 06:00 and ends on December 03 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average U (zonal) component of wind at 250 hPa?", + "response": "Based on the provided data, South Korea experienced the highest average U (zonal) component of wind at 250 hPa over the specified time-period, with an average U (zonal) component of wind at 250 hPa of 56.28 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "South Korea", + "true_value": "56.276791019408144", + "geofeature": "country", + "target_variable": "u_component_of_wind_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "7d0a30d41f44229c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40777:40794:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90019:90020:1'} The data corresponds to corresponds to a snapshot on August 12 18:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 150 hPa in Antarctica?", + "response": "Based on the provided data, the average U (zonal) component of wind at 150 hPa at Antarctica is -0.7797 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Antarctica", + "operation": "average", + "variable": "u_component_of_wind_150", + "true_value": "-0.7797030030043979", + "target_variable": "u_component_of_wind_150", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d26cc16c5d4aab2c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90019:90020:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59171:59178:1'} The data starts from July 02 18:00 and ends on July 04 06:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average Geopotential at 250 hPa?", + "response": "Based on the provided data, Porpoise Bay experienced the lowest average Geopotential at 250 hPa over the specified time-period, with an average Geopotential at 250 hPa of 9.094e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Porpoise Bay", + "true_value": "90942.13850991784", + "geofeature": "water_body", + "target_variable": "geopotential_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ccd9896cb886c8db", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59171:59178:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48334:48343:1'} The data starts from January 31 12:00 and ends on February 02 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 700 hPa in Alboran Sea?", + "response": "Based on the provided data, the maximum U (zonal) component of wind at 700 hPa at Alboran Sea is 5.335 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Alboran Sea", + "operation": "maximum", + "variable": "u_component_of_wind_700", + "true_value": "5.334851264953613", + "target_variable": "u_component_of_wind_700", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "66272111b07b64c9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48334:48343:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82578:82599:1'} The data starts from July 10 12:00 and ends on July 15 12:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the lowest average V (meridional) component of wind at 100 hPa?", + "response": "Based on the provided data, Lagoa dos Patos experienced the lowest average V (meridional) component of wind at 100 hPa over the specified time-period, with an average V (meridional) component of wind at 100 hPa of -9.74 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Lagoa dos Patos", + "true_value": "-9.739831029680436", + "geofeature": "water_body", + "target_variable": "v_component_of_wind_100", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "a61e5fcc65689959", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82578:82599:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33645:33655:1'} The data starts from January 11 06:00 and ends on January 13 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure in Germany?", + "response": "Based on the provided data, the minimum Surface pressure at Germany is 9.116e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Germany", + "operation": "minimum", + "variable": "surface_pressure", + "true_value": "91157.234375", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4722c69c475c227b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33645:33655:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42673:42697:1'} The data starts from March 17 06:00 and ends on March 23 00:00. Based on the above data, answer the following question:", + "question": "Which state in Montenegro has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Rožaje has the lowest recorded Mean sea level pressure among the states in Montenegro with the lowest recorded Mean sea level pressure of 9.974e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Rožaje, Montenegro", + "true_value": "99737.0546875", + "geofeature": "state", + "upper_level_location": "Montenegro", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "c8d9fb51d3430eea", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42673:42697:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79749:79776:1'} The data starts from August 02 06:00 and ends on August 08 18:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 500 hPa in Bay of Plenty?", + "response": "Based on the provided data, the average U (zonal) component of wind at 500 hPa at Bay of Plenty is 7.152 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Bay of Plenty", + "operation": "average", + "variable": "u_component_of_wind_500", + "true_value": "7.152424346851115", + "target_variable": "u_component_of_wind_500", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "443f217bdd2639a1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79749:79776:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50706:50724:1'} The data starts from September 15 12:00 and ends on September 19 18:00. Based on the above data, answer the following question:", + "question": "What is the median Surface pressure in Bahía Blanca?", + "response": "Based on the provided data, the median Surface pressure at Bahía Blanca is 1.004e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Bahía Blanca", + "operation": "median", + "variable": "surface_pressure", + "true_value": "100405.0625", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2461db3ed97b4439", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50706:50724:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53003:53010:1'} The data starts from April 12 18:00 and ends on April 14 06:00. Based on the above data, answer the following question:", + "question": "Which reef has the highest recorded Surface pressure?", + "response": "Based on the provided data, Great Barrier Reef has the highest recorded Surface pressure among the reefs with the highest recorded Surface pressure of 1.021e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Great Barrier Reef", + "true_value": "102056.2421875", + "geofeature": "reef", + "upper_level_location": "reef", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "fa6bc79e51bed50c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53003:53010:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76945:76967:1'} The data starts from September 01 06:00 and ends on September 06 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Geopotential at 600 hPa?", + "response": "Based on the provided data, Africa experienced the highest average Geopotential at 600 hPa over the specified time-period, with an average Geopotential at 600 hPa of 4.347e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Africa", + "true_value": "43465.31252884888", + "geofeature": "continent", + "target_variable": "geopotential_600", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "87630f4bbc50d8f5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76945:76967:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31348:31373:1'} The data starts from June 16 00:00 and ends on June 22 00:00. Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure in Dakhlet Nouadhibou, Mauritania?", + "response": "Based on the provided data, the average Mean sea level pressure at Dakhlet Nouadhibou, Mauritania is 1.013e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Dakhlet Nouadhibou, Mauritania", + "operation": "average", + "variable": "mean_sea_level_pressure", + "true_value": "101345.24189263849", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f46abbc6069f2a62", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31348:31373:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60019:60026:1'} The data starts from January 30 18:00 and ends on February 01 06:00. Based on the above data, answer the following question:", + "question": "Which state in Slovakia has the lowest recorded Surface temperature?", + "response": "Based on the provided data, Žilinský has the lowest recorded Surface temperature among the states in Slovakia with the lowest recorded Surface temperature of 273.1 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Žilinský, Slovakia", + "true_value": "273.0751953125", + "geofeature": "state", + "upper_level_location": "Slovakia", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "af1e7082e3e2e5db", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60019:60026:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51773:51778:1'} The data starts from June 09 06:00 and ends on June 10 06:00. Based on the above data, answer the following question:", + "question": "Which country experienced the highest average 10-meter U component of wind?", + "response": "Based on the provided data, Heard Island and McDonald Islands experienced the highest average 10-meter U component of wind over the specified time-period, with an average 10-meter U component of wind of 10.66 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Heard Island and McDonald Islands", + "true_value": "10.664501451882591", + "geofeature": "country", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "15ec9c2c98a519a5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51773:51778:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47853:47872:1'} The data starts from October 03 06:00 and ends on October 07 18:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average 10-meter V component of wind?", + "response": "Based on the provided data, Iceland experienced the lowest average 10-meter V component of wind over the specified time-period, with an average 10-meter V component of wind of -7.348 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Iceland", + "true_value": "-7.34845983907767", + "geofeature": "country", + "target_variable": "10m_v_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "2fc73ab13e117c38", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47853:47872:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80136:80153:1'} The data starts from November 07 00:00 and ends on November 11 00:00. Based on the above data, answer the following question:", + "question": "Which state in Heard Island and McDonald Islands has the highest recorded U (zonal) component of wind at 100 hPa?", + "response": "Based on the provided data, Heard Island and McDonald Islands has the highest recorded U (zonal) component of wind at 100 hPa among the states in Heard Island and McDonald Islands with the highest recorded U (zonal) component of wind at 100 hPa of 28.3 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Heard Island and McDonald Islands, Heard Island and McDonald Islands", + "true_value": "28.303266525268555", + "geofeature": "state", + "upper_level_location": "Heard Island and McDonald Islands", + "target_variable": "u_component_of_wind_100", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "50e61df72a65a456", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80136:80153:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58365:58386:1'} The data starts from December 13 06:00 and ends on December 18 06:00. Based on the above data, answer the following question:", + "question": "Which sound has the highest recorded Surface temperature?", + "response": "Based on the provided data, Pamlico Sound has the highest recorded Surface temperature among the sounds with the highest recorded Surface temperature of 293.3 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Pamlico Sound", + "true_value": "293.3058776855469", + "geofeature": "sound", + "upper_level_location": "sound", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1b3a82bdd50c41b5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58365:58386:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86975:86978:1'} The data starts from July 13 18:00 and ends on July 14 06:00. Based on the above data, answer the following question:", + "question": "What is the median Mean sea level pressure in Poland?", + "response": "Based on the provided data, the median Mean sea level pressure at Poland is 1.014e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Poland", + "operation": "median", + "variable": "mean_sea_level_pressure", + "true_value": "101444.9375", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cd2590c1949309d0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86975:86978:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78037:78042:1'} The data starts from May 31 06:00 and ends on June 01 06:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature in Turks and Caicos Islands?", + "response": "Based on the provided data, the median Surface temperature at Turks and Caicos Islands is 300.3 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Turks and Caicos Islands", + "operation": "median", + "variable": "2m_temperature", + "true_value": "300.32135009765625", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f4d491e28a07de62", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78037:78042:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62754:62758:1'} The data starts from December 14 12:00 and ends on December 15 06:00. Based on the above data, answer the following question:", + "question": "Which country in Europe has the lowest recorded Surface temperature?", + "response": "Based on the provided data, Russia has the lowest recorded Surface temperature among the countries in Europe with the lowest recorded Surface temperature of 221.3 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Russia", + "true_value": "221.3047637939453", + "geofeature": "country", + "upper_level_location": "Europe", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "387c2a96ab33763c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62754:62758:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81829:81836:1'} The data starts from January 04 06:00 and ends on January 05 18:00. Based on the above data, answer the following question:", + "question": "Which country experienced the lowest average Temperature at 700 hPa?", + "response": "Based on the provided data, Greenland experienced the lowest average Temperature at 700 hPa over the specified time-period, with an average Temperature at 700 hPa of 248.2 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Greenland", + "true_value": "248.23838740798175", + "geofeature": "country", + "target_variable": "temperature_700", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1ad5b568dcf91705", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81829:81836:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68570:68586:1'} The data starts from December 07 12:00 and ends on December 11 06:00. Based on the above data, answer the following question:", + "question": "Which inlet has the highest recorded Geopotential at 600 hPa?", + "response": "Based on the provided data, Baird Inlet has the highest recorded Geopotential at 600 hPa among the inlets with the highest recorded Geopotential at 600 hPa of 3.818e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Baird Inlet", + "true_value": "38176.93359375", + "geofeature": "inlet", + "upper_level_location": "inlet", + "target_variable": "geopotential_600", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "3d746a981b4a36b8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68570:68586:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70634:70658:1'} The data starts from May 07 12:00 and ends on May 13 06:00. Based on the above data, answer the following question:", + "question": "What is the average Specific humidity at 200 hPa in North America?", + "response": "Based on the provided data, the average Specific humidity at 200 hPa at North America is 1.427e-05 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "North America", + "operation": "average", + "variable": "specific_humidity_200", + "true_value": "1.426641188371634e-05", + "target_variable": "specific_humidity_200", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5089b6e7d9c7c7ec", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70634:70658:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57177:57187:1'} The data starts from February 19 06:00 and ends on February 21 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface pressure in Ghardaïa, Algeria?", + "response": "Based on the provided data, the maximum Surface pressure at Ghardaïa, Algeria is 9.844e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Ghardaïa, Algeria", + "operation": "maximum", + "variable": "surface_pressure", + "true_value": "98443.984375", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "907e2ce09523e063", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57177:57187:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61033:61053:1'} The data starts from October 10 06:00 and ends on October 15 00:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average Geopotential at 250 hPa?", + "response": "Based on the provided data, Antarctica experienced the lowest average Geopotential at 250 hPa over the specified time-period, with an average Geopotential at 250 hPa of 9.095e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "90948.0736139993", + "geofeature": "continent", + "target_variable": "geopotential_250", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "01da084cda69321b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61033:61053:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67275:67289:1'} The data starts from January 17 18:00 and ends on January 21 00:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature in Zanjan, Iran?", + "response": "Based on the provided data, the median Surface temperature at Zanjan, Iran is 269.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Zanjan, Iran", + "operation": "median", + "variable": "2m_temperature", + "true_value": "269.9052734375", + "target_variable": "2m_temperature", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6ce931c1fced8aec", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67275:67289:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46863:46885:1'} The data starts from January 28 18:00 and ends on February 03 00:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the lowest average U (zonal) component of wind at 300 hPa?", + "response": "Based on the provided data, Antarctica experienced the lowest average U (zonal) component of wind at 300 hPa over the specified time-period, with an average U (zonal) component of wind at 300 hPa of 1.628 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Antarctica", + "true_value": "1.628038685892737", + "geofeature": "continent", + "target_variable": "u_component_of_wind_300", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "0a8da421d0f26ebb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46863:46885:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64155:64174:1'} The data starts from November 29 18:00 and ends on December 04 06:00. Based on the above data, answer the following question:", + "question": "Which state in United States Virgin Islands has the lowest recorded Mean sea level pressure?", + "response": "Based on the provided data, Saint Croix has the lowest recorded Mean sea level pressure among the states in United States Virgin Islands with the lowest recorded Mean sea level pressure of 1.014e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Saint Croix, United States Virgin Islands", + "true_value": "101380.7265625", + "geofeature": "state", + "upper_level_location": "United States Virgin Islands", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "1b44cd6924e8c58d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64155:64174:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65212:65219:1'} The data starts from August 21 00:00 and ends on August 22 12:00. Based on the above data, answer the following question:", + "question": "Which country in North America has the lowest recorded 10-meter U component of wind?", + "response": "Based on the provided data, Canada has the lowest recorded 10-meter U component of wind among the countries in North America with the lowest recorded 10-meter U component of wind of -15.77 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Canada", + "true_value": "-15.768387794494629", + "geofeature": "country", + "upper_level_location": "North America", + "target_variable": "10m_u_component_of_wind", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "6b637bb7c306aab3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65212:65219:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45424:45426:1'} The data starts from February 03 00:00 and ends on February 03 06:00. Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure in Øresund?", + "response": "Based on the provided data, the average Mean sea level pressure at Øresund is 1.007e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Øresund", + "operation": "average", + "variable": "mean_sea_level_pressure", + "true_value": "100693.33984375", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c9fd58c0faa6271c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45424:45426:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69564:69589:1'} The data starts from August 13 00:00 and ends on August 19 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Mean sea level pressure in Antarctica?", + "response": "Based on the provided data, the minimum Mean sea level pressure at Antarctica is 9.64e+04 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "dvzBtE", + "location": "Antarctica", + "operation": "minimum", + "variable": "mean_sea_level_pressure", + "true_value": "96403.2578125", + "target_variable": "mean_sea_level_pressure", + "level": "1a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bdac050467d40b04", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69564:69589:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48285:48292:1'} The data starts from January 19 06:00 and ends on January 20 18:00. Based on the above data, answer the following question:", + "question": "Which water_body experienced the highest average Surface pressure?", + "response": "Based on the provided data, Yellow Sea experienced the highest average Surface pressure over the specified time-period, with an average Surface pressure of 1.031e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Yellow Sea", + "true_value": "103073.52259251192", + "geofeature": "water_body", + "target_variable": "surface_pressure", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "a219a702345eb4fa", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48285:48292:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56953:56954:1'} The data corresponds to corresponds to a snapshot on December 25 06:00. Based on the above data, answer the following question:", + "question": "Which state in Latvia has the lowest recorded U (zonal) component of wind at 500 hPa?", + "response": "Based on the provided data, Nauksenu has the lowest recorded U (zonal) component of wind at 500 hPa among the states in Latvia with the lowest recorded U (zonal) component of wind at 500 hPa of 10.91 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "tshMBh", + "true_location": "Nauksenu, Latvia", + "true_value": "10.90519905090332", + "geofeature": "state", + "upper_level_location": "Latvia", + "target_variable": "u_component_of_wind_500", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "95af456e1f8e6c87", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56953:56954:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30163:30167:1'} The data starts from August 24 18:00 and ends on August 25 12:00. Based on the above data, answer the following question:", + "question": "Which continent experienced the highest average Specific humidity at 700 hPa?", + "response": "Based on the provided data, Africa experienced the highest average Specific humidity at 700 hPa over the specified time-period, with an average Specific humidity at 700 hPa of 0.005652 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "QaajpN", + "true_location": "Africa", + "true_value": "0.0056517200258234535", + "geofeature": "continent", + "target_variable": "specific_humidity_700", + "level": "1a", + "eval_type": "location", + "forced_extreme_window": false, + "task_id": "ee45b8647a4158ea", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30163:30167:1" + } + } +] \ No newline at end of file diff --git a/level1b_part0.json b/level1b_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..a297c1433bc1fd86f64d164d60258f0c9bece3e3 --- /dev/null +++ b/level1b_part0.json @@ -0,0 +1,3302 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65786:65790:1'} The data starts from January 11 12:00 and ends on January 12 06:00. Based on the above data, answer the following question:", + "question": "What is the median Surface pressure experienced by English Channel at 0 hours from the initial timeframe?", + "response": "Based on the provided data, English Channel experienced an median Surface pressure of 1.002e+05 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "100220.625", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7d7636bbe132f824", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65786:65790:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91988:92010:1'} The data starts from December 18 00:00 and ends on December 23 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Trinidad and Tobago experience its highest Geopotential at 500 hPa?", + "response": "Based on the provided data, Trinidad and Tobago experienced its highest Geopotential at 500 hPa at 108 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "57797.457", + "true_value": 108, + "target_variable": "geopotential_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8b9169a03bcb152f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91988:92010:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51512:51522:1'} The data starts from April 05 00:00 and ends on April 07 06:00. Based on the above data, answer the following question:", + "question": "What is the average Geopotential at 250 hPa experienced by Saint Pierre and Miquelon at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Saint Pierre and Miquelon experienced an average Geopotential at 250 hPa of 1.004e+05 m²/s² at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "100370.4296875", + "target_variable": "geopotential_250", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e9cd3c9e4be7d62e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51512:51522:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71518:71542:1'} The data starts from December 14 12:00 and ends on December 20 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum Temperature at 300 hPa experienced by Iligan, Philippines at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Iligan, Philippines experienced an minimum Temperature at 300 hPa of 242.7 K at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "242.67491149902344", + "target_variable": "temperature_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6462cbe8686f93ff", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71518:71542:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76071:76099:1'} The data starts from January 25 18:00 and ends on February 01 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Ostrobothnia, Finland experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Ostrobothnia, Finland experienced its highest 10-meter V component of wind at 132 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "8.714817", + "true_value": 132, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "413a8ff9fd7a25b7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76071:76099:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59779:59804:1'} The data starts from December 01 18:00 and ends on December 07 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Oceania experience its lowest Surface temperature?", + "response": "Based on the provided data, Oceania experienced its lowest Surface temperature at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "276.33218", + "true_value": 12, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "14f2ae6b64152c4b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59779:59804:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75816:75828:1'} The data starts from November 23 00:00 and ends on November 25 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Specific humidity at 300 hPa experienced by Laccadive Sea at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Laccadive Sea experienced an maximum Specific humidity at 300 hPa of 0.0006871 kg/kg at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "0.0006870570359751582", + "target_variable": "specific_humidity_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8c2c8232366e09b3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75816:75828:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48191:48204:1'} The data starts from December 26 18:00 and ends on December 29 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Viscount Melville Sound experience its highest Specific humidity at 250 hPa?", + "response": "Based on the provided data, Viscount Melville Sound experienced its highest Specific humidity at 250 hPa at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "1.4592131e-05", + "true_value": 72, + "target_variable": "specific_humidity_250", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "0c3d40f19cd20de2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48191:48204:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48891:48904:1'} The data starts from June 18 18:00 and ends on June 21 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did South America experience its highest Temperature at 850 hPa?", + "response": "Based on the provided data, South America experienced its highest Temperature at 850 hPa at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "295.02777", + "true_value": 48, + "target_variable": "temperature_850", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8ccdd8d26b9810f6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48891:48904:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59103:59108:1'} The data starts from June 15 18:00 and ends on June 16 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Pohnpei, Federated States of Micronesia experience its highest Mean sea level pressure?", + "response": "Based on the provided data, Pohnpei, Federated States of Micronesia experienced its highest Mean sea level pressure at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101212.695", + "true_value": 18, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "eb4435dc53cbc848", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59103:59108:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48217:48245:1'} The data starts from January 02 06:00 and ends on January 09 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Øresund experience its lowest Surface pressure?", + "response": "Based on the provided data, Øresund experienced its lowest Surface pressure at 162 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "99657.62", + "true_value": 162, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "7bd7337cd6791da6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48217:48245:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88441:88444:1'} The data starts from July 15 06:00 and ends on July 15 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum V (meridional) component of wind at 925 hPa experienced by Yucatan Channel at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Yucatan Channel experienced an maximum V (meridional) component of wind at 925 hPa of 2.783 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "2.7826759815216064", + "target_variable": "v_component_of_wind_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "79afb0b5a4250f64", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88441:88444:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85393:85396:1'} The data starts from June 13 06:00 and ends on June 13 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Antarctica experience its lowest 10-meter U component of wind?", + "response": "Based on the provided data, Antarctica experienced its lowest 10-meter U component of wind at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-19.430695", + "true_value": 0, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8b96ef0193775b4a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85393:85396:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74687:74701:1'} The data starts from February 13 18:00 and ends on February 17 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Ionian Sea experience its lowest Specific humidity at 925 hPa?", + "response": "Based on the provided data, Ionian Sea experienced its lowest Specific humidity at 925 hPa at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.0016853493", + "true_value": 60, + "target_variable": "specific_humidity_925", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "3f6a227067bd653f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74687:74701:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74530:74543:1'} The data starts from January 05 12:00 and ends on January 08 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did El Oro, Ecuador experience its lowest U (zonal) component of wind at 400 hPa?", + "response": "Based on the provided data, El Oro, Ecuador experienced its lowest U (zonal) component of wind at 400 hPa at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-14.209126", + "true_value": 54, + "target_variable": "u_component_of_wind_400", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "98906ad4dc8d4ebb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74530:74543:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85237:85257:1'} The data starts from May 05 06:00 and ends on May 10 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Celebes Sea experience its highest Surface temperature?", + "response": "Based on the provided data, Celebes Sea experienced its highest Surface temperature at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "305.57623", + "true_value": 96, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "abd783d9092657e8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85237:85257:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84056:84083:1'} The data starts from July 14 00:00 and ends on July 20 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Pitcairn Islands experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Pitcairn Islands experienced its highest 10-meter V component of wind at 138 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "7.302941", + "true_value": 138, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "0531874d91daaf7c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84056:84083:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81606:81632:1'} The data starts from November 09 12:00 and ends on November 15 18:00. Based on the above data, answer the following question:", + "question": "What is the average Geopotential at 850 hPa experienced by North America at 120 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an average Geopotential at 850 hPa of 1.422e+04 m²/s² at 120 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 120, + "true_value": "14216.611475250946", + "target_variable": "geopotential_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3cee0152c0f9f994", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81606:81632:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44936:44948:1'} The data starts from October 04 00:00 and ends on October 06 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did South America experience its lowest Surface pressure?", + "response": "Based on the provided data, South America experienced its lowest Surface pressure at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "60151.836", + "true_value": 0, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f8598559aac97ef0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44936:44948:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77982:77988:1'} The data starts from May 17 12:00 and ends on May 18 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Davis Strait experience its highest Surface temperature?", + "response": "Based on the provided data, Davis Strait experienced its highest Surface temperature at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "280.39566", + "true_value": 30, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ce911acd3c7d9bb6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77982:77988:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75242:75246:1'} The data starts from July 02 12:00 and ends on July 03 06:00. Based on the above data, answer the following question:", + "question": "What is the average Temperature at 850 hPa experienced by Liddon Gulf at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Liddon Gulf experienced an average Temperature at 850 hPa of 279.5 K at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "279.4631397441009", + "target_variable": "temperature_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "059c6206b531b857", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75242:75246:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58306:58320:1'} The data starts from November 28 12:00 and ends on December 01 18:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 200 hPa experienced by South America at 72 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an median V (meridional) component of wind at 200 hPa of 7.487 m/s at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "7.48723030090332", + "target_variable": "v_component_of_wind_200", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8ce4620c03196182", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58306:58320:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73754:73762:1'} The data starts from June 25 12:00 and ends on June 27 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Pitcairn Islands experience its highest Temperature at 700 hPa?", + "response": "Based on the provided data, Pitcairn Islands experienced its highest Temperature at 700 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "281.45215", + "true_value": 6, + "target_variable": "temperature_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "4f376d43ed28396c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73754:73762:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70969:70986:1'} The data starts from July 30 06:00 and ends on August 03 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Puntarenas, Costa Rica experience its lowest Specific humidity at 600 hPa?", + "response": "Based on the provided data, Puntarenas, Costa Rica experienced its lowest Specific humidity at 600 hPa at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.002245588", + "true_value": 78, + "target_variable": "specific_humidity_600", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5c35153652084d2b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70969:70986:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87919:87930:1'} The data starts from March 06 18:00 and ends on March 09 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Netherlands experience its highest Surface temperature?", + "response": "Based on the provided data, Netherlands experienced its highest Surface temperature at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "299.08148", + "true_value": 48, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "d0bbffed02e7f060", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87919:87930:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81940:81943:1'} The data starts from February 01 00:00 and ends on February 01 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Africa experience its highest Surface temperature?", + "response": "Based on the provided data, Africa experienced its highest Surface temperature at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "314.5318", + "true_value": 12, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "36b91f5839701243", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81940:81943:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82945:82967:1'} The data starts from October 10 06:00 and ends on October 15 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Rheinland-Pfalz, Germany experience its lowest Temperature at 700 hPa?", + "response": "Based on the provided data, Rheinland-Pfalz, Germany experienced its lowest Temperature at 700 hPa at 114 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "261.29898", + "true_value": 114, + "target_variable": "temperature_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "40f09428529ab0ab", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82945:82967:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78041:78053:1'} The data starts from June 01 06:00 and ends on June 04 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Mean sea level pressure experienced by Lovrenc na Pohorju, Slovenia at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Lovrenc na Pohorju, Slovenia experienced an minimum Mean sea level pressure of 1.014e+05 Pa at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "101387.109375", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "72935c856e9af566", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78041:78053:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81006:81024:1'} The data starts from June 12 12:00 and ends on June 16 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Asia experience its highest Specific humidity at 1000 hPa?", + "response": "Based on the provided data, Asia experienced its highest Specific humidity at 1000 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.025365496", + "true_value": 6, + "target_variable": "specific_humidity_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a1d790fac549e8d0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81006:81024:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62381:62392:1'} The data starts from September 12 06:00 and ends on September 14 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure experienced by Gorontalo, Indonesia at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Gorontalo, Indonesia experienced an minimum Surface pressure of 9.932e+04 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "99315.2265625", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2984a0a6e397435b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62381:62392:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51401:51424:1'} The data starts from March 08 06:00 and ends on March 13 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind experienced by Gulf of Aqaba at 78 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Aqaba experienced an maximum 10-meter U component of wind of 7.45 m/s at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 78, + "true_value": "7.449925422668457", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0710c1195937155f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51401:51424:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62253:62273:1'} The data starts from August 11 06:00 and ends on August 16 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Antarctica experience its lowest V (meridional) component of wind at 250 hPa?", + "response": "Based on the provided data, Antarctica experienced its lowest V (meridional) component of wind at 250 hPa at 102 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-40.331646", + "true_value": 102, + "target_variable": "v_component_of_wind_250", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f7411efe5e01db29", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62253:62273:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84428:84429:1'} The data corresponds to corresponds to a snapshot on October 15 00:00. Based on the above data, answer the following question:", + "question": "What is the median U (zonal) component of wind at 1000 hPa experienced by South America at 0 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an median U (zonal) component of wind at 1000 hPa of -0.7736 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "-0.7735934853553772", + "target_variable": "u_component_of_wind_1000", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "75598dec7123a5f4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84428:84429:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45981:45986:1'} The data starts from June 22 06:00 and ends on June 23 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Bight of Benin experience its lowest Geopotential at 1000 hPa?", + "response": "Based on the provided data, Bight of Benin experienced its lowest Geopotential at 1000 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "966.449", + "true_value": 12, + "target_variable": "geopotential_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "824ed8fa49c50a63", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45981:45986:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77751:77756:1'} The data starts from March 20 18:00 and ends on March 21 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Savu Sea experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Savu Sea experienced its lowest Mean sea level pressure at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "100634.17", + "true_value": 12, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a0f1d9d1595f7536", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77751:77756:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80582:80587:1'} The data starts from February 26 12:00 and ends on February 27 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Sulawesi Selatan, Indonesia experience its lowest U (zonal) component of wind at 500 hPa?", + "response": "Based on the provided data, Sulawesi Selatan, Indonesia experienced its lowest U (zonal) component of wind at 500 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.04400629", + "true_value": 24, + "target_variable": "u_component_of_wind_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "81f5d3dc88a5694d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80582:80587:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63192:63211:1'} The data starts from April 03 00:00 and ends on April 07 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Olbia-Tempio, Italy experience its lowest Specific humidity at 250 hPa?", + "response": "Based on the provided data, Olbia-Tempio, Italy experienced its lowest Specific humidity at 250 hPa at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "5.633227e-06", + "true_value": 30, + "target_variable": "specific_humidity_250", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c9da95056177d514", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63192:63211:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70032:70059:1'} The data starts from December 08 00:00 and ends on December 14 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Straits of Florida experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Straits of Florida experienced its lowest Mean sea level pressure at 156 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101537.086", + "true_value": 156, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a2829574dd974cc0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70032:70059:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65668:65672:1'} The data starts from December 13 00:00 and ends on December 13 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Dardanelles experience its lowest Geopotential at 1000 hPa?", + "response": "Based on the provided data, Dardanelles experienced its lowest Geopotential at 1000 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "1744.0288", + "true_value": 0, + "target_variable": "geopotential_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "bed355a82d5f1e12", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65668:65672:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62383:62389:1'} The data starts from September 12 18:00 and ends on September 14 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 500 hPa experienced by Estrecho de Magellanes at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Estrecho de Magellanes experienced an maximum U (zonal) component of wind at 500 hPa of 33.1 m/s at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "33.10405731201172", + "target_variable": "u_component_of_wind_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b49f5eaf700596c0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62383:62389:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70316:70332:1'} The data starts from February 17 00:00 and ends on February 20 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 600 hPa experienced by Powys, United Kingdom at 72 hours from the initial timeframe?", + "response": "Based on the provided data, Powys, United Kingdom experienced an minimum U (zonal) component of wind at 600 hPa of 6.903 m/s at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "6.9027323722839355", + "target_variable": "u_component_of_wind_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "081f37f83725df8d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70316:70332:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77013:77014:1'} The data corresponds to corresponds to a snapshot on September 18 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Africa experience its highest V (meridional) component of wind at 150 hPa?", + "response": "Based on the provided data, Africa experienced its highest V (meridional) component of wind at 150 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "20.373098", + "true_value": 0, + "target_variable": "v_component_of_wind_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8cf61eca7d536c7e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77013:77014:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38492:38520:1'} The data starts from May 07 00:00 and ends on May 13 18:00. Based on the above data, answer the following question:", + "question": "What is the median U (zonal) component of wind at 100 hPa experienced by James Bay at 6 hours from the initial timeframe?", + "response": "Based on the provided data, James Bay experienced an median U (zonal) component of wind at 100 hPa of 13.69 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "13.688614845275879", + "target_variable": "u_component_of_wind_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "100aa4dde87c35b9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38492:38520:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45767:45776:1'} The data starts from April 29 18:00 and ends on May 01 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Nepal experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Nepal experienced its lowest Mean sea level pressure at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "100252.59", + "true_value": 42, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b3de0513dc0011f7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45767:45776:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69031:69046:1'} The data starts from April 01 18:00 and ends on April 05 06:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 100 hPa experienced by Brazilian Island at 54 hours from the initial timeframe?", + "response": "Based on the provided data, Brazilian Island experienced an median Specific humidity at 100 hPa of 2.246e-06 kg/kg at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 54, + "true_value": "2.2462531887867954e-06", + "target_variable": "specific_humidity_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "934bbbb46329c554", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69031:69046:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41414:41424:1'} The data starts from May 07 12:00 and ends on May 09 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface pressure experienced by Yvelines, France at 48 hours from the initial timeframe?", + "response": "Based on the provided data, Yvelines, France experienced an maximum Surface pressure of 1.007e+05 Pa at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 48, + "true_value": "100734.3671875", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c0207eb2957e86e1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41414:41424:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37835:37849:1'} The data starts from November 23 18:00 and ends on November 27 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 700 hPa experienced by Andorra at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Andorra experienced an minimum U (zonal) component of wind at 700 hPa of 19.98 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "19.981544494628906", + "target_variable": "u_component_of_wind_700", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a348ead63e6aeed3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37835:37849:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58842:58866:1'} The data starts from April 11 12:00 and ends on April 17 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Tyrrhenian Sea experience its lowest 10-meter V component of wind?", + "response": "Based on the provided data, Tyrrhenian Sea experienced its lowest 10-meter V component of wind at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-8.841613", + "true_value": 48, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "2a97c251a2f64ee9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58842:58866:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34389:34411:1'} The data starts from July 16 06:00 and ends on July 21 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Yaracuy, Venezuela experience its lowest 10-meter U component of wind?", + "response": "Based on the provided data, Yaracuy, Venezuela experienced its lowest 10-meter U component of wind at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-3.3331864", + "true_value": 18, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b5bc7960a704c4a2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34389:34411:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79314:79338:1'} The data starts from April 15 12:00 and ends on April 21 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Antarctica experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Antarctica experienced its lowest Mean sea level pressure at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "95339.49", + "true_value": 96, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e336111114e79ed6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79314:79338:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45549:45571:1'} The data starts from March 06 06:00 and ends on March 11 12:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 50 hPa experienced by Djibouti at 108 hours from the initial timeframe?", + "response": "Based on the provided data, Djibouti experienced an average U (zonal) component of wind at 50 hPa of -16.36 m/s at 108 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 108, + "true_value": "-16.357495414153757", + "target_variable": "u_component_of_wind_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8c7d9daef3c0f4f4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45549:45571:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67699:67725:1'} The data starts from May 03 18:00 and ends on May 10 00:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 500 hPa experienced by Asia at 96 hours from the initial timeframe?", + "response": "Based on the provided data, Asia experienced an median Temperature at 500 hPa of 262.6 K at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 96, + "true_value": "262.5992431640625", + "target_variable": "temperature_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "89f9b7b102cfb6a6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67699:67725:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40982:41000:1'} The data starts from January 19 12:00 and ends on January 23 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Isingiro, Uganda experience its lowest V (meridional) component of wind at 700 hPa?", + "response": "Based on the provided data, Isingiro, Uganda experienced its lowest V (meridional) component of wind at 700 hPa at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-10.418706", + "true_value": 90, + "target_variable": "v_component_of_wind_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b10115cd13a150d4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40982:41000:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40074:40100:1'} The data starts from June 06 12:00 and ends on June 12 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Cerro Largo, Uruguay experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Cerro Largo, Uruguay experienced its lowest Mean sea level pressure at 108 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101101.32", + "true_value": 108, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "277b54b0ea2af327", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40074:40100:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80171:80198:1'} The data starts from November 15 18:00 and ends on November 22 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum V (meridional) component of wind at 150 hPa experienced by Gulf of Oman at 132 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Oman experienced an maximum V (meridional) component of wind at 150 hPa of 22.08 m/s at 132 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 132, + "true_value": "22.084501266479492", + "target_variable": "v_component_of_wind_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "292962b5b4677d55", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80171:80198:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75643:75656:1'} The data starts from October 10 18:00 and ends on October 13 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface temperature experienced by Chuquisaca, Bolivia at 66 hours from the initial timeframe?", + "response": "Based on the provided data, Chuquisaca, Bolivia experienced an maximum Surface temperature of 300.3 K at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "300.31585693359375", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e5123b4c2182a164", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75643:75656:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53890:53894:1'} The data starts from November 20 12:00 and ends on November 21 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Qatar experience its highest Surface temperature?", + "response": "Based on the provided data, Qatar experienced its highest Surface temperature at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "301.71936", + "true_value": 0, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "762c8b19dac70366", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53890:53894:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34899:34913:1'} The data starts from November 20 18:00 and ends on November 24 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Barents Sea experience its lowest Surface pressure?", + "response": "Based on the provided data, Barents Sea experienced its lowest Surface pressure at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "93565.22", + "true_value": 6, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "9fef10f6d6c76eed", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34899:34913:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32966:32973:1'} The data starts from July 25 12:00 and ends on July 27 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Sierra Leone experience its lowest 10-meter V component of wind?", + "response": "Based on the provided data, Sierra Leone experienced its lowest 10-meter V component of wind at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.047627438", + "true_value": 36, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "806109d188308c3a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32966:32973:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30673:30686:1'} The data starts from December 30 06:00 and ends on January 02 06:00 (1 year later). Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure experienced by Angola at 66 hours from the initial timeframe?", + "response": "Based on the provided data, Angola experienced an average Mean sea level pressure of 1.011e+05 Pa at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "101145.38736312266", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b1e633e4aca22209", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30673:30686:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82700:82723:1'} The data starts from August 10 00:00 and ends on August 15 12:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind experienced by Gitega, Burundi at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Gitega, Burundi experienced an median 10-meter V component of wind of -0.08037 m/s at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "-0.08037173748016357", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6b9e95dcb586969a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82700:82723:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37972:37995:1'} The data starts from December 28 00:00 and ends on January 02 12:00 (1 year later). Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did San Marino experience its highest Surface pressure?", + "response": "Based on the provided data, San Marino experienced its highest Surface pressure at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "97225.21", + "true_value": 60, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ebc6e3c784ca9e1e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37972:37995:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39858:39872:1'} The data starts from April 13 12:00 and ends on April 16 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Hamilton Inlet experience its lowest 10-meter V component of wind?", + "response": "Based on the provided data, Hamilton Inlet experienced its lowest 10-meter V component of wind at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-1.2420189", + "true_value": 78, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ccc091205b9e3ad6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39858:39872:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69494:69513:1'} The data starts from July 26 12:00 and ends on July 31 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Temperature at 150 hPa experienced by Gulf of Sakhalin at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Sakhalin experienced an maximum Temperature at 150 hPa of 222.7 K at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "222.65306091308594", + "target_variable": "temperature_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "619382ad9a160a81", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69494:69513:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71047:71069:1'} The data starts from August 18 18:00 and ends on August 24 00:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 400 hPa experienced by Denmark Strait at 96 hours from the initial timeframe?", + "response": "Based on the provided data, Denmark Strait experienced an average V (meridional) component of wind at 400 hPa of 10.08 m/s at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 96, + "true_value": "10.082657942162118", + "target_variable": "v_component_of_wind_400", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8c6cfd921736a5ad", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71047:71069:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64794:64815:1'} The data starts from May 08 12:00 and ends on May 13 12:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 100 hPa experienced by An Nabatiyah, Lebanon at 66 hours from the initial timeframe?", + "response": "Based on the provided data, An Nabatiyah, Lebanon experienced an median Temperature at 100 hPa of 211 K at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "211.0044708251953", + "target_variable": "temperature_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "08d78b384008fd46", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64794:64815:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32926:32942:1'} The data starts from July 15 12:00 and ends on July 19 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Cabo Verde experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Cabo Verde experienced its highest 10-meter V component of wind at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "1.4461064", + "true_value": 54, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "4783580c53fe3ad5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32926:32942:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74390:74417:1'} The data starts from December 01 12:00 and ends on December 08 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Libya experience its highest U (zonal) component of wind at 300 hPa?", + "response": "Based on the provided data, Libya experienced its highest U (zonal) component of wind at 300 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "59.645023", + "true_value": 6, + "target_variable": "u_component_of_wind_300", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "88a0473780f12a7b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74390:74417:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55874:55897:1'} The data starts from March 30 12:00 and ends on April 05 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Africa experience its lowest Surface pressure?", + "response": "Based on the provided data, Africa experienced its lowest Surface pressure at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "77094.484", + "true_value": 72, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "2d13034acf9a76d0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55874:55897:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41988:42002:1'} The data starts from September 28 00:00 and ends on October 01 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Antarctica experience its highest Specific humidity at 925 hPa?", + "response": "Based on the provided data, Antarctica experienced its highest Specific humidity at 925 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.0035641084", + "true_value": 6, + "target_variable": "specific_humidity_925", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f37aebf1b518ece9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41988:42002:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82002:82030:1'} The data starts from February 16 12:00 and ends on February 23 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Lakes, South Sudan experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Lakes, South Sudan experienced its lowest Mean sea level pressure at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "100137.41", + "true_value": 72, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "02b75bd207512516", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82002:82030:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74275:74298:1'} The data starts from November 02 18:00 and ends on November 08 06:00. Based on the above data, answer the following question:", + "question": "What is the median U (zonal) component of wind at 50 hPa experienced by Africa at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Africa experienced an median U (zonal) component of wind at 50 hPa of -0.2266 m/s at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "-0.2266252487897873", + "target_variable": "u_component_of_wind_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "58389d83c30a8e53", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74275:74298:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78162:78178:1'} The data starts from July 01 12:00 and ends on July 05 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum Temperature at 600 hPa experienced by Peacock Sound at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Peacock Sound experienced an maximum Temperature at 600 hPa of 245.4 K at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "245.3580322265625", + "target_variable": "temperature_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b2e5761016170da8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78162:78178:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43787:43800:1'} The data starts from December 20 18:00 and ends on December 23 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Ungava Bay experience its lowest U (zonal) component of wind at 150 hPa?", + "response": "Based on the provided data, Ungava Bay experienced its lowest U (zonal) component of wind at 150 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "13.775823", + "true_value": 0, + "target_variable": "u_component_of_wind_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "d5c00056a3a5702d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43787:43800:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80126:80140:1'} The data starts from November 04 12:00 and ends on November 07 18:00. Based on the above data, answer the following question:", + "question": "What is the median Geopotential at 925 hPa experienced by Poland at 48 hours from the initial timeframe?", + "response": "Based on the provided data, Poland experienced an median Geopotential at 925 hPa of 6043 m²/s² at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 48, + "true_value": "6043.421875", + "target_variable": "geopotential_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "95403cac9e6485d4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80126:80140:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57555:57582:1'} The data starts from May 24 18:00 and ends on May 31 06:00. Based on the above data, answer the following question:", + "question": "What is the average Surface pressure experienced by Quthing, Lesotho at 84 hours from the initial timeframe?", + "response": "Based on the provided data, Quthing, Lesotho experienced an average Surface pressure of 8.432e+04 Pa at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "84318.328125", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "042101b0fa1942fc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57555:57582:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61152:61173:1'} The data starts from November 09 00:00 and ends on November 14 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Bering Sea experience its lowest Surface pressure?", + "response": "Based on the provided data, Bering Sea experienced its lowest Surface pressure at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "89399.055", + "true_value": 30, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ab61a08a46cb82d5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61152:61173:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36220:36234:1'} The data starts from October 17 00:00 and ends on October 20 06:00. Based on the above data, answer the following question:", + "question": "What is the median Surface pressure experienced by Syria at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Syria experienced an median Surface pressure of 9.51e+04 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "95096.2421875", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a8aa38dd2d39b97d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36220:36234:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49575:49579:1'} The data starts from December 06 18:00 and ends on December 07 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter V component of wind experienced by Wager Bay at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Wager Bay experienced an maximum 10-meter V component of wind of -4.246 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "-4.245517730712891", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4493211a6bba9fc4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49575:49579:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41152:41156:1'} The data starts from March 03 00:00 and ends on March 03 18:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature experienced by Amundsen Gulf at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Amundsen Gulf experienced an median Surface temperature of 241.1 K at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "241.06776428222656", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "86e6171b111cba3a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41152:41156:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37032:37052:1'} The data starts from May 07 00:00 and ends on May 11 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Murchison Sound experience its lowest V (meridional) component of wind at 100 hPa?", + "response": "Based on the provided data, Murchison Sound experienced its lowest V (meridional) component of wind at 100 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-7.626159", + "true_value": 0, + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "4ae9e58cc6d3677c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37032:37052:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56180:56201:1'} The data starts from June 15 00:00 and ends on June 20 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Oceania experience its highest U (zonal) component of wind at 100 hPa?", + "response": "Based on the provided data, Oceania experienced its highest U (zonal) component of wind at 100 hPa at 102 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "42.825832", + "true_value": 102, + "target_variable": "u_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c74f518061ee693b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56180:56201:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91991:92009:1'} The data starts from December 18 18:00 and ends on December 23 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Europe experience its lowest Specific humidity at 1000 hPa?", + "response": "Based on the provided data, Europe experienced its lowest Specific humidity at 1000 hPa at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "2.5279243e-05", + "true_value": 54, + "target_variable": "specific_humidity_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "6425218194d01f44", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91991:92009:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87679:87703:1'} The data starts from January 05 18:00 and ends on January 11 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Lanao del Sur, Philippines experience its lowest Temperature at 250 hPa?", + "response": "Based on the provided data, Lanao del Sur, Philippines experienced its lowest Temperature at 250 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "232.0017", + "true_value": 6, + "target_variable": "temperature_250", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "131d08fe0a8fe213", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87679:87703:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79707:79725:1'} The data starts from July 22 18:00 and ends on July 27 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface temperature experienced by Africa at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Africa experienced an maximum Surface temperature of 320.1 K at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "320.12591552734375", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c37af2011d815a60", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79707:79725:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62971:62990:1'} The data starts from February 06 18:00 and ends on February 11 06:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter U component of wind experienced by Venezuela at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Venezuela experienced an median 10-meter U component of wind of -2.547 m/s at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "-2.5472412109375", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1caba93b2aea8a8b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62971:62990:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89675:89678:1'} The data starts from May 18 18:00 and ends on May 19 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum V (meridional) component of wind at 500 hPa experienced by Turks and Caicos Islands at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Turks and Caicos Islands experienced an maximum V (meridional) component of wind at 500 hPa of 7.426 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "7.426264762878418", + "target_variable": "v_component_of_wind_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d5f7324567a26c1a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89675:89678:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33925:33952:1'} The data starts from March 22 06:00 and ends on March 28 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Specific humidity at 50 hPa experienced by Gulf of Mannar at 78 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Mannar experienced an minimum Specific humidity at 50 hPa of 2.279e-06 kg/kg at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 78, + "true_value": "2.279388581882813e-06", + "target_variable": "specific_humidity_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "64ca413c613b8557", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33925:33952:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77734:77758:1'} The data starts from March 16 12:00 and ends on March 22 06:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter U component of wind experienced by Sheema, Uganda at 114 hours from the initial timeframe?", + "response": "Based on the provided data, Sheema, Uganda experienced an median 10-meter U component of wind of -0.2732 m/s at 114 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 114, + "true_value": "-0.27321863174438477", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e0de6f333ff20804", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77734:77758:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34922:34942:1'} The data starts from November 26 12:00 and ends on December 01 06:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 150 hPa experienced by Brežice, Slovenia at 60 hours from the initial timeframe?", + "response": "Based on the provided data, Brežice, Slovenia experienced an average U (zonal) component of wind at 150 hPa of 0.3586 m/s at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 60, + "true_value": "0.35862740874290466", + "target_variable": "u_component_of_wind_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1a702613928ed864", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34922:34942:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50638:50652:1'} The data starts from August 29 12:00 and ends on September 01 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface temperature experienced by Samar Sea at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Samar Sea experienced an minimum Surface temperature of 298.5 K at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "298.514404296875", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c1fe2f8e511a8c97", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50638:50652:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76108:76120:1'} The data starts from February 04 00:00 and ends on February 06 18:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 400 hPa experienced by Borski, Republic of Serbia at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Borski, Republic of Serbia experienced an median V (meridional) component of wind at 400 hPa of -17.94 m/s at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "-17.93826675415039", + "target_variable": "v_component_of_wind_400", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "54a9d55f61d9cc75", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76108:76120:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30681:30691:1'} The data starts from January 01 06:00 and ends on January 03 12:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature experienced by South America at 36 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an median Surface temperature of 300.2 K at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "300.17626953125", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "84cb97e3c89b6b2b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30681:30691:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93344:93357:1'} The data starts from November 22 00:00 and ends on November 25 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Varaždinska, Croatia experience its highest Mean sea level pressure?", + "response": "Based on the provided data, Varaždinska, Croatia experienced its highest Mean sea level pressure at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101878.39", + "true_value": 72, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "43d9640d3897fb25", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93344:93357:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93253:93273:1'} The data starts from October 30 06:00 and ends on November 04 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Kyrgyzstan experience its highest Geopotential at 500 hPa?", + "response": "Based on the provided data, Kyrgyzstan experienced its highest Geopotential at 500 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "56441.23", + "true_value": 0, + "target_variable": "geopotential_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8ca45487c9f89fe1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93253:93273:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86472:86488:1'} The data starts from March 10 00:00 and ends on March 13 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 500 hPa experienced by South America at 24 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an minimum U (zonal) component of wind at 500 hPa of -14.32 m/s at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "-14.31657600402832", + "target_variable": "u_component_of_wind_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "20f8c672e72c2608", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86472:86488:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72867:72875:1'} The data starts from November 15 18:00 and ends on November 17 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Djibouti experience its highest Surface temperature?", + "response": "Based on the provided data, Djibouti experienced its highest Surface temperature at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "304.6777", + "true_value": 18, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e08a0f5aa140961c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72867:72875:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54450:54460:1'} The data starts from April 08 12:00 and ends on April 10 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure experienced by Menabe, Madagascar at 54 hours from the initial timeframe?", + "response": "Based on the provided data, Menabe, Madagascar experienced an minimum Surface pressure of 9.705e+04 Pa at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 54, + "true_value": "97054.328125", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6dd803eabc16ba08", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54450:54460:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76757:76773:1'} The data starts from July 16 06:00 and ends on July 20 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Cagliari, Italy experience its lowest V (meridional) component of wind at 400 hPa?", + "response": "Based on the provided data, Cagliari, Italy experienced its lowest V (meridional) component of wind at 400 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-11.981972", + "true_value": 12, + "target_variable": "v_component_of_wind_400", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f3b959a966822c99", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76757:76773:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70549:70556:1'} The data starts from April 16 06:00 and ends on April 17 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Antarctica experience its highest Geopotential at 500 hPa?", + "response": "Based on the provided data, Antarctica experienced its highest Geopotential at 500 hPa at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "52813.39", + "true_value": 18, + "target_variable": "geopotential_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f66ffa3b1fe1a441", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70549:70556:1" + } + } +] \ No newline at end of file diff --git a/level1b_part1.json b/level1b_part1.json new file mode 100644 index 0000000000000000000000000000000000000000..4b06850708c713486483d643ca8a6826ada75d12 --- /dev/null +++ b/level1b_part1.json @@ -0,0 +1,3302 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78603:78608:1'} The data starts from October 19 18:00 and ends on October 20 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure experienced by Chiang Mai, Thailand at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Chiang Mai, Thailand experienced an maximum Mean sea level pressure of 1.014e+05 Pa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "101377.734375", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e76f9bf2ba7d60bc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78603:78608:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46698:46720:1'} The data starts from December 18 12:00 and ends on December 23 18:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter V component of wind experienced by Dardanelles at 126 hours from the initial timeframe?", + "response": "Based on the provided data, Dardanelles experienced an average 10-meter V component of wind of -5.1 m/s at 126 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 126, + "true_value": "-5.099837779998779", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "682b771147e5a0f0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46698:46720:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43956:43959:1'} The data starts from February 01 00:00 and ends on February 01 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind experienced by Oceania at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Oceania experienced an maximum 10-meter U component of wind of 9.689 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "9.688642501831055", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "78ce7cdea40dbdad", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43956:43959:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40881:40891:1'} The data starts from December 25 06:00 and ends on December 27 12:00. Based on the above data, answer the following question:", + "question": "What is the average Surface pressure experienced by Djibouti at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Djibouti experienced an average Surface pressure of 9.525e+04 Pa at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "95251.73574068538", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0880ea2b810c3cad", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40881:40891:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33101:33116:1'} The data starts from August 28 06:00 and ends on August 31 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Matochkin Shar Strait experience its lowest Surface pressure?", + "response": "Based on the provided data, Matochkin Shar Strait experienced its lowest Surface pressure at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "98644.17", + "true_value": 60, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b92b0c7e3ddc29c7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33101:33116:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80726:80731:1'} The data starts from April 03 12:00 and ends on April 04 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Africa experience its highest V (meridional) component of wind at 400 hPa?", + "response": "Based on the provided data, Africa experienced its highest V (meridional) component of wind at 400 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "34.315437", + "true_value": 12, + "target_variable": "v_component_of_wind_400", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a80cb64869cba6ba", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80726:80731:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69737:69750:1'} The data starts from September 25 06:00 and ends on September 28 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did South America experience its lowest Temperature at 400 hPa?", + "response": "Based on the provided data, South America experienced its lowest Temperature at 400 hPa at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "231.58044", + "true_value": 30, + "target_variable": "temperature_400", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "d8d190e404ca0537", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69737:69750:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48593:48600:1'} The data starts from April 05 06:00 and ends on April 06 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did South America experience its lowest 10-meter V component of wind?", + "response": "Based on the provided data, South America experienced its lowest 10-meter V component of wind at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-15.1773405", + "true_value": 36, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a60c197d0dbe1846", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48593:48600:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63697:63716:1'} The data starts from August 07 06:00 and ends on August 11 18:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 600 hPa experienced by Antarctica at 96 hours from the initial timeframe?", + "response": "Based on the provided data, Antarctica experienced an median Temperature at 600 hPa of 236.1 K at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 96, + "true_value": "236.0713348388672", + "target_variable": "temperature_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "842b612dbb12b65f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63697:63716:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54955:54967:1'} The data starts from August 12 18:00 and ends on August 15 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 850 hPa experienced by Bahía de Campeche at 48 hours from the initial timeframe?", + "response": "Based on the provided data, Bahía de Campeche experienced an maximum Geopotential at 850 hPa of 1.536e+04 m²/s² at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 48, + "true_value": "15359.66015625", + "target_variable": "geopotential_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "44f095c335395b0d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54955:54967:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88905:88912:1'} The data starts from November 08 06:00 and ends on November 09 18:00. Based on the above data, answer the following question:", + "question": "What is the average Surface pressure experienced by Baía de São Marcos at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Baía de São Marcos experienced an average Surface pressure of 1.005e+05 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "100536.35455819352", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7cc7ffd5e5ee2034", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88905:88912:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47570:47576:1'} The data starts from July 24 12:00 and ends on July 25 18:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 50 hPa experienced by Delaware Bay at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Delaware Bay experienced an median V (meridional) component of wind at 50 hPa of 2.133 m/s at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "2.1330673694610596", + "target_variable": "v_component_of_wind_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d37e86f541598f31", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47570:47576:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92922:92941:1'} The data starts from August 08 12:00 and ends on August 13 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure experienced by Puno, Peru at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Puno, Peru experienced an minimum Surface pressure of 6.046e+04 Pa at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "60464.15625", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fc3f37297cc7e8b4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92922:92941:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33596:33613:1'} The data starts from December 30 00:00 and ends on January 03 00:00 (1 year later). Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 150 hPa experienced by North America at 90 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an median V (meridional) component of wind at 150 hPa of 1.012 m/s at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 90, + "true_value": "1.0119937658309937", + "target_variable": "v_component_of_wind_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1944660bcb1136ba", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33596:33613:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84246:84258:1'} The data starts from August 30 12:00 and ends on September 02 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Pavilostas, Latvia experience its lowest Surface pressure?", + "response": "Based on the provided data, Pavilostas, Latvia experienced its lowest Surface pressure at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101185.47", + "true_value": 60, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "9c0964fa3808485d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84246:84258:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52738:52756:1'} The data starts from February 05 12:00 and ends on February 09 18:00. Based on the above data, answer the following question:", + "question": "What is the median Mean sea level pressure experienced by Africa at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Africa experienced an median Mean sea level pressure of 1.014e+05 Pa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "101351.34375", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "74fe90641a788744", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52738:52756:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38249:38263:1'} The data starts from March 07 06:00 and ends on March 10 12:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter V component of wind experienced by Prince William Sound at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Prince William Sound experienced an average 10-meter V component of wind of 3.783 m/s at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "3.7832327187441432", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d4e22c5b90781b8e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38249:38263:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41214:41239:1'} The data starts from March 18 12:00 and ends on March 24 12:00. Based on the above data, answer the following question:", + "question": "What is the median Geopotential at 925 hPa experienced by Antarctica at 48 hours from the initial timeframe?", + "response": "Based on the provided data, Antarctica experienced an median Geopotential at 925 hPa of 5499 m²/s² at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 48, + "true_value": "5498.79736328125", + "target_variable": "geopotential_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a82ac898fb843d4f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41214:41239:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70769:70792:1'} The data starts from June 10 06:00 and ends on June 15 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure experienced by Finland at 84 hours from the initial timeframe?", + "response": "Based on the provided data, Finland experienced an maximum Mean sea level pressure of 1.003e+05 Pa at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "100263.375", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c2e926214500e433", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70769:70792:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30254:30264:1'} The data starts from September 16 12:00 and ends on September 18 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Pitcairn Islands experience its highest Specific humidity at 300 hPa?", + "response": "Based on the provided data, Pitcairn Islands experienced its highest Specific humidity at 300 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.00037497596", + "true_value": 24, + "target_variable": "specific_humidity_300", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "39d9852d34718903", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30254:30264:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32095:32105:1'} The data starts from December 19 18:00 and ends on December 22 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Samoa experience its lowest Geopotential at 300 hPa?", + "response": "Based on the provided data, Samoa experienced its lowest Geopotential at 300 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "94445.62", + "true_value": 12, + "target_variable": "geopotential_300", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b48963a27b3a751b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32095:32105:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64359:64381:1'} The data starts from January 19 18:00 and ends on January 25 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 925 hPa experienced by Golfo de Guayaquil at 114 hours from the initial timeframe?", + "response": "Based on the provided data, Golfo de Guayaquil experienced an minimum V (meridional) component of wind at 925 hPa of -2.132 m/s at 114 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 114, + "true_value": "-2.131645917892456", + "target_variable": "v_component_of_wind_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "078dc4b03a70c821", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64359:64381:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89540:89554:1'} The data starts from April 15 00:00 and ends on April 18 06:00. Based on the above data, answer the following question:", + "question": "What is the average Specific humidity at 50 hPa experienced by Gulf of Anadyr' at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Anadyr' experienced an average Specific humidity at 50 hPa of 2.916e-06 kg/kg at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "2.91593313111561e-06", + "target_variable": "specific_humidity_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d70212b6bfc2ea67", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89540:89554:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51939:51957:1'} The data starts from July 20 18:00 and ends on July 25 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Kâmpóng Thum, Cambodia experience its highest U (zonal) component of wind at 700 hPa?", + "response": "Based on the provided data, Kâmpóng Thum, Cambodia experienced its highest U (zonal) component of wind at 700 hPa at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "12.675913", + "true_value": 90, + "target_variable": "u_component_of_wind_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "0782d2edb98ecc9a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51939:51957:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77080:77102:1'} The data starts from October 05 00:00 and ends on October 10 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 300 hPa experienced by Niue at 126 hours from the initial timeframe?", + "response": "Based on the provided data, Niue experienced an minimum V (meridional) component of wind at 300 hPa of -9.281 m/s at 126 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 126, + "true_value": "-9.281013488769531", + "target_variable": "v_component_of_wind_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "701ee21248d252b8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77080:77102:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70486:70502:1'} The data starts from March 31 12:00 and ends on April 04 06:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 850 hPa experienced by South America at 90 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an average V (meridional) component of wind at 850 hPa of -0.6073 m/s at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 90, + "true_value": "-0.6073373574721408", + "target_variable": "v_component_of_wind_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c87e64d4425c2cd6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70486:70502:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85602:85604:1'} The data starts from August 04 12:00 and ends on August 04 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter V component of wind experienced by Angola at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Angola experienced an minimum 10-meter V component of wind of -2.514 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "-2.513798475265503", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1c340d8b7514eaa7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85602:85604:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92193:92209:1'} The data starts from February 07 06:00 and ends on February 11 00:00. Based on the above data, answer the following question:", + "question": "What is the average Surface temperature experienced by Midway Islands, United States Minor Outlying Islands at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Midway Islands, United States Minor Outlying Islands experienced an average Surface temperature of 293.8 K at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "293.8415222167969", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "19d469fe23080119", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92193:92209:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40383:40410:1'} The data starts from August 22 18:00 and ends on August 29 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Mackenzie Bay experience its highest V (meridional) component of wind at 1000 hPa?", + "response": "Based on the provided data, Mackenzie Bay experienced its highest V (meridional) component of wind at 1000 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "7.7341905", + "true_value": 0, + "target_variable": "v_component_of_wind_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "08fc1a35db167b69", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40383:40410:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88200:88212:1'} The data starts from May 16 00:00 and ends on May 18 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Torres Strait experience its highest Surface pressure?", + "response": "Based on the provided data, Torres Strait experienced its highest Surface pressure at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101339.836", + "true_value": 48, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "cc6ab8f993c06cc4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88200:88212:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63993:64012:1'} The data starts from October 20 06:00 and ends on October 24 18:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 300 hPa experienced by Ararat, Armenia at 108 hours from the initial timeframe?", + "response": "Based on the provided data, Ararat, Armenia experienced an median V (meridional) component of wind at 300 hPa of 0.4658 m/s at 108 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 108, + "true_value": "0.46580642461776733", + "target_variable": "v_component_of_wind_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "15fde1da665f3a0a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63993:64012:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90518:90520:1'} The data starts from December 15 12:00 and ends on December 15 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did South Atlantic Ocean experience its lowest Surface temperature?", + "response": "Based on the provided data, South Atlantic Ocean experienced its lowest Surface temperature at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "271.65518", + "true_value": 6, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8d96648c004e081d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90518:90520:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56154:56159:1'} The data starts from June 08 12:00 and ends on June 09 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter V component of wind experienced by Bosporus at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Bosporus experienced an minimum 10-meter V component of wind of -2.924 m/s at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "-2.9236063957214355", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0ca46a51e15ac037", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56154:56159:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37376:37395:1'} The data starts from August 01 00:00 and ends on August 05 12:00. Based on the above data, answer the following question:", + "question": "What is the average Temperature at 150 hPa experienced by Gulf of Oman at 102 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Oman experienced an average Temperature at 150 hPa of 209 K at 102 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 102, + "true_value": "209.03243959714206", + "target_variable": "temperature_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "12db0d32f32195dc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37376:37395:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64727:64752:1'} The data starts from April 21 18:00 and ends on April 27 18:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 400 hPa experienced by Hong Kong S.A.R. at 144 hours from the initial timeframe?", + "response": "Based on the provided data, Hong Kong S.A.R. experienced an average V (meridional) component of wind at 400 hPa of 2.137 m/s at 144 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 144, + "true_value": "2.1368139511444446", + "target_variable": "v_component_of_wind_400", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "38e90fef060981d0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64727:64752:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71113:71121:1'} The data starts from September 04 06:00 and ends on September 06 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Kosovo experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Kosovo experienced its highest 10-meter V component of wind at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "4.264428", + "true_value": 12, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "563bb219e2290f9d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71113:71121:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88874:88885:1'} The data starts from October 31 12:00 and ends on November 03 00:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 700 hPa experienced by Bristol Channel at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Bristol Channel experienced an average V (meridional) component of wind at 700 hPa of 9.067 m/s at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "9.066652895418962", + "target_variable": "v_component_of_wind_700", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "648d02b3be501946", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88874:88885:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84651:84657:1'} The data starts from December 09 18:00 and ends on December 11 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Armenia experience its lowest U (zonal) component of wind at 1000 hPa?", + "response": "Based on the provided data, Armenia experienced its lowest U (zonal) component of wind at 1000 hPa at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-0.4872278", + "true_value": 18, + "target_variable": "u_component_of_wind_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "1aae4345f3d57a13", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84651:84657:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79798:79815:1'} The data starts from August 14 12:00 and ends on August 18 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Dominica experience its lowest 10-meter V component of wind?", + "response": "Based on the provided data, Dominica experienced its lowest 10-meter V component of wind at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-3.6854095", + "true_value": 36, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "21e21e5801a0cfa5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79798:79815:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71639:71652:1'} The data starts from January 13 18:00 and ends on January 16 18:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter U component of wind experienced by Lebanon at 48 hours from the initial timeframe?", + "response": "Based on the provided data, Lebanon experienced an average 10-meter U component of wind of -1.532 m/s at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 48, + "true_value": "-1.5324169407853245", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ded6ddb98c5a5aa5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71639:71652:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47062:47089:1'} The data starts from March 19 12:00 and ends on March 26 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Ville de N'Djamena, Chad experience its lowest 10-meter V component of wind?", + "response": "Based on the provided data, Ville de N'Djamena, Chad experienced its lowest 10-meter V component of wind at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-5.091153", + "true_value": 48, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "dcf5c0f16e27d4ba", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47062:47089:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86406:86427:1'} The data starts from February 21 12:00 and ends on February 26 12:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind experienced by Atsimo-Andrefana, Madagascar at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Atsimo-Andrefana, Madagascar experienced an median 10-meter V component of wind of 0.3243 m/s at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "0.32430508732795715", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "61e48ab5938a9daa", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86406:86427:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54645:54670:1'} The data starts from May 27 06:00 and ends on June 02 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Kocenu, Latvia experience its highest U (zonal) component of wind at 600 hPa?", + "response": "Based on the provided data, Kocenu, Latvia experienced its highest U (zonal) component of wind at 600 hPa at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "21.393717", + "true_value": 54, + "target_variable": "u_component_of_wind_600", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e94ffd93762c3374", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54645:54670:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57037:57060:1'} The data starts from January 15 06:00 and ends on January 20 18:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 150 hPa experienced by Šempeter-Vrtojba, Slovenia at 120 hours from the initial timeframe?", + "response": "Based on the provided data, Šempeter-Vrtojba, Slovenia experienced an median Specific humidity at 150 hPa of 2.8e-06 kg/kg at 120 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 120, + "true_value": "2.799617050186498e-06", + "target_variable": "specific_humidity_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "08b6463d066a43f5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57037:57060:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67887:67908:1'} The data starts from June 19 18:00 and ends on June 24 18:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter U component of wind experienced by Piacenza, Italy at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Piacenza, Italy experienced an average 10-meter U component of wind of 0.3816 m/s at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "0.3815503418445587", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "65628d1ff07b4305", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67887:67908:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52609:52610:1'} The data corresponds to corresponds to a snapshot on January 04 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Samoa experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Samoa experienced its lowest Mean sea level pressure at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "100635.22", + "true_value": 0, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "20d66afb6ac516f1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52609:52610:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60659:60684:1'} The data starts from July 08 18:00 and ends on July 14 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Temperature at 250 hPa experienced by Banja Luka, Bosnia and Herzegovina at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Banja Luka, Bosnia and Herzegovina experienced an minimum Temperature at 250 hPa of 225.2 K at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "225.17210388183594", + "target_variable": "temperature_250", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e3cf3c1eda9900de", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60659:60684:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58855:58866:1'} The data starts from April 14 18:00 and ends on April 17 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did East China Sea experience its lowest U (zonal) component of wind at 1000 hPa?", + "response": "Based on the provided data, East China Sea experienced its lowest U (zonal) component of wind at 1000 hPa at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-15.854783", + "true_value": 60, + "target_variable": "u_component_of_wind_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "44c8ca7c39034827", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58855:58866:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84380:84385:1'} The data starts from October 03 00:00 and ends on October 04 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Guinea experience its highest V (meridional) component of wind at 150 hPa?", + "response": "Based on the provided data, Guinea experienced its highest V (meridional) component of wind at 150 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.6351862", + "true_value": 24, + "target_variable": "v_component_of_wind_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "22dad4fac3f2647b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84380:84385:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88390:88398:1'} The data starts from July 02 12:00 and ends on July 04 06:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter U component of wind experienced by Asia at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Asia experienced an average 10-meter U component of wind of 0.5139 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "0.5139046457948268", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9f0d6086f69c6a1e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88390:88398:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68092:68098:1'} The data starts from August 10 00:00 and ends on August 11 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Sudan experience its highest 10-meter U component of wind?", + "response": "Based on the provided data, Sudan experienced its highest 10-meter U component of wind at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "6.177621", + "true_value": 12, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "4725c812ff668d69", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68092:68098:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52287:52296:1'} The data starts from October 15 18:00 and ends on October 17 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Glacis, Seychelles experience its highest Surface pressure?", + "response": "Based on the provided data, Glacis, Seychelles experienced its highest Surface pressure at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101415.305", + "true_value": 12, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5dbfeb458fa841ae", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52287:52296:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84194:84215:1'} The data starts from August 17 12:00 and ends on August 22 12:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 150 hPa experienced by Oceania at 84 hours from the initial timeframe?", + "response": "Based on the provided data, Oceania experienced an median Temperature at 150 hPa of 208.7 K at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "208.7330322265625", + "target_variable": "temperature_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a26e711d3b1308d6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84194:84215:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76588:76602:1'} The data starts from June 04 00:00 and ends on June 07 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Bay of Bengal experience its highest Specific humidity at 925 hPa?", + "response": "Based on the provided data, Bay of Bengal experienced its highest Specific humidity at 925 hPa at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.019240443", + "true_value": 60, + "target_variable": "specific_humidity_925", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "fc6823f658ca562b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76588:76602:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83651:83666:1'} The data starts from April 03 18:00 and ends on April 07 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Northern Cyprus experience its lowest V (meridional) component of wind at 100 hPa?", + "response": "Based on the provided data, Northern Cyprus experienced its lowest V (meridional) component of wind at 100 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-12.580192", + "true_value": 0, + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "123b3697911106b1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83651:83666:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67995:68001:1'} The data starts from July 16 18:00 and ends on July 18 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Africa experience its lowest Temperature at 850 hPa?", + "response": "Based on the provided data, Africa experienced its lowest Temperature at 850 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "272.48804", + "true_value": 0, + "target_variable": "temperature_850", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a8b956bed94a0adb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67995:68001:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69708:69717:1'} The data starts from September 18 00:00 and ends on September 20 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Scarborough Reef experience its lowest Temperature at 500 hPa?", + "response": "Based on the provided data, Scarborough Reef experienced its lowest Temperature at 500 hPa at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "267.8127", + "true_value": 30, + "target_variable": "temperature_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "32a4b048346be2a9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69708:69717:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64089:64094:1'} The data starts from November 13 06:00 and ends on November 14 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum Mean sea level pressure experienced by Nigeria at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Nigeria experienced an minimum Mean sea level pressure of 1.013e+05 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "101260.78125", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2122ba13905b0d06", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64089:64094:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78862:78863:1'} The data corresponds to corresponds to a snapshot on December 23 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Sakhalin experience its highest Surface temperature?", + "response": "Based on the provided data, Gulf of Sakhalin experienced its highest Surface temperature at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "256.42694", + "true_value": 0, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8441c638e3d70bca", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78862:78863:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92136:92144:1'} The data starts from January 24 00:00 and ends on January 25 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Sea of Marmara experience its highest V (meridional) component of wind at 300 hPa?", + "response": "Based on the provided data, Sea of Marmara experienced its highest V (meridional) component of wind at 300 hPa at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "5.41912", + "true_value": 18, + "target_variable": "v_component_of_wind_300", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "61aa73dfa7c866d9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92136:92144:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46006:46009:1'} The data starts from June 28 12:00 and ends on June 29 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum V (meridional) component of wind at 100 hPa experienced by South America at 6 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an maximum V (meridional) component of wind at 100 hPa of 15.55 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "15.549494743347168", + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "41b7f10ba187da65", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46006:46009:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71403:71420:1'} The data starts from November 15 18:00 and ends on November 19 18:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 1000 hPa experienced by Bender, Moldova at 54 hours from the initial timeframe?", + "response": "Based on the provided data, Bender, Moldova experienced an median Temperature at 1000 hPa of 273.2 K at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 54, + "true_value": "273.22808837890625", + "target_variable": "temperature_1000", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "175eae49110dd5fe", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71403:71420:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37725:37751:1'} The data starts from October 27 06:00 and ends on November 02 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Katakwi, Uganda experience its highest Temperature at 300 hPa?", + "response": "Based on the provided data, Katakwi, Uganda experienced its highest Temperature at 300 hPa at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "242.6867", + "true_value": 54, + "target_variable": "temperature_300", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c7099e0dbc35cb10", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37725:37751:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69384:69410:1'} The data starts from June 29 00:00 and ends on July 05 06:00. Based on the above data, answer the following question:", + "question": "What is the median U (zonal) component of wind at 700 hPa experienced by Equatorial Guinea at 114 hours from the initial timeframe?", + "response": "Based on the provided data, Equatorial Guinea experienced an median U (zonal) component of wind at 700 hPa of -7.435 m/s at 114 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 114, + "true_value": "-7.434864044189453", + "target_variable": "u_component_of_wind_700", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "149f2072118c3838", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69384:69410:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49698:49709:1'} The data starts from January 06 12:00 and ends on January 09 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface temperature experienced by Gulf of Papua at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Papua experienced an maximum Surface temperature of 300.7 K at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "300.721923828125", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f2d9f3c58d5e5563", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49698:49709:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38558:38574:1'} The data starts from May 23 12:00 and ends on May 27 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did São Miguel, Cabo Verde experience its lowest U (zonal) component of wind at 700 hPa?", + "response": "Based on the provided data, São Miguel, Cabo Verde experienced its lowest U (zonal) component of wind at 700 hPa at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-11.562306", + "true_value": 90, + "target_variable": "u_component_of_wind_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "7d192cc93ffcd6fc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38558:38574:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33910:33914:1'} The data starts from March 18 12:00 and ends on March 19 06:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature experienced by Cyprus No Mans Area at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Cyprus No Mans Area experienced an median Surface temperature of 287.3 K at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "287.26263427734375", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "19ad302918d1a96a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33910:33914:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51304:51320:1'} The data starts from February 12 00:00 and ends on February 15 18:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter U component of wind experienced by Europe at 84 hours from the initial timeframe?", + "response": "Based on the provided data, Europe experienced an median 10-meter U component of wind of 0.389 m/s at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "0.3889950215816498", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3cd394b72129ed22", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51304:51320:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83378:83393:1'} The data starts from January 26 12:00 and ends on January 30 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface temperature experienced by Bathurst Inlet at 84 hours from the initial timeframe?", + "response": "Based on the provided data, Bathurst Inlet experienced an maximum Surface temperature of 246.8 K at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "246.8065643310547", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5399c9f482e9acec", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83378:83393:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51721:51726:1'} The data starts from May 27 06:00 and ends on May 28 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Nauru experience its lowest Specific humidity at 500 hPa?", + "response": "Based on the provided data, Nauru experienced its lowest Specific humidity at 500 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.0006941264", + "true_value": 24, + "target_variable": "specific_humidity_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "d1037739702ecccf", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51721:51726:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50094:50101:1'} The data starts from April 15 12:00 and ends on April 17 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Saint John, Barbados experience its lowest Temperature at 200 hPa?", + "response": "Based on the provided data, Saint John, Barbados experienced its lowest Temperature at 200 hPa at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "218.75389", + "true_value": 30, + "target_variable": "temperature_200", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5963c1bcd71e86fb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50094:50101:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74069:74090:1'} The data starts from September 12 06:00 and ends on September 17 06:00. Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure experienced by North America at 72 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an average Mean sea level pressure of 1.015e+05 Pa at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "101542.75721773205", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "be9b01ab3fca500c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74069:74090:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29964:29983:1'} The data starts from July 06 00:00 and ends on July 10 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Castries, Saint Lucia experience its highest Geopotential at 150 hPa?", + "response": "Based on the provided data, Castries, Saint Lucia experienced its highest Geopotential at 150 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "139638.8", + "true_value": 0, + "target_variable": "geopotential_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "cfa848da69e8fa68", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29964:29983:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56482:56490:1'} The data starts from August 29 12:00 and ends on August 31 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind experienced by Tulcea, Romania at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Tulcea, Romania experienced an maximum 10-meter U component of wind of 2.858 m/s at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "2.857774496078491", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "23b148d88349e0c4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56482:56490:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37184:37201:1'} The data starts from June 14 00:00 and ends on June 18 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Maule, Chile experience its highest U (zonal) component of wind at 100 hPa?", + "response": "Based on the provided data, Maule, Chile experienced its highest U (zonal) component of wind at 100 hPa at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "31.144142", + "true_value": 96, + "target_variable": "u_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c9b1c98cf267dedc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37184:37201:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56059:56072:1'} The data starts from May 15 18:00 and ends on May 18 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Benin experience its highest V (meridional) component of wind at 100 hPa?", + "response": "Based on the provided data, Benin experienced its highest V (meridional) component of wind at 100 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "11.171251", + "true_value": 24, + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "2dbaac9b0dcf4c07", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56059:56072:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86668:86685:1'} The data starts from April 28 00:00 and ends on May 02 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Oceania experience its highest Temperature at 150 hPa?", + "response": "Based on the provided data, Oceania experienced its highest Temperature at 150 hPa at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "225.81355", + "true_value": 54, + "target_variable": "temperature_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "93154028986efe7d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86668:86685:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75930:75944:1'} The data starts from December 21 12:00 and ends on December 24 18:00. Based on the above data, answer the following question:", + "question": "What is the average Specific humidity at 850 hPa experienced by Central Bosnia, Bosnia and Herzegovina at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Central Bosnia, Bosnia and Herzegovina experienced an average Specific humidity at 850 hPa of 0.005438 kg/kg at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "0.005437938502451951", + "target_variable": "specific_humidity_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "475724cdd0bdfb14", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75930:75944:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64511:64535:1'} The data starts from February 26 18:00 and ends on March 04 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 50 hPa experienced by Frobisher Bay at 96 hours from the initial timeframe?", + "response": "Based on the provided data, Frobisher Bay experienced an minimum Geopotential at 50 hPa of 1.972e+05 m²/s² at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 96, + "true_value": "197227.40625", + "target_variable": "geopotential_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a45f4be585367e43", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64511:64535:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68089:68117:1'} The data starts from August 09 06:00 and ends on August 16 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 50 hPa experienced by Antarctica at 72 hours from the initial timeframe?", + "response": "Based on the provided data, Antarctica experienced an maximum U (zonal) component of wind at 50 hPa of 65.27 m/s at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "65.27426147460938", + "target_variable": "u_component_of_wind_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d431ff488ab59cd3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68089:68117:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65761:65772:1'} The data starts from January 05 06:00 and ends on January 07 18:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 200 hPa experienced by Asia at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Asia experienced an median Temperature at 200 hPa of 218.1 K at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "218.05929565429688", + "target_variable": "temperature_200", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1b05434b51444165", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65761:65772:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63480:63491:1'} The data starts from June 14 00:00 and ends on June 16 12:00. Based on the above data, answer the following question:", + "question": "What is the median Geopotential at 150 hPa experienced by Porpoise Bay at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Porpoise Bay experienced an median Geopotential at 150 hPa of 1.251e+05 m²/s² at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "125141.375", + "target_variable": "geopotential_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "53e5b54dd159830d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63480:63491:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59987:59991:1'} The data starts from January 22 18:00 and ends on January 23 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind experienced by Hong Kong S.A.R. at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Hong Kong S.A.R. experienced an maximum 10-meter U component of wind of -1.064 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "-1.0638647079467773", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c1677fc01b460f4b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59987:59991:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59450:59474:1'} The data starts from September 10 12:00 and ends on September 16 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Japan experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Japan experienced its lowest Mean sea level pressure at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "99550.99", + "true_value": 30, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "485cd92a58206dbf", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59450:59474:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59168:59178:1'} The data starts from July 02 00:00 and ends on July 04 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum Temperature at 300 hPa experienced by Grand Kru, Liberia at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Grand Kru, Liberia experienced an minimum Temperature at 300 hPa of 240.6 K at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "240.57289123535156", + "target_variable": "temperature_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ea54f70c33e8abd5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59168:59178:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50678:50694:1'} The data starts from September 08 12:00 and ends on September 12 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Golfo San Matías experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Golfo San Matías experienced its lowest Mean sea level pressure at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101189.79", + "true_value": 12, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "dcde9af9a88f5a30", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50678:50694:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87062:87074:1'} The data starts from August 04 12:00 and ends on August 07 06:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 600 hPa experienced by North America at 36 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an median Specific humidity at 600 hPa of 0.001977 kg/kg at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "0.00197723344899714", + "target_variable": "specific_humidity_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c5e0db3f39051e20", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87062:87074:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72172:72178:1'} The data starts from May 26 00:00 and ends on May 27 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 500 hPa experienced by Senegal at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Senegal experienced an minimum V (meridional) component of wind at 500 hPa of -2.235 m/s at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "-2.2347569465637207", + "target_variable": "v_component_of_wind_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c5d6ef2279634f7a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72172:72178:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60940:60958:1'} The data starts from September 17 00:00 and ends on September 21 06:00. Based on the above data, answer the following question:", + "question": "What is the median Geopotential at 100 hPa experienced by Macquarie Island, Australia at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Macquarie Island, Australia experienced an median Geopotential at 100 hPa of 1.53e+05 m²/s² at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "153020.21875", + "target_variable": "geopotential_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "894102db59b77c85", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60940:60958:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73932:73950:1'} The data starts from August 09 00:00 and ends on August 13 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Kutch experience its lowest Temperature at 100 hPa?", + "response": "Based on the provided data, Gulf of Kutch experienced its lowest Temperature at 100 hPa at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "192.67262", + "true_value": 66, + "target_variable": "temperature_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "cdd33d6574b77223", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73932:73950:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39070:39075:1'} The data starts from September 28 12:00 and ends on September 29 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Cuenca, Spain experience its lowest Specific humidity at 200 hPa?", + "response": "Based on the provided data, Cuenca, Spain experienced its lowest Specific humidity at 200 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "1.536152e-05", + "true_value": 0, + "target_variable": "specific_humidity_200", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "20598d8716699bdf", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39070:39075:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79814:79818:1'} The data starts from August 18 12:00 and ends on August 19 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Asia experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Asia experienced its highest 10-meter V component of wind at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "11.701563", + "true_value": 18, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "86576b32783da81e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79814:79818:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82987:83012:1'} The data starts from October 20 18:00 and ends on October 26 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Tyrrhenian Sea experience its highest V (meridional) component of wind at 100 hPa?", + "response": "Based on the provided data, Tyrrhenian Sea experienced its highest V (meridional) component of wind at 100 hPa at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "4.759498", + "true_value": 30, + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a7b51635757177e6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82987:83012:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68945:68958:1'} The data starts from March 11 06:00 and ends on March 14 06:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 400 hPa experienced by Antarctica at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Antarctica experienced an median Specific humidity at 400 hPa of 8.311e-05 kg/kg at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "8.311473357025534e-05", + "target_variable": "specific_humidity_400", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f59f558da33f656b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68945:68958:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40082:40110:1'} The data starts from June 08 12:00 and ends on June 15 06:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 925 hPa experienced by Antarctica at 78 hours from the initial timeframe?", + "response": "Based on the provided data, Antarctica experienced an median Temperature at 925 hPa of 251.3 K at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 78, + "true_value": "251.31492614746094", + "target_variable": "temperature_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "16ff018286fb2875", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40082:40110:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36193:36214:1'} The data starts from October 10 06:00 and ends on October 15 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Lebanon experience its highest Geopotential at 150 hPa?", + "response": "Based on the provided data, Lebanon experienced its highest Geopotential at 150 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "139242.9", + "true_value": 12, + "target_variable": "geopotential_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "82bcdd2d05188227", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36193:36214:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80010:80024:1'} The data starts from October 06 12:00 and ends on October 09 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Saint Vincent and the Grenadines experience its highest Temperature at 1000 hPa?", + "response": "Based on the provided data, Saint Vincent and the Grenadines experienced its highest Temperature at 1000 hPa at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "300.36774", + "true_value": 78, + "target_variable": "temperature_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c17d9f4ef586180f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80010:80024:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39221:39249:1'} The data starts from November 05 06:00 and ends on November 12 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Suriname experience its highest Surface pressure?", + "response": "Based on the provided data, Suriname experienced its highest Surface pressure at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101234.76", + "true_value": 30, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5611453be0978f76", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39221:39249:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65202:65217:1'} The data starts from August 18 12:00 and ends on August 22 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure experienced by Flemish Brabant, Belgium at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Flemish Brabant, Belgium experienced an minimum Surface pressure of 1.013e+05 Pa at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "101332.0703125", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ecc663d69702124a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65202:65217:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67924:67932:1'} The data starts from June 29 00:00 and ends on June 30 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Walloon Brabant, Belgium experience its highest Temperature at 600 hPa?", + "response": "Based on the provided data, Walloon Brabant, Belgium experienced its highest Temperature at 600 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "268.8646", + "true_value": 0, + "target_variable": "temperature_600", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c62fab7b5665daca", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67924:67932:1" + } + } +] \ No newline at end of file diff --git a/level1b_part2.json b/level1b_part2.json new file mode 100644 index 0000000000000000000000000000000000000000..4c2db858ee74374f5c5d3d2e1cd3234b6dd616e8 --- /dev/null +++ b/level1b_part2.json @@ -0,0 +1,3302 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46346:46373:1'} The data starts from September 21 12:00 and ends on September 28 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Singapore experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Singapore experienced its lowest Mean sea level pressure at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "100951.07", + "true_value": 42, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "12217aa373e372aa", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46346:46373:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36238:36261:1'} The data starts from October 21 12:00 and ends on October 27 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Saint Lucia experience its highest U (zonal) component of wind at 50 hPa?", + "response": "Based on the provided data, Saint Lucia experienced its highest U (zonal) component of wind at 50 hPa at 120 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "2.1251626", + "true_value": 120, + "target_variable": "u_component_of_wind_50", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "d2f5e36b393ec8cd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36238:36261:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64692:64694:1'} The data starts from April 13 00:00 and ends on April 13 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Vilniaus, Lithuania experience its highest 10-meter U component of wind?", + "response": "Based on the provided data, Vilniaus, Lithuania experienced its highest 10-meter U component of wind at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "4.110471", + "true_value": 6, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "21ebc04d2641d095", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64692:64694:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78696:78715:1'} The data starts from November 12 00:00 and ends on November 16 12:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter V component of wind experienced by Odessa, Ukraine at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Odessa, Ukraine experienced an average 10-meter V component of wind of -3.847 m/s at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "-3.8468396425566076", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3d8acf1ce336980d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78696:78715:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40821:40847:1'} The data starts from December 10 06:00 and ends on December 16 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Kiribati experience its highest U (zonal) component of wind at 250 hPa?", + "response": "Based on the provided data, Kiribati experienced its highest U (zonal) component of wind at 250 hPa at 150 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "13.497579", + "true_value": 150, + "target_variable": "u_component_of_wind_250", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "17711f31ba783e7b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40821:40847:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75821:75824:1'} The data starts from November 24 06:00 and ends on November 24 18:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 500 hPa experienced by Bahrain at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Bahrain experienced an median Temperature at 500 hPa of 259.6 K at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "259.5704650878906", + "target_variable": "temperature_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b2bf019c6e7191a9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75821:75824:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62756:62776:1'} The data starts from December 15 00:00 and ends on December 19 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did South America experience its highest 10-meter U component of wind?", + "response": "Based on the provided data, South America experienced its highest 10-meter U component of wind at 114 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "15.173789", + "true_value": 114, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8215fe1f23bf98e6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62756:62776:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39856:39865:1'} The data starts from April 13 00:00 and ends on April 15 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Specific humidity at 50 hPa experienced by East Timor at 0 hours from the initial timeframe?", + "response": "Based on the provided data, East Timor experienced an minimum Specific humidity at 50 hPa of 2.539e-06 kg/kg at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "2.539354454711429e-06", + "target_variable": "specific_humidity_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6aa5bdb845391188", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39856:39865:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56046:56050:1'} The data starts from May 12 12:00 and ends on May 13 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Oman experience its lowest V (meridional) component of wind at 100 hPa?", + "response": "Based on the provided data, Gulf of Oman experienced its lowest V (meridional) component of wind at 100 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-4.0199103", + "true_value": 0, + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "0891ab5df0c9a9f2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56046:56050:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80782:80799:1'} The data starts from April 17 12:00 and ends on April 21 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Norfolk Island, Norfolk Island experience its lowest Geopotential at 600 hPa?", + "response": "Based on the provided data, Norfolk Island, Norfolk Island experienced its lowest Geopotential at 600 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "41998.727", + "true_value": 6, + "target_variable": "geopotential_600", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "03d12885aa8cbf94", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80782:80799:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44018:44043:1'} The data starts from February 16 12:00 and ends on February 22 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Specific humidity at 50 hPa experienced by Weddell Sea at 72 hours from the initial timeframe?", + "response": "Based on the provided data, Weddell Sea experienced an maximum Specific humidity at 50 hPa of 2.877e-06 kg/kg at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "2.8769238724635215e-06", + "target_variable": "specific_humidity_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2fc007acfe80aa0f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44018:44043:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62624:62644:1'} The data starts from November 12 00:00 and ends on November 16 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter U component of wind experienced by Mackenzie Bay at 84 hours from the initial timeframe?", + "response": "Based on the provided data, Mackenzie Bay experienced an minimum 10-meter U component of wind of -4.884 m/s at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "-4.883816719055176", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "72cbb182b80f5746", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62624:62644:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85787:85814:1'} The data starts from September 19 18:00 and ends on September 26 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 850 hPa experienced by Balearic Sea at 90 hours from the initial timeframe?", + "response": "Based on the provided data, Balearic Sea experienced an maximum U (zonal) component of wind at 850 hPa of 3.331 m/s at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 90, + "true_value": "3.3307011127471924", + "target_variable": "u_component_of_wind_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fc87597f8df848b1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85787:85814:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85074:85099:1'} The data starts from March 25 12:00 and ends on March 31 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Batanes, Philippines experience its lowest Temperature at 300 hPa?", + "response": "Based on the provided data, Batanes, Philippines experienced its lowest Temperature at 300 hPa at 132 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "240.0483", + "true_value": 132, + "target_variable": "temperature_300", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "0311608c44ebcd28", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85074:85099:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67006:67033:1'} The data starts from November 11 12:00 and ends on November 18 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure experienced by Manisa, Turkey at 132 hours from the initial timeframe?", + "response": "Based on the provided data, Manisa, Turkey experienced an maximum Mean sea level pressure of 1.011e+05 Pa at 132 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 132, + "true_value": "101083.2734375", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "31723211ddd9646c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67006:67033:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36479:36486:1'} The data starts from December 20 18:00 and ends on December 22 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure experienced by Indonesia at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Indonesia experienced an maximum Mean sea level pressure of 1.013e+05 Pa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "101291.9921875", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dc364c5d07cc94bd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36479:36486:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30672:30686:1'} The data starts from December 30 00:00 and ends on January 02 06:00 (1 year later). Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 250 hPa experienced by St. Helena Bay at 12 hours from the initial timeframe?", + "response": "Based on the provided data, St. Helena Bay experienced an minimum Geopotential at 250 hPa of 1.067e+05 m²/s² at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "106703.203125", + "target_variable": "geopotential_250", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3bc1ed2b757d734e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30672:30686:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71922:71933:1'} The data starts from March 24 12:00 and ends on March 27 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Strait of Gibraltar experience its lowest V (meridional) component of wind at 50 hPa?", + "response": "Based on the provided data, Strait of Gibraltar experienced its lowest V (meridional) component of wind at 50 hPa at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-7.684415", + "true_value": 54, + "target_variable": "v_component_of_wind_50", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "30194e3142a78d22", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71922:71933:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62926:62946:1'} The data starts from January 26 12:00 and ends on January 31 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 850 hPa experienced by Philippine Sea at 48 hours from the initial timeframe?", + "response": "Based on the provided data, Philippine Sea experienced an maximum Geopotential at 850 hPa of 1.522e+04 m²/s² at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 48, + "true_value": "15221.8154296875", + "target_variable": "geopotential_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4e0e8abc0259bc73", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62926:62946:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39677:39700:1'} The data starts from February 27 06:00 and ends on March 04 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter V component of wind experienced by Uttaradit, Thailand at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Uttaradit, Thailand experienced an maximum 10-meter V component of wind of 1.485 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "1.4847122430801392", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b9498bbd17049465", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39677:39700:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64919:64930:1'} The data starts from June 08 18:00 and ends on June 11 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Timor Sea experience its highest 10-meter U component of wind?", + "response": "Based on the provided data, Timor Sea experienced its highest 10-meter U component of wind at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-1.297811", + "true_value": 18, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "2701318703bc633a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64919:64930:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93375:93398:1'} The data starts from November 29 18:00 and ends on December 05 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did West Bengal, India experience its lowest 10-meter U component of wind?", + "response": "Based on the provided data, West Bengal, India experienced its lowest 10-meter U component of wind at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-2.6710808", + "true_value": 6, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "edbd65aa4fb24bc0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93375:93398:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67045:67071:1'} The data starts from November 21 06:00 and ends on November 27 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface temperature experienced by Saint Martin at 66 hours from the initial timeframe?", + "response": "Based on the provided data, Saint Martin experienced an minimum Surface temperature of 299.2 K at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "299.204345703125", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4c91fc2395ddad1c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67045:67071:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58063:58071:1'} The data starts from September 28 18:00 and ends on September 30 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Manafwa, Uganda experience its lowest U (zonal) component of wind at 100 hPa?", + "response": "Based on the provided data, Manafwa, Uganda experienced its lowest U (zonal) component of wind at 100 hPa at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-9.9314375", + "true_value": 42, + "target_variable": "u_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "32d9aa195d869c04", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58063:58071:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90760:90765:1'} The data starts from February 14 00:00 and ends on February 15 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface temperature experienced by Central Singapore, Singapore at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Central Singapore, Singapore experienced an minimum Surface temperature of 298.2 K at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "298.17913818359375", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "06a7159eff0c53ed", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90760:90765:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54215:54238:1'} The data starts from February 09 18:00 and ends on February 15 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Chiradzulu, Malawi experience its lowest V (meridional) component of wind at 50 hPa?", + "response": "Based on the provided data, Chiradzulu, Malawi experienced its lowest V (meridional) component of wind at 50 hPa at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-6.1027303", + "true_value": 48, + "target_variable": "v_component_of_wind_50", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "72534d11e07662fd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54215:54238:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60050:60062:1'} The data starts from February 07 12:00 and ends on February 10 06:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 200 hPa experienced by North America at 12 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an average U (zonal) component of wind at 200 hPa of 22.12 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "22.118911564392413", + "target_variable": "u_component_of_wind_200", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bc76256b7a882773", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60050:60062:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50972:50986:1'} The data starts from November 21 00:00 and ends on November 24 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did United Kingdom experience its highest Temperature at 50 hPa?", + "response": "Based on the provided data, United Kingdom experienced its highest Temperature at 50 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "217.82372", + "true_value": 24, + "target_variable": "temperature_50", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "50624eb860016474", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50972:50986:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89578:89588:1'} The data starts from April 24 12:00 and ends on April 26 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Princes Town, Trinidad and Tobago experience its lowest Temperature at 850 hPa?", + "response": "Based on the provided data, Princes Town, Trinidad and Tobago experienced its lowest Temperature at 850 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "288.99136", + "true_value": 24, + "target_variable": "temperature_850", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "988c94b60cba73a8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89578:89588:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81929:81947:1'} The data starts from January 29 06:00 and ends on February 02 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter V component of wind experienced by Ratnapura, Sri Lanka at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Ratnapura, Sri Lanka experienced an maximum 10-meter V component of wind of -2.98 m/s at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "-2.9800143241882324", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c71cfef6ba631ff9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81929:81947:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74269:74283:1'} The data starts from November 01 06:00 and ends on November 04 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Mean sea level pressure experienced by Burkina Faso at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Burkina Faso experienced an minimum Mean sea level pressure of 1.009e+05 Pa at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "100855.8046875", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2cfec204b71025d9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74269:74283:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65662:65681:1'} The data starts from December 11 12:00 and ends on December 16 00:00. Based on the above data, answer the following question:", + "question": "What is the median Geopotential at 500 hPa experienced by Antarctica at 102 hours from the initial timeframe?", + "response": "Based on the provided data, Antarctica experienced an median Geopotential at 500 hPa of 5.161e+04 m²/s² at 102 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 102, + "true_value": "51609.40625", + "target_variable": "geopotential_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2d8ea5e61aa0c115", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65662:65681:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30720:30728:1'} The data starts from January 11 00:00 and ends on January 12 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Surface temperature experienced by Baykonur Cosmodrome at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Baykonur Cosmodrome experienced an maximum Surface temperature of 260.1 K at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "260.146484375", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4ed4a0f318f07201", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30720:30728:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70311:70322:1'} The data starts from February 15 18:00 and ends on February 18 06:00. Based on the above data, answer the following question:", + "question": "What is the average Specific humidity at 400 hPa experienced by Europe at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Europe experienced an average Specific humidity at 400 hPa of 9.019e-05 kg/kg at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "9.019359290003217e-05", + "target_variable": "specific_humidity_400", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cf6fdd7d5250dcef", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70311:70322:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89307:89319:1'} The data starts from February 16 18:00 and ends on February 19 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Southern Patagonian Ice Field experience its lowest U (zonal) component of wind at 150 hPa?", + "response": "Based on the provided data, Southern Patagonian Ice Field experienced its lowest U (zonal) component of wind at 150 hPa at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "16.323154", + "true_value": 66, + "target_variable": "u_component_of_wind_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "0d7650eae666b10c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89307:89319:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56490:56518:1'} The data starts from August 31 12:00 and ends on September 07 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Taiwan Strait experience its lowest U (zonal) component of wind at 925 hPa?", + "response": "Based on the provided data, Taiwan Strait experienced its lowest U (zonal) component of wind at 925 hPa at 156 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-1.4978856", + "true_value": 156, + "target_variable": "u_component_of_wind_925", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "658424b13e6e60f7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56490:56518:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63255:63268:1'} The data starts from April 18 18:00 and ends on April 21 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Temperature at 50 hPa experienced by North America at 60 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an maximum Temperature at 50 hPa of 224.1 K at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 60, + "true_value": "224.09991455078125", + "target_variable": "temperature_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "473f6898e8dd7a76", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63255:63268:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67104:67117:1'} The data starts from December 06 00:00 and ends on December 09 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Specific humidity at 250 hPa experienced by San José, Costa Rica at 72 hours from the initial timeframe?", + "response": "Based on the provided data, San José, Costa Rica experienced an minimum Specific humidity at 250 hPa of 0.0001796 kg/kg at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "0.00017957837553694844", + "target_variable": "specific_humidity_250", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "12068e908513b789", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67104:67117:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52351:52373:1'} The data starts from October 31 18:00 and ends on November 06 00:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 1000 hPa experienced by Ohangwena, Namibia at 120 hours from the initial timeframe?", + "response": "Based on the provided data, Ohangwena, Namibia experienced an median Specific humidity at 1000 hPa of 0.008331 kg/kg at 120 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 120, + "true_value": "0.00833061896264553", + "target_variable": "specific_humidity_1000", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "17c1d79968dff4eb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52351:52373:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35325:35329:1'} The data starts from March 07 06:00 and ends on March 08 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum V (meridional) component of wind at 300 hPa experienced by Lovech, Bulgaria at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Lovech, Bulgaria experienced an maximum V (meridional) component of wind at 300 hPa of -14.87 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "-14.867430686950684", + "target_variable": "v_component_of_wind_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4d3adf9646701363", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35325:35329:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91474:91479:1'} The data starts from August 11 12:00 and ends on August 12 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Anadyr' experience its highest 10-meter U component of wind?", + "response": "Based on the provided data, Gulf of Anadyr' experienced its highest 10-meter U component of wind at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "1.8730232", + "true_value": 0, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "fa49d7b0179de39b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91474:91479:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37972:37994:1'} The data starts from December 28 00:00 and ends on January 02 06:00 (1 year later). Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Bismarck Sea experience its lowest V (meridional) component of wind at 500 hPa?", + "response": "Based on the provided data, Bismarck Sea experienced its lowest V (meridional) component of wind at 500 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-5.9724727", + "true_value": 24, + "target_variable": "v_component_of_wind_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "83394966dae757d9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37972:37994:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47169:47184:1'} The data starts from April 15 06:00 and ends on April 18 18:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter U component of wind experienced by Gnjilane, Kosovo at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Gnjilane, Kosovo experienced an median 10-meter U component of wind of 1.102 m/s at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "1.101516842842102", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2922127d0f6c071c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47169:47184:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55459:55463:1'} The data starts from December 16 18:00 and ends on December 17 12:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature experienced by Cameroon at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Cameroon experienced an median Surface temperature of 303.2 K at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "303.231201171875", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6614efa421981999", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55459:55463:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75805:75832:1'} The data starts from November 20 06:00 and ends on November 26 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Oman experience its highest Temperature at 1000 hPa?", + "response": "Based on the provided data, Gulf of Oman experienced its highest Temperature at 1000 hPa at 150 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "301.5594", + "true_value": 150, + "target_variable": "temperature_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a02945bd220724a5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75805:75832:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45054:45060:1'} The data starts from November 02 12:00 and ends on November 03 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter U component of wind experienced by Monagas, Venezuela at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Monagas, Venezuela experienced an minimum 10-meter U component of wind of -3.42 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "-3.419818878173828", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "21dc2ad56eacb626", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45054:45060:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91043:91054:1'} The data starts from April 25 18:00 and ends on April 28 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Mannar experience its highest Surface temperature?", + "response": "Based on the provided data, Gulf of Mannar experienced its highest Surface temperature at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "307.14685", + "true_value": 60, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "7c835ca0b453d012", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91043:91054:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49305:49318:1'} The data starts from September 30 06:00 and ends on October 03 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Queen Charlotte Strait experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Queen Charlotte Strait experienced its lowest Mean sea level pressure at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "100732.86", + "true_value": 0, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "6420f143d238358d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49305:49318:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70681:70687:1'} The data starts from May 19 06:00 and ends on May 20 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Baleares, Spain experience its lowest Temperature at 850 hPa?", + "response": "Based on the provided data, Baleares, Spain experienced its lowest Temperature at 850 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "286.03897", + "true_value": 6, + "target_variable": "temperature_850", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "56902b31c36c2453", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70681:70687:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55521:55536:1'} The data starts from January 01 06:00 and ends on January 04 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did North America experience its lowest Specific humidity at 700 hPa?", + "response": "Based on the provided data, North America experienced its lowest Specific humidity at 700 hPa at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "3.320419e-05", + "true_value": 72, + "target_variable": "specific_humidity_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "57d8539b99bec7c6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55521:55536:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63609:63618:1'} The data starts from July 16 06:00 and ends on July 18 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Northland, New Zealand experience its highest Mean sea level pressure?", + "response": "Based on the provided data, Northland, New Zealand experienced its highest Mean sea level pressure at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "102745.664", + "true_value": 48, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "bed789a5ee193f2e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63609:63618:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68286:68308:1'} The data starts from September 27 12:00 and ends on October 02 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 600 hPa experienced by Saint Vincent and the Grenadines at 96 hours from the initial timeframe?", + "response": "Based on the provided data, Saint Vincent and the Grenadines experienced an maximum Geopotential at 600 hPa of 4.34e+04 m²/s² at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 96, + "true_value": "43399.625", + "target_variable": "geopotential_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a21f0a73560473e8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68286:68308:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84415:84424:1'} The data starts from October 11 18:00 and ends on October 13 18:00. Based on the above data, answer the following question:", + "question": "What is the median U (zonal) component of wind at 600 hPa experienced by Sea of Japan at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Sea of Japan experienced an median U (zonal) component of wind at 600 hPa of 19.13 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "19.12572479248047", + "target_variable": "u_component_of_wind_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "777749c791609800", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84415:84424:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85384:85402:1'} The data starts from June 11 00:00 and ends on June 15 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Asia experience its lowest U (zonal) component of wind at 200 hPa?", + "response": "Based on the provided data, Asia experienced its lowest U (zonal) component of wind at 200 hPa at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-38.794075", + "true_value": 78, + "target_variable": "u_component_of_wind_200", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "936269e01f58322c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85384:85402:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60657:60663:1'} The data starts from July 08 06:00 and ends on July 09 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Kiboga, Uganda experience its lowest Temperature at 700 hPa?", + "response": "Based on the provided data, Kiboga, Uganda experienced its lowest Temperature at 700 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "280.2218", + "true_value": 0, + "target_variable": "temperature_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "af2d562c44cb8b66", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60657:60663:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42029:42032:1'} The data starts from October 08 06:00 and ends on October 08 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Bujumbura Mairie, Burundi experience its highest Geopotential at 50 hPa?", + "response": "Based on the provided data, Bujumbura Mairie, Burundi experienced its highest Geopotential at 50 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "202473.19", + "true_value": 12, + "target_variable": "geopotential_50", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "aa6224c23b87f204", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42029:42032:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80818:80821:1'} The data starts from April 26 12:00 and ends on April 27 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did North America experience its highest Surface pressure?", + "response": "Based on the provided data, North America experienced its highest Surface pressure at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "103545.14", + "true_value": 0, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "bd8bafc1646203f1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80818:80821:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71952:71954:1'} The data starts from April 01 00:00 and ends on April 01 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter V component of wind experienced by Sea of Azov at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Sea of Azov experienced an minimum 10-meter V component of wind of -2.278 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "-2.278226137161255", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "25da5fab1356aa50", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71952:71954:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44276:44298:1'} The data starts from April 22 00:00 and ends on April 27 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter V component of wind experienced by Dunaújváros, Hungary at 126 hours from the initial timeframe?", + "response": "Based on the provided data, Dunaújváros, Hungary experienced an minimum 10-meter V component of wind of -0.163 m/s at 126 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 126, + "true_value": "-0.16300299763679504", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7a6d49047d4cdfce", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44276:44298:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50720:50730:1'} The data starts from September 19 00:00 and ends on September 21 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Europe experience its lowest 10-meter V component of wind?", + "response": "Based on the provided data, Europe experienced its lowest 10-meter V component of wind at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-15.764209", + "true_value": 12, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "fadef09160be579d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50720:50730:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42045:42068:1'} The data starts from October 12 06:00 and ends on October 17 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure experienced by Africa at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Africa experienced an maximum Mean sea level pressure of 1.022e+05 Pa at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "102232.25", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "458ab73b37d4a4f0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42045:42068:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39446:39463:1'} The data starts from December 31 12:00 and ends on January 04 12:00 (1 year later). Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did North America experience its highest Temperature at 600 hPa?", + "response": "Based on the provided data, North America experienced its highest Temperature at 600 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "277.88696", + "true_value": 12, + "target_variable": "temperature_600", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ba2ca3105c55f471", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39446:39463:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71465:71477:1'} The data starts from December 01 06:00 and ends on December 04 00:00. Based on the above data, answer the following question:", + "question": "What is the median U (zonal) component of wind at 250 hPa experienced by Saint Lawrence River at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Saint Lawrence River experienced an median U (zonal) component of wind at 250 hPa of 29.28 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "29.281890869140625", + "target_variable": "u_component_of_wind_250", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d1d98e26ed32870f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71465:71477:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83640:83646:1'} The data starts from April 01 00:00 and ends on April 02 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Europe experience its lowest Geopotential at 500 hPa?", + "response": "Based on the provided data, Europe experienced its lowest Geopotential at 500 hPa at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "49244.74", + "true_value": 30, + "target_variable": "geopotential_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e79298ecb3612468", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83640:83646:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40201:40220:1'} The data starts from July 08 06:00 and ends on July 12 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did North America experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, North America experienced its lowest Mean sea level pressure at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "99128.82", + "true_value": 36, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a9b4f947683e22f9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40201:40220:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29848:29853:1'} The data starts from June 07 00:00 and ends on June 08 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Camden, United Kingdom experience its lowest Surface pressure?", + "response": "Based on the provided data, Camden, United Kingdom experienced its lowest Surface pressure at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "100988.445", + "true_value": 6, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c980ae5444d18eef", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29848:29853:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37457:37465:1'} The data starts from August 21 06:00 and ends on August 23 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter U component of wind experienced by South Cotabato, Philippines at 30 hours from the initial timeframe?", + "response": "Based on the provided data, South Cotabato, Philippines experienced an minimum 10-meter U component of wind of -1.683 m/s at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "-1.6827259063720703", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9927fbac8dc50036", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37457:37465:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84267:84270:1'} The data starts from September 04 18:00 and ends on September 05 06:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 925 hPa experienced by Gulf of Sakhalin at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Sakhalin experienced an median Temperature at 925 hPa of 281.6 K at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "281.5704040527344", + "target_variable": "temperature_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c91be4441c9897db", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84267:84270:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81138:81150:1'} The data starts from July 15 12:00 and ends on July 18 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did St. Helena Bay experience its lowest V (meridional) component of wind at 200 hPa?", + "response": "Based on the provided data, St. Helena Bay experienced its lowest V (meridional) component of wind at 200 hPa at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-22.833353", + "true_value": 48, + "target_variable": "v_component_of_wind_200", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "11d9b64c7ad31ebd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81138:81150:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59362:59373:1'} The data starts from August 19 12:00 and ends on August 22 00:00. Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure experienced by Kraslavas, Latvia at 54 hours from the initial timeframe?", + "response": "Based on the provided data, Kraslavas, Latvia experienced an average Mean sea level pressure of 1.01e+05 Pa at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 54, + "true_value": "100990.1597878574", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "58cf9209f7953fe6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59362:59373:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92111:92130:1'} The data starts from January 17 18:00 and ends on January 22 06:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter U component of wind experienced by San Cristóbal, Dominican Republic at 90 hours from the initial timeframe?", + "response": "Based on the provided data, San Cristóbal, Dominican Republic experienced an average 10-meter U component of wind of -4.314 m/s at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 90, + "true_value": "-4.314384739401801", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "efd59fb8b0ebbf77", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92111:92130:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67877:67903:1'} The data starts from June 17 06:00 and ends on June 23 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Specific humidity at 850 hPa experienced by Falcón, Venezuela at 126 hours from the initial timeframe?", + "response": "Based on the provided data, Falcón, Venezuela experienced an maximum Specific humidity at 850 hPa of 0.01259 kg/kg at 126 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 126, + "true_value": "0.012588640674948692", + "target_variable": "specific_humidity_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "eb9b6ad7e84355f1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67877:67903:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54483:54491:1'} The data starts from April 16 18:00 and ends on April 18 12:00. Based on the above data, answer the following question:", + "question": "What is the median Temperature at 50 hPa experienced by Persian Gulf at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Persian Gulf experienced an median Temperature at 50 hPa of 208.5 K at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "208.53472900390625", + "target_variable": "temperature_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9c899ce6376b7416", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54483:54491:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48755:48774:1'} The data starts from May 15 18:00 and ends on May 20 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Tuzla, Bosnia and Herzegovina experience its lowest 10-meter U component of wind?", + "response": "Based on the provided data, Tuzla, Bosnia and Herzegovina experienced its lowest 10-meter U component of wind at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-2.6612723", + "true_value": 90, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "dd772fff37424e26", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48755:48774:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62591:62607:1'} The data starts from November 03 18:00 and ends on November 07 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 150 hPa experienced by Northern Cyprus at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Northern Cyprus experienced an maximum Geopotential at 150 hPa of 1.354e+05 m²/s² at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "135374.421875", + "target_variable": "geopotential_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a6bcd74e860a045a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62591:62607:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69313:69316:1'} The data starts from June 11 06:00 and ends on June 11 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure experienced by Africa at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Africa experienced an minimum Surface pressure of 7.723e+04 Pa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "77232.2265625", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cdb716afbf653939", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69313:69316:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65803:65821:1'} The data starts from January 15 18:00 and ends on January 20 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 300 hPa experienced by Molucca Sea at 54 hours from the initial timeframe?", + "response": "Based on the provided data, Molucca Sea experienced an maximum U (zonal) component of wind at 300 hPa of 9.829 m/s at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 54, + "true_value": "9.828654289245605", + "target_variable": "u_component_of_wind_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "075357056f0990c0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65803:65821:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53756:53763:1'} The data starts from October 18 00:00 and ends on October 19 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Lucca, Italy experience its lowest Surface pressure?", + "response": "Based on the provided data, Lucca, Italy experienced its lowest Surface pressure at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "98968.734", + "true_value": 18, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "7eb1b3e2b3ba8168", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53756:53763:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84159:84162:1'} The data starts from August 08 18:00 and ends on August 09 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Lake Pontchartrain experience its highest Geopotential at 700 hPa?", + "response": "Based on the provided data, Lake Pontchartrain experienced its highest Geopotential at 700 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "31171.037", + "true_value": 0, + "target_variable": "geopotential_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "d0a41e516e03fe98", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84159:84162:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53243:53253:1'} The data starts from June 11 18:00 and ends on June 14 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface temperature experienced by Golfo de Guayaquil at 54 hours from the initial timeframe?", + "response": "Based on the provided data, Golfo de Guayaquil experienced an minimum Surface temperature of 294.8 K at 54 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 54, + "true_value": "294.801513671875", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ebd6457a04582298", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53243:53253:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85782:85799:1'} The data starts from September 18 12:00 and ends on September 22 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 250 hPa experienced by Africa at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Africa experienced an minimum Geopotential at 250 hPa of 9.898e+04 m²/s² at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "98975.6484375", + "target_variable": "geopotential_250", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "53a292df7513f0cd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85782:85799:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60764:60766:1'} The data starts from August 04 00:00 and ends on August 04 06:00. Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure experienced by Gulf of Maine at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Maine experienced an average Mean sea level pressure of 1.012e+05 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "101212.54778762789", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "412d584ef49ab6dc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60764:60766:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91421:91436:1'} The data starts from July 29 06:00 and ends on August 01 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 200 hPa experienced by M'Clure Strait at 42 hours from the initial timeframe?", + "response": "Based on the provided data, M'Clure Strait experienced an maximum Geopotential at 200 hPa of 1.162e+05 m²/s² at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "116242.828125", + "target_variable": "geopotential_200", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fe9f28a301e0a0f4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91421:91436:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34044:34071:1'} The data starts from April 21 00:00 and ends on April 27 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface pressure experienced by Lérida, Spain at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Lérida, Spain experienced an minimum Surface pressure of 9.16e+04 Pa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "91604.8671875", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "944ae27c9d564a0b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34044:34071:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92627:92648:1'} The data starts from May 26 18:00 and ends on May 31 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Specific humidity at 200 hPa experienced by Solomon Islands at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Solomon Islands experienced an maximum Specific humidity at 200 hPa of 9.928e-05 kg/kg at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "9.927859355229884e-05", + "target_variable": "specific_humidity_200", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e8575548694d8bc3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92627:92648:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75659:75666:1'} The data starts from October 14 18:00 and ends on October 16 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Zou, Benin experience its lowest V (meridional) component of wind at 500 hPa?", + "response": "Based on the provided data, Zou, Benin experienced its lowest V (meridional) component of wind at 500 hPa at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-1.6069547", + "true_value": 36, + "target_variable": "v_component_of_wind_500", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "29c39904c03c7709", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75659:75666:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40977:40991:1'} The data starts from January 18 06:00 and ends on January 21 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Joseph Bonaparte Gulf experience its lowest Specific humidity at 100 hPa?", + "response": "Based on the provided data, Joseph Bonaparte Gulf experienced its lowest Specific humidity at 100 hPa at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "1.0718657e-06", + "true_value": 78, + "target_variable": "specific_humidity_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a846d111723e0a9f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40977:40991:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47639:47662:1'} The data starts from August 10 18:00 and ends on August 16 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did North America experience its highest Temperature at 150 hPa?", + "response": "Based on the provided data, North America experienced its highest Temperature at 150 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "235.75581", + "true_value": 0, + "target_variable": "temperature_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "9cb6d6dba28c9677", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47639:47662:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63986:64009:1'} The data starts from October 18 12:00 and ends on October 24 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Haute-Marne, France experience its highest Geopotential at 1000 hPa?", + "response": "Based on the provided data, Haute-Marne, France experienced its highest Geopotential at 1000 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "1774.3098", + "true_value": 24, + "target_variable": "geopotential_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8347175374d7e7f0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63986:64009:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82604:82631:1'} The data starts from July 17 00:00 and ends on July 23 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Coral Sea Islands, Coral Sea Islands experience its lowest U (zonal) component of wind at 850 hPa?", + "response": "Based on the provided data, Coral Sea Islands, Coral Sea Islands experienced its lowest U (zonal) component of wind at 850 hPa at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-12.830981", + "true_value": 90, + "target_variable": "u_component_of_wind_850", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "70aef98da2a982f6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82604:82631:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68585:68601:1'} The data starts from December 11 06:00 and ends on December 15 00:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 50 hPa experienced by Hövsgöl, Mongolia at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Hövsgöl, Mongolia experienced an median V (meridional) component of wind at 50 hPa of -2.119 m/s at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "-2.1186769008636475", + "target_variable": "v_component_of_wind_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b5e531cd652d064b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68585:68601:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33454:33460:1'} The data starts from November 24 12:00 and ends on November 25 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Bight of Biafra experience its highest Surface pressure?", + "response": "Based on the provided data, Bight of Biafra experienced its highest Surface pressure at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101106.5", + "true_value": 24, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "79a7897080024ff0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33454:33460:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83048:83057:1'} The data starts from November 05 00:00 and ends on November 07 00:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter V component of wind experienced by Europe at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Europe experienced an median 10-meter V component of wind of 0.3086 m/s at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "0.30861684679985046", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "af4c1c3b9ee983d3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83048:83057:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37534:37562:1'} The data starts from September 09 12:00 and ends on September 16 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum Temperature at 150 hPa experienced by Ağsu, Azerbaijan at 144 hours from the initial timeframe?", + "response": "Based on the provided data, Ağsu, Azerbaijan experienced an minimum Temperature at 150 hPa of 211.6 K at 144 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 144, + "true_value": "211.6427001953125", + "target_variable": "temperature_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0677f5a5801067b4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37534:37562:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92410:92435:1'} The data starts from April 02 12:00 and ends on April 08 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter V component of wind experienced by Lagos, Nigeria at 114 hours from the initial timeframe?", + "response": "Based on the provided data, Lagos, Nigeria experienced an minimum 10-meter V component of wind of 2.68 m/s at 114 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 114, + "true_value": "2.6797890663146973", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4f13f7c16e8f4337", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92410:92435:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68244:68264:1'} The data starts from September 17 00:00 and ends on September 21 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did North America experience its highest V (meridional) component of wind at 150 hPa?", + "response": "Based on the provided data, North America experienced its highest V (meridional) component of wind at 150 hPa at 114 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "24.78329", + "true_value": 114, + "target_variable": "v_component_of_wind_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c297979b32238a5c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68244:68264:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61155:61183:1'} The data starts from November 09 18:00 and ends on November 16 12:00. Based on the above data, answer the following question:", + "question": "What is the average Surface pressure experienced by Spratly Islands, Spratly Islands at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Spratly Islands, Spratly Islands experienced an average Surface pressure of 1.009e+05 Pa at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "100943.98790031526", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d20c7e5e60ba51cc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61155:61183:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33246:33269:1'} The data starts from October 03 12:00 and ends on October 09 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Asia experience its lowest Specific humidity at 50 hPa?", + "response": "Based on the provided data, Asia experienced its lowest Specific humidity at 50 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "2.6527891e-06", + "true_value": 6, + "target_variable": "specific_humidity_50", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ece81706ef0ed7a3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33246:33269:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82890:82917:1'} The data starts from September 26 12:00 and ends on October 03 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 300 hPa experienced by South America at 84 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an minimum Geopotential at 300 hPa of 8.665e+04 m²/s² at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "86647.6953125", + "target_variable": "geopotential_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e6f39072b1398c82", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82890:82917:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83124:83136:1'} The data starts from November 24 00:00 and ends on November 26 18:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 100 hPa experienced by INDIAN OCEAN at 0 hours from the initial timeframe?", + "response": "Based on the provided data, INDIAN OCEAN experienced an median V (meridional) component of wind at 100 hPa of -0.1691 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "-0.1690678596496582", + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9a4c92327f86a53d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83124:83136:1" + } + } +] \ No newline at end of file diff --git a/level1b_part3.json b/level1b_part3.json new file mode 100644 index 0000000000000000000000000000000000000000..e29cb03e424ae07367abe93a461e4a80a6e23e57 --- /dev/null +++ b/level1b_part3.json @@ -0,0 +1,3302 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87001:87021:1'} The data starts from July 20 06:00 and ends on July 25 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 100 hPa experienced by Europe at 72 hours from the initial timeframe?", + "response": "Based on the provided data, Europe experienced an minimum U (zonal) component of wind at 100 hPa of -9.678 m/s at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "-9.677873611450195", + "target_variable": "u_component_of_wind_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "abab9949da7423a9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87001:87021:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80523:80544:1'} The data starts from February 11 18:00 and ends on February 16 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Tolima, Colombia experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, Tolima, Colombia experienced its lowest Mean sea level pressure at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "100929.16", + "true_value": 6, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "34cd4c8b5fc04da8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80523:80544:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62558:62580:1'} The data starts from October 26 12:00 and ends on October 31 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface temperature experienced by Ashmore and Cartier Islands at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Ashmore and Cartier Islands experienced an minimum Surface temperature of 300.6 K at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "300.56878662109375", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "530f4be4fa196396", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62558:62580:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42110:42129:1'} The data starts from October 28 12:00 and ends on November 02 00:00. Based on the above data, answer the following question:", + "question": "What is the average Surface temperature experienced by Africa at 102 hours from the initial timeframe?", + "response": "Based on the provided data, Africa experienced an average Surface temperature of 298.4 K at 102 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 102, + "true_value": "298.41929073383574", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bd3b14516d7f4535", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42110:42129:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54015:54030:1'} The data starts from December 21 18:00 and ends on December 25 06:00. Based on the above data, answer the following question:", + "question": "What is the median U (zonal) component of wind at 100 hPa experienced by Auckland, New Zealand at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Auckland, New Zealand experienced an median U (zonal) component of wind at 100 hPa of 4.399 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "4.39874267578125", + "target_variable": "u_component_of_wind_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "52d06f8c13836f49", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54015:54030:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32594:32612:1'} The data starts from April 23 12:00 and ends on April 27 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum V (meridional) component of wind at 300 hPa experienced by San Francisco Bay at 96 hours from the initial timeframe?", + "response": "Based on the provided data, San Francisco Bay experienced an minimum V (meridional) component of wind at 300 hPa of -45.28 m/s at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 96, + "true_value": "-45.2773323059082", + "target_variable": "v_component_of_wind_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "606a3dea9029f124", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32594:32612:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86280:86304:1'} The data starts from January 21 00:00 and ends on January 26 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did North America experience its lowest Mean sea level pressure?", + "response": "Based on the provided data, North America experienced its lowest Mean sea level pressure at 102 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "97705.04", + "true_value": 102, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "4c44e2a95335ba11", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86280:86304:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66965:66979:1'} The data starts from November 01 06:00 and ends on November 04 12:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 300 hPa experienced by Africa at 66 hours from the initial timeframe?", + "response": "Based on the provided data, Africa experienced an average U (zonal) component of wind at 300 hPa of 8.128 m/s at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "8.127706942112164", + "target_variable": "u_component_of_wind_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c3a043b23b1f0a03", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66965:66979:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42255:42281:1'} The data starts from December 03 18:00 and ends on December 10 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Finland experience its highest Temperature at 200 hPa?", + "response": "Based on the provided data, Gulf of Finland experienced its highest Temperature at 200 hPa at 102 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "222.64534", + "true_value": 102, + "target_variable": "temperature_200", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "03b7960c4b38f597", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42255:42281:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65811:65818:1'} The data starts from January 17 18:00 and ends on January 19 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Sognefjorden experience its lowest Surface pressure?", + "response": "Based on the provided data, Sognefjorden experienced its lowest Surface pressure at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "87183.67", + "true_value": 24, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "42fafd3fb9ec0d56", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65811:65818:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34045:34052:1'} The data starts from April 21 06:00 and ends on April 22 18:00. Based on the above data, answer the following question:", + "question": "What is the average Geopotential at 925 hPa experienced by Celebes Sea at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Celebes Sea experienced an average Geopotential at 925 hPa of 7362 m²/s² at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "7362.33318647526", + "target_variable": "geopotential_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "47db861901711528", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34045:34052:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79376:79384:1'} The data starts from May 01 00:00 and ends on May 02 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Specific humidity at 850 hPa experienced by Oceania at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Oceania experienced an maximum Specific humidity at 850 hPa of 0.0145 kg/kg at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "0.014499403536319733", + "target_variable": "specific_humidity_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "26bbfcd65dca42a5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79376:79384:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58548:58549:1'} The data corresponds to corresponds to a snapshot on January 28 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Southern Patagonian Ice Field experience its highest U (zonal) component of wind at 700 hPa?", + "response": "Based on the provided data, Southern Patagonian Ice Field experienced its highest U (zonal) component of wind at 700 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "17.070269", + "true_value": 0, + "target_variable": "u_component_of_wind_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a16c577623d83e94", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58548:58549:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74403:74418:1'} The data starts from December 04 18:00 and ends on December 08 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Rezina, Moldova experience its highest 10-meter U component of wind?", + "response": "Based on the provided data, Rezina, Moldova experienced its highest 10-meter U component of wind at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "0.12299655", + "true_value": 30, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "0365f8eb226ee4ab", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74403:74418:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50665:50666:1'} The data corresponds to corresponds to a snapshot on September 05 06:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 300 hPa experienced by Selat Dampier at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Selat Dampier experienced an average V (meridional) component of wind at 300 hPa of -2.144 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "-2.1439639239751562", + "target_variable": "v_component_of_wind_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8f021ca311542d98", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50665:50666:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88598:88622:1'} The data starts from August 23 12:00 and ends on August 29 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter V component of wind experienced by South America at 132 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an minimum 10-meter V component of wind of -9.094 m/s at 132 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 132, + "true_value": "-9.093582153320312", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a874165b1c9e1074", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88598:88622:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61384:61408:1'} The data starts from January 06 00:00 and ends on January 11 18:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter V component of wind experienced by North America at 138 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an average 10-meter V component of wind of 0.2232 m/s at 138 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 138, + "true_value": "0.22322553408826076", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1ed70baa8c5a696c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61384:61408:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55426:55452:1'} The data starts from December 08 12:00 and ends on December 14 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Baía de São Marcos experience its lowest 10-meter U component of wind?", + "response": "Based on the provided data, Baía de São Marcos experienced its lowest 10-meter U component of wind at 144 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-7.3967233", + "true_value": 144, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ed89ff3899c55a17", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55426:55452:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55709:55722:1'} The data starts from February 17 06:00 and ends on February 20 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum 10-meter V component of wind experienced by Tuvalu at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Tuvalu experienced an minimum 10-meter V component of wind of -4.795 m/s at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "-4.795251369476318", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ac5d2c94a5f8fade", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55709:55722:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45242:45248:1'} The data starts from December 19 12:00 and ends on December 20 18:00. Based on the above data, answer the following question:", + "question": "What is the median U (zonal) component of wind at 925 hPa experienced by Mali at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Mali experienced an median U (zonal) component of wind at 925 hPa of -9.953 m/s at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "-9.95274543762207", + "target_variable": "u_component_of_wind_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9fc3d68d4b520dac", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45242:45248:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33984:33996:1'} The data starts from April 06 00:00 and ends on April 08 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Golfe du Lion experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Golfe du Lion experienced its highest 10-meter V component of wind at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "3.8509", + "true_value": 42, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "2c632e732cd7728f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33984:33996:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90081:90092:1'} The data starts from August 28 06:00 and ends on August 30 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Norwegian Sea experience its highest 10-meter U component of wind?", + "response": "Based on the provided data, Norwegian Sea experienced its highest 10-meter U component of wind at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "14.860716", + "true_value": 36, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "19c22f6d18ba8f89", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90081:90092:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60334:60350:1'} The data starts from April 18 12:00 and ends on April 22 06:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature experienced by North America at 6 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an median Surface temperature of 270.4 K at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "270.3954162597656", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "52bce99a7f7f877d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60334:60350:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75045:75058:1'} The data starts from May 14 06:00 and ends on May 17 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Costa Rica experience its highest Mean sea level pressure?", + "response": "Based on the provided data, Costa Rica experienced its highest Mean sea level pressure at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101334.73", + "true_value": 48, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5801a151e782d3d0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75045:75058:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39776:39798:1'} The data starts from March 24 00:00 and ends on March 29 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Kuwait experience its highest Temperature at 925 hPa?", + "response": "Based on the provided data, Kuwait experienced its highest Temperature at 925 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "297.98984", + "true_value": 12, + "target_variable": "temperature_925", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "950777ed77008a8f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39776:39798:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76786:76810:1'} The data starts from July 23 12:00 and ends on July 29 06:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 600 hPa experienced by Luxembourg at 120 hours from the initial timeframe?", + "response": "Based on the provided data, Luxembourg experienced an average V (meridional) component of wind at 600 hPa of -0.4478 m/s at 120 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 120, + "true_value": "-0.44778746916381096", + "target_variable": "v_component_of_wind_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3060bb4528625cb5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76786:76810:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58129:58140:1'} The data starts from October 15 06:00 and ends on October 17 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Chaun Bay experience its lowest Geopotential at 150 hPa?", + "response": "Based on the provided data, Chaun Bay experienced its lowest Geopotential at 150 hPa at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "128382.88", + "true_value": 42, + "target_variable": "geopotential_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b0693b4d22314f14", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58129:58140:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43528:43530:1'} The data starts from October 17 00:00 and ends on October 17 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Riga experience its highest Specific humidity at 200 hPa?", + "response": "Based on the provided data, Gulf of Riga experienced its highest Specific humidity at 200 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "2.059917e-05", + "true_value": 6, + "target_variable": "specific_humidity_200", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e5580a6cca3cfd6d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43528:43530:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92576:92599:1'} The data starts from May 14 00:00 and ends on May 19 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Africa experience its lowest 10-meter U component of wind?", + "response": "Based on the provided data, Africa experienced its lowest 10-meter U component of wind at 90 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-11.541672", + "true_value": 90, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "7c6b03fbc4cd66cb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92576:92599:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66263:66268:1'} The data starts from May 09 18:00 and ends on May 10 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Singapore experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Singapore experienced its highest 10-meter V component of wind at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "2.578771", + "true_value": 12, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "cf68219566a9cd23", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66263:66268:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76827:76830:1'} The data starts from August 02 18:00 and ends on August 03 06:00. Based on the above data, answer the following question:", + "question": "What is the average Geopotential at 250 hPa experienced by Livanu, Latvia at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Livanu, Latvia experienced an average Geopotential at 250 hPa of 1.028e+05 m²/s² at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "102811.25093363167", + "target_variable": "geopotential_250", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c6cc4f7c432ac2ca", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76827:76830:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54796:54798:1'} The data starts from July 04 00:00 and ends on July 04 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum V (meridional) component of wind at 100 hPa experienced by South America at 0 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an maximum V (meridional) component of wind at 100 hPa of 15.4 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "15.400553703308105", + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5106729e19f9f896", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54796:54798:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86426:86446:1'} The data starts from February 26 12:00 and ends on March 03 06:00. Based on the above data, answer the following question:", + "question": "What is the median Mean sea level pressure experienced by Viseu, Portugal at 66 hours from the initial timeframe?", + "response": "Based on the provided data, Viseu, Portugal experienced an median Mean sea level pressure of 9.801e+04 Pa at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "98013.578125", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ee4e69aecb403764", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86426:86446:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64495:64501:1'} The data starts from February 22 18:00 and ends on February 24 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Idaho, United States of America experience its highest Temperature at 850 hPa?", + "response": "Based on the provided data, Idaho, United States of America experienced its highest Temperature at 850 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "276.66196", + "true_value": 6, + "target_variable": "temperature_850", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f58e3c9c4e76d23d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64495:64501:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53124:53125:1'} The data corresponds to corresponds to a snapshot on May 13 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Aden experience its highest Surface temperature?", + "response": "Based on the provided data, Gulf of Aden experienced its highest Surface temperature at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "302.10376", + "true_value": 0, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "40bfe885180ff17b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53124:53125:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47117:47142:1'} The data starts from April 02 06:00 and ends on April 08 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Namutumba, Uganda experience its highest Mean sea level pressure?", + "response": "Based on the provided data, Namutumba, Uganda experienced its highest Mean sea level pressure at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101611.84", + "true_value": 72, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ded28c228bc2e720", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47117:47142:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45603:45608:1'} The data starts from March 19 18:00 and ends on March 20 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Saint Barthelemy experience its highest Temperature at 925 hPa?", + "response": "Based on the provided data, Saint Barthelemy experienced its highest Temperature at 925 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "291.64886", + "true_value": 24, + "target_variable": "temperature_925", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "54f04bc8ee0e1b9f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45603:45608:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66475:66494:1'} The data starts from July 01 18:00 and ends on July 06 06:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter U component of wind experienced by Halmahera Sea at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Halmahera Sea experienced an median 10-meter U component of wind of -2.33 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "-2.330394744873047", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a26c70f9e18c5ad5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66475:66494:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50932:50943:1'} The data starts from November 11 00:00 and ends on November 13 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Saint Lawrence River experience its lowest U (zonal) component of wind at 200 hPa?", + "response": "Based on the provided data, Saint Lawrence River experienced its lowest U (zonal) component of wind at 200 hPa at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "23.74358", + "true_value": 42, + "target_variable": "u_component_of_wind_200", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f1785d64d105a82a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50932:50943:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57040:57052:1'} The data starts from January 16 00:00 and ends on January 18 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 925 hPa experienced by Morocco at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Morocco experienced an minimum U (zonal) component of wind at 925 hPa of -13.34 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "-13.335769653320312", + "target_variable": "u_component_of_wind_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d0e2a504b500c7af", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57040:57052:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80740:80764:1'} The data starts from April 07 00:00 and ends on April 12 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Vestfjorden experience its lowest 10-meter V component of wind?", + "response": "Based on the provided data, Vestfjorden experienced its lowest 10-meter V component of wind at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-4.2994666", + "true_value": 0, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "3ff71900c76c767d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80740:80764:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85926:85933:1'} The data starts from October 24 12:00 and ends on October 26 00:00. Based on the above data, answer the following question:", + "question": "What is the average 10-meter V component of wind experienced by Prince of Wales Strait at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Prince of Wales Strait experienced an average 10-meter V component of wind of 1.469 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "1.4693780061927035", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "520e69de2c0d341f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85926:85933:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60814:60825:1'} The data starts from August 16 12:00 and ends on August 19 00:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 400 hPa experienced by Long Island Sound at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Long Island Sound experienced an median V (meridional) component of wind at 400 hPa of -4.691 m/s at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "-4.690646648406982", + "target_variable": "v_component_of_wind_400", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d0195c4f5379e81f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60814:60825:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36863:36880:1'} The data starts from March 25 18:00 and ends on March 29 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 250 hPa experienced by Portugal at 72 hours from the initial timeframe?", + "response": "Based on the provided data, Portugal experienced an minimum U (zonal) component of wind at 250 hPa of 1.418 m/s at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "1.4182366132736206", + "target_variable": "u_component_of_wind_250", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "948fdc4ef2975046", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36863:36880:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81343:81357:1'} The data starts from September 04 18:00 and ends on September 08 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Temperature at 600 hPa experienced by Sibuyan Sea at 30 hours from the initial timeframe?", + "response": "Based on the provided data, Sibuyan Sea experienced an minimum Temperature at 600 hPa of 276.8 K at 30 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 30, + "true_value": "276.8427429199219", + "target_variable": "temperature_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8e5b46e239f5464c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81343:81357:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69198:69216:1'} The data starts from May 13 12:00 and ends on May 17 18:00. Based on the above data, answer the following question:", + "question": "What is the median Surface pressure experienced by Tonga at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Tonga experienced an median Surface pressure of 1.01e+05 Pa at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "101046.0625", + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3aefc300cd668fa6", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69198:69216:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33154:33167:1'} The data starts from September 10 12:00 and ends on September 13 12:00. Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure experienced by L'viv, Ukraine at 60 hours from the initial timeframe?", + "response": "Based on the provided data, L'viv, Ukraine experienced an average Mean sea level pressure of 1.018e+05 Pa at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 60, + "true_value": "101773.1670958398", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "437f88f1fddf36a8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33154:33167:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64467:64468:1'} The data corresponds to corresponds to a snapshot on February 15 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Mean sea level pressure experienced by Laghman, Afghanistan at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Laghman, Afghanistan experienced an minimum Mean sea level pressure of 1.021e+05 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "102080.453125", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "479697054d683297", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64467:64468:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83453:83477:1'} The data starts from February 14 06:00 and ends on February 20 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Yangtze River experience its lowest Surface pressure?", + "response": "Based on the provided data, Yangtze River experienced its lowest Surface pressure at 96 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101868.7", + "true_value": 96, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8f18bf1cf444fec7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83453:83477:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84537:84552:1'} The data starts from November 11 06:00 and ends on November 14 18:00. Based on the above data, answer the following question:", + "question": "What is the median V (meridional) component of wind at 150 hPa experienced by Bistrita-Nasaud, Romania at 66 hours from the initial timeframe?", + "response": "Based on the provided data, Bistrita-Nasaud, Romania experienced an median V (meridional) component of wind at 150 hPa of -4.627 m/s at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "-4.626900672912598", + "target_variable": "v_component_of_wind_150", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0ca08a9e95755c53", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84537:84552:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32763:32765:1'} The data starts from June 04 18:00 and ends on June 05 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Brunei experience its highest 10-meter U component of wind?", + "response": "Based on the provided data, Brunei experienced its highest 10-meter U component of wind at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "1.6382104", + "true_value": 0, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "997b009534c9beec", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32763:32765:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65587:65600:1'} The data starts from November 22 18:00 and ends on November 25 18:00. Based on the above data, answer the following question:", + "question": "What is the median Mean sea level pressure experienced by Inner Sea at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Inner Sea experienced an median Mean sea level pressure of 1.031e+05 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "103108.203125", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b259a877acdf1b9f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65587:65600:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58218:58230:1'} The data starts from November 06 12:00 and ends on November 09 06:00. Based on the above data, answer the following question:", + "question": "What is the average V (meridional) component of wind at 400 hPa experienced by Yangtze River at 42 hours from the initial timeframe?", + "response": "Based on the provided data, Yangtze River experienced an average V (meridional) component of wind at 400 hPa of 6.639 m/s at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 42, + "true_value": "6.639337051457919", + "target_variable": "v_component_of_wind_400", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "76d7b562fb4065c3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58218:58230:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66237:66254:1'} The data starts from May 03 06:00 and ends on May 07 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum Temperature at 925 hPa experienced by Oceania at 84 hours from the initial timeframe?", + "response": "Based on the provided data, Oceania experienced an maximum Temperature at 925 hPa of 297.6 K at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "297.57208251953125", + "target_variable": "temperature_925", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a96c1bc9267c71b1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66237:66254:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72495:72516:1'} The data starts from August 14 18:00 and ends on August 19 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum Temperature at 300 hPa experienced by Coral Sea Islands at 114 hours from the initial timeframe?", + "response": "Based on the provided data, Coral Sea Islands experienced an minimum Temperature at 300 hPa of 237 K at 114 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 114, + "true_value": "236.9620361328125", + "target_variable": "temperature_300", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d308ea1bfaf44a3c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72495:72516:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37030:37045:1'} The data starts from May 06 12:00 and ends on May 10 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Prydz Bay experience its lowest Surface temperature?", + "response": "Based on the provided data, Prydz Bay experienced its lowest Surface temperature at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "239.73204", + "true_value": 24, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "3ae227de90fa3e6e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37030:37045:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86780:86799:1'} The data starts from May 26 00:00 and ends on May 30 12:00. Based on the above data, answer the following question:", + "question": "What is the minimum Mean sea level pressure experienced by North Caicos, Turks and Caicos Islands at 0 hours from the initial timeframe?", + "response": "Based on the provided data, North Caicos, Turks and Caicos Islands experienced an minimum Mean sea level pressure of 1.016e+05 Pa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "101571.9140625", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b92d3333fae6e546", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86780:86799:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61632:61633:1'} The data corresponds to corresponds to a snapshot on March 09 00:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 100 hPa experienced by Uda Bay at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Uda Bay experienced an average U (zonal) component of wind at 100 hPa of 3.676 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "3.6761834308599597", + "target_variable": "u_component_of_wind_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c8ac980b6dc479f2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61632:61633:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66026:66050:1'} The data starts from March 11 12:00 and ends on March 17 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Bay of Plenty experience its highest Geopotential at 250 hPa?", + "response": "Based on the provided data, Bay of Plenty experienced its highest Geopotential at 250 hPa at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "105190.54", + "true_value": 36, + "target_variable": "geopotential_250", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "36b151c283aba16a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66026:66050:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65338:65356:1'} The data starts from September 21 12:00 and ends on September 25 18:00. Based on the above data, answer the following question:", + "question": "What is the minimum U (zonal) component of wind at 600 hPa experienced by Malta at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Malta experienced an minimum U (zonal) component of wind at 600 hPa of 2.076 m/s at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "2.076298713684082", + "target_variable": "u_component_of_wind_600", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d4f07f563c744b7d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65338:65356:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33757:33776:1'} The data starts from February 08 06:00 and ends on February 12 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Ethiopia experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Ethiopia experienced its highest 10-meter V component of wind at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "5.436374", + "true_value": 0, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "cda80240a7b70d9a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33757:33776:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32191:32203:1'} The data starts from January 12 18:00 and ends on January 15 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Oceania experience its highest Surface pressure?", + "response": "Based on the provided data, Oceania experienced its highest Surface pressure at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "102126.305", + "true_value": 48, + "target_variable": "surface_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "517aa73744583925", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32191:32203:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55499:55506:1'} The data starts from December 26 18:00 and ends on December 28 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Sea of Marmara experience its lowest U (zonal) component of wind at 50 hPa?", + "response": "Based on the provided data, Sea of Marmara experienced its lowest U (zonal) component of wind at 50 hPa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "4.65624", + "true_value": 24, + "target_variable": "u_component_of_wind_50", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "7eed3f5ff37ff003", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55499:55506:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44304:44307:1'} The data starts from April 29 00:00 and ends on April 29 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did North Sea experience its highest Temperature at 100 hPa?", + "response": "Based on the provided data, North Sea experienced its highest Temperature at 100 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "223.54321", + "true_value": 6, + "target_variable": "temperature_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "91cfbc7ffa0e6394", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44304:44307:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39506:39510:1'} The data starts from January 15 12:00 and ends on January 16 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 850 hPa experienced by Germany at 12 hours from the initial timeframe?", + "response": "Based on the provided data, Germany experienced an minimum Geopotential at 850 hPa of 1.276e+04 m²/s² at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 12, + "true_value": "12762.515625", + "target_variable": "geopotential_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d56e7f8c660aee6b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39506:39510:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82707:82727:1'} The data starts from August 11 18:00 and ends on August 16 12:00. Based on the above data, answer the following question:", + "question": "What is the average U (zonal) component of wind at 100 hPa experienced by Laghman, Afghanistan at 18 hours from the initial timeframe?", + "response": "Based on the provided data, Laghman, Afghanistan experienced an average U (zonal) component of wind at 100 hPa of 8.665 m/s at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 18, + "true_value": "8.664648174517563", + "target_variable": "u_component_of_wind_100", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ec1739903038039f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82707:82727:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52657:52685:1'} The data starts from January 16 06:00 and ends on January 23 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind experienced by Bosporus at 36 hours from the initial timeframe?", + "response": "Based on the provided data, Bosporus experienced an maximum 10-meter U component of wind of 0.5364 m/s at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "0.5363929271697998", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7f5f2940d8de85fc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52657:52685:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40678:40681:1'} The data starts from November 04 12:00 and ends on November 05 00:00. Based on the above data, answer the following question:", + "question": "What is the median Specific humidity at 50 hPa experienced by Oceania at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Oceania experienced an median Specific humidity at 50 hPa of 2.673e-06 kg/kg at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "2.6728630473371595e-06", + "target_variable": "specific_humidity_50", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "af4f4aef66cb3452", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40678:40681:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54567:54582:1'} The data starts from May 07 18:00 and ends on May 11 06:00. Based on the above data, answer the following question:", + "question": "What is the maximum Temperature at 700 hPa experienced by South Carolina, United States of America at 48 hours from the initial timeframe?", + "response": "Based on the provided data, South Carolina, United States of America experienced an maximum Temperature at 700 hPa of 277.3 K at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 48, + "true_value": "277.291748046875", + "target_variable": "temperature_700", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "729fbabe954635f7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54567:54582:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62766:62785:1'} The data starts from December 17 12:00 and ends on December 22 00:00. Based on the above data, answer the following question:", + "question": "What is the median Surface temperature experienced by North America at 66 hours from the initial timeframe?", + "response": "Based on the provided data, North America experienced an median Surface temperature of 261.7 K at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "261.7147521972656", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0b1bf2e9decf8926", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62766:62785:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37649:37653:1'} The data starts from October 08 06:00 and ends on October 09 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Gulf of Thailand experience its lowest U (zonal) component of wind at 1000 hPa?", + "response": "Based on the provided data, Gulf of Thailand experienced its lowest U (zonal) component of wind at 1000 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-3.3232052", + "true_value": 6, + "target_variable": "u_component_of_wind_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "7cc38a2bbb8b0730", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37649:37653:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76987:77014:1'} The data starts from September 11 18:00 and ends on September 18 06:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Strait of Gibraltar experience its highest Geopotential at 150 hPa?", + "response": "Based on the provided data, Strait of Gibraltar experienced its highest Geopotential at 150 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "139674.31", + "true_value": 6, + "target_variable": "geopotential_150", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b2a0cafacd3c2736", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76987:77014:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70507:70509:1'} The data starts from April 05 18:00 and ends on April 06 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum Specific humidity at 850 hPa experienced by Bay of Fundy at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Bay of Fundy experienced an maximum Specific humidity at 850 hPa of 0.00394 kg/kg at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "0.003940099384635687", + "target_variable": "specific_humidity_850", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dc3ad65fc916f198", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70507:70509:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44931:44932:1'} The data corresponds to corresponds to a snapshot on October 02 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum Geopotential at 700 hPa experienced by United States of America at 0 hours from the initial timeframe?", + "response": "Based on the provided data, United States of America experienced an maximum Geopotential at 700 hPa of 3.135e+04 m²/s² at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "31347.61328125", + "target_variable": "geopotential_700", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7617abd36b7c4265", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44931:44932:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89279:89295:1'} The data starts from February 09 18:00 and ends on February 13 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Asia experience its lowest Geopotential at 700 hPa?", + "response": "Based on the provided data, Asia experienced its lowest Geopotential at 700 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "26742.352", + "true_value": 0, + "target_variable": "geopotential_700", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "1087d782a699d21d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89279:89295:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49137:49155:1'} The data starts from August 19 06:00 and ends on August 23 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum Mean sea level pressure experienced by Río de la Plata at 24 hours from the initial timeframe?", + "response": "Based on the provided data, Río de la Plata experienced an maximum Mean sea level pressure of 1.025e+05 Pa at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 24, + "true_value": "102492.875", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "72bba2d427815981", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49137:49155:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46973:46995:1'} The data starts from February 25 06:00 and ends on March 02 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Bauchi, Nigeria experience its highest Mean sea level pressure?", + "response": "Based on the provided data, Bauchi, Nigeria experienced its highest Mean sea level pressure at 24 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "101368.086", + "true_value": 24, + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "564602414a231b1d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46973:46995:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77683:77692:1'} The data starts from March 03 18:00 and ends on March 05 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Estrecho de Magellanes experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Estrecho de Magellanes experienced its highest 10-meter V component of wind at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "3.0104964", + "true_value": 12, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e89c674ce1b35a13", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77683:77692:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57917:57924:1'} The data starts from August 23 06:00 and ends on August 24 18:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter U component of wind experienced by South America at 36 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an maximum 10-meter U component of wind of 16.59 m/s at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 36, + "true_value": "16.594486236572266", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "33523cb4529eec2d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57917:57924:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37202:37204:1'} The data starts from June 18 12:00 and ends on June 18 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Thaa, Maldives experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Thaa, Maldives experienced its highest 10-meter V component of wind at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "3.3819573", + "true_value": 0, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "98fe5d8b4d864346", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37202:37204:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82281:82291:1'} The data starts from April 27 06:00 and ends on April 29 12:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 200 hPa experienced by Oceania at 6 hours from the initial timeframe?", + "response": "Based on the provided data, Oceania experienced an maximum U (zonal) component of wind at 200 hPa of 64.98 m/s at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 6, + "true_value": "64.97938537597656", + "target_variable": "u_component_of_wind_200", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5d90b882bf840914", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82281:82291:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82912:82921:1'} The data starts from October 02 00:00 and ends on October 04 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did South America experience its highest Surface temperature?", + "response": "Based on the provided data, South America experienced its highest Surface temperature at 42 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "313.02167", + "true_value": 42, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "fd57bea9c2f3a327", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82912:82921:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81061:81087:1'} The data starts from June 26 06:00 and ends on July 02 12:00. Based on the above data, answer the following question:", + "question": "What is the median 10-meter U component of wind experienced by Gulf of Papua at 0 hours from the initial timeframe?", + "response": "Based on the provided data, Gulf of Papua experienced an median 10-meter U component of wind of -3.72 m/s at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 0, + "true_value": "-3.720439910888672", + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "588e0c83c760300b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81061:81087:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78706:78727:1'} The data starts from November 14 12:00 and ends on November 19 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Serranilla Bank experience its lowest 10-meter U component of wind?", + "response": "Based on the provided data, Serranilla Bank experienced its lowest 10-meter U component of wind at 60 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-5.125201", + "true_value": 60, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "aa4f9c5dcbe5abb0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78706:78727:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91570:91591:1'} The data starts from September 04 12:00 and ends on September 09 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Antarctica experience its lowest 10-meter U component of wind?", + "response": "Based on the provided data, Antarctica experienced its lowest 10-meter U component of wind at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-27.788536", + "true_value": 18, + "target_variable": "10m_u_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "bda30340094c3dee", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91570:91591:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42781:42803:1'} The data starts from April 13 06:00 and ends on April 18 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Peru experience its highest 10-meter V component of wind?", + "response": "Based on the provided data, Peru experienced its highest 10-meter V component of wind at 78 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "7.1397657", + "true_value": 78, + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "bc7397911dc3c6d0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42781:42803:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87703:87704:1'} The data corresponds to corresponds to a snapshot on January 11 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Moroto, Uganda experience its lowest Temperature at 600 hPa?", + "response": "Based on the provided data, Moroto, Uganda experienced its lowest Temperature at 600 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "275.36115", + "true_value": 0, + "target_variable": "temperature_600", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "cba55f5ef59c9e7b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87703:87704:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48195:48207:1'} The data starts from December 27 18:00 and ends on December 30 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Syria experience its lowest Geopotential at 1000 hPa?", + "response": "Based on the provided data, Syria experienced its lowest Geopotential at 1000 hPa at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "435.64526", + "true_value": 0, + "target_variable": "geopotential_1000", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "85a48111f5f4fb2e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48195:48207:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46493:46514:1'} The data starts from October 28 06:00 and ends on November 02 06:00. Based on the above data, answer the following question:", + "question": "What is the minimum Surface temperature experienced by Ecuador at 72 hours from the initial timeframe?", + "response": "Based on the provided data, Ecuador experienced an minimum Surface temperature of 285.4 K at 72 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 72, + "true_value": "285.3721923828125", + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5471552c578259bc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46493:46514:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89605:89629:1'} The data starts from May 01 06:00 and ends on May 07 00:00. Based on the above data, answer the following question:", + "question": "What is the minimum Geopotential at 500 hPa experienced by Kafr ash Shaykh, Egypt at 132 hours from the initial timeframe?", + "response": "Based on the provided data, Kafr ash Shaykh, Egypt experienced an minimum Geopotential at 500 hPa of 5.687e+04 m²/s² at 132 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 132, + "true_value": "56873.70703125", + "target_variable": "geopotential_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c289cbee878c457d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89605:89629:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91964:91982:1'} The data starts from December 12 00:00 and ends on December 16 06:00. Based on the above data, answer the following question:", + "question": "What is the average Mean sea level pressure experienced by South America at 48 hours from the initial timeframe?", + "response": "Based on the provided data, South America experienced an average Mean sea level pressure of 1.011e+05 Pa at 48 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 48, + "true_value": "101080.85467258094", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fdbe03cac0d94ab7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91964:91982:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40973:40976:1'} The data starts from January 17 06:00 and ends on January 17 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Republic of Serbia experience its lowest Surface temperature?", + "response": "Based on the provided data, Republic of Serbia experienced its lowest Surface temperature at 0 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "268.68912", + "true_value": 0, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "9f8c16cf50ada039", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40973:40976:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83012:83040:1'} The data starts from October 27 00:00 and ends on November 02 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Saint Thomas Middle Island, Saint Kitts and Nevis experience its lowest Temperature at 850 hPa?", + "response": "Based on the provided data, Saint Thomas Middle Island, Saint Kitts and Nevis experienced its lowest Temperature at 850 hPa at 12 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "290.3966", + "true_value": 12, + "target_variable": "temperature_850", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e0b6626678d69c27", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83012:83040:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60020:60047:1'} The data starts from January 31 00:00 and ends on February 06 12:00. Based on the above data, answer the following question:", + "question": "What is the median Mean sea level pressure experienced by Ozolnieku, Latvia at 102 hours from the initial timeframe?", + "response": "Based on the provided data, Ozolnieku, Latvia experienced an median Mean sea level pressure of 1.018e+05 Pa at 102 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 102, + "true_value": "101794.203125", + "target_variable": "mean_sea_level_pressure", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c5dce3e5f3154eae", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60020:60047:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84132:84140:1'} The data starts from August 02 00:00 and ends on August 03 18:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did INDIAN OCEAN experience its lowest V (meridional) component of wind at 100 hPa?", + "response": "Based on the provided data, INDIAN OCEAN experienced its lowest V (meridional) component of wind at 100 hPa at 6 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "-38.778362", + "true_value": 6, + "target_variable": "v_component_of_wind_100", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e979e73e912fd166", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84132:84140:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70638:70645:1'} The data starts from May 08 12:00 and ends on May 10 00:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Oceania experience its lowest Temperature at 925 hPa?", + "response": "Based on the provided data, Oceania experienced its lowest Temperature at 925 hPa at 18 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "267.46997", + "true_value": 18, + "target_variable": "temperature_925", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "50247efb93657c11", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70638:70645:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55281:55293:1'} The data starts from November 02 06:00 and ends on November 05 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum U (zonal) component of wind at 500 hPa experienced by Boca Grande at 66 hours from the initial timeframe?", + "response": "Based on the provided data, Boca Grande experienced an maximum U (zonal) component of wind at 500 hPa of 3.054 m/s at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "3.0536181926727295", + "target_variable": "u_component_of_wind_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "da45e57e003cfe65", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55281:55293:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85965:85993:1'} The data starts from November 03 06:00 and ends on November 10 00:00. Based on the above data, answer the following question:", + "question": "What is the maximum 10-meter V component of wind experienced by Uttaradit, Thailand at 66 hours from the initial timeframe?", + "response": "Based on the provided data, Uttaradit, Thailand experienced an maximum 10-meter V component of wind of -0.7946 m/s at 66 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 66, + "true_value": "-0.7945788502693176", + "target_variable": "10m_v_component_of_wind", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "33b2d3c0d68e3f7f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85965:85993:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36270:36279:1'} The data starts from October 29 12:00 and ends on October 31 12:00. Based on the above data, answer the following question:", + "question": "How many hours from the initial timeframe did Mudug, Somalia experience its lowest Surface temperature?", + "response": "Based on the provided data, Mudug, Somalia experienced its lowest Surface temperature at 36 hours from the initial timeframe.", + "metadata": { + "question_id": "ipApbm", + "prompt_id": "HIuSnl", + "extremum_value": "293.02917", + "true_value": 36, + "target_variable": "2m_temperature", + "level": "1b", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "d63aa8e63e11443a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36270:36279:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80353:80370:1'} The data starts from December 31 06:00 and ends on January 04 06:00 (1 year later). Based on the above data, answer the following question:", + "question": "What is the minimum Temperature at 500 hPa experienced by Kane Basin at 84 hours from the initial timeframe?", + "response": "Based on the provided data, Kane Basin experienced an minimum Temperature at 500 hPa of 230 K at 84 hours from the initial timeframe.", + "metadata": { + "question_id": "ebBRPc", + "prompt_id": "HIuSnl", + "time": 84, + "true_value": "230.0439910888672", + "target_variable": "temperature_500", + "level": "1b", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9bbdd179e9b06bc8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80353:80370:1" + } + } +] \ No newline at end of file diff --git a/level2a_part0.json b/level2a_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..a0b05f8b00fad0303edfd77f14d0f7228780341a --- /dev/null +++ b/level2a_part0.json @@ -0,0 +1,5134 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93183:93196:1'} The data starts from October 12 18:00 and ends on October 15 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Mean sea level pressure lies outside the climatological 5th–90th percentile envelope for the SON seasonal climatology. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 5th–90th percentile envelope for Mean sea level pressure during SON seasonal climatology: SOUTHERN OCEAN(average -311.9 Pa)\nNorth Atlantic Ocean(average 184 Pa)\nNorth Pacific Ocean(average 34.62 Pa)\nSouth Pacific Ocean(average 79.26 Pa)\nINDIAN OCEAN(average -88.25 Pa)\nSouth Atlantic Ocean(average 0.9375 Pa)\nPhilippine Sea(average -35.97 Pa)\nTasman Sea(average 31.49 Pa)\nSouth China Sea(average -16.18 Pa)\nLabrador Sea(average 252.2 Pa)\nHudson Bay(average -62.83 Pa)\nSea of Okhotsk(average 41.21 Pa)\nBellingshausen Sea(average -14.42 Pa)\nAmundsen Sea(average -271.8 Pa)\nDavis Strait(average 152.5 Pa)\nHudson Strait(average 100.4 Pa)\nGulf of Saint Lawrence(average 149.5 Pa)\nGolfo Corcovado(average 89.71 Pa)\nWrigley Gulf(average -480.4 Pa)\nSulzberger Bay(average -33.55 Pa)\nCumberland Sound(average 95.23 Pa)\nFrobisher Bay(average 92.12 Pa)\nHamilton Inlet(average 94.23 Pa)\nGolfo de Guayaquil(average 17.12 Pa)\nCook Strait(average 113.6 Pa)\nStrait of Belle Isle(average 228.7 Pa)\nPeacock Sound(average -56.11 Pa)\nLagoa dos Patos(average 0.9375 Pa)\nRoss Sea(average -387.1 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "SON seasonal climatology", + "lower_quantile": "0.05", + "upper_quantile": "0.9", + "true_value": [ + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Tasman Sea", + "South China Sea", + "Labrador Sea", + "Hudson Bay", + "Sea of Okhotsk", + "Bellingshausen Sea", + "Amundsen Sea", + "Davis Strait", + "Hudson Strait", + "Gulf of Saint Lawrence", + "Golfo Corcovado", + "Wrigley Gulf", + "Sulzberger Bay", + "Cumberland Sound", + "Frobisher Bay", + "Hamilton Inlet", + "Golfo de Guayaquil", + "Cook Strait", + "Strait of Belle Isle", + "Peacock Sound", + "Lagoa dos Patos", + "Ross Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "e9420186b05d266c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93183:93196:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90454:90469:1'} The data starts from November 29 12:00 and ends on December 03 00:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Geopotential at 400 hPa differs from the monthly climatology for November mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Geopotential at 400 hPa values.", + "response": "Based on the provided data, no significant Geopotential at 400 hPa anomalies were detected relative to the monthly climatology for November baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "geopotential", + 400 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for November", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ead828818e82a0cb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90454:90469:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88334:88348:1'} The data starts from June 18 12:00 and ends on June 21 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Specific humidity at 1000 hPa values running above the 99th percentile climatology for the monthly climatology for June? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Specific humidity at 1000 hPa values above the 99th percentile climatology for monthly climatology for June: China(average 6.447e-05 kg/kg)\nCosta Rica(average 0.0001513 kg/kg)\nNicaragua(average 0.0001206 kg/kg)\nUkraine(average 0.0002205 kg/kg)\nRussia(average 0.0002175 kg/kg)\nGeorgia(average 0.0002147 kg/kg)\nNorth Macedonia(average 5.48e-05 kg/kg)\nAlbania(average 0.0002407 kg/kg)\nKosovo(average 9.914e-05 kg/kg)\nTurkey(average 0.0003983 kg/kg)\nRomania(average 0.000121 kg/kg)\nHungary(average 6.14e-05 kg/kg)\nGreece(average 0.0005757 kg/kg)\nRepublic of Serbia(average 0.0001 kg/kg)\nBulgaria(average 9.338e-05 kg/kg)\nEl Salvador(average 0.0002291 kg/kg)\nGuatemala(average 0.0001518 kg/kg)\nMontenegro(average 0.0001049 kg/kg)\nCuba(average 5.703e-06 kg/kg)\nHonduras(average 0.0001461 kg/kg)\nColombia(average 9.846e-05 kg/kg)\nMoldova(average 0.0002943 kg/kg)\nTurkmenistan(average 0.0001118 kg/kg)\nUnited States of America(average 0.0001769 kg/kg)\nCanada(average 3.387e-05 kg/kg)\nMexico(average 0.0003115 kg/kg)\nPanama(average 0.0001777 kg/kg)\nTaiwan(average 7.662e-05 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "specific_humidity", + 1000 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for June", + "quantile": "0.99", + "threshold_direction": "above", + "true_value": [ + "China", + "Costa Rica", + "Nicaragua", + "Ukraine", + "Russia", + "Georgia", + "North Macedonia", + "Albania", + "Kosovo", + "Turkey", + "Romania", + "Hungary", + "Greece", + "Republic of Serbia", + "Bulgaria", + "El Salvador", + "Guatemala", + "Montenegro", + "Cuba", + "Honduras", + "Colombia", + "Moldova", + "Turkmenistan", + "United States of America", + "Canada", + "Mexico", + "Panama", + "Taiwan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "19754547904f172c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88334:88348:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72247:72274:1'} The data starts from June 13 18:00 and ends on June 20 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in 10-meter V component of wind values? An exceedance is defined as a period of at least 48 consecutive hours where the 10-meter V component of wind values exceed the 99th percentile climatology for the six-hourly climatology for day 165 at 18 UTC.", + "response": "The following water body(s) are currently experiencing an exceedance in 10-meter V component of wind: Arctic Ocean(average 0.3985 m/s)\nNorth Atlantic Ocean(average 0.06958 m/s)\nNorth Pacific Ocean(average 0.7693 m/s)\nSouth Pacific Ocean(average 0.6387 m/s)\nINDIAN OCEAN(average 0.2434 m/s)\nPhilippine Sea(average 0.08332 m/s)\nTasman Sea(average 0.1244 m/s)\nArabian Sea(average 0.8712 m/s)\nRed Sea(average 0.5948 m/s)\nBay of Biscay(average 0.1197 m/s)\nEast China Sea(average 0.08332 m/s)\nKara Sea(average 0.0517 m/s)\nGulf of Aden(average 1.546 m/s)\nThe North Western Passages(average 0.2833 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 165 at 18 UTC", + "quantile": "0.99", + "min_duration_days": 2, + "true_value": [ + "Arctic Ocean", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "Philippine Sea", + "Tasman Sea", + "Arabian Sea", + "Red Sea", + "Bay of Biscay", + "East China Sea", + "Kara Sea", + "Gulf of Aden", + "The North Western Passages" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "5b6c1ea2285b024f", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72247:72274:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65379:65405:1'} The data starts from October 01 18:00 and ends on October 08 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Storm (General) is occuring in the country of New Zealand. Specifically the region(s) being affected are: Kapiti Coast District area (Wellington province)\nA Storm (General) is occuring in the country of Haiti. Specifically the region(s) being affected are: Port-au-Prince district (Ouest province)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "New Zealand", + "Haiti" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "cf0f7ffade5aa1e4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65379:65405:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54657:54685:1'} The data starts from May 30 06:00 and ends on June 06 00:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will Sudan experience its highest V (meridional) component of wind at 925 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Sudan will experience its highest V (meridional) component of wind at 925 hPa of 12.4 m/s 24 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 24, + "location": "Sudan", + "extremum_value": "12.396456", + "target_variable": "v_component_of_wind_925", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "d96461ec5d1f16a2", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54657:54685:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78258:78284:1'} The data starts from July 25 12:00 and ends on July 31 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Geopotential at 850 hPa lies outside the climatological 5th–90th percentile envelope for the monthly climatology for July. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 5th–90th percentile envelope for Geopotential at 850 hPa during monthly climatology for July: North Pacific Ocean(average 136.7 m²/s²)\nSouth Pacific Ocean(average 98.76 m²/s²)\nINDIAN OCEAN(average 5.259 m²/s²)\nPhilippine Sea(average -73.82 m²/s²)\nGreat Australian Bight(average 3.836 m²/s²)\nSibuyan Sea(average -16.77 m²/s²)\nBohol Sea(average -0.0791 m²/s²)\nSurigao Strait(average -0.0791 m²/s²)\nRagay Gulf(average -14.11 m²/s²)\nSamar Sea(average -12.95 m²/s²)\nLeyte Gulf(average -0.0791 m²/s²)\nVisayan Sea(average -19.43 m²/s²)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "geopotential", + 850 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for July", + "lower_quantile": "0.05", + "upper_quantile": "0.9", + "true_value": [ + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "Philippine Sea", + "Great Australian Bight", + "Sibuyan Sea", + "Bohol Sea", + "Surigao Strait", + "Ragay Gulf", + "Samar Sea", + "Leyte Gulf", + "Visayan Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0f1f785836753a06", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78258:78284:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69847:69872:1'} The data starts from October 22 18:00 and ends on October 28 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Specific humidity at 700 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the Specific humidity at 700 hPa values exceed the 90th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Specific humidity at 700 hPa: Indonesia(average 6.918e-05 kg/kg)\nMalaysia(average 0.0001027 kg/kg)\nIndia(average 0.0001022 kg/kg)\nEthiopia(average 9.54e-05 kg/kg)\nSouth Sudan(average 0.0001444 kg/kg)\nSomalia(average 0.0001667 kg/kg)\nKenya(average 0.0001643 kg/kg)\nUnited Republic of Tanzania(average 5.503e-05 kg/kg)\nFrance(average 0.0002458 kg/kg)\nMorocco(average 0.0003225 kg/kg)\nDemocratic Republic of the Congo(average 5.986e-05 kg/kg)\nNamibia(average 4.54e-05 kg/kg)\nSaint Martin(average 0.000321 kg/kg)\nSint Maarten(average 0.000321 kg/kg)\nBrazil(average 0.0001445 kg/kg)\nSpain(average 0.0002347 kg/kg)\nCentral African Republic(average 4.361e-05 kg/kg)\nSudan(average 0.00026 kg/kg)\nEritrea(average 0.0004414 kg/kg)\nIraq(average 0.0002666 kg/kg)\nIran(average 0.0003126 kg/kg)\nNetherlands(average 0.0003415 kg/kg)\nAngola(average 4.54e-05 kg/kg)\nSaudi Arabia(average 0.0002397 kg/kg)\nHaiti(average 0.0007873 kg/kg)\nDominican Republic(average 0.001026 kg/kg)\nBurundi(average 5.503e-05 kg/kg)\nCuba(average 0.0001719 kg/kg)\nPortugal(average 0.0002054 kg/kg)\nUnited States of America(average 0.0005106 kg/kg)\nPapua New Guinea(average 9.234e-05 kg/kg)\nSri Lanka(average 9.828e-05 kg/kg)\nThe Bahamas(average 0.0003469 kg/kg)\nTurks and Caicos Islands(average 0.0005757 kg/kg)\nKiribati(average 0.0006994 kg/kg)\nMontserrat(average 0.0002458 kg/kg)\nAntigua and Barbuda(average 0.0003039 kg/kg)\nSaint Kitts and Nevis(average 0.0003039 kg/kg)\nUnited States Virgin Islands(average 0.0001204 kg/kg)\nSaint Barthelemy(average 0.0003621 kg/kg)\nPuerto Rico(average 0.0003752 kg/kg)\nAnguilla(average 0.0003415 kg/kg)\nBritish Virgin Islands(average 0.0002207 kg/kg)\nSingapore(average 3.508e-05 kg/kg)\nCook Islands(average 0.0004601 kg/kg)\nMaldives(average 0.0002445 kg/kg)\nAmerican Samoa(average 0.0001915 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 700 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.9", + "min_duration_days": 3, + "true_value": [ + "Indonesia", + "Malaysia", + "India", + "Ethiopia", + "South Sudan", + "Somalia", + "Kenya", + "United Republic of Tanzania", + "France", + "Morocco", + "Democratic Republic of the Congo", + "Namibia", + "Saint Martin", + "Sint Maarten", + "Brazil", + "Spain", + "Central African Republic", + "Sudan", + "Eritrea", + "Iraq", + "Iran", + "Netherlands", + "Angola", + "Saudi Arabia", + "Haiti", + "Dominican Republic", + "Burundi", + "Cuba", + "Portugal", + "United States of America", + "Papua New Guinea", + "Sri Lanka", + "The Bahamas", + "Turks and Caicos Islands", + "Kiribati", + "Montserrat", + "Antigua and Barbuda", + "Saint Kitts and Nevis", + "United States Virgin Islands", + "Saint Barthelemy", + "Puerto Rico", + "Anguilla", + "British Virgin Islands", + "Singapore", + "Cook Islands", + "Maldives", + "American Samoa" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "a7a1cd50c0260a73", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69847:69872:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74682:74693:1'} The data starts from February 12 12:00 and ends on February 15 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Specific humidity at 1000 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the Specific humidity at 1000 hPa values exceed the 95th percentile climatology for the six-hourly climatology for day 43 at 12 UTC. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Specific humidity at 1000 hPa: Indonesia(average 0.0001446 kg/kg)\nMalaysia(average 8.939e-06 kg/kg)\nChile(average 0.0005488 kg/kg)\nBolivia(average 0.0007545 kg/kg)\nPeru(average 0.000813 kg/kg)\nArgentina(average 0.0004481 kg/kg)\nIndia(average 0.001027 kg/kg)\nEthiopia(average 0.0006495 kg/kg)\nSouth Sudan(average 0.001297 kg/kg)\nSomalia(average 0.0003098 kg/kg)\nSyria(average 0.0003012 kg/kg)\nSomaliland(average 0.0005512 kg/kg)\nMorocco(average 0.0008942 kg/kg)\nWestern Sahara(average 0.0009973 kg/kg)\nCosta Rica(average 1.171e-05 kg/kg)\nRepublic of the Congo(average 0.0004932 kg/kg)\nDemocratic Republic of the Congo(average 0.001424 kg/kg)\nNamibia(average 0.0007924 kg/kg)\nSouth Africa(average 0.0005126 kg/kg)\nBrazil(average 0.0004677 kg/kg)\nUruguay(average 0.000444 kg/kg)\nRussia(average 5.912e-05 kg/kg)\nCambodia(average 0.0006358 kg/kg)\nGeorgia(average 0.0003117 kg/kg)\nAzerbaijan(average 0.0002351 kg/kg)\nTurkey(average 0.0002208 kg/kg)\nSpain(average 0.0008099 kg/kg)\nArmenia(average 0.0002351 kg/kg)\nZambia(average 0.0005042 kg/kg)\nSierra Leone(average 0.0004533 kg/kg)\nGuinea(average 0.0002534 kg/kg)\nLiberia(average 0.0004749 kg/kg)\nCentral African Republic(average 0.002177 kg/kg)\nSudan(average 0.001261 kg/kg)\nEritrea(average 0.00161 kg/kg)\nIraq(average 0.000539 kg/kg)\nIran(average 0.0001793 kg/kg)\nNetherlands(average 0.0002196 kg/kg)\nIvory Coast(average 0.0008903 kg/kg)\nMali(average 0.001436 kg/kg)\nSenegal(average 0.0009024 kg/kg)\nNigeria(average 0.0005936 kg/kg)\nBenin(average 0.002042 kg/kg)\nAngola(average 0.001431 kg/kg)\nSaudi Arabia(average 0.0008152 kg/kg)\nZimbabwe(average 0.0006101 kg/kg)\nThailand(average 0.0006358 kg/kg)\nChad(average 0.001023 kg/kg)\nKuwait(average 0.0004855 kg/kg)\nEast Timor(average 5.964e-05 kg/kg)\nAlgeria(average 0.0006553 kg/kg)\nMozambique(average 0.0005197 kg/kg)\nMyanmar(average 0.0003731 kg/kg)\nEcuador(average 0.0005783 kg/kg)\nColombia(average 0.000713 kg/kg)\nParaguay(average 0.0002414 kg/kg)\nBrazilian Island(average 0.0007078 kg/kg)\nLesotho(average 7.78e-05 kg/kg)\nCameroon(average 0.0004139 kg/kg)\nGabon(average 0.0003831 kg/kg)\nNiger(average 0.001759 kg/kg)\nBurkina Faso(average 0.001901 kg/kg)\nTogo(average 0.0004254 kg/kg)\nGhana(average 0.000646 kg/kg)\nGuinea-Bissau(average 0.0008193 kg/kg)\nUnited States of America(average 0.000214 kg/kg)\nCanada(average 0.000316 kg/kg)\nMexico(average 7.358e-05 kg/kg)\nPanama(average 0.0003943 kg/kg)\nVenezuela(average 0.0004616 kg/kg)\nEgypt(average 0.001807 kg/kg)\nYemen(average 0.0006077 kg/kg)\nMauritania(average 0.0006855 kg/kg)\nEquatorial Guinea(average 0.000464 kg/kg)\nGambia(average 0.001138 kg/kg)\nAustralia(average 0.0005947 kg/kg)\nGreenland(average 0.0001536 kg/kg)\nNew Zealand(average 0.0005986 kg/kg)\nMadagascar(average 0.0002446 kg/kg)\nFrench Southern and Antarctic Lands(average 8.448e-05 kg/kg)\nKiribati(average 0.0003916 kg/kg)\nAntigua and Barbuda(average 0.0002196 kg/kg)\nSaint Kitts and Nevis(average 0.0002196 kg/kg)\nSaint Barthelemy(average 0.0002196 kg/kg)\nAnguilla(average 0.0002196 kg/kg)\nSaint Helena(average 0.001066 kg/kg)\nCabo Verde(average 0.0004234 kg/kg)\nCook Islands(average 0.000454 kg/kg)\nTonga(average 0.0004677 kg/kg)\nSamoa(average 0.00053 kg/kg)\nNiue(average 0.0008254 kg/kg)\nAmerican Samoa(average 0.0006936 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 1000 + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 43 at 12 UTC", + "quantile": "0.95", + "min_duration_days": 2, + "true_value": [ + "Indonesia", + "Malaysia", + "Chile", + "Bolivia", + "Peru", + "Argentina", + "India", + "Ethiopia", + "South Sudan", + "Somalia", + "Syria", + "Somaliland", + "Morocco", + "Western Sahara", + "Costa Rica", + "Republic of the Congo", + "Democratic Republic of the Congo", + "Namibia", + "South Africa", + "Brazil", + "Uruguay", + "Russia", + "Cambodia", + "Georgia", + "Azerbaijan", + "Turkey", + "Spain", + "Armenia", + "Zambia", + "Sierra Leone", + "Guinea", + "Liberia", + "Central African Republic", + "Sudan", + "Eritrea", + "Iraq", + "Iran", + "Netherlands", + "Ivory Coast", + "Mali", + "Senegal", + "Nigeria", + "Benin", + "Angola", + "Saudi Arabia", + "Zimbabwe", + "Thailand", + "Chad", + "Kuwait", + "East Timor", + "Algeria", + "Mozambique", + "Myanmar", + "Ecuador", + "Colombia", + "Paraguay", + "Brazilian Island", + "Lesotho", + "Cameroon", + "Gabon", + "Niger", + "Burkina Faso", + "Togo", + "Ghana", + "Guinea-Bissau", + "United States of America", + "Canada", + "Mexico", + "Panama", + "Venezuela", + "Egypt", + "Yemen", + "Mauritania", + "Equatorial Guinea", + "Gambia", + "Australia", + "Greenland", + "New Zealand", + "Madagascar", + "French Southern and Antarctic Lands", + "Kiribati", + "Antigua and Barbuda", + "Saint Kitts and Nevis", + "Saint Barthelemy", + "Anguilla", + "Saint Helena", + "Cabo Verde", + "Cook Islands", + "Tonga", + "Samoa", + "Niue", + "American Samoa" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "c55a5e6caf93961d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74682:74693:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68335:68347:1'} The data starts from October 09 18:00 and ends on October 12 12:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Derecho currently happening? Specify the affected countries or regions, or respond 'No Derecho detected.'", + "response": "No Derecho detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Derecho", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "72e2634961a17549", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68335:68347:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83601:83609:1'} The data starts from March 22 06:00 and ends on March 24 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, there is no extreme weather event occuring.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "0e61bd46c9acfb7a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83601:83609:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89058:89067:1'} The data starts from December 16 12:00 and ends on December 18 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 42 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 42 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 42 hours:\nA Extra-tropical storm is expected in the country of Spain in approximately the next 12 to 84 hours. Specifically the region(s) that might get affected are: Catalogne\nA Extra-tropical storm is expected in the country of France in approximately the next 12 to 84 hours. Specifically the region(s) that might get affected are: Corse,Dordogne, Cote d'Azur\nA Extra-tropical storm is expected in the country of Portugal in approximately the next 12 to 84 hours. Specifically the region(s) that might get affected are: Montijo, Castro Daire, Águeda, Amarante, Chaves, Lisbon, Porto\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Spain", + "France", + "Portugal" + ], + "extreme_event_hours": 42, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "eda23b281a83dd0c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89058:89067:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80709:80722:1'} The data starts from March 30 06:00 and ends on April 02 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Temperature at 925 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the Temperature at 925 hPa values exceed the 95th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Temperature at 925 hPa: Indonesia(average 0.06853 K)\nPeru(average 0.3408 K)\nIndia(average 0.9397 K)\nBrazil(average 0.09717 K)\nVietnam(average 0.6105 K)\nLaos(average 0.7048 K)\nThailand(average 0.8789 K)\nMyanmar(average 1.242 K)\nBangladesh(average 0.8019 K)\nColombia(average 0.4066 K)\nMexico(average 0.3951 K)\nVenezuela(average 0.425 K)\nNew Zealand(average 1.428 K)\nFrench Southern and Antarctic Lands(average 1.08 K)\nHeard Island and McDonald Islands(average 0.3452 K)\nIndian Ocean Territories(average 0.2336 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "temperature", + 925 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.95", + "min_duration_days": 2, + "true_value": [ + "Indonesia", + "Peru", + "India", + "Brazil", + "Vietnam", + "Laos", + "Thailand", + "Myanmar", + "Bangladesh", + "Colombia", + "Mexico", + "Venezuela", + "New Zealand", + "French Southern and Antarctic Lands", + "Heard Island and McDonald Islands", + "Indian Ocean Territories" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "35b717c298b334ce", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80709:80722:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50248:50257:1'} The data starts from May 24 00:00 and ends on May 26 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Storm (General) is occuring in the country of Romania. Specifically the region(s) being affected are: North-East\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Romania" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "9940aed312a8139b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50248:50257:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83919:83943:1'} The data starts from June 09 18:00 and ends on June 15 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) V (meridional) component of wind at 925 hPa lies outside the climatological 1st–90th percentile envelope for the monthly climatology for June. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–90th percentile envelope for V (meridional) component of wind at 925 hPa during monthly climatology for June: SOUTHERN OCEAN(average 0.2502 m/s)\nNorth Pacific Ocean(average 0.08617 m/s)\nSouth Pacific Ocean(average 0.8017 m/s)\nSouth Atlantic Ocean(average 0.1497 m/s)\nBlack Sea(average 0.2681 m/s)\nPhilippine Sea(average 0.02817 m/s)\nRed Sea(average 0.8333 m/s)\nSea of Okhotsk(average 0.1253 m/s)\nAdriatic Sea(average 0.1839 m/s)\nThe North Western Passages(average 0.1769 m/s)\nSolomon Sea(average 0.09841 m/s)\nTaiwan Strait(average 0.02817 m/s)\nWrigley Gulf(average 0.3619 m/s)\nSulzberger Bay(average 1.063 m/s)\nGulf of Suez(average 0.9818 m/s)\nSea of Marmara(average 0.08465 m/s)\nGulf of Aqaba(average 0.8333 m/s)\nMediterranean Sea(average 0.1233 m/s)\nRoss Sea(average 0.8204 m/s)\nCoral Sea(average 0.8017 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 925 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for June", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "SOUTHERN OCEAN", + "North Pacific Ocean", + "South Pacific Ocean", + "South Atlantic Ocean", + "Black Sea", + "Philippine Sea", + "Red Sea", + "Sea of Okhotsk", + "Adriatic Sea", + "The North Western Passages", + "Solomon Sea", + "Taiwan Strait", + "Wrigley Gulf", + "Sulzberger Bay", + "Gulf of Suez", + "Sea of Marmara", + "Gulf of Aqaba", + "Mediterranean Sea", + "Ross Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ad0760d9c17a69b9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83919:83943:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89781:89801:1'} The data starts from June 14 06:00 and ends on June 19 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "No Tropical Cyclone detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "21590f2f94fa72d3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89781:89801:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82968:82983:1'} The data starts from October 16 00:00 and ends on October 19 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in V (meridional) component of wind at 400 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the V (meridional) component of wind at 400 hPa values exceed the 95th percentile climatology for the daily climatology for day 289.", + "response": "The following water body(s) are currently experiencing an exceedance in V (meridional) component of wind at 400 hPa: Arctic Ocean(average 1.321 m/s)\nNorth Atlantic Ocean(average 2.767 m/s)\nNorth Pacific Ocean(average 0.3928 m/s)\nSouth Pacific Ocean(average 3.574 m/s)\nINDIAN OCEAN(average 1.02 m/s)\nPhilippine Sea(average 4.768 m/s)\nSouth China Sea(average 1.385 m/s)\nCaribbean Sea(average 0.9136 m/s)\nWeddell Sea(average 1.597 m/s)\nGreenland Sea(average 3.408 m/s)\nGulf of Honduras(average 0.08998 m/s)\nKangertittivaq(average 2.517 m/s)\nDenmark Strait(average 3.097 m/s)\nBohol Sea(average 2.898 m/s)\nSurigao Strait(average 2.898 m/s)\nSamar Sea(average 2.898 m/s)\nLeyte Gulf(average 2.898 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "v_component_of_wind", + 400 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 289", + "quantile": "0.95", + "min_duration_days": 3, + "true_value": [ + "Arctic Ocean", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "Philippine Sea", + "South China Sea", + "Caribbean Sea", + "Weddell Sea", + "Greenland Sea", + "Gulf of Honduras", + "Kangertittivaq", + "Denmark Strait", + "Bohol Sea", + "Surigao Strait", + "Samar Sea", + "Leyte Gulf" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "003f19341651c6fd", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82968:82983:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51380:51391:1'} The data starts from March 03 00:00 and ends on March 05 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 42 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 42 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 42 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 42, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "f58109ef1714beaa", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51380:51391:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33265:33269:1'} The data starts from October 08 06:00 and ends on October 09 00:00. Based on the above data, answer the following question:", + "question": "In the 18 hours after the end of the given time window, when will Namibia experience its highest 10-meter V component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Namibia will experience its highest 10-meter V component of wind of 7.977 m/s 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Namibia", + "extremum_value": "7.9770527", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "6afce8bc44263f40", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33265:33269:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81076:81096:1'} The data starts from June 30 00:00 and ends on July 04 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface pressure lies outside the climatological 5th–99th percentile envelope for the daily climatology for day 181. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 5th–99th percentile envelope for Surface pressure during daily climatology for day 181: Arctic Ocean(average 55.83 Pa)\nSOUTHERN OCEAN(average -275.8 Pa)\nNorth Atlantic Ocean(average -375.3 Pa)\nNorth Pacific Ocean(average -102.3 Pa)\nSouth Pacific Ocean(average -146.7 Pa)\nINDIAN OCEAN(average -13.48 Pa)\nSouth Atlantic Ocean(average 269.2 Pa)\nPhilippine Sea(average -80.5 Pa)\nTasman Sea(average -13.48 Pa)\nHudson Bay(average -178.8 Pa)\nCaspian Sea(average 166.4 Pa)\nNorwegian Sea(average -375.2 Pa)\nGreenland Sea(average -437.6 Pa)\nLaptev Sea(average 77.2 Pa)\nJames Bay(average -78.39 Pa)\nEast Siberian Sea(average 47.06 Pa)\nDenmark Strait(average -460.5 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 181", + "lower_quantile": "0.05", + "upper_quantile": "0.99", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Tasman Sea", + "Hudson Bay", + "Caspian Sea", + "Norwegian Sea", + "Greenland Sea", + "Laptev Sea", + "James Bay", + "East Siberian Sea", + "Denmark Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "4e638af3443e9d65", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81076:81096:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92006:92019:1'} The data starts from December 22 12:00 and ends on December 25 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Geopotential at 50 hPa values running above the 95th percentile climatology for the monthly climatology for December? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Geopotential at 50 hPa values above the 95th percentile climatology for monthly climatology for December: Chile(average 790.3 m²/s²)\nBolivia(average 54.21 m²/s²)\nPeru(average 26.86 m²/s²)\nArgentina(average 792.6 m²/s²)\nChina(average 123.6 m²/s²)\nFrance(average 34.49 m²/s²)\nNicaragua(average 13.91 m²/s²)\nSaint Martin(average 86.66 m²/s²)\nSint Maarten(average 86.66 m²/s²)\nKazakhstan(average 93.67 m²/s²)\nBrazil(average 192.8 m²/s²)\nUruguay(average 564.1 m²/s²)\nMongolia(average 324.7 m²/s²)\nRussia(average 433 m²/s²)\nNetherlands(average 81.95 m²/s²)\nHaiti(average 79.37 m²/s²)\nDominican Republic(average 82.16 m²/s²)\nEl Salvador(average 9.141 m²/s²)\nGuatemala(average 24.7 m²/s²)\nUS Naval Base Guantanamo Bay(average 84.72 m²/s²)\nCuba(average 56.52 m²/s²)\nHonduras(average 22.25 m²/s²)\nColombia(average 20.63 m²/s²)\nParaguay(average 135.4 m²/s²)\nBrazilian Island(average 471.2 m²/s²)\nUnited States of America(average 137.2 m²/s²)\nMexico(average 22.7 m²/s²)\nBelize(average 33.61 m²/s²)\nVenezuela(average 15.83 m²/s²)\nSouthern Patagonian Ice Field(average 1181 m²/s²)\nAustralia(average 171.5 m²/s²)\nFiji(average 73.08 m²/s²)\nNew Zealand(average 232.2 m²/s²)\nNew Caledonia(average 83.5 m²/s²)\nThe Bahamas(average 53.94 m²/s²)\nTurks and Caicos Islands(average 95.45 m²/s²)\nPitcairn Islands(average 18.67 m²/s²)\nFrench Polynesia(average 84.32 m²/s²)\nDominica(average 15.47 m²/s²)\nUnited States Minor Outlying Islands(average 66.02 m²/s²)\nMontserrat(average 44.11 m²/s²)\nAntigua and Barbuda(average 60.68 m²/s²)\nSaint Kitts and Nevis(average 60.68 m²/s²)\nUnited States Virgin Islands(average 91.08 m²/s²)\nSaint Barthelemy(average 77.25 m²/s²)\nPuerto Rico(average 80.08 m²/s²)\nAnguilla(average 81.95 m²/s²)\nBritish Virgin Islands(average 88.87 m²/s²)\nJamaica(average 65.43 m²/s²)\nCayman Islands(average 48.94 m²/s²)\nNorfolk Island(average 264.3 m²/s²)\nCook Islands(average 80.45 m²/s²)\nTonga(average 132.5 m²/s²)\nFalkland Islands(average 765.8 m²/s²)\nVanuatu(average 58.07 m²/s²)\nNiue(average 118.5 m²/s²)\nBajo Nuevo Bank (Petrel Is.)(average 48.03 m²/s²)\nSerranilla Bank(average 43.73 m²/s²)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "geopotential", + 50 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for December", + "quantile": "0.95", + "threshold_direction": "above", + "true_value": [ + "Chile", + "Bolivia", + "Peru", + "Argentina", + "China", + "France", + "Nicaragua", + "Saint Martin", + "Sint Maarten", + "Kazakhstan", + "Brazil", + "Uruguay", + "Mongolia", + "Russia", + "Netherlands", + "Haiti", + "Dominican Republic", + "El Salvador", + "Guatemala", + "US Naval Base Guantanamo Bay", + "Cuba", + "Honduras", + "Colombia", + "Paraguay", + "Brazilian Island", + "United States of America", + "Mexico", + "Belize", + "Venezuela", + "Southern Patagonian Ice Field", + "Australia", + "Fiji", + "New Zealand", + "New Caledonia", + "The Bahamas", + "Turks and Caicos Islands", + "Pitcairn Islands", + "French Polynesia", + "Dominica", + "United States Minor Outlying Islands", + "Montserrat", + "Antigua and Barbuda", + "Saint Kitts and Nevis", + "United States Virgin Islands", + "Saint Barthelemy", + "Puerto Rico", + "Anguilla", + "British Virgin Islands", + "Jamaica", + "Cayman Islands", + "Norfolk Island", + "Cook Islands", + "Tonga", + "Falkland Islands", + "Vanuatu", + "Niue", + "Bajo Nuevo Bank (Petrel Is.)", + "Serranilla Bank" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "93ec9cf92bd98535", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92006:92019:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72261:72274:1'} The data starts from June 17 06:00 and ends on June 20 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Mean sea level pressure values running above the 99th percentile climatology for the six-hourly climatology for day 169 at 06 UTC? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Mean sea level pressure values above the 99th percentile climatology for six-hourly climatology for day 169 at 06 UTC: Indonesia(average 12.09 Pa)\nSouth Africa(average 503.7 Pa)\nUnited States of America(average 61.41 Pa)\nCanada(average 90.39 Pa)\nPapua New Guinea(average 23.4 Pa)\nAustralia(average 46.12 Pa)\nGreenland(average 38.85 Pa)\nNew Zealand(average 289.3 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 169 at 06 UTC", + "quantile": "0.99", + "threshold_direction": "above", + "true_value": [ + "Indonesia", + "South Africa", + "United States of America", + "Canada", + "Papua New Guinea", + "Australia", + "Greenland", + "New Zealand" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "f228fad38cbc41ba", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72261:72274:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44244:44257:1'} The data starts from April 14 00:00 and ends on April 17 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Australia", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Australia" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "456e5130d9855291", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44244:44257:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65642:65670:1'} The data starts from December 06 12:00 and ends on December 13 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 42 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 42 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 42 hours:\nA Storm (General) is expected in the country of United States of America in approximately the next 42 to 66 hours. Specifically the region(s) that might get affected are: Beaumont, Port Arthur areas (Jefferson district, Texas province), Sugar Land area (Fort Bend district, Texas province), Smith district (Mississipi province), Dallas, Harris districts (Texas province), Los Angeles district (California province), Alabama, Arkansas, Florida, Georgia, Louisiana, Maryland, Missouri, North Carolina, Oklahoma, South Carolina, Tennesse, Virginia, West Virginia provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "United States of America" + ], + "extreme_event_hours": 42, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "90ccab3c19c6608e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65642:65670:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74033:74047:1'} The data starts from September 03 06:00 and ends on September 06 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 36 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 36 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 36 hours:\nA Severe weather is expected in the country of Paraguay in approximately the next 36 hours. Specifically the region(s) that might get affected are: Neembucu, San Pedro, Paraguari, Cordillera, Canindeyu, Caaguazu provinces\nA Tropical cyclone is expected in the country of Philippines in approximately the next 36 to 60 hours. Specifically the region(s) that might get affected are: Botolan, Iba, San Antonio, San Felipe, San Marcelino areas (Zambales district, Region III (Central Luzon) province), Abucay, Bagac, Dinalupihan, Hermosa, Morong, Pilar, Samal areas (Bataan district, Region III (Central Luzon) province), Apalit, Arayat, Bacolor, Floridabanca, Guagua, Lubao, Masantol, Mexico, Minalin, San Luis, Sasmuan, Stanta Ana, Santo Thomas areas (Pampanga district, Region III (Central Luzon) province), Balagtas, Calumpit, Guiguinto, Marilao, Meycauayan, San Miguel areas (Bulacan district, Region III (Central Luzon) province), Calamba city (Laguna district, Region IV-A (Calabarzon) province)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Paraguay", + "Philippines" + ], + "extreme_event_hours": 36, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "297dddb09d6c919d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74033:74047:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78368:78376:1'} The data starts from August 22 00:00 and ends on August 23 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) V (meridional) component of wind at 500 hPa lies outside the climatological 1st–90th percentile envelope for the monthly climatology for August. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–90th percentile envelope for V (meridional) component of wind at 500 hPa during monthly climatology for August: Arctic Ocean(average 1.529 m/s)\nSOUTHERN OCEAN(average 2.802 m/s)\nNorth Atlantic Ocean(average 1.295 m/s)\nNorth Pacific Ocean(average 1.394 m/s)\nSouth Pacific Ocean(average 4.234 m/s)\nINDIAN OCEAN(average 1.087 m/s)\nSouth Atlantic Ocean(average 3.865 m/s)\nPhilippine Sea(average 2.435 m/s)\nBay of Bengal(average 0.6205 m/s)\nArabian Sea(average 0.2842 m/s)\nBeaufort Sea(average 1.971 m/s)\nCaribbean Sea(average 0.1495 m/s)\nGulf of Mexico(average 0.6221 m/s)\nSea of Okhotsk(average 3.32 m/s)\nGreenland Sea(average -0.1997 m/s)\nBanda Sea(average 0.307 m/s)\nMozambique Channel(average 2.812 m/s)\nGulf of Guinea(average 0.7185 m/s)\nBarents Sea(average 3.496 m/s)\nJava Sea(average 0.2627 m/s)\nEast China Sea(average 1.486 m/s)\nChukchi Sea(average 2.155 m/s)\nArafura Sea(average 0.5113 m/s)\nTimor Sea(average 0.7282 m/s)\nLaccadive Sea(average 1.058 m/s)\nBellingshausen Sea(average 5.574 m/s)\nAmundsen Sea(average 3.13 m/s)\nKara Sea(average 3.485 m/s)\nGreat Australian Bight(average 1.303 m/s)\nStraits of Florida(average 0.9687 m/s)\nThe North Western Passages(average 0.9307 m/s)\nChesapeake Bay(average 0.2948 m/s)\nGulf of Mannar(average 0.7256 m/s)\nShelikhova Gulf(average 2.607 m/s)\nGulf of Khambhät(average 0.4661 m/s)\nUchiura Bay(average 0.2513 m/s)\nTsugaru Strait(average 0.232 m/s)\nTatar Strait(average 3.169 m/s)\nKotzebue Sound(average 0.8532 m/s)\nGulf of Boothia(average 1.58 m/s)\nLa Pérouse Strait(average 1.161 m/s)\nBight of Benin(average 1.202 m/s)\nBight of Biafra(average 0.7784 m/s)\nBahía Blanca(average -1.564 m/s)\nDavis Sea(average 1.442 m/s)\nLützow-Holm Bay(average 9.676 m/s)\nAntongila Bay(average 0.4392 m/s)\nKaraginskiy Gulf(average 0.1128 m/s)\nJoseph Bonaparte Gulf(average 0.3755 m/s)\nGulf of Sakhalin(average 2.587 m/s)\nYenisey Gulf(average 1.623 m/s)\nAlbemarle Sound(average 0.8963 m/s)\nPamlico Sound(average 1.066 m/s)\nPeacock Sound(average 2.755 m/s)\nBali Sea(average 0.1994 m/s)\nSelat Bali(average 0.1707 m/s)\nFlores Sea(average 0.2036 m/s)\nSavu Sea(average 0.3833 m/s)\nRoss Sea(average 2.524 m/s)\nSea of Japan(average 1.585 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 500 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for August", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Bay of Bengal", + "Arabian Sea", + "Beaufort Sea", + "Caribbean Sea", + "Gulf of Mexico", + "Sea of Okhotsk", + "Greenland Sea", + "Banda Sea", + "Mozambique Channel", + "Gulf of Guinea", + "Barents Sea", + "Java Sea", + "East China Sea", + "Chukchi Sea", + "Arafura Sea", + "Timor Sea", + "Laccadive Sea", + "Bellingshausen Sea", + "Amundsen Sea", + "Kara Sea", + "Great Australian Bight", + "Straits of Florida", + "The North Western Passages", + "Chesapeake Bay", + "Gulf of Mannar", + "Shelikhova Gulf", + "Gulf of Khambhät", + "Uchiura Bay", + "Tsugaru Strait", + "Tatar Strait", + "Kotzebue Sound", + "Gulf of Boothia", + "La Pérouse Strait", + "Bight of Benin", + "Bight of Biafra", + "Bahía Blanca", + "Davis Sea", + "Lützow-Holm Bay", + "Antongila Bay", + "Karaginskiy Gulf", + "Joseph Bonaparte Gulf", + "Gulf of Sakhalin", + "Yenisey Gulf", + "Albemarle Sound", + "Pamlico Sound", + "Peacock Sound", + "Bali Sea", + "Selat Bali", + "Flores Sea", + "Savu Sea", + "Ross Sea", + "Sea of Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0359af24060f9b56", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78368:78376:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85193:85220:1'} The data starts from April 24 06:00 and ends on April 30 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) Specific humidity at 500 hPa values running above the 95th percentile climatology for the monthly climatology for April? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show Specific humidity at 500 hPa values above the 95th percentile climatology for monthly climatology for April: North Atlantic Ocean(average 0.0002441 kg/kg)\nNorth Pacific Ocean(average 0.0002535 kg/kg)\nSouth Pacific Ocean(average 7.756e-05 kg/kg)\nPhilippine Sea(average 0.000733 kg/kg)\nCaribbean Sea(average 0.0001641 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "specific_humidity", + 500 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for April", + "quantile": "0.95", + "threshold_direction": "above", + "true_value": [ + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "Philippine Sea", + "Caribbean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0db6b05bb570f869", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85193:85220:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81988:82011:1'} The data starts from February 13 00:00 and ends on February 18 12:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Blizzard/Winter Storm currently happening? Specify the affected countries or regions, or respond 'No Blizzard/Winter Storm detected.'", + "response": "Based on the provided data, the Blizzard/Winter Storm is affecting: United States of America; United States of America", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "United States of America", + "United States of America" + ], + "target_disaster": "Blizzard/Winter Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "8137998faf37aa10", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81988:82011:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72624:72648:1'} The data starts from September 16 00:00 and ends on September 21 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter V component of wind lies outside the climatological 1st–90th percentile envelope for the six-hourly climatology for day 260 at 00 UTC. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 1st–90th percentile envelope for 10-meter V component of wind during six-hourly climatology for day 260 at 00 UTC: Indonesia(average 0.1088 m/s)\nMalaysia(average -0.05556 m/s)\nPeru(average -0.03306 m/s)\nDhekelia Sovereign Base Area(average 0.6902 m/s)\nCyprus(average 1.043 m/s)\nIndia(average 0.3441 m/s)\nChina(average 0.3551 m/s)\nEthiopia(average 0.05674 m/s)\nSouth Sudan(average 0.2343 m/s)\nSomalia(average 0.2161 m/s)\nKenya(average 0.1418 m/s)\nUnited Republic of Tanzania(average 0.1915 m/s)\nSyria(average 0.6533 m/s)\nSomaliland(average -0.3174 m/s)\nFrance(average 0.3177 m/s)\nSuriname(average 0.317 m/s)\nGuyana(average 0.4309 m/s)\nMorocco(average 0.724 m/s)\nWestern Sahara(average 0.8846 m/s)\nDemocratic Republic of the Congo(average 0.03128 m/s)\nBhutan(average 0.226 m/s)\nUkraine(average -0.4934 m/s)\nBelarus(average -0.3148 m/s)\nNamibia(average 0.3002 m/s)\nOman(average 0.2385 m/s)\nUzbekistan(average 0.1408 m/s)\nKazakhstan(average -0.2058 m/s)\nTajikistan(average 0.1207 m/s)\nBrazil(average 0.6189 m/s)\nMongolia(average 0.2414 m/s)\nRussia(average -0.0453 m/s)\nCzechia(average -0.5234 m/s)\nGermany(average -0.3027 m/s)\nNorway(average 1.714 m/s)\nVietnam(average 0.3319 m/s)\nGeorgia(average 0.1496 m/s)\nNorth Macedonia(average -0.05172 m/s)\nAzerbaijan(average 0.1468 m/s)\nKosovo(average -0.273 m/s)\nTurkey(average 0.6782 m/s)\nSpain(average 0.785 m/s)\nLaos(average 0.09493 m/s)\nKyrgyzstan(average 0.1277 m/s)\nArmenia(average 0.1381 m/s)\nLibya(average -0.2286 m/s)\nRomania(average -0.2639 m/s)\nHungary(average -0.8563 m/s)\nSlovakia(average -0.9141 m/s)\nPoland(average -0.5226 m/s)\nGreece(average -0.05172 m/s)\nZambia(average 0.2707 m/s)\nSudan(average 0.1878 m/s)\nEritrea(average -0.2277 m/s)\nAustria(average -0.712 m/s)\nIraq(average 0.3744 m/s)\nItaly(average -0.6266 m/s)\nIran(average 0.1989 m/s)\nRepublic of Serbia(average -0.5045 m/s)\nMali(average 0.876 m/s)\nNigeria(average -0.02134 m/s)\nAngola(average 0.0661 m/s)\nCroatia(average -1.128 m/s)\nSlovenia(average -0.6121 m/s)\nSaudi Arabia(average 0.07436 m/s)\nZimbabwe(average 0.06962 m/s)\nPakistan(average 0.1465 m/s)\nBulgaria(average -0.1533 m/s)\nThailand(average 0.06827 m/s)\nChad(average -0.157 m/s)\nAlgeria(average 0.2158 m/s)\nMozambique(average -0.08455 m/s)\nBurundi(average 0.1182 m/s)\nMyanmar(average 0.1386 m/s)\nBangladesh(average 0.2409 m/s)\nAndorra(average 0.2795 m/s)\nAfghanistan(average -0.2408 m/s)\nMontenegro(average -0.4293 m/s)\nBosnia and Herzegovina(average -0.9709 m/s)\nUganda(average -0.05569 m/s)\nColombia(average 0.2756 m/s)\nPortugal(average 1.385 m/s)\nTurkmenistan(average -0.6292 m/s)\nJordan(average 0.03268 m/s)\nNepal(average 0.5185 m/s)\nCameroon(average -0.08554 m/s)\nTogo(average -0.08966 m/s)\nGhana(average -0.08966 m/s)\nGibraltar(average 0.2059 m/s)\nUnited States of America(average -0.2325 m/s)\nCanada(average -1.428 m/s)\nMexico(average -0.07666 m/s)\nVenezuela(average 0.377 m/s)\nPapua New Guinea(average -0.7322 m/s)\nEgypt(average 0.1643 m/s)\nYemen(average 0.4421 m/s)\nMauritania(average 1.625 m/s)\nNorthern Cyprus(average 1.171 m/s)\nCyprus No Mans Area(average 1.043 m/s)\nAkrotiri Sovereign Base Area(average 1.043 m/s)\nAustralia(average -0.5197 m/s)\nGreenland(average -0.8315 m/s)\nSri Lanka(average 0.2766 m/s)\nCuraçao(average 0.4899 m/s)\nAruba(average 0.4899 m/s)\nTaiwan(average 0.7361 m/s)\nIceland(average 2.357 m/s)\nTrinidad and Tobago(average 0.399 m/s)\nGrenada(average 0.6209 m/s)\nSaint Vincent and the Grenadines(average 0.119 m/s)\nSaint Lucia(average 0.03256 m/s)\nMauritius(average -0.4605 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 260 at 00 UTC", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "Indonesia", + "Malaysia", + "Peru", + "Dhekelia Sovereign Base Area", + "Cyprus", + "India", + "China", + "Ethiopia", + "South Sudan", + "Somalia", + "Kenya", + "United Republic of Tanzania", + "Syria", + "Somaliland", + "France", + "Suriname", + "Guyana", + "Morocco", + "Western Sahara", + "Democratic Republic of the Congo", + "Bhutan", + "Ukraine", + "Belarus", + "Namibia", + "Oman", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Brazil", + "Mongolia", + "Russia", + "Czechia", + "Germany", + "Norway", + "Vietnam", + "Georgia", + "North Macedonia", + "Azerbaijan", + "Kosovo", + "Turkey", + "Spain", + "Laos", + "Kyrgyzstan", + "Armenia", + "Libya", + "Romania", + "Hungary", + "Slovakia", + "Poland", + "Greece", + "Zambia", + "Sudan", + "Eritrea", + "Austria", + "Iraq", + "Italy", + "Iran", + "Republic of Serbia", + "Mali", + "Nigeria", + "Angola", + "Croatia", + "Slovenia", + "Saudi Arabia", + "Zimbabwe", + "Pakistan", + "Bulgaria", + "Thailand", + "Chad", + "Algeria", + "Mozambique", + "Burundi", + "Myanmar", + "Bangladesh", + "Andorra", + "Afghanistan", + "Montenegro", + "Bosnia and Herzegovina", + "Uganda", + "Colombia", + "Portugal", + "Turkmenistan", + "Jordan", + "Nepal", + "Cameroon", + "Togo", + "Ghana", + "Gibraltar", + "United States of America", + "Canada", + "Mexico", + "Venezuela", + "Papua New Guinea", + "Egypt", + "Yemen", + "Mauritania", + "Northern Cyprus", + "Cyprus No Mans Area", + "Akrotiri Sovereign Base Area", + "Australia", + "Greenland", + "Sri Lanka", + "Curaçao", + "Aruba", + "Taiwan", + "Iceland", + "Trinidad and Tobago", + "Grenada", + "Saint Vincent and the Grenadines", + "Saint Lucia", + "Mauritius" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ef5f98857cad41e7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72624:72648:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81626:81645:1'} The data starts from November 14 12:00 and ends on November 19 00:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) V (meridional) component of wind at 1000 hPa lies outside the climatological 10th–99th percentile envelope for the daily climatology for day 318. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–99th percentile envelope for V (meridional) component of wind at 1000 hPa during daily climatology for day 318: Arctic Ocean(average -0.7542 m/s)\nSOUTHERN OCEAN(average -0.9151 m/s)\nNorth Atlantic Ocean(average -0.3888 m/s)\nNorth Pacific Ocean(average -0.5073 m/s)\nSouth Pacific Ocean(average -1.569 m/s)\nINDIAN OCEAN(average -0.9829 m/s)\nSouth Atlantic Ocean(average -1.299 m/s)\nGreat Barrier Reef(average -1.198 m/s)\nSouth China Sea(average -0.2424 m/s)\nArabian Sea(average -0.3651 m/s)\nGulf of Alaska(average -0.6262 m/s)\nSea of Okhotsk(average -1.013 m/s)\nWeddell Sea(average -0.1504 m/s)\nBanda Sea(average -0.0592 m/s)\nBarents Sea(average -0.8664 m/s)\nKara Sea(average -1.326 m/s)\nGreat Australian Bight(average -0.9651 m/s)\nThe North Western Passages(average -0.8795 m/s)\nSolomon Sea(average -0.3041 m/s)\nCook Inlet(average -0.4227 m/s)\nViscount Melville Sound(average -0.06343 m/s)\nBering Sea(average -0.6719 m/s)\nMcMurdo Sound(average -0.3084 m/s)\nKarskiye Strait(average -0.9458 m/s)\nKronotskiy Gulf(average -0.00119 m/s)\nGulf of Boothia(average -0.07465 m/s)\nPrydz Bay(average -0.8272 m/s)\nGulf of Kamchatka(average -0.6091 m/s)\nMinto Inlet(average -0.04736 m/s)\nRichard Collinson Inlet(average -0.06262 m/s)\nPrince ALbert Sound(average -0.1797 m/s)\nHadley Bay(average -0.06343 m/s)\nBathurst Inlet(average -1.334 m/s)\nJones Sound(average -0.02856 m/s)\nMatochkin Shar Strait(average -0.3403 m/s)\nSavu Sea(average -0.0592 m/s)\nSalish Sea(average -0.006033 m/s)\nRoss Sea(average -0.6354 m/s)\nCoral Sea(average -1.06 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 1000 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 318", + "lower_quantile": "0.1", + "upper_quantile": "0.99", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Great Barrier Reef", + "South China Sea", + "Arabian Sea", + "Gulf of Alaska", + "Sea of Okhotsk", + "Weddell Sea", + "Banda Sea", + "Barents Sea", + "Kara Sea", + "Great Australian Bight", + "The North Western Passages", + "Solomon Sea", + "Cook Inlet", + "Viscount Melville Sound", + "Bering Sea", + "McMurdo Sound", + "Karskiye Strait", + "Kronotskiy Gulf", + "Gulf of Boothia", + "Prydz Bay", + "Gulf of Kamchatka", + "Minto Inlet", + "Richard Collinson Inlet", + "Prince ALbert Sound", + "Hadley Bay", + "Bathurst Inlet", + "Jones Sound", + "Matochkin Shar Strait", + "Savu Sea", + "Salish Sea", + "Ross Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "9ac379bd98a7036e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81626:81645:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62566:62577:1'} The data starts from October 28 12:00 and ends on October 31 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Honduras; Cayman Islands; Haiti", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Honduras", + "Cayman Islands", + "Haiti" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "2a66f36fc045e2de", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62566:62577:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76932:76960:1'} The data starts from August 29 00:00 and ends on September 04 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Japan. Specifically the region(s) being affected are: Wakayama, Nara, Ehime, Kooti provinces\nA Tropical cyclone is occuring in the country of United States of America. Specifically the region(s) being affected are: Louisiana, Mississippi, Alabama, Texas, New York, Pennsylvania, District of Columbia, Georgia, Maryland, Tennessee, Virginia provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Japan", + "United States of America" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "d79a92762db6f26b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76932:76960:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92046:92057:1'} The data starts from January 01 12:00 and ends on January 04 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Blizzard/Winter Storm currently happening? Specify the affected countries or regions, or respond 'No Blizzard/Winter Storm detected.'", + "response": "Based on the provided data, the Blizzard/Winter Storm is affecting: United States of America", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "United States of America" + ], + "target_disaster": "Blizzard/Winter Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "709761cc18f4f77e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92046:92057:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70648:70666:1'} The data starts from May 11 00:00 and ends on May 15 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface pressure lies outside the climatological 10th–95th percentile envelope for the monthly climatology for May. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–95th percentile envelope for Surface pressure during monthly climatology for May: SOUTHERN OCEAN(average -504.2 Pa)\nSouth Pacific Ocean(average -24.71 Pa)\nINDIAN OCEAN(average -757.4 Pa)\nBay of Bengal(average -98.98 Pa)\nLabrador Sea(average -244.7 Pa)\nNorwegian Sea(average -62.92 Pa)\nBaltic Sea(average -88.53 Pa)\nNorth Sea(average -110.4 Pa)\nBristol Channel(average -16.88 Pa)\nIrish Sea(average -30.72 Pa)\nAndaman Sea(average -87.22 Pa)\nChukchi Sea(average 29.44 Pa)\nDavis Strait(average -244.2 Pa)\nEnglish Channel(average -19.18 Pa)\nGulf of Bothnia(average -54.99 Pa)\nBering Sea(average 102.3 Pa)\nVestfjorden(average -29.73 Pa)\nSkagerrak(average -274.6 Pa)\nSognefjorden(average -102.5 Pa)\nTrondheimsfjorden(average -91.27 Pa)\nKattegat(average -221.4 Pa)\nGulf of Riga(average -3.539 Pa)\nWaddenzee(average -84.59 Pa)\nBoknafjorden(average -101.1 Pa)\nGulf of Martaban(average -33.18 Pa)\nØresund(average -105.2 Pa)\nMecklenburger Bucht(average -94.08 Pa)\nGulf of Anadyr'(average 107.2 Pa)\nMediterranean Sea(average -22.97 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for May", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "SOUTHERN OCEAN", + "South Pacific Ocean", + "INDIAN OCEAN", + "Bay of Bengal", + "Labrador Sea", + "Norwegian Sea", + "Baltic Sea", + "North Sea", + "Bristol Channel", + "Irish Sea", + "Andaman Sea", + "Chukchi Sea", + "Davis Strait", + "English Channel", + "Gulf of Bothnia", + "Bering Sea", + "Vestfjorden", + "Skagerrak", + "Sognefjorden", + "Trondheimsfjorden", + "Kattegat", + "Gulf of Riga", + "Waddenzee", + "Boknafjorden", + "Gulf of Martaban", + "Øresund", + "Mecklenburger Bucht", + "Gulf of Anadyr'", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ac0c430d41e812d2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70648:70666:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32228:32237:1'} The data starts from January 22 00:00 and ends on January 24 00:00. Based on the above data, answer the following question:", + "question": "What will the average 10-meter V component of wind be in Scotia Sea, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the average 10-meter V component of wind in Scotia Sea 12 hours after the given time window will be 1.423 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "1.4226081", + "location": "Scotia Sea", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "768de55019c6f14e", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32228:32237:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67290:67301:1'} The data starts from January 21 12:00 and ends on January 24 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Mean sea level pressure lies outside the climatological 10th–99th percentile envelope for the monthly climatology for January. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–99th percentile envelope for Mean sea level pressure during monthly climatology for January: Chile(average -63.08 Pa)\nArgentina(average -78.46 Pa)\nDhekelia Sovereign Base Area(average -218.6 Pa)\nCyprus(average -216.2 Pa)\nChina(average -48.05 Pa)\nIsrael(average -86.21 Pa)\nPalestine(average -128.6 Pa)\nLebanon(average -219.1 Pa)\nSyria(average -196 Pa)\nUkraine(average -241.7 Pa)\nBelarus(average -315.3 Pa)\nSouth Africa(average -43.98 Pa)\nLithuania(average -246.7 Pa)\nRussia(average -193.8 Pa)\nEstonia(average -86.04 Pa)\nLatvia(average -174.4 Pa)\nGeorgia(average -213.8 Pa)\nAzerbaijan(average -83.08 Pa)\nTurkey(average -194.6 Pa)\nArmenia(average -128.1 Pa)\nLibya(average -6.406 Pa)\nRomania(average -131.4 Pa)\nHungary(average -93.14 Pa)\nSlovakia(average -90.35 Pa)\nPoland(average -124.6 Pa)\nGreece(average -58.15 Pa)\nIraq(average -101.7 Pa)\nIran(average -18.05 Pa)\nSaudi Arabia(average -72.74 Pa)\nZimbabwe(average -32.34 Pa)\nBulgaria(average -71.15 Pa)\nMozambique(average -76.87 Pa)\neSwatini(average -46.08 Pa)\nMoldova(average -228.3 Pa)\nJordan(average -124.4 Pa)\nLesotho(average -63.39 Pa)\nUnited States of America(average -167.8 Pa)\nEgypt(average -55.35 Pa)\nNorthern Cyprus(average -236.5 Pa)\nCyprus No Mans Area(average -216.2 Pa)\nAkrotiri Sovereign Base Area(average -216.2 Pa)\nAustralia(average -53.85 Pa)\nUnited States Minor Outlying Islands(average -40.95 Pa)\nSaint Helena(average -10.15 Pa)\nFalkland Islands(average -90.32 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for January", + "lower_quantile": "0.1", + "upper_quantile": "0.99", + "true_value": [ + "Chile", + "Argentina", + "Dhekelia Sovereign Base Area", + "Cyprus", + "China", + "Israel", + "Palestine", + "Lebanon", + "Syria", + "Ukraine", + "Belarus", + "South Africa", + "Lithuania", + "Russia", + "Estonia", + "Latvia", + "Georgia", + "Azerbaijan", + "Turkey", + "Armenia", + "Libya", + "Romania", + "Hungary", + "Slovakia", + "Poland", + "Greece", + "Iraq", + "Iran", + "Saudi Arabia", + "Zimbabwe", + "Bulgaria", + "Mozambique", + "eSwatini", + "Moldova", + "Jordan", + "Lesotho", + "United States of America", + "Egypt", + "Northern Cyprus", + "Cyprus No Mans Area", + "Akrotiri Sovereign Base Area", + "Australia", + "United States Minor Outlying Islands", + "Saint Helena", + "Falkland Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "fd03cd7ffe246172", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67290:67301:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80622:80640:1'} The data starts from March 08 12:00 and ends on March 12 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in 10-meter U component of wind values? An exceedance is defined as a period of at least 72 consecutive hours where the 10-meter U component of wind values exceed the 99th percentile climatology for the MAM seasonal climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in 10-meter U component of wind: Finland(average 0.1097 m/s)\nSolomon Islands(average 2.657 m/s)\nVanuatu(average 3.658 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "MAM seasonal climatology", + "quantile": "0.99", + "min_duration_days": 3, + "true_value": [ + "Finland", + "Solomon Islands", + "Vanuatu" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "f98069f0fab90d51", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80622:80640:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61879:61893:1'} The data starts from May 09 18:00 and ends on May 13 00:00. Based on the above data, answer the following question:", + "question": "In the 42 hours after the end of the given time window, when will Saint Pierre and Miquelon experience its lowest Specific humidity at 925 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Saint Pierre and Miquelon will experience its lowest Specific humidity at 925 hPa of 0.005822 kg/kg 42 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 42, + "location": "Saint Pierre and Miquelon", + "extremum_value": "0.005821958", + "target_variable": "specific_humidity_925", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e871850e36afa42e", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61879:61893:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79784:79798:1'} The data starts from August 11 00:00 and ends on August 14 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) U (zonal) component of wind at 850 hPa differs from the monthly climatology for August mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above U (zonal) component of wind at 850 hPa values.", + "response": "Based on the provided data, no significant U (zonal) component of wind at 850 hPa anomalies were detected relative to the monthly climatology for August baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "u_component_of_wind", + 850 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for August", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "12626cf2543f7f2b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79784:79798:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90469:90473:1'} The data starts from December 03 06:00 and ends on December 04 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Surface temperature values? An exceedance is defined as a period of at least 24 consecutive hours where the Surface temperature values exceed the 95th percentile climatology for the all-time climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in Surface temperature: SOUTHERN OCEAN(average 0.017 K)\nNorth Atlantic Ocean(average 0.4278 K)\nNorth Pacific Ocean(average 0.3288 K)\nSouth Pacific Ocean(average 0.2219 K)\nINDIAN OCEAN(average 0.09063 K)\nSouth Atlantic Ocean(average 0.4278 K)\nPhilippine Sea(average 0.09792 K)\nGreat Barrier Reef(average 0.04257 K)\nSouth China Sea(average 0.002045 K)\nArabian Sea(average 0.09833 K)\nWeddell Sea(average 1.277 K)\nCelebes Sea(average 0.1969 K)\nBanda Sea(average 0.08524 K)\nMozambique Channel(average 0.02057 K)\nArafura Sea(average 0.2952 K)\nLaccadive Sea(average 0.1484 K)\nGreat Australian Bight(average 2.495 K)\nGulf of Carpentaria(average 0.2815 K)\nBismarck Sea(average 0.3104 K)\nTorres Strait(average 0.1123 K)\nDavao Gulf(average 0.06204 K)\nCoral Sea(average 0.04257 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.95", + "min_duration_days": 1, + "true_value": [ + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Great Barrier Reef", + "South China Sea", + "Arabian Sea", + "Weddell Sea", + "Celebes Sea", + "Banda Sea", + "Mozambique Channel", + "Arafura Sea", + "Laccadive Sea", + "Great Australian Bight", + "Gulf of Carpentaria", + "Bismarck Sea", + "Torres Strait", + "Davao Gulf", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "4293dd01f7aaf19a", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90469:90473:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40557:40576:1'} The data starts from October 05 06:00 and ends on October 09 18:00. Based on the above data, answer the following question:", + "question": "In the 42 hours after the end of the given time window, when will Africa experience its highest Mean sea level pressure? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Africa will experience its highest Mean sea level pressure of 1.021e+05 Pa 42 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 42, + "location": "Africa", + "extremum_value": "102107.01", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "973b1fc15557f1f0", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40557:40576:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55064:55082:1'} The data starts from September 09 00:00 and ends on September 13 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Viet Nam.\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Viet Nam" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "a0dc74e0b93edc32", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55064:55082:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89051:89054:1'} The data starts from December 14 18:00 and ends on December 15 06:00. Based on the above data, answer the following question:", + "question": "What will the median 10-meter V component of wind be in Oceania, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the median 10-meter V component of wind in Oceania 12 hours after the given time window will be 0.2118 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "0.21175879", + "location": "Oceania", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1a3a976abb41bb1a", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89051:89054:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70734:70747:1'} The data starts from June 01 12:00 and ends on June 04 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Temperature at 850 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the Temperature at 850 hPa values exceed the 90th percentile climatology for the monthly climatology for June. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Temperature at 850 hPa: Chile(average 0.8145 K)\nArgentina(average 0.7209 K)\nIndia(average 0.5377 K)\nChina(average 0.1387 K)\nSomalia(average 0.1126 K)\nKenya(average 0.1066 K)\nUnited Republic of Tanzania(average 0.183 K)\nFrance(average 0.5884 K)\nMorocco(average 0.6393 K)\nNicaragua(average 0.2495 K)\nSouth Africa(average 0.2144 K)\nRussia(average 1.019 K)\nGuinea(average 0.1696 K)\nIvory Coast(average 0.5344 K)\nMali(average 0.6005 K)\nSenegal(average 0.3154 K)\nNigeria(average 0.06613 K)\nBenin(average 0.06613 K)\nMozambique(average 0.1756 K)\nMyanmar(average 0.2743 K)\nBangladesh(average 0.5551 K)\nHonduras(average 0.325 K)\nNepal(average 0.501 K)\nBurkina Faso(average 0.3564 K)\nTogo(average 0.02136 K)\nGhana(average 0.1265 K)\nGuinea-Bissau(average 0.2841 K)\nUnited States of America(average 2.415 K)\nCanada(average 1.325 K)\nMauritania(average 0.6061 K)\nGambia(average 0.2599 K)\nAustralia(average 0.2774 K)\nMadagascar(average 0.3318 K)\nSri Lanka(average 0.1556 K)\nFrench Southern and Antarctic Lands(average 0.4339 K)\nSeychelles(average 0.2051 K)\nComoros(average 0.4032 K)\nCoral Sea Islands(average 0.4017 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "temperature", + 850 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for June", + "quantile": "0.9", + "min_duration_days": 2, + "true_value": [ + "Chile", + "Argentina", + "India", + "China", + "Somalia", + "Kenya", + "United Republic of Tanzania", + "France", + "Morocco", + "Nicaragua", + "South Africa", + "Russia", + "Guinea", + "Ivory Coast", + "Mali", + "Senegal", + "Nigeria", + "Benin", + "Mozambique", + "Myanmar", + "Bangladesh", + "Honduras", + "Nepal", + "Burkina Faso", + "Togo", + "Ghana", + "Guinea-Bissau", + "United States of America", + "Canada", + "Mauritania", + "Gambia", + "Australia", + "Madagascar", + "Sri Lanka", + "French Southern and Antarctic Lands", + "Seychelles", + "Comoros", + "Coral Sea Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "de8009eeb810087f", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70734:70747:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90935:90955:1'} The data starts from March 29 18:00 and ends on April 03 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Specific humidity at 600 hPa differs from the six-hourly climatology for day 88 at 18 UTC mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Specific humidity at 600 hPa values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for Specific humidity at 600 hPa relative to the six-hourly climatology for day 88 at 18 UTC mean: North Atlantic Ocean(average -0.002476 kg/kg)\nGulf of Guinea(average -0.00222 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "specific_humidity", + 600 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 88 at 18 UTC", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "North Atlantic Ocean", + "Gulf of Guinea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "4a01a3b88062a4e8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90935:90955:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68669:68684:1'} The data starts from January 01 06:00 and ends on January 04 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) U (zonal) component of wind at 50 hPa lies outside the climatological 10th–99th percentile envelope for the six-hourly climatology for day 1 at 06 UTC. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–99th percentile envelope for U (zonal) component of wind at 50 hPa during six-hourly climatology for day 1 at 06 UTC: Arctic Ocean(average -1.743 m/s)\nSOUTHERN OCEAN(average -1.178 m/s)\nNorth Atlantic Ocean(average -1.41 m/s)\nNorth Pacific Ocean(average -2.924 m/s)\nSouth Pacific Ocean(average -1.785 m/s)\nINDIAN OCEAN(average -1.494 m/s)\nSouth Atlantic Ocean(average -1.001 m/s)\nPhilippine Sea(average -0.911 m/s)\nGreat Barrier Reef(average -2.45 m/s)\nTasman Sea(average 0.5864 m/s)\nArabian Sea(average -0.5943 m/s)\nGulf of Mexico(average -0.7897 m/s)\nLabrador Sea(average -0.5039 m/s)\nHudson Bay(average -0.5213 m/s)\nGulf of Alaska(average -0.8954 m/s)\nCelebes Sea(average -1.106 m/s)\nSulu Sea(average -0.6557 m/s)\nBanda Sea(average -1.38 m/s)\nMozambique Channel(average -0.2585 m/s)\nGulf of Guinea(average -2.061 m/s)\nBaltic Sea(average -0.6189 m/s)\nNorth Sea(average -2.01 m/s)\nBristol Channel(average -1.748 m/s)\nInner Seas(average -2.05 m/s)\nIrish Sea(average -1.982 m/s)\nJava Sea(average -0.5078 m/s)\nEast China Sea(average 0.3561 m/s)\nBahía de Campeche(average -0.3449 m/s)\nArafura Sea(average -2.953 m/s)\nTimor Sea(average -2.793 m/s)\nGreat Australian Bight(average -0.985 m/s)\nJames Bay(average -1.079 m/s)\nGulf of Carpentaria(average -3.558 m/s)\nEnglish Channel(average -1.119 m/s)\nQueen Charlotte Sound(average -2.945 m/s)\nGulf of Saint Lawrence(average -1.241 m/s)\nMolucca Sea(average -0.608 m/s)\nBismarck Sea(average -2.817 m/s)\nSolomon Sea(average -2 m/s)\nBay of Fundy(average -2.109 m/s)\nCeram Sea(average -1.065 m/s)\nGulf of Maine(average -1.244 m/s)\nChesapeake Bay(average -3.891 m/s)\nGolfo San Jorge(average -0.2425 m/s)\nDixon Entrance(average -1.993 m/s)\nGolfo San Matías(average -1.396 m/s)\nGolfo Corcovado(average -0.6945 m/s)\nWrigley Gulf(average -0.8621 m/s)\nHamilton Inlet(average -1.408 m/s)\nSkagerrak(average -0.9466 m/s)\nKattegat(average -0.4313 m/s)\nShark Bay(average -0.4645 m/s)\nGolfo de Tehuantepec(average -0.6753 m/s)\nTorres Strait(average -2.546 m/s)\nGeographe Bay(average -1.516 m/s)\nGulf of Papua(average -2.063 m/s)\nWaddenzee(average -2.205 m/s)\nBight of Benin(average -2.94 m/s)\nBight of Biafra(average -1.027 m/s)\nStrait of Belle Isle(average -0.429 m/s)\nEstrecho de Magellanes(average -0.5782 m/s)\nBahía Blanca(average -1.791 m/s)\nGolfo de Penas(average -1.239 m/s)\nBahía Grande(average -0.9871 m/s)\nJoseph Bonaparte Gulf(average -4.168 m/s)\nSaint Lawrence River(average -0.786 m/s)\nMassachusetts Bay(average -1.079 m/s)\nDelaware Bay(average -2.708 m/s)\nLong Island Sound(average -2.584 m/s)\nAlbemarle Sound(average -4.852 m/s)\nPamlico Sound(average -4.128 m/s)\nSmith Sound(average -2.659 m/s)\nQueen Charlotte Strait(average -1.951 m/s)\nLake Pontchartrain(average -0.6939 m/s)\nSeno de Skyring(average -0.6178 m/s)\nSeno Otway(average -0.6178 m/s)\nBay Inútil(average -0.7294 m/s)\nBras d'Or Lake(average -1.202 m/s)\nBali Sea(average -1.832 m/s)\nDavao Gulf(average -2.114 m/s)\nHalmahera Sea(average -0.8982 m/s)\nSelat Bali(average -1.509 m/s)\nFlores Sea(average -2.535 m/s)\nSelat Dampier(average -0.7131 m/s)\nGulf of Buli(average -0.8812 m/s)\nGulf of Kau(average -0.7229 m/s)\nBohol Sea(average -1.481 m/s)\nSurigao Strait(average -1.608 m/s)\nSavu Sea(average -4.072 m/s)\nHecate Strait(average -2.358 m/s)\nCordova Bay(average -0.8916 m/s)\nØresund(average -0.4672 m/s)\nMecklenburger Bucht(average -0.777 m/s)\nSargasso Sea(average -0.902 m/s)\nSalish Sea(average -1.442 m/s)\nRoss Sea(average -1.009 m/s)\nCoral Sea(average -1.457 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "u_component_of_wind", + 50 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 1 at 06 UTC", + "lower_quantile": "0.1", + "upper_quantile": "0.99", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Great Barrier Reef", + "Tasman Sea", + "Arabian Sea", + "Gulf of Mexico", + "Labrador Sea", + "Hudson Bay", + "Gulf of Alaska", + "Celebes Sea", + "Sulu Sea", + "Banda Sea", + "Mozambique Channel", + "Gulf of Guinea", + "Baltic Sea", + "North Sea", + "Bristol Channel", + "Inner Seas", + "Irish Sea", + "Java Sea", + "East China Sea", + "Bahía de Campeche", + "Arafura Sea", + "Timor Sea", + "Great Australian Bight", + "James Bay", + "Gulf of Carpentaria", + "English Channel", + "Queen Charlotte Sound", + "Gulf of Saint Lawrence", + "Molucca Sea", + "Bismarck Sea", + "Solomon Sea", + "Bay of Fundy", + "Ceram Sea", + "Gulf of Maine", + "Chesapeake Bay", + "Golfo San Jorge", + "Dixon Entrance", + "Golfo San Matías", + "Golfo Corcovado", + "Wrigley Gulf", + "Hamilton Inlet", + "Skagerrak", + "Kattegat", + "Shark Bay", + "Golfo de Tehuantepec", + "Torres Strait", + "Geographe Bay", + "Gulf of Papua", + "Waddenzee", + "Bight of Benin", + "Bight of Biafra", + "Strait of Belle Isle", + "Estrecho de Magellanes", + "Bahía Blanca", + "Golfo de Penas", + "Bahía Grande", + "Joseph Bonaparte Gulf", + "Saint Lawrence River", + "Massachusetts Bay", + "Delaware Bay", + "Long Island Sound", + "Albemarle Sound", + "Pamlico Sound", + "Smith Sound", + "Queen Charlotte Strait", + "Lake Pontchartrain", + "Seno de Skyring", + "Seno Otway", + "Bay Inútil", + "Bras d'Or Lake", + "Bali Sea", + "Davao Gulf", + "Halmahera Sea", + "Selat Bali", + "Flores Sea", + "Selat Dampier", + "Gulf of Buli", + "Gulf of Kau", + "Bohol Sea", + "Surigao Strait", + "Savu Sea", + "Hecate Strait", + "Cordova Bay", + "Øresund", + "Mecklenburger Bucht", + "Sargasso Sea", + "Salish Sea", + "Ross Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "14d406b8b29f4bad", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68669:68684:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88940:88961:1'} The data starts from November 17 00:00 and ends on November 22 00:00. Based on the above data, answer the following question:", + "question": "What will the median V (meridional) component of wind at 400 hPa be in Andaman Sea, 6 hours after the end of the given time window?", + "response": "Based on the provided data, the median V (meridional) component of wind at 400 hPa in Andaman Sea 6 hours after the given time window will be -4.163 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-4.1628494", + "location": "Andaman Sea", + "target_variable": "v_component_of_wind_400", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "df956567b8e672d3", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88940:88961:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77412:77429:1'} The data starts from December 27 00:00 and ends on December 31 00:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) 10-meter V component of wind differs from the monthly climatology for December mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below 10-meter V component of wind values.", + "response": "Based on the provided data, no significant 10-meter V component of wind anomalies were detected relative to the monthly climatology for December baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for December", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "42c6832562f2cc72", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77412:77429:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69830:69856:1'} The data starts from October 18 12:00 and ends on October 24 18:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Extra-Tropical Storm currently happening? Specify the affected countries or regions, or respond 'No Extra-Tropical Storm detected.'", + "response": "No Extra-Tropical Storm detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Extra-Tropical Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "d1508dc5bc62c420", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69830:69856:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83840:83842:1'} The data starts from May 21 00:00 and ends on May 21 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Bangladesh. Specifically the region(s) being affected are: Barisal; Noakhali, Lakshmipur, Chandpur (Chittagong); Cox’s Bazar, Bhola, Barguna, Patuakhali\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Bangladesh" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "a276134975afe043", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83840:83842:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80321:80336:1'} The data starts from December 23 06:00 and ends on December 26 18:00. Based on the above data, answer the following question:", + "question": "What will the median Surface temperature be in Gulf of Aqaba, 24 hours after the end of the given time window?", + "response": "Based on the provided data, the median Surface temperature in Gulf of Aqaba 24 hours after the given time window will be 289.4 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "289.43536", + "location": "Gulf of Aqaba", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3d2dc637b9c56fbc", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80321:80336:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87452:87457:1'} The data starts from November 10 00:00 and ends on November 11 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter V component of wind lies outside the climatological 5th–95th percentile envelope for the daily climatology for day 314. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 5th–95th percentile envelope for 10-meter V component of wind during daily climatology for day 314: Indonesia(average 0.3734 m/s)\nMalaysia(average 0.4107 m/s)\nChile(average -0.4892 m/s)\nBolivia(average -0.3182 m/s)\nPeru(average -0.3377 m/s)\nArgentina(average -0.3847 m/s)\nIndia(average 0.5624 m/s)\nChina(average 0.5055 m/s)\nEthiopia(average -0.08587 m/s)\nSouth Sudan(average -0.08587 m/s)\nMalawi(average 0.2649 m/s)\nUnited Republic of Tanzania(average 0.6712 m/s)\nFrance(average 1.035 m/s)\nDemocratic Republic of the Congo(average 0.3744 m/s)\nNamibia(average -0.431 m/s)\nSouth Africa(average -0.4361 m/s)\nOman(average 0.5246 m/s)\nKazakhstan(average -0.2715 m/s)\nBrazil(average -0.3775 m/s)\nUruguay(average -0.4082 m/s)\nMongolia(average 0.4775 m/s)\nRussia(average 0.7662 m/s)\nGermany(average 0.3852 m/s)\nNorway(average 0.2846 m/s)\nFinland(average 0.2598 m/s)\nLuxembourg(average 0.2835 m/s)\nBelgium(average 0.3239 m/s)\nSpain(average 0.6884 m/s)\nLibya(average -0.3268 m/s)\nTunisia(average -0.6085 m/s)\nZambia(average 0.2335 m/s)\nSudan(average 0.9358 m/s)\nItaly(average 0.1651 m/s)\nSwitzerland(average 0.158 m/s)\nMali(average -0.2504 m/s)\nSaudi Arabia(average 0.5445 m/s)\nBotswana(average -0.431 m/s)\nPakistan(average 0.00734 m/s)\nThailand(average 0.645 m/s)\nChad(average 0.2331 m/s)\nMonaco(average 1.274 m/s)\nAlgeria(average -0.5886 m/s)\nMozambique(average 1.452 m/s)\nBrazilian Island(average -0.5829 m/s)\nPortugal(average 0.6813 m/s)\nNiger(average 0.1927 m/s)\nUnited States of America(average -0.6652 m/s)\nCanada(average -0.1539 m/s)\nMexico(average 0.09785 m/s)\nEgypt(average 0.481 m/s)\nYemen(average 0.3229 m/s)\nMauritania(average -0.1894 m/s)\nSiachen Glacier(average 0.00734 m/s)\nAustralia(average -0.1186 m/s)\nGreenland(average -0.7502 m/s)\nFiji(average 0.3045 m/s)\nNew Zealand(average 0.2511 m/s)\nMadagascar(average 2.319 m/s)\nPhilippines(average -0.3533 m/s)\nSri Lanka(average -0.693 m/s)\nJapan(average -0.6353 m/s)\nFrench Polynesia(average 1.124 m/s)\nFrench Southern and Antarctic Lands(average 2.415 m/s)\nSeychelles(average 1.836 m/s)\nKiribati(average 0.9025 m/s)\nMarshall Islands(average 1.508 m/s)\nMauritius(average -3.183 m/s)\nComoros(average 6.985 m/s)\nIndian Ocean Territories(average 0.6321 m/s)\nCook Islands(average -0.7577 m/s)\nTonga(average 0.04712 m/s)\nSolomon Islands(average -0.5937 m/s)\nNauru(average 0.3178 m/s)\nFederated States of Micronesia(average -0.3302 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 314", + "lower_quantile": "0.05", + "upper_quantile": "0.95", + "true_value": [ + "Indonesia", + "Malaysia", + "Chile", + "Bolivia", + "Peru", + "Argentina", + "India", + "China", + "Ethiopia", + "South Sudan", + "Malawi", + "United Republic of Tanzania", + "France", + "Democratic Republic of the Congo", + "Namibia", + "South Africa", + "Oman", + "Kazakhstan", + "Brazil", + "Uruguay", + "Mongolia", + "Russia", + "Germany", + "Norway", + "Finland", + "Luxembourg", + "Belgium", + "Spain", + "Libya", + "Tunisia", + "Zambia", + "Sudan", + "Italy", + "Switzerland", + "Mali", + "Saudi Arabia", + "Botswana", + "Pakistan", + "Thailand", + "Chad", + "Monaco", + "Algeria", + "Mozambique", + "Brazilian Island", + "Portugal", + "Niger", + "United States of America", + "Canada", + "Mexico", + "Egypt", + "Yemen", + "Mauritania", + "Siachen Glacier", + "Australia", + "Greenland", + "Fiji", + "New Zealand", + "Madagascar", + "Philippines", + "Sri Lanka", + "Japan", + "French Polynesia", + "French Southern and Antarctic Lands", + "Seychelles", + "Kiribati", + "Marshall Islands", + "Mauritius", + "Comoros", + "Indian Ocean Territories", + "Cook Islands", + "Tonga", + "Solomon Islands", + "Nauru", + "Federated States of Micronesia" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d632e972263aa200", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87452:87457:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79084:79106:1'} The data starts from February 17 00:00 and ends on February 22 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Blizzard/Winter Storm currently happening? Specify the affected countries or regions, or respond 'No Blizzard/Winter Storm detected.'", + "response": "Based on the provided data, the Blizzard/Winter Storm is affecting: China", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "China" + ], + "target_disaster": "Blizzard/Winter Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "97edea4175cd1900", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79084:79106:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51874:51899:1'} The data starts from July 04 12:00 and ends on July 10 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Philippines. Specifically the region(s) being affected are: La Union, Pangasinan, Ilocos Sur, Ilocos Norte provinces, Laoag city (Luzon)\nA Tropical cyclone is occuring in the country of China. Specifically the region(s) being affected are: Fujian province\nA Tropical cyclone is occuring in the country of Russian Federation.\nA Tropical cyclone is occuring in the country of Taiwan (Province of China).\nA Tropical cyclone is occuring in the country of Philippines. Specifically the region(s) being affected are: Kalinga-Apayao province\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Philippines", + "China", + "Russian Federation", + "Taiwan (Province of China)", + "Philippines" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "70e96f1fa99b1f3c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51874:51899:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78208:78220:1'} The data starts from July 13 00:00 and ends on July 15 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface temperature lies outside the climatological 10th–95th percentile envelope for the monthly climatology for July. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–95th percentile envelope for Surface temperature during monthly climatology for July: Arctic Ocean(average 0.01801 K)\nSOUTHERN OCEAN(average -0.5183 K)\nNorth Atlantic Ocean(average 0.6881 K)\nNorth Pacific Ocean(average -0.2142 K)\nSouth Pacific Ocean(average 0.1683 K)\nINDIAN OCEAN(average 0.001317 K)\nSouth Atlantic Ocean(average 0.295 K)\nBlack Sea(average 0.4931 K)\nPhilippine Sea(average -0.01263 K)\nTasman Sea(average 0.2579 K)\nArabian Sea(average 0.113 K)\nBeaufort Sea(average 2.066 K)\nGulf of Mexico(average -0.1429 K)\nLabrador Sea(average 0.1303 K)\nHudson Bay(average 0.8057 K)\nBaffin Bay(average 1.61 K)\nGulf of Alaska(average -0.278 K)\nRed Sea(average 0.1174 K)\nWeddell Sea(average -0.2784 K)\nNorwegian Sea(average 0.03372 K)\nGreenland Sea(average 0.5845 K)\nBay of Biscay(average -0.05804 K)\nMozambique Channel(average 0.09985 K)\nBarents Sea(average 0.5644 K)\nJava Sea(average -0.005615 K)\nLaccadive Sea(average 0.1419 K)\nDavis Strait(average 0.7794 K)\nKara Sea(average 0.3719 K)\nLaptev Sea(average 0.9709 K)\nTyrrhenian Sea(average 0.5261 K)\nWhite Sea(average 0.5674 K)\nJames Bay(average 1.221 K)\nHudson Strait(average 0.9587 K)\nAdriatic Sea(average 1.188 K)\nStraits of Florida(average -0.2309 K)\nThe North Western Passages(average 0.7928 K)\nIonian Sea(average 1.115 K)\nSolomon Sea(average 0.115 K)\nBay of Fundy(average 0.3628 K)\nCook Inlet(average -0.3239 K)\nMelville Bay(average 1.786 K)\nGulf of Maine(average 0.2354 K)\nAegean Sea(average 0.6358 K)\nAmundsen Gulf(average 1.706 K)\nViscount Melville Sound(average 1.051 K)\nBering Sea(average -0.0705 K)\nEast Siberian Sea(average 0.4181 K)\nLincoln Sea(average 1.002 K)\nM'Clure Strait(average 0.9316 K)\nMcMurdo Sound(average -0.7064 K)\nDisko Bay(average 0.6567 K)\nStorfjorden(average 0.2772 K)\nKarskiye Strait(average 0.6484 K)\nFoxe Basin(average 1.19 K)\nGulf of Yana(average 1.293 K)\nEast Korea Bay(average -0.1111 K)\nCook Strait(average 0.3249 K)\nGulf of Papua(average 0.09084 K)\nGulf of Sidra(average 0.3138 K)\nSea of Crete(average 0.8494 K)\nPrince William Sound(average -0.3857 K)\nLützow-Holm Bay(average -0.1149 K)\nSt. Helena Bay(average -0.1957 K)\nKangertittivaq(average 0.8163 K)\nDardanelles(average 0.00589 K)\nSea of Marmara(average 0.00589 K)\nDenmark Strait(average 0.6962 K)\nDarnley Bay(average 1.012 K)\nPrince of Wales Strait(average 0.9344 K)\nMinto Inlet(average 0.3916 K)\nRichard Collinson Inlet(average 1.019 K)\nPrince ALbert Sound(average 0.3916 K)\nLiddon Gulf(average 1.434 K)\nWynniatt Bay(average 0.221 K)\nJones Sound(average 0.1338 K)\nUummannaq Fjord(average 0.3891 K)\nBali Sea(average -0.02884 K)\nSelat Bali(average -0.02884 K)\nMurchison Sound(average 0.3803 K)\nRobeson Channel(average 1.245 K)\nMonterey Bay(average -0.5728 K)\nGulf of Anadyr'(average 0.02692 K)\nMediterranean Sea(average 0.7664 K)\nRoss Sea(average -0.6455 K)\nCoral Sea(average 0.2707 K)\nSea of Japan(average -0.142 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for July", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Black Sea", + "Philippine Sea", + "Tasman Sea", + "Arabian Sea", + "Beaufort Sea", + "Gulf of Mexico", + "Labrador Sea", + "Hudson Bay", + "Baffin Bay", + "Gulf of Alaska", + "Red Sea", + "Weddell Sea", + "Norwegian Sea", + "Greenland Sea", + "Bay of Biscay", + "Mozambique Channel", + "Barents Sea", + "Java Sea", + "Laccadive Sea", + "Davis Strait", + "Kara Sea", + "Laptev Sea", + "Tyrrhenian Sea", + "White Sea", + "James Bay", + "Hudson Strait", + "Adriatic Sea", + "Straits of Florida", + "The North Western Passages", + "Ionian Sea", + "Solomon Sea", + "Bay of Fundy", + "Cook Inlet", + "Melville Bay", + "Gulf of Maine", + "Aegean Sea", + "Amundsen Gulf", + "Viscount Melville Sound", + "Bering Sea", + "East Siberian Sea", + "Lincoln Sea", + "M'Clure Strait", + "McMurdo Sound", + "Disko Bay", + "Storfjorden", + "Karskiye Strait", + "Foxe Basin", + "Gulf of Yana", + "East Korea Bay", + "Cook Strait", + "Gulf of Papua", + "Gulf of Sidra", + "Sea of Crete", + "Prince William Sound", + "Lützow-Holm Bay", + "St. Helena Bay", + "Kangertittivaq", + "Dardanelles", + "Sea of Marmara", + "Denmark Strait", + "Darnley Bay", + "Prince of Wales Strait", + "Minto Inlet", + "Richard Collinson Inlet", + "Prince ALbert Sound", + "Liddon Gulf", + "Wynniatt Bay", + "Jones Sound", + "Uummannaq Fjord", + "Bali Sea", + "Selat Bali", + "Murchison Sound", + "Robeson Channel", + "Monterey Bay", + "Gulf of Anadyr'", + "Mediterranean Sea", + "Ross Sea", + "Coral Sea", + "Sea of Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "f2d5ba23b5fdda2d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78208:78220:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88765:88772:1'} The data starts from October 04 06:00 and ends on October 05 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) U (zonal) component of wind at 50 hPa values running below the 10th percentile climatology for the SON seasonal climatology? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show U (zonal) component of wind at 50 hPa values below the 10th percentile climatology for SON seasonal climatology: Arctic Ocean(average -0.6928 m/s)\nSOUTHERN OCEAN(average -3.205 m/s)\nNorth Atlantic Ocean(average -0.8135 m/s)\nNorth Pacific Ocean(average -0.6055 m/s)\nSouth Pacific Ocean(average -1.983 m/s)\nBeaufort Sea(average -0.8937 m/s)\nLabrador Sea(average -1.269 m/s)\nWeddell Sea(average -2.374 m/s)\nNorwegian Sea(average -0.3359 m/s)\nBarents Sea(average -0.3464 m/s)\nDavis Strait(average -1.691 m/s)\nEast Siberian Sea(average -0.2657 m/s)\nM'Clure Strait(average -0.3038 m/s)\nDisko Bay(average -0.1065 m/s)\nVestfjorden(average -0.06753 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "u_component_of_wind", + 50 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "SON seasonal climatology", + "quantile": "0.1", + "threshold_direction": "below", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "Beaufort Sea", + "Labrador Sea", + "Weddell Sea", + "Norwegian Sea", + "Barents Sea", + "Davis Strait", + "East Siberian Sea", + "M'Clure Strait", + "Disko Bay", + "Vestfjorden" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "34b934e466f54a19", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88765:88772:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67415:67420:1'} The data starts from February 21 18:00 and ends on February 22 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in U (zonal) component of wind at 50 hPa values? An exceedance is defined as a period of at least 24 consecutive hours where the U (zonal) component of wind at 50 hPa values exceed the 90th percentile climatology for the all-time climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in U (zonal) component of wind at 50 hPa: Arctic Ocean(average 9.824 m/s)\nNorth Atlantic Ocean(average 2.441 m/s)\nNorth Pacific Ocean(average 2.234 m/s)\nINDIAN OCEAN(average 0.2017 m/s)\nBlack Sea(average 10.25 m/s)\nPhilippine Sea(average 3.082 m/s)\nBay of Bengal(average 0.9479 m/s)\nSouth China Sea(average 3.961 m/s)\nBeaufort Sea(average 3.856 m/s)\nCaribbean Sea(average 0.5731 m/s)\nGulf of Mexico(average 0.9719 m/s)\nLabrador Sea(average 2.936 m/s)\nHudson Bay(average 7.238 m/s)\nCaspian Sea(average 0.6676 m/s)\nCelebes Sea(average 2.731 m/s)\nSulu Sea(average 3.586 m/s)\nGreenland Sea(average 10.34 m/s)\nLuzon Strait(average 4.922 m/s)\nBay of Biscay(average 3.95 m/s)\nGulf of Guinea(average 1.774 m/s)\nBarents Sea(average 2.884 m/s)\nNorth Sea(average 2.93 m/s)\nAndaman Sea(average 1.543 m/s)\nEast China Sea(average 2.444 m/s)\nBahía de Campeche(average 0.6897 m/s)\nGulf of Thailand(average 1.885 m/s)\nLaptev Sea(average 4.121 m/s)\nTyrrhenian Sea(average 15.49 m/s)\nSea of Azov(average 9.055 m/s)\nJames Bay(average 18.51 m/s)\nEnglish Channel(average 4.404 m/s)\nGolfe du Lion(average 13.68 m/s)\nAdriatic Sea(average 20.46 m/s)\nStraits of Florida(average 0.816 m/s)\nThe North Western Passages(average 2.094 m/s)\nIonian Sea(average 12.63 m/s)\nGulf of Saint Lawrence(average 2.49 m/s)\nMolucca Sea(average 2.014 m/s)\nGulf of Tonkin(average 3.223 m/s)\nBay of Fundy(average 1.08 m/s)\nStrait of Malacca(average 0.4274 m/s)\nMakassar Strait(average 0.216 m/s)\nTaiwan Strait(average 3.536 m/s)\nGulf of Maine(average 1.724 m/s)\nStrait of Gibraltar(average 2.038 m/s)\nBalearic Sea(average 9.098 m/s)\nAegean Sea(average 10.56 m/s)\nAmundsen Gulf(average 3.22 m/s)\nYucatan Channel(average 0.7519 m/s)\nEast Siberian Sea(average 10.46 m/s)\nFranklin Bay(average 5.141 m/s)\nHamilton Inlet(average 3.047 m/s)\nGulf of Gabès(average 5.722 m/s)\nMackenzie Bay(average 0.6923 m/s)\nGolfo de Tehuantepec(average 0.3651 m/s)\nGulf of Yana(average 5.745 m/s)\nDmitriy Laptev Strait(average 7.979 m/s)\nQiongzhou Strait(average 3.331 m/s)\nGulf of Sidra(average 3.662 m/s)\nSea of Crete(average 7.986 m/s)\nLigurian Sea(average 20.64 m/s)\nAlboran Sea(average 3.041 m/s)\nHangzhou Bay(average 0.5361 m/s)\nBosporus(average 12.73 m/s)\nGulf of Martaban(average 1.94 m/s)\nKangertittivaq(average 4.858 m/s)\nSaint Lawrence River(average 6.067 m/s)\nDardanelles(average 12.42 m/s)\nSea of Marmara(average 12.94 m/s)\nDenmark Strait(average 0.5285 m/s)\nMassachusetts Bay(average 0.2509 m/s)\nDelaware Bay(average 1.484 m/s)\nLong Island Sound(average 1.312 m/s)\nHusky Lakes(average 2.21 m/s)\nDarnley Bay(average 4.165 m/s)\nMinto Inlet(average 0.4868 m/s)\nPrince ALbert Sound(average 0.4868 m/s)\nAmazon River(average 0.06499 m/s)\nOzero Mogotoyevo(average 7.159 m/s)\nGuba Gusinaya(average 7.364 m/s)\nDavao Gulf(average 3.875 m/s)\nSibuyan Sea(average 4.105 m/s)\nGulf of Buli(average 0.5729 m/s)\nGulf of Kau(average 1.432 m/s)\nBohol Sea(average 3.588 m/s)\nSurigao Strait(average 3.566 m/s)\nRagay Gulf(average 4.153 m/s)\nSamar Sea(average 3.665 m/s)\nTayabas Bay(average 4.286 m/s)\nLeyte Gulf(average 3.463 m/s)\nVisayan Sea(average 3.621 m/s)\nCanal do Norte(average 0.7802 m/s)\nSargasso Sea(average 1.073 m/s)\nMediterranean Sea(average 7.787 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "u_component_of_wind", + 50 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.9", + "min_duration_days": 1, + "true_value": [ + "Arctic Ocean", + "North Atlantic Ocean", + "North Pacific Ocean", + "INDIAN OCEAN", + "Black Sea", + "Philippine Sea", + "Bay of Bengal", + "South China Sea", + "Beaufort Sea", + "Caribbean Sea", + "Gulf of Mexico", + "Labrador Sea", + "Hudson Bay", + "Caspian Sea", + "Celebes Sea", + "Sulu Sea", + "Greenland Sea", + "Luzon Strait", + "Bay of Biscay", + "Gulf of Guinea", + "Barents Sea", + "North Sea", + "Andaman Sea", + "East China Sea", + "Bahía de Campeche", + "Gulf of Thailand", + "Laptev Sea", + "Tyrrhenian Sea", + "Sea of Azov", + "James Bay", + "English Channel", + "Golfe du Lion", + "Adriatic Sea", + "Straits of Florida", + "The North Western Passages", + "Ionian Sea", + "Gulf of Saint Lawrence", + "Molucca Sea", + "Gulf of Tonkin", + "Bay of Fundy", + "Strait of Malacca", + "Makassar Strait", + "Taiwan Strait", + "Gulf of Maine", + "Strait of Gibraltar", + "Balearic Sea", + "Aegean Sea", + "Amundsen Gulf", + "Yucatan Channel", + "East Siberian Sea", + "Franklin Bay", + "Hamilton Inlet", + "Gulf of Gabès", + "Mackenzie Bay", + "Golfo de Tehuantepec", + "Gulf of Yana", + "Dmitriy Laptev Strait", + "Qiongzhou Strait", + "Gulf of Sidra", + "Sea of Crete", + "Ligurian Sea", + "Alboran Sea", + "Hangzhou Bay", + "Bosporus", + "Gulf of Martaban", + "Kangertittivaq", + "Saint Lawrence River", + "Dardanelles", + "Sea of Marmara", + "Denmark Strait", + "Massachusetts Bay", + "Delaware Bay", + "Long Island Sound", + "Husky Lakes", + "Darnley Bay", + "Minto Inlet", + "Prince ALbert Sound", + "Amazon River", + "Ozero Mogotoyevo", + "Guba Gusinaya", + "Davao Gulf", + "Sibuyan Sea", + "Gulf of Buli", + "Gulf of Kau", + "Bohol Sea", + "Surigao Strait", + "Ragay Gulf", + "Samar Sea", + "Tayabas Bay", + "Leyte Gulf", + "Visayan Sea", + "Canal do Norte", + "Sargasso Sea", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "81d5031bc079f581", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67415:67420:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90462:90483:1'} The data starts from December 01 12:00 and ends on December 06 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) U (zonal) component of wind at 850 hPa values running below the 5th percentile climatology for the six-hourly climatology for day 336 at 12 UTC? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show U (zonal) component of wind at 850 hPa values below the 5th percentile climatology for six-hourly climatology for day 336 at 12 UTC: Arctic Ocean(average -2.572 m/s)\nSOUTHERN OCEAN(average -1.494 m/s)\nNorth Atlantic Ocean(average -2.528 m/s)\nNorth Pacific Ocean(average -1.746 m/s)\nSouth Pacific Ocean(average -0.8684 m/s)\nINDIAN OCEAN(average -1.181 m/s)\nSouth Atlantic Ocean(average -0.4486 m/s)\nBlack Sea(average -0.2666 m/s)\nPhilippine Sea(average -2.756 m/s)\nGreat Barrier Reef(average -1.499 m/s)\nBay of Bengal(average -0.7412 m/s)\nSouth China Sea(average -1.518 m/s)\nGulf of Alaska(average -0.4313 m/s)\nWeddell Sea(average -1.152 m/s)\nNorth Sea(average -0.03824 m/s)\nEast China Sea(average -2.453 m/s)\nArafura Sea(average -1.09 m/s)\nTimor Sea(average -1.225 m/s)\nGulf of Carpentaria(average -1.01 m/s)\nThe North Western Passages(average -0.5034 m/s)\nSolomon Sea(average -0.1992 m/s)\nTaiwan Strait(average -0.2533 m/s)\nBering Sea(average -2.619 m/s)\nSkagerrak(average -0.8506 m/s)\nTorres Strait(average -1.546 m/s)\nGulf of Papua(average -0.8368 m/s)\nPrydz Bay(average -0.09369 m/s)\nBoknafjorden(average -0.03824 m/s)\nJoseph Bonaparte Gulf(average -0.4387 m/s)\nSargasso Sea(average -2.109 m/s)\nMediterranean Sea(average -0.6131 m/s)\nRoss Sea(average -0.7629 m/s)\nCoral Sea(average -1.073 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "u_component_of_wind", + 850 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 336 at 12 UTC", + "quantile": "0.05", + "threshold_direction": "below", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Black Sea", + "Philippine Sea", + "Great Barrier Reef", + "Bay of Bengal", + "South China Sea", + "Gulf of Alaska", + "Weddell Sea", + "North Sea", + "East China Sea", + "Arafura Sea", + "Timor Sea", + "Gulf of Carpentaria", + "The North Western Passages", + "Solomon Sea", + "Taiwan Strait", + "Bering Sea", + "Skagerrak", + "Torres Strait", + "Gulf of Papua", + "Prydz Bay", + "Boknafjorden", + "Joseph Bonaparte Gulf", + "Sargasso Sea", + "Mediterranean Sea", + "Ross Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0acfe2e36d47a182", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90462:90483:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48077:48104:1'} The data starts from November 28 06:00 and ends on December 04 18:00. Based on the above data, answer the following question:", + "question": "What will the minimum V (meridional) component of wind at 250 hPa be in Asia, 6 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum V (meridional) component of wind at 250 hPa in Asia 6 hours after the given time window will be -17.77 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-17.767454", + "location": "Asia", + "target_variable": "v_component_of_wind_250", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "210da20b75d68ad6", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48077:48104:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76371:76385:1'} The data starts from April 10 18:00 and ends on April 14 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Temperature at 500 hPa values running below the 1st percentile climatology for the MAM seasonal climatology? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Temperature at 500 hPa values below the 1st percentile climatology for MAM seasonal climatology: Dominican Republic(average -0.1618 K)\nNew Zealand(average -1.281 K)\nKiribati(average -0.09341 K)\nUnited States Minor Outlying Islands(average -0.3376 K)\nTonga(average -0.05563 K)\nWallis and Futuna(average -0.6689 K)\nSamoa(average -0.4916 K)\nAmerican Samoa(average -1.254 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "temperature", + 500 + ], + "geofeature": "country", + "climatology_timescale_desc": "MAM seasonal climatology", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "Dominican Republic", + "New Zealand", + "Kiribati", + "United States Minor Outlying Islands", + "Tonga", + "Wallis and Futuna", + "Samoa", + "American Samoa" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "37f6ebb353284510", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76371:76385:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67992:67997:1'} The data starts from July 16 00:00 and ends on July 17 00:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Mean sea level pressure differs from the all-time climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Mean sea level pressure values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for Mean sea level pressure relative to the all-time climatology mean: Philippine Sea(average -1976 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "Philippine Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "5e3f9815288621c2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67992:67997:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88994:89011:1'} The data starts from November 30 12:00 and ends on December 04 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in 10-meter V component of wind values? An exceedance is defined as a period of at least 96 consecutive hours where the 10-meter V component of wind values exceed the 99th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant 10-meter V component of wind anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.99", + "min_duration_days": 4, + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d96e9cb2ab5aafb0", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88994:89011:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82851:82879:1'} The data starts from September 16 18:00 and ends on September 23 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Temperature at 400 hPa lies outside the climatological 10th–99th percentile envelope for the SON seasonal climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–99th percentile envelope for Temperature at 400 hPa during SON seasonal climatology: India(average 0.3683 K)\nOman(average 0.3344 K)\nBrazil(average 0.06811 K)\nIran(average 0.5099 K)\nPakistan(average 0.7869 K)\nUnited States of America(average 0.1443 K)\nAustralia(average -0.4103 K)\nKiribati(average 0.1817 K)\nUnited States Minor Outlying Islands(average 0.2489 K)\nSouth Georgia and the Islands(average -0.9837 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "temperature", + 400 + ], + "geofeature": "country", + "climatology_timescale_desc": "SON seasonal climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.99", + "true_value": [ + "India", + "Oman", + "Brazil", + "Iran", + "Pakistan", + "United States of America", + "Australia", + "Kiribati", + "United States Minor Outlying Islands", + "South Georgia and the Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "179d6702f1205603", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82851:82879:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74776:74795:1'} The data starts from March 08 00:00 and ends on March 12 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 36 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 36 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 36 hours:\nA Tropical cyclone is expected in the country of Fiji in approximately the next 36 to 84 hours. Specifically the region(s) that might get affected are: Central, Easter, Northern, Western provinces\nA Storm (General) is expected in the country of United States of America in approximately the next 12 to 60 hours. Specifically the region(s) that might get affected are: Connecticut, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Fiji", + "United States of America" + ], + "extreme_event_hours": 36, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "b9c0007dd22e06ce", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74776:74795:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75096:75106:1'} The data starts from May 27 00:00 and ends on May 29 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Specific humidity at 150 hPa values running below the 5th percentile climatology for the six-hourly climatology for day 147 at 00 UTC? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Specific humidity at 150 hPa values below the 5th percentile climatology for six-hourly climatology for day 147 at 00 UTC: Chile(average -3.146e-07 kg/kg)\nPeru(average -1.094e-07 kg/kg)\nMorocco(average -8.837e-07 kg/kg)\nRussia(average -2.368e-08 kg/kg)\nSpain(average -7.245e-07 kg/kg)\nTunisia(average -5.396e-07 kg/kg)\nCentral African Republic(average -2.787e-07 kg/kg)\nNigeria(average -4.796e-07 kg/kg)\nChad(average -7.231e-07 kg/kg)\nAlgeria(average -7.385e-07 kg/kg)\nCameroon(average -3.401e-07 kg/kg)\nGabon(average -1.599e-07 kg/kg)\nUnited States of America(average -1.592e-08 kg/kg)\nEquatorial Guinea(average -2.492e-07 kg/kg)\nAustralia(average -3.802e-08 kg/kg)\nNew Zealand(average -4.256e-08 kg/kg)\nBritish Indian Ocean Territory(average -3.427e-07 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "specific_humidity", + 150 + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 147 at 00 UTC", + "quantile": "0.05", + "threshold_direction": "below", + "true_value": [ + "Chile", + "Peru", + "Morocco", + "Russia", + "Spain", + "Tunisia", + "Central African Republic", + "Nigeria", + "Chad", + "Algeria", + "Cameroon", + "Gabon", + "United States of America", + "Equatorial Guinea", + "Australia", + "New Zealand", + "British Indian Ocean Territory" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "7f5cf90430ed3b3c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75096:75106:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81117:81120:1'} The data starts from July 10 06:00 and ends on July 10 18:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will Samar Sea experience its highest Temperature at 300 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Samar Sea will experience its highest Temperature at 300 hPa of 244 K 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "Samar Sea", + "extremum_value": "243.96075", + "target_variable": "temperature_300", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "4ffa032d35d58f0b", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81117:81120:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86453:86458:1'} The data starts from March 05 06:00 and ends on March 06 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface pressure differs from the daily climatology for day 64 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Surface pressure values.", + "response": "Based on the provided data, no significant Surface pressure anomalies were detected relative to the daily climatology for day 64 baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 64", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "5a8f0df019396c9d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86453:86458:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88742:88752:1'} The data starts from September 28 12:00 and ends on September 30 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) Surface temperature values running below the 1st percentile climatology for the SON seasonal climatology? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show Surface temperature values below the 1st percentile climatology for SON seasonal climatology: Weddell Sea(average -0.292 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "SON seasonal climatology", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "Weddell Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "c992eba5193c7290", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88742:88752:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76249:76272:1'} The data starts from March 11 06:00 and ends on March 16 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) V (meridional) component of wind at 700 hPa differs from the all-time climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below V (meridional) component of wind at 700 hPa values. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) exceed the ±3σ anomaly threshold for V (meridional) component of wind at 700 hPa relative to the all-time climatology mean: Chad(average -9.909 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "v_component_of_wind", + 700 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "Chad" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "71b06298ecf56b03", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76249:76272:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86152:86168:1'} The data starts from December 20 00:00 and ends on December 23 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Storm (General) is occuring in the country of Philippines. Specifically the region(s) being affected are: Valencia, (Mindanao),Salvador, Sapad, Dalama village (Tubod) (Lanao del Norte), Piagapo, Sibuco,Tugaya, Marawi (Lanao del Sud), Zamboanga del Norte, Zamboanga Sibugay, Cagayan de Oro (Misamis Oriental)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Philippines" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "5c11477bdcebcc36", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86152:86168:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73549:73564:1'} The data starts from May 05 06:00 and ends on May 08 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Specific humidity at 250 hPa values running above the 99th percentile climatology for the daily climatology for day 125? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Specific humidity at 250 hPa values above the 99th percentile climatology for daily climatology for day 125: Russia(average 1.012e-06 kg/kg)\nSudan(average 4.554e-06 kg/kg)\nIraq(average 9.582e-06 kg/kg)\nIran(average 9.18e-06 kg/kg)\nSaudi Arabia(average 1.756e-05 kg/kg)\nKuwait(average 3.379e-06 kg/kg)\nEgypt(average 8.166e-06 kg/kg)\nPhilippines(average 4.283e-06 kg/kg)\nNiue(average 2.35e-06 kg/kg)\nNorthern Mariana Islands(average 3.036e-05 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "specific_humidity", + 250 + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 125", + "quantile": "0.99", + "threshold_direction": "above", + "true_value": [ + "Russia", + "Sudan", + "Iraq", + "Iran", + "Saudi Arabia", + "Kuwait", + "Egypt", + "Philippines", + "Niue", + "Northern Mariana Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "7b5ea9ad05b0bb1a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73549:73564:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31748:31776:1'} The data starts from September 24 00:00 and ends on September 30 18:00. Based on the above data, answer the following question:", + "question": "In the 36 hours after the end of the given time window, when will Nepal experience its lowest Surface pressure? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Nepal will experience its lowest Surface pressure of 5.438e+04 Pa 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Nepal", + "extremum_value": "54384.94", + "target_variable": "surface_pressure", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "394e3ae321cce59e", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31748:31776:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90800:90812:1'} The data starts from February 24 00:00 and ends on February 26 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Mean sea level pressure lies outside the climatological 10th–95th percentile envelope for the all-time climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–95th percentile envelope for Mean sea level pressure during all-time climatology: Indonesia(average -48.1 Pa)\nMalaysia(average -10.34 Pa)\nChile(average -25.33 Pa)\nPeru(average -17.27 Pa)\nArgentina(average -24.86 Pa)\nIndia(average -111.7 Pa)\nChina(average -86.37 Pa)\nFrance(average 269.5 Pa)\nUkraine(average 158.4 Pa)\nSaint Martin(average 15.19 Pa)\nSint Maarten(average 15.19 Pa)\nUzbekistan(average 165.2 Pa)\nKazakhstan(average 127 Pa)\nTajikistan(average 42.02 Pa)\nRussia(average 184.3 Pa)\nCzechia(average 20.13 Pa)\nGermany(average 13.18 Pa)\nGeorgia(average 315.5 Pa)\nNorth Macedonia(average 284.2 Pa)\nAlbania(average 324.1 Pa)\nAzerbaijan(average 260.2 Pa)\nKosovo(average 248 Pa)\nTurkey(average 259.5 Pa)\nSpain(average 179.5 Pa)\nKyrgyzstan(average 59.54 Pa)\nArmenia(average 198.7 Pa)\nLibya(average 149.7 Pa)\nTunisia(average 262.8 Pa)\nRomania(average 156.6 Pa)\nHungary(average 97.38 Pa)\nSlovakia(average 65.14 Pa)\nGreece(average 260 Pa)\nAustria(average 122.2 Pa)\nItaly(average 366.8 Pa)\nSwitzerland(average 152 Pa)\nIran(average 268.7 Pa)\nNetherlands(average 15.19 Pa)\nLiechtenstein(average 145.5 Pa)\nRepublic of Serbia(average 183.2 Pa)\nCroatia(average 203.5 Pa)\nSlovenia(average 180.1 Pa)\nPakistan(average -111.1 Pa)\nBulgaria(average 236.8 Pa)\nSan Marino(average 286 Pa)\nDominican Republic(average 8.102 Pa)\nEast Timor(average -29.8 Pa)\nMonaco(average 437 Pa)\nAlgeria(average 156 Pa)\nMozambique(average -5.409 Pa)\nAndorra(average 288.7 Pa)\nAfghanistan(average 178 Pa)\nMontenegro(average 284.8 Pa)\nBosnia and Herzegovina(average 244.4 Pa)\nColombia(average -24.36 Pa)\nPortugal(average 73.04 Pa)\nMoldova(average 84.15 Pa)\nTurkmenistan(average 295.5 Pa)\nUnited States of America(average 215.3 Pa)\nCanada(average -76.44 Pa)\nPapua New Guinea(average -33.91 Pa)\nEgypt(average 47.51 Pa)\nVatican(average 378.6 Pa)\nSiachen Glacier(average -66.01 Pa)\nAustralia(average -52.14 Pa)\nFiji(average -107.8 Pa)\nNew Zealand(average -59.8 Pa)\nNew Caledonia(average -4.676 Pa)\nMadagascar(average -33.04 Pa)\nPhilippines(average -15 Pa)\nSaint Pierre and Miquelon(average -169.9 Pa)\nPitcairn Islands(average -563.5 Pa)\nFrench Polynesia(average -204.9 Pa)\nFrench Southern and Antarctic Lands(average -32.92 Pa)\nSeychelles(average -13.38 Pa)\nKiribati(average -42.73 Pa)\nUnited States Minor Outlying Islands(average -67.21 Pa)\nUnited States Virgin Islands(average 9.844 Pa)\nPuerto Rico(average 4.934 Pa)\nAnguilla(average 15.19 Pa)\nBritish Virgin Islands(average 12.52 Pa)\nComoros(average -15.63 Pa)\nMalta(average 445 Pa)\nIndian Ocean Territories(average -334.7 Pa)\nCook Islands(average -209.6 Pa)\nTonga(average -151.7 Pa)\nWallis and Futuna(average -67.38 Pa)\nSamoa(average -33.12 Pa)\nSolomon Islands(average -107.2 Pa)\nTuvalu(average -71.03 Pa)\nSouth Georgia and the Islands(average -534.5 Pa)\nVanuatu(average -125.2 Pa)\nNiue(average -258.3 Pa)\nAmerican Samoa(average -56.19 Pa)\nCoral Sea Islands(average -9.656 Pa)\nAshmore and Cartier Islands(average -27.23 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "Indonesia", + "Malaysia", + "Chile", + "Peru", + "Argentina", + "India", + "China", + "France", + "Ukraine", + "Saint Martin", + "Sint Maarten", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Russia", + "Czechia", + "Germany", + "Georgia", + "North Macedonia", + "Albania", + "Azerbaijan", + "Kosovo", + "Turkey", + "Spain", + "Kyrgyzstan", + "Armenia", + "Libya", + "Tunisia", + "Romania", + "Hungary", + "Slovakia", + "Greece", + "Austria", + "Italy", + "Switzerland", + "Iran", + "Netherlands", + "Liechtenstein", + "Republic of Serbia", + "Croatia", + "Slovenia", + "Pakistan", + "Bulgaria", + "San Marino", + "Dominican Republic", + "East Timor", + "Monaco", + "Algeria", + "Mozambique", + "Andorra", + "Afghanistan", + "Montenegro", + "Bosnia and Herzegovina", + "Colombia", + "Portugal", + "Moldova", + "Turkmenistan", + "United States of America", + "Canada", + "Papua New Guinea", + "Egypt", + "Vatican", + "Siachen Glacier", + "Australia", + "Fiji", + "New Zealand", + "New Caledonia", + "Madagascar", + "Philippines", + "Saint Pierre and Miquelon", + "Pitcairn Islands", + "French Polynesia", + "French Southern and Antarctic Lands", + "Seychelles", + "Kiribati", + "United States Minor Outlying Islands", + "United States Virgin Islands", + "Puerto Rico", + "Anguilla", + "British Virgin Islands", + "Comoros", + "Malta", + "Indian Ocean Territories", + "Cook Islands", + "Tonga", + "Wallis and Futuna", + "Samoa", + "Solomon Islands", + "Tuvalu", + "South Georgia and the Islands", + "Vanuatu", + "Niue", + "American Samoa", + "Coral Sea Islands", + "Ashmore and Cartier Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "2ca21be6a3dbdc7f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90800:90812:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48290:48303:1'} The data starts from January 20 12:00 and ends on January 23 12:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Africa experience its lowest Surface temperature? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Africa will experience its lowest Surface temperature of 271.8 K 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Africa", + "extremum_value": "271.80917", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b12063ea9b01295a", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48290:48303:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74732:74758:1'} The data starts from February 25 00:00 and ends on March 03 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Extra-Tropical Storm currently happening? Specify the affected countries or regions, or respond 'No Extra-Tropical Storm detected.'", + "response": "Based on the provided data, the Extra-Tropical Storm is affecting: Belgium; Switzerland; Germany; Spain; France; United Kingdom of Great Britain and Northern Ireland; Luxembourg; Netherlands (Kingdom of the); Portugal", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Belgium", + "Switzerland", + "Germany", + "Spain", + "France", + "United Kingdom of Great Britain and Northern Ireland", + "Luxembourg", + "Netherlands (Kingdom of the)", + "Portugal" + ], + "target_disaster": "Extra-Tropical Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "7fe7840da16ddc5d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74732:74758:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78779:78785:1'} The data starts from December 02 18:00 and ends on December 04 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 24 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 24 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 24 hours:\nA Tropical cyclone is expected in the country of Philippines in approximately the next 0 hours. Specifically the region(s) that might get affected are: Region XI (Davao Region) province\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Philippines" + ], + "extreme_event_hours": 24, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "a21030e29e6c84d2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78779:78785:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57739:57764:1'} The data starts from July 09 18:00 and ends on July 15 18:00. Based on the above data, answer the following question:", + "question": "What will the median 10-meter U component of wind be in Estonia, 24 hours after the end of the given time window?", + "response": "Based on the provided data, the median 10-meter U component of wind in Estonia 24 hours after the given time window will be 2.981 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "2.9811", + "location": "Estonia", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "97efb3e31e9f3d29", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57739:57764:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34182:34210:1'} The data starts from May 25 12:00 and ends on June 01 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Storm (General) is occuring in the country of Tonga. Specifically the region(s) being affected are: Tongatapu\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Tonga" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "6bbfe73b76d05529", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34182:34210:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82694:82701:1'} The data starts from August 08 12:00 and ends on August 10 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: China", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "China" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "4c6571643ff1404a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82694:82701:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49739:49763:1'} The data starts from January 16 18:00 and ends on January 22 12:00. Based on the above data, answer the following question:", + "question": "In the 36 hours after the end of the given time window, when will Africa experience its highest Specific humidity at 700 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Africa will experience its highest Specific humidity at 700 hPa of 0.009871 kg/kg 24 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 24, + "location": "Africa", + "extremum_value": "0.009870905", + "target_variable": "specific_humidity_700", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5ba5a2d3c33ab3b1", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49739:49763:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93285:93302:1'} The data starts from November 07 06:00 and ends on November 11 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Specific humidity at 300 hPa values running below the 5th percentile climatology for the six-hourly climatology for day 311 at 06 UTC? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Specific humidity at 300 hPa values below the 5th percentile climatology for six-hourly climatology for day 311 at 06 UTC: Bolivia(average -2.993e-05 kg/kg)\nPeru(average -2.079e-05 kg/kg)\nRepublic of the Congo(average -3.878e-06 kg/kg)\nKazakhstan(average -3.156e-06 kg/kg)\nBrazil(average -5.471e-05 kg/kg)\nRussia(average -2.49e-06 kg/kg)\nCentral African Republic(average -2.989e-06 kg/kg)\nSudan(average -8.475e-06 kg/kg)\nAngola(average -1.772e-05 kg/kg)\nChad(average -1.168e-05 kg/kg)\nColombia(average -5.244e-05 kg/kg)\nTurkmenistan(average -3.156e-06 kg/kg)\nCameroon(average -4.708e-06 kg/kg)\nUnited States of America(average -1.107e-05 kg/kg)\nCanada(average -1.685e-06 kg/kg)\nMexico(average -2.099e-05 kg/kg)\nGreenland(average -1.855e-06 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "specific_humidity", + 300 + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 311 at 06 UTC", + "quantile": "0.05", + "threshold_direction": "below", + "true_value": [ + "Bolivia", + "Peru", + "Republic of the Congo", + "Kazakhstan", + "Brazil", + "Russia", + "Central African Republic", + "Sudan", + "Angola", + "Chad", + "Colombia", + "Turkmenistan", + "Cameroon", + "United States of America", + "Canada", + "Mexico", + "Greenland" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "e134fe915f83a989", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93285:93302:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89054:89078:1'} The data starts from December 15 12:00 and ends on December 21 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) 10-meter U component of wind differs from the daily climatology for day 349 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below 10-meter U component of wind values.", + "response": "Based on the provided data, no significant 10-meter U component of wind anomalies were detected relative to the daily climatology for day 349 baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 349", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0d2141a88972d3c4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89054:89078:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61104:61126:1'} The data starts from October 28 00:00 and ends on November 02 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Philippines. Specifically the region(s) being affected are: Cavite district (Region IV-A (Calabarzon) province), Sorsogon, Catanduanes, Albay districts (Region V (Bicol region) province), Metropolitan Manila district (National Capital region (NCR) province), Samar districts (Region VIII (Eastern Visayas) province)\nA Tropical cyclone is occuring in the country of Taiwan (Province of China). Specifically the region(s) being affected are: Taipei, Pingtung, Kaohsiung areas (Taiwan Sheng province)\nA Tropical cyclone is occuring in the country of Bangladesh. Specifically the region(s) being affected are: Barisal, Barguna, Jhalokati, Bhola, Pirojpur districts (Barisal province), Khulna district (Khulna province), Noakhali, Lakshmipur, Cox's Bazar, Chandpur districts (Chittagong province), Dhaka, Mymensingh, Shariatpur districts (Dhaka province)\nA Severe weather is occuring in the country of United Kingdom of Great Britain and Northern Ireland. Specifically the region(s) being affected are: Larkhill city (Wiltshire district), Bognor Regis village, Selsey city (West Sussex district) (England province) ; West Yorkshire, South Yorkshire, North Yorkshire, Humberside (Yorkshire), West Sussex, East Sussex (Sussex), Kent, Wiltshire, Hamphshire districts (England province)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Philippines", + "Taiwan (Province of China)", + "Bangladesh", + "United Kingdom of Great Britain and Northern Ireland" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "dd2f78ff4ef0cb80", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61104:61126:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92184:92185:1'} The data corresponds to corresponds to a snapshot on February 05 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Madagascar. Specifically the region(s) being affected are: Fianarantsoa and Ambositra ; Manakara Atsimo, Ikongo District (Fitovinany Region); Nosy-Varika, Mananjary (Vatovavy Region)\nA Tropical cyclone is occuring in the country of Réunion.\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Madagascar", + "Réunion" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "0b1f464f2609d310", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92184:92185:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87545:87554:1'} The data starts from December 03 06:00 and ends on December 05 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface temperature lies outside the climatological 1st–99th percentile envelope for the six-hourly climatology for day 337 at 06 UTC. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–99th percentile envelope for Surface temperature during six-hourly climatology for day 337 at 06 UTC: Arctic Ocean(average 1.584 K)\nSOUTHERN OCEAN(average 0.06744 K)\nNorth Atlantic Ocean(average 0.3387 K)\nNorth Pacific Ocean(average 0.6534 K)\nSouth Pacific Ocean(average 0.1656 K)\nINDIAN OCEAN(average 0.2102 K)\nSouth Atlantic Ocean(average 0.4397 K)\nPhilippine Sea(average 0.4594 K)\nGreat Barrier Reef(average 0.1973 K)\nBay of Bengal(average -1.072 K)\nSouth China Sea(average 0.4001 K)\nArabian Sea(average -0.05302 K)\nBeaufort Sea(average 3.492 K)\nCaribbean Sea(average 0.5444 K)\nGulf of Mexico(average 0.6174 K)\nBaffin Bay(average -0.815 K)\nGulf of Alaska(average 0.8489 K)\nRed Sea(average 0.9407 K)\nSea of Okhotsk(average 0.83 K)\nWeddell Sea(average 0.9226 K)\nCelebes Sea(average -0.3471 K)\nNorwegian Sea(average -0.2091 K)\nGreenland Sea(average -0.09129 K)\nBanda Sea(average 0.1228 K)\nLuzon Strait(average 0.4304 K)\nBay of Biscay(average 0.05838 K)\nMozambique Channel(average 0.2379 K)\nGulf of Guinea(average 0.6115 K)\nBarents Sea(average 1.16 K)\nJava Sea(average -0.5501 K)\nAndaman Sea(average 0.1924 K)\nEast China Sea(average 0.9128 K)\nChukchi Sea(average 2.404 K)\nBahía de Campeche(average 1.121 K)\nArafura Sea(average 0.4224 K)\nTimor Sea(average 0.1314 K)\nKara Sea(average 0.1756 K)\nTyrrhenian Sea(average 0.741 K)\nGulf of Carpentaria(average 0.3725 K)\nGolfo de California(average 0.3933 K)\nGulf of Honduras(average 1.08 K)\nAdriatic Sea(average 0.231 K)\nStraits of Florida(average 0.5393 K)\nThe North Western Passages(average -1.588 K)\nIonian Sea(average 0.2504 K)\nGulf of Tonkin(average 0.9503 K)\nStrait of Malacca(average -1.05 K)\nCeram Sea(average 0.005127 K)\nInner Sea(average 0.7213 K)\nTaiwan Strait(average 0.5273 K)\nBristol Bay(average 0.5683 K)\nGolfo San Jorge(average 1.062 K)\nBalearic Sea(average 0.3467 K)\nGulf of Kutch(average -0.2466 K)\nGolfo de Panamá(average 0.8786 K)\nYucatan Channel(average 1.022 K)\nBering Sea(average 0.6502 K)\nLincoln Sea(average -1.156 K)\nBoca Grande(average 0.8893 K)\nBaía de Marajó(average 0.08121 K)\nStorfjorden(average 0.09747 K)\nGulf of Khambhät(average -0.4882 K)\nUchiura Bay(average 1.53 K)\nTsugaru Strait(average 2.671 K)\nShark Bay(average -0.7035 K)\nGulf of Gabès(average 0.8076 K)\nMackenzie Bay(average 4.963 K)\nKotzebue Sound(average 0.1072 K)\nKane Basin(average -2.157 K)\nGolfo de Tehuantepec(average 1.194 K)\nLa Pérouse Strait(average 1.239 K)\nTorres Strait(average 0.3915 K)\nGulf of Sidra(average 0.5748 K)\nLigurian Sea(average 1.545 K)\nAlboran Sea(average 0.9384 K)\nBight of Benin(average 0.7622 K)\nBight of Biafra(average 0.3971 K)\nPrince William Sound(average 0.443 K)\nDavis Sea(average -0.2115 K)\nBab el Mandeb(average 0.3136 K)\nGolfo de Urabá(average 1.059 K)\nJoseph Bonaparte Gulf(average 0.09927 K)\nGulf of Martaban(average 0.07452 K)\nLago de Maracaibo(average 0.7918 K)\nHusky Lakes(average 5.818 K)\nJones Sound(average -0.9796 K)\nHall Basin(average -0.1615 K)\nRobeson Channel(average -0.1615 K)\nCanal do Norte(average 0.4496 K)\nSargasso Sea(average 0.4191 K)\nMediterranean Sea(average 0.8064 K)\nRoss Sea(average 0.9706 K)\nCoral Sea(average 0.0784 K)\nSea of Japan(average 1.016 K)\nKorea Strait(average 0.6132 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 337 at 06 UTC", + "lower_quantile": "0.01", + "upper_quantile": "0.99", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Great Barrier Reef", + "Bay of Bengal", + "South China Sea", + "Arabian Sea", + "Beaufort Sea", + "Caribbean Sea", + "Gulf of Mexico", + "Baffin Bay", + "Gulf of Alaska", + "Red Sea", + "Sea of Okhotsk", + "Weddell Sea", + "Celebes Sea", + "Norwegian Sea", + "Greenland Sea", + "Banda Sea", + "Luzon Strait", + "Bay of Biscay", + "Mozambique Channel", + "Gulf of Guinea", + "Barents Sea", + "Java Sea", + "Andaman Sea", + "East China Sea", + "Chukchi Sea", + "Bahía de Campeche", + "Arafura Sea", + "Timor Sea", + "Kara Sea", + "Tyrrhenian Sea", + "Gulf of Carpentaria", + "Golfo de California", + "Gulf of Honduras", + "Adriatic Sea", + "Straits of Florida", + "The North Western Passages", + "Ionian Sea", + "Gulf of Tonkin", + "Strait of Malacca", + "Ceram Sea", + "Inner Sea", + "Taiwan Strait", + "Bristol Bay", + "Golfo San Jorge", + "Balearic Sea", + "Gulf of Kutch", + "Golfo de Panamá", + "Yucatan Channel", + "Bering Sea", + "Lincoln Sea", + "Boca Grande", + "Baía de Marajó", + "Storfjorden", + "Gulf of Khambhät", + "Uchiura Bay", + "Tsugaru Strait", + "Shark Bay", + "Gulf of Gabès", + "Mackenzie Bay", + "Kotzebue Sound", + "Kane Basin", + "Golfo de Tehuantepec", + "La Pérouse Strait", + "Torres Strait", + "Gulf of Sidra", + "Ligurian Sea", + "Alboran Sea", + "Bight of Benin", + "Bight of Biafra", + "Prince William Sound", + "Davis Sea", + "Bab el Mandeb", + "Golfo de Urabá", + "Joseph Bonaparte Gulf", + "Gulf of Martaban", + "Lago de Maracaibo", + "Husky Lakes", + "Jones Sound", + "Hall Basin", + "Robeson Channel", + "Canal do Norte", + "Sargasso Sea", + "Mediterranean Sea", + "Ross Sea", + "Coral Sea", + "Sea of Japan", + "Korea Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "6c6b26b4d7932b1e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87545:87554:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67908:67930:1'} The data starts from June 25 00:00 and ends on June 30 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) V (meridional) component of wind at 1000 hPa values running above the 90th percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show V (meridional) component of wind at 1000 hPa values above the 90th percentile climatology for all-time climatology: SOUTHERN OCEAN(average 0.975 m/s)\nNorth Atlantic Ocean(average 0.3377 m/s)\nNorth Pacific Ocean(average 0.414 m/s)\nSouth Pacific Ocean(average 0.3514 m/s)\nINDIAN OCEAN(average 0.9383 m/s)\nSouth Atlantic Ocean(average 0.07709 m/s)\nPhilippine Sea(average 0.7154 m/s)\nBay of Bengal(average 0.6738 m/s)\nSouth China Sea(average 0.6239 m/s)\nArabian Sea(average 1.992 m/s)\nCaspian Sea(average 0.298 m/s)\nWeddell Sea(average 0.4114 m/s)\nGreenland Sea(average 0.04933 m/s)\nBanda Sea(average 0.6792 m/s)\nLuzon Strait(average 1.567 m/s)\nGulf of Guinea(average 0.2036 m/s)\nAndaman Sea(average 0.5438 m/s)\nYellow Sea(average 1.791 m/s)\nEast China Sea(average 2.027 m/s)\nChukchi Sea(average 0.3301 m/s)\nArafura Sea(average 1.102 m/s)\nTimor Sea(average 0.8224 m/s)\nGulf of Thailand(average 0.1431 m/s)\nLaccadive Sea(average 1.843 m/s)\nBellingshausen Sea(average 0.008314 m/s)\nGulf of Aden(average 0.2528 m/s)\nGulf of Carpentaria(average 1.337 m/s)\nGolfo de California(average 0.3966 m/s)\nBay of Plenty(average 0.169 m/s)\nGulf of Tonkin(average 0.04429 m/s)\nStrait of Malacca(average 0.08924 m/s)\nStrait of Singapore(average 0.1227 m/s)\nCeram Sea(average 0.5609 m/s)\nInner Sea(average 0.902 m/s)\nTaiwan Strait(average 0.9703 m/s)\nShelikhova Gulf(average 0.03259 m/s)\nMcMurdo Sound(average 0.03094 m/s)\nGulf of Khambhät(average 0.8406 m/s)\nGulf of Masira(average 0.6564 m/s)\nQiongzhou Strait(average 0.08482 m/s)\nTorres Strait(average 0.002063 m/s)\nHangzhou Bay(average 0.2207 m/s)\nJoseph Bonaparte Gulf(average 0.977 m/s)\nGulf of Martaban(average 0.659 m/s)\nKangertittivaq(average 0.03242 m/s)\nPeacock Sound(average 0.008314 m/s)\nGeorge VI Sound(average 0.9674 m/s)\nCanal do Norte(average 0.02218 m/s)\nGulf of Anadyr'(average 0.2422 m/s)\nRoss Sea(average 0.05184 m/s)\nCoral Sea(average 0.1981 m/s)\nSea of Japan(average 1.906 m/s)\nKorea Strait(average 2.623 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "v_component_of_wind", + 1000 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.9", + "threshold_direction": "above", + "true_value": [ + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Bay of Bengal", + "South China Sea", + "Arabian Sea", + "Caspian Sea", + "Weddell Sea", + "Greenland Sea", + "Banda Sea", + "Luzon Strait", + "Gulf of Guinea", + "Andaman Sea", + "Yellow Sea", + "East China Sea", + "Chukchi Sea", + "Arafura Sea", + "Timor Sea", + "Gulf of Thailand", + "Laccadive Sea", + "Bellingshausen Sea", + "Gulf of Aden", + "Gulf of Carpentaria", + "Golfo de California", + "Bay of Plenty", + "Gulf of Tonkin", + "Strait of Malacca", + "Strait of Singapore", + "Ceram Sea", + "Inner Sea", + "Taiwan Strait", + "Shelikhova Gulf", + "McMurdo Sound", + "Gulf of Khambhät", + "Gulf of Masira", + "Qiongzhou Strait", + "Torres Strait", + "Hangzhou Bay", + "Joseph Bonaparte Gulf", + "Gulf of Martaban", + "Kangertittivaq", + "Peacock Sound", + "George VI Sound", + "Canal do Norte", + "Gulf of Anadyr'", + "Ross Sea", + "Coral Sea", + "Sea of Japan", + "Korea Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0e137364fb33fbba", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67908:67930:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78333:78360:1'} The data starts from August 13 06:00 and ends on August 19 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) V (meridional) component of wind at 300 hPa lies outside the climatological 10th–95th percentile envelope for the JJA seasonal climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–95th percentile envelope for V (meridional) component of wind at 300 hPa during JJA seasonal climatology: Ukraine(average -0.6816 m/s)\nBelarus(average -0.2796 m/s)\nNamibia(average -0.5006 m/s)\nSouth Africa(average -0.6406 m/s)\nNorway(average -1.123 m/s)\nRomania(average -0.8907 m/s)\nSlovakia(average -0.125 m/s)\nPoland(average -0.2023 m/s)\nIreland(average 0.7015 m/s)\nUnited Kingdom(average 0.7006 m/s)\nMoldova(average -1.135 m/s)\nUnited States of America(average -1.131 m/s)\nCanada(average 3.084 m/s)\nGreenland(average 3.716 m/s)\nPitcairn Islands(average -1.856 m/s)\nIndian Ocean Territories(average -1.94 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 300 + ], + "geofeature": "country", + "climatology_timescale_desc": "JJA seasonal climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "Ukraine", + "Belarus", + "Namibia", + "South Africa", + "Norway", + "Romania", + "Slovakia", + "Poland", + "Ireland", + "United Kingdom", + "Moldova", + "United States of America", + "Canada", + "Greenland", + "Pitcairn Islands", + "Indian Ocean Territories" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "7ef45bf6ad961e9c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78333:78360:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91831:91856:1'} The data starts from November 08 18:00 and ends on November 14 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Surface temperature values running below the 10th percentile climatology for the monthly climatology for November? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Surface temperature values below the 10th percentile climatology for monthly climatology for November: Chile(average -0.1428 K)\nPeru(average -0.2741 K)\nBrazil(average -0.06226 K)\nEcuador(average -0.1746 K)\nUnited States of America(average -0.3486 K)\nAustralia(average -0.4017 K)\nGreenland(average -0.7634 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for November", + "quantile": "0.1", + "threshold_direction": "below", + "true_value": [ + "Chile", + "Peru", + "Brazil", + "Ecuador", + "United States of America", + "Australia", + "Greenland" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "673b4eabf536f13c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91831:91856:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86550:86561:1'} The data starts from March 29 12:00 and ends on April 01 00:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface pressure differs from the monthly climatology for March mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Surface pressure values.", + "response": "Based on the provided data, no significant Surface pressure anomalies were detected relative to the monthly climatology for March baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for March", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "54292a89296b1bfb", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86550:86561:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82005:82013:1'} The data starts from February 17 06:00 and ends on February 19 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 24 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 24 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 24 hours:\nA Tropical cyclone is expected in the country of Australia in approximately the next 24 hours. Specifically the region(s) that might get affected are: Northern Territory, Western Australia provinces\nA Blizzard/Winter storm is expected in the country of State of Palestine in approximately the next 0 to 72 hours. Specifically the region(s) that might get affected are: West Bank, Gaza regions\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Australia", + "State of Palestine" + ], + "extreme_event_hours": 24, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "21e16da8ef0b9c12", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82005:82013:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51941:51956:1'} The data starts from July 21 06:00 and ends on July 24 18:00. Based on the above data, answer the following question:", + "question": "What will the minimum 10-meter V component of wind be in Cabo Verde, 48 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum 10-meter V component of wind in Cabo Verde 48 hours after the given time window will be -6.496 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-6.49648", + "location": "Cabo Verde", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c56d9d3e696ed487", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51941:51956:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65130:65143:1'} The data starts from July 31 12:00 and ends on August 03 12:00. Based on the above data, answer the following question:", + "question": "In the 18 hours after the end of the given time window, when will Boca Grande experience its highest Surface pressure? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Boca Grande will experience its highest Surface pressure of 1.01e+05 Pa 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "Boca Grande", + "extremum_value": "100951.484", + "target_variable": "surface_pressure", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e6d08e7e0930043e", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65130:65143:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31999:32006:1'} The data starts from November 25 18:00 and ends on November 27 06:00. Based on the above data, answer the following question:", + "question": "What will the maximum Specific humidity at 600 hPa be in Africa, 30 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum Specific humidity at 600 hPa in Africa 30 hours after the given time window will be 0.006449 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "0.0064494396", + "location": "Africa", + "target_variable": "specific_humidity_600", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d664a87c5eabdd2d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31999:32006:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30747:30770:1'} The data starts from January 17 18:00 and ends on January 23 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the General Storm currently happening? Specify the affected countries or regions, or respond 'No General Storm detected.'", + "response": "No General Storm detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "General Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "f84d4f29cb84a20c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30747:30770:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76055:76078:1'} The data starts from January 21 18:00 and ends on January 27 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Surface pressure differs from the DJF seasonal climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Surface pressure values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Surface pressure anomalies were detected relative to the DJF seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "DJF seasonal climatology", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "83150df179b50cd4", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76055:76078:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78315:78322:1'} The data starts from August 08 18:00 and ends on August 10 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Temperature at 1000 hPa differs from the all-time climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Temperature at 1000 hPa values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Temperature at 1000 hPa anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "temperature", + 1000 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "871c6aeb604da49c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78315:78322:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87205:87219:1'} The data starts from September 09 06:00 and ends on September 12 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) U (zonal) component of wind at 300 hPa differs from the all-time climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below U (zonal) component of wind at 300 hPa values.", + "response": "Based on the provided data, no significant U (zonal) component of wind at 300 hPa anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "u_component_of_wind", + 300 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "11ca8f7c441c42d1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87205:87219:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36819:36833:1'} The data starts from March 14 18:00 and ends on March 18 00:00. Based on the above data, answer the following question:", + "question": "In the 18 hours after the end of the given time window, when will Venezuela experience its highest Specific humidity at 250 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Venezuela will experience its highest Specific humidity at 250 hPa of 0.0002898 kg/kg 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Venezuela", + "extremum_value": "0.00028978047", + "target_variable": "specific_humidity_250", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "29c6d04bb47e1d65", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36819:36833:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85039:85058:1'} The data starts from March 16 18:00 and ends on March 21 06:00. Based on the above data, answer the following question:", + "question": "What will the maximum Surface temperature be in Queen Charlotte Sound, 48 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum Surface temperature in Queen Charlotte Sound 48 hours after the given time window will be 278.5 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "278.51978", + "location": "Queen Charlotte Sound", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "929f50fffd14af20", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85039:85058:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64317:64334:1'} The data starts from January 09 06:00 and ends on January 13 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 18 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 18 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 18 hours:\nA Tropical cyclone is expected in the country of Fiji in approximately the next 18 hours. Specifically the region(s) that might get affected are: Northern, Eastern, Western provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Fiji" + ], + "extreme_event_hours": 18, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "4cda33a6002bb0a5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64317:64334:1" + } + } +] \ No newline at end of file diff --git a/level2a_part1.json b/level2a_part1.json new file mode 100644 index 0000000000000000000000000000000000000000..1e0574508bb0c8464a7a189a1805ee4b7e85d767 --- /dev/null +++ b/level2a_part1.json @@ -0,0 +1,4444 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55447:55474:1'} The data starts from December 13 18:00 and ends on December 20 06:00. Based on the above data, answer the following question:", + "question": "In the 24 hours after the end of the given time window, when will Spain experience its lowest Surface temperature? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Spain will experience its lowest Surface temperature of 276.1 K 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Spain", + "extremum_value": "276.08728", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "cd84cd86eaef5724", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55447:55474:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40363:40364:1'} The data corresponds to corresponds to a snapshot on August 17 18:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will Beaufort Sea experience its lowest 10-meter V component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Beaufort Sea will experience its lowest 10-meter V component of wind of -5.272 m/s 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "Beaufort Sea", + "extremum_value": "-5.2716603", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "60d3d3fe0a0cf1ce", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40363:40364:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66225:66248:1'} The data starts from April 30 06:00 and ends on May 05 18:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Blizzard/Winter Storm currently happening? Specify the affected countries or regions, or respond 'No Blizzard/Winter Storm detected.'", + "response": "No Blizzard/Winter Storm detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Blizzard/Winter Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "d624477172945690", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66225:66248:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83759:83786:1'} The data starts from April 30 18:00 and ends on May 07 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Surface temperature differs from the daily climatology for day 121 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Surface temperature values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Surface temperature anomalies were detected relative to the daily climatology for day 121 baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 121", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ad6988f26990e241", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83759:83786:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86658:86686:1'} The data starts from April 25 12:00 and ends on May 02 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Sand/Dust storm is occuring in the country of India. Specifically the region(s) being affected are: Rajasthan, Uttar Pradesh\nA Storm (General) is occuring in the country of Argentina. Specifically the region(s) being affected are: La Matanza, Quilmes, Padre Varela de Luján,Lomas de Zamora districts (Buenos Aires)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "India", + "Argentina" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "02f9d3896f89c4f5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86658:86686:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71677:71702:1'} The data starts from January 23 06:00 and ends on January 29 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Madagascar; Fiji", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Madagascar", + "Fiji" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "8f6321b21808b6a7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71677:71702:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76509:76529:1'} The data starts from May 15 06:00 and ends on May 20 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) U (zonal) component of wind at 200 hPa differs from the MAM seasonal climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below U (zonal) component of wind at 200 hPa values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant U (zonal) component of wind at 200 hPa anomalies were detected relative to the MAM seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "u_component_of_wind", + 200 + ], + "geofeature": "country", + "climatology_timescale_desc": "MAM seasonal climatology", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "b0db9b571e68da7d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76509:76529:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72595:72612:1'} The data starts from September 08 18:00 and ends on September 12 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in U (zonal) component of wind at 500 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the U (zonal) component of wind at 500 hPa values exceed the 99th percentile climatology for the all-time climatology.", + "response": "Based on the provided data, no significant U (zonal) component of wind at 500 hPa anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "u_component_of_wind", + 500 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.99", + "min_duration_days": 2, + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "6bda2653b171d228", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72595:72612:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87066:87075:1'} The data starts from August 05 12:00 and ends on August 07 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) V (meridional) component of wind at 700 hPa values running below the 5th percentile climatology for the monthly climatology for August? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show V (meridional) component of wind at 700 hPa values below the 5th percentile climatology for monthly climatology for August: Indonesia(average -0.3884 m/s)\nPeru(average -0.2654 m/s)\nIndia(average -0.3411 m/s)\nChina(average -0.1267 m/s)\nUnited Republic of Tanzania(average -0.4573 m/s)\nMorocco(average -1.048 m/s)\nBrazil(average -0.8574 m/s)\nNorway(average -1.145 m/s)\nVietnam(average -0.1488 m/s)\nSpain(average -0.7427 m/s)\nSudan(average -0.1557 m/s)\nIran(average -0.1661 m/s)\nPakistan(average -0.01352 m/s)\nAlgeria(average -0.3944 m/s)\nAfghanistan(average -0.1937 m/s)\nEcuador(average -0.1529 m/s)\nTurkmenistan(average -0.143 m/s)\nMexico(average -0.6755 m/s)\nEgypt(average -0.3248 m/s)\nGreenland(average -0.7052 m/s)\nSri Lanka(average -0.3008 m/s)\nJapan(average -0.6422 m/s)\nIceland(average -0.797 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "v_component_of_wind", + 700 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for August", + "quantile": "0.05", + "threshold_direction": "below", + "true_value": [ + "Indonesia", + "Peru", + "India", + "China", + "United Republic of Tanzania", + "Morocco", + "Brazil", + "Norway", + "Vietnam", + "Spain", + "Sudan", + "Iran", + "Pakistan", + "Algeria", + "Afghanistan", + "Ecuador", + "Turkmenistan", + "Mexico", + "Egypt", + "Greenland", + "Sri Lanka", + "Japan", + "Iceland" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "9a2c846acd3148de", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87066:87075:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77207:77234:1'} The data starts from November 05 18:00 and ends on November 12 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) V (meridional) component of wind at 600 hPa lies outside the climatological 10th–99th percentile envelope for the monthly climatology for November. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–99th percentile envelope for V (meridional) component of wind at 600 hPa during monthly climatology for November: Indonesia(average -0.424 m/s)\nChile(average -0.5001 m/s)\nDhekelia Sovereign Base Area(average -0.7518 m/s)\nCyprus(average -0.7518 m/s)\nIndia(average -1.454 m/s)\nChina(average -0.2154 m/s)\nIsrael(average -0.8337 m/s)\nPalestine(average -0.8337 m/s)\nLebanon(average -1.657 m/s)\nSyria(average -1.635 m/s)\nFrance(average -0.6479 m/s)\nBhutan(average -0.42 m/s)\nUkraine(average -4.587 m/s)\nBelarus(average -1.261 m/s)\nRussia(average -2.648 m/s)\nGeorgia(average -1.35 m/s)\nTurkey(average -1.898 m/s)\nIraq(average -0.775 m/s)\nSaudi Arabia(average -0.3423 m/s)\nThailand(average -0.8133 m/s)\nMyanmar(average -2.045 m/s)\nBangladesh(average -1.81 m/s)\nJordan(average -0.7161 m/s)\nNepal(average -0.3092 m/s)\nPanama(average -0.09163 m/s)\nNorthern Cyprus(average -1.275 m/s)\nCyprus No Mans Area(average -0.7518 m/s)\nAkrotiri Sovereign Base Area(average -0.7518 m/s)\nGreenland(average -1.539 m/s)\nThe Bahamas(average -0.515 m/s)\nJapan(average -0.9499 m/s)\nNorthern Mariana Islands(average -1.636 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 600 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for November", + "lower_quantile": "0.1", + "upper_quantile": "0.99", + "true_value": [ + "Indonesia", + "Chile", + "Dhekelia Sovereign Base Area", + "Cyprus", + "India", + "China", + "Israel", + "Palestine", + "Lebanon", + "Syria", + "France", + "Bhutan", + "Ukraine", + "Belarus", + "Russia", + "Georgia", + "Turkey", + "Iraq", + "Saudi Arabia", + "Thailand", + "Myanmar", + "Bangladesh", + "Jordan", + "Nepal", + "Panama", + "Northern Cyprus", + "Cyprus No Mans Area", + "Akrotiri Sovereign Base Area", + "Greenland", + "The Bahamas", + "Japan", + "Northern Mariana Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d03bdb2f75082627", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77207:77234:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67925:67939:1'} The data starts from June 29 06:00 and ends on July 02 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Geopotential at 250 hPa lies outside the climatological 1st–99th percentile envelope for the JJA seasonal climatology. Regions outside that envelope are anomalous.", + "response": "Based on the provided data, no significant Geopotential at 250 hPa anomalies were detected relative to the JJA seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "geopotential", + 250 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "JJA seasonal climatology", + "lower_quantile": "0.01", + "upper_quantile": "0.99", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "7b8ae1bd4e258d11", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67925:67939:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81736:81741:1'} The data starts from December 12 00:00 and ends on December 13 00:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Nigeria experience its highest 10-meter U component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Nigeria will experience its highest 10-meter U component of wind of 3.008 m/s 24 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 24, + "location": "Nigeria", + "extremum_value": "3.008109", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "56ab56e389c5e6b2", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81736:81741:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91330:91354:1'} The data starts from July 06 12:00 and ends on July 12 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface pressure lies outside the climatological 10th–99th percentile envelope for the six-hourly climatology for day 187 at 12 UTC. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–99th percentile envelope for Surface pressure during six-hourly climatology for day 187 at 12 UTC: Arctic Ocean(average -598.4 Pa)\nSOUTHERN OCEAN(average -604.3 Pa)\nNorth Atlantic Ocean(average -6.209 Pa)\nNorth Pacific Ocean(average 126.6 Pa)\nSouth Pacific Ocean(average -194.4 Pa)\nINDIAN OCEAN(average -213.7 Pa)\nSouth Atlantic Ocean(average -283.6 Pa)\nPhilippine Sea(average 20.35 Pa)\nGreat Barrier Reef(average -50.77 Pa)\nBay of Bengal(average 27.73 Pa)\nSouth China Sea(average 49.22 Pa)\nBeaufort Sea(average -88.04 Pa)\nLabrador Sea(average -106.3 Pa)\nRed Sea(average -1.867 Pa)\nBanda Sea(average -3.977 Pa)\nLuzon Strait(average 14.77 Pa)\nBarents Sea(average -260.9 Pa)\nEast China Sea(average 23.55 Pa)\nChukchi Sea(average -130.7 Pa)\nArafura Sea(average -20.71 Pa)\nTimor Sea(average -10.25 Pa)\nDavis Strait(average -108.4 Pa)\nKara Sea(average -594.7 Pa)\nLaptev Sea(average -773.6 Pa)\nDrake Passage(average -190.5 Pa)\nSea of Azov(average 97.53 Pa)\nGulf of Carpentaria(average -15.03 Pa)\nSolomon Sea(average -9.643 Pa)\nGulf of Tonkin(average 163.7 Pa)\nTaiwan Strait(average 19.52 Pa)\nEast Siberian Sea(average -655.2 Pa)\nVil'kitskogo Strait(average -669.8 Pa)\nShark Bay(average -159.7 Pa)\nKotzebue Sound(average -54.1 Pa)\nGulf of Yana(average -443.7 Pa)\nDmitriy Laptev Strait(average -419.8 Pa)\nQiongzhou Strait(average 107 Pa)\nTorres Strait(average -49.9 Pa)\nGeographe Bay(average -226.9 Pa)\nGulf of Papua(average -48.04 Pa)\nGulf of Olen‘k(average -373.5 Pa)\nEstrecho de Magellanes(average -174 Pa)\nChaun Bay(average -315.3 Pa)\nKhatanga Gulf(average -242.7 Pa)\nGulf of Ob(average -289.1 Pa)\nYenisey Gulf(average -224.1 Pa)\nSeno de Skyring(average -78.83 Pa)\nSeno Otway(average -118.6 Pa)\nBay Inútil(average -109.7 Pa)\nOzero Mogotoyevo(average -410.3 Pa)\nGuba Gusinaya(average -455.9 Pa)\nSargasso Sea(average 46.59 Pa)\nCoral Sea(average -23.31 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 187 at 12 UTC", + "lower_quantile": "0.1", + "upper_quantile": "0.99", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Great Barrier Reef", + "Bay of Bengal", + "South China Sea", + "Beaufort Sea", + "Labrador Sea", + "Red Sea", + "Banda Sea", + "Luzon Strait", + "Barents Sea", + "East China Sea", + "Chukchi Sea", + "Arafura Sea", + "Timor Sea", + "Davis Strait", + "Kara Sea", + "Laptev Sea", + "Drake Passage", + "Sea of Azov", + "Gulf of Carpentaria", + "Solomon Sea", + "Gulf of Tonkin", + "Taiwan Strait", + "East Siberian Sea", + "Vil'kitskogo Strait", + "Shark Bay", + "Kotzebue Sound", + "Gulf of Yana", + "Dmitriy Laptev Strait", + "Qiongzhou Strait", + "Torres Strait", + "Geographe Bay", + "Gulf of Papua", + "Gulf of Olen‘k", + "Estrecho de Magellanes", + "Chaun Bay", + "Khatanga Gulf", + "Gulf of Ob", + "Yenisey Gulf", + "Seno de Skyring", + "Seno Otway", + "Bay Inútil", + "Ozero Mogotoyevo", + "Guba Gusinaya", + "Sargasso Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "859edbb8dc22e7b3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91330:91354:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32097:32098:1'} The data corresponds to corresponds to a snapshot on December 20 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 24 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 24 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 24 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 24, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "1c496ea98d5b6f85", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32097:32098:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69280:69289:1'} The data starts from June 03 00:00 and ends on June 05 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Specific humidity at 200 hPa lies outside the climatological 1st–90th percentile envelope for the monthly climatology for June. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 1st–90th percentile envelope for Specific humidity at 200 hPa during monthly climatology for June: Bolivia(average 2.737e-06 kg/kg)\nPeru(average 2.702e-06 kg/kg)\nArgentina(average 6.373e-06 kg/kg)\nIndia(average 1.398e-07 kg/kg)\nChina(average 7.782e-06 kg/kg)\nEthiopia(average 5.134e-06 kg/kg)\nSomalia(average 9.393e-06 kg/kg)\nKenya(average 9.153e-06 kg/kg)\nUnited Republic of Tanzania(average 4.019e-06 kg/kg)\nFrance(average 5.028e-06 kg/kg)\nSuriname(average 3.357e-06 kg/kg)\nGuyana(average 3.579e-06 kg/kg)\nRepublic of the Congo(average 3.616e-06 kg/kg)\nNamibia(average 3.32e-06 kg/kg)\nSouth Africa(average 3.347e-06 kg/kg)\nBrazil(average 7.08e-06 kg/kg)\nCzechia(average -2.496e-07 kg/kg)\nSweden(average -1.463e-08 kg/kg)\nAlbania(average -6.19e-07 kg/kg)\nRomania(average -1.163e-07 kg/kg)\nHungary(average -5.198e-07 kg/kg)\nSlovakia(average -2.869e-07 kg/kg)\nPoland(average -2.286e-08 kg/kg)\nLiberia(average 2.183e-07 kg/kg)\nAustria(average -4.515e-07 kg/kg)\nItaly(average -1.104e-06 kg/kg)\nIvory Coast(average 9.478e-07 kg/kg)\nRepublic of Serbia(average -5.816e-07 kg/kg)\nNigeria(average 5.611e-06 kg/kg)\nBenin(average 4.645e-06 kg/kg)\nCroatia(average -7.213e-07 kg/kg)\nSlovenia(average -4.361e-07 kg/kg)\nChad(average 1.248e-06 kg/kg)\nMozambique(average 2.978e-06 kg/kg)\nMontenegro(average -7.75e-07 kg/kg)\nBosnia and Herzegovina(average -9.006e-07 kg/kg)\nParaguay(average 5.727e-06 kg/kg)\nPortugal(average -2.046e-06 kg/kg)\nNepal(average 8.495e-07 kg/kg)\nGabon(average 3.899e-06 kg/kg)\nNiger(average 5.926e-06 kg/kg)\nBurkina Faso(average 3.565e-06 kg/kg)\nTogo(average 4.769e-06 kg/kg)\nGhana(average 3.058e-06 kg/kg)\nUnited States of America(average 1.073e-06 kg/kg)\nCanada(average -8.499e-08 kg/kg)\nAustralia(average 4.016e-07 kg/kg)\nMadagascar(average 5.83e-06 kg/kg)\nFrench Southern and Antarctic Lands(average 2.57e-06 kg/kg)\nSeychelles(average 9.86e-06 kg/kg)\nComoros(average 9.584e-06 kg/kg)\nCabo Verde(average 2.432e-06 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "specific_humidity", + 200 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for June", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "Bolivia", + "Peru", + "Argentina", + "India", + "China", + "Ethiopia", + "Somalia", + "Kenya", + "United Republic of Tanzania", + "France", + "Suriname", + "Guyana", + "Republic of the Congo", + "Namibia", + "South Africa", + "Brazil", + "Czechia", + "Sweden", + "Albania", + "Romania", + "Hungary", + "Slovakia", + "Poland", + "Liberia", + "Austria", + "Italy", + "Ivory Coast", + "Republic of Serbia", + "Nigeria", + "Benin", + "Croatia", + "Slovenia", + "Chad", + "Mozambique", + "Montenegro", + "Bosnia and Herzegovina", + "Paraguay", + "Portugal", + "Nepal", + "Gabon", + "Niger", + "Burkina Faso", + "Togo", + "Ghana", + "United States of America", + "Canada", + "Australia", + "Madagascar", + "French Southern and Antarctic Lands", + "Seychelles", + "Comoros", + "Cabo Verde" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "daf5b1ac8c731962", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69280:69289:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68351:68379:1'} The data starts from October 13 18:00 and ends on October 20 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Bahamas.\nA Tropical cyclone is occuring in the country of Cuba. Specifically the region(s) being affected are: Santiago de Cuba, Granma, Guantanamo, Matanzas, Sancti Spiritus, Villa Clara, Cienfuegos, Havana, Ciudad Havana, Pinar del Rio, Isla De La Juventud provinces\nA Tropical cyclone is occuring in the country of Haiti. Specifically the region(s) being affected are: Sud, Sud Est provinces\nA Tropical cyclone is occuring in the country of Jamaica. Specifically the region(s) being affected are: Saint Thomas, Saint Catherine, Trelawny, Saint Andrew And Kingston provinces\nA Tropical cyclone is occuring in the country of Mexico. Specifically the region(s) being affected are: Benito Juarez, Isla Mujeres, Cozumel districts (Quintana Roo province)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Bahamas", + "Cuba", + "Haiti", + "Jamaica", + "Mexico" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "71414b5b56cb7d62", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68351:68379:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63103:63129:1'} The data starts from March 11 18:00 and ends on March 18 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 42 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 42 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 42 hours:\nA Storm (General) is expected in the country of United States of America in approximately the next 24 hours. Specifically the region(s) that might get affected are: Sevier, Blount, Knox, Hawkins districts (Tennessee province), Cumberland, Bell districts (Kentucky province), Wise, Smyth, Washington districts (Virginia province), Cabell district (West Virginia province), North Carolina, Mississippi, Arkansas, Texas provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "United States of America" + ], + "extreme_event_hours": 42, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "7fe87905ff0cfa60", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63103:63129:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78589:78615:1'} The data starts from October 16 06:00 and ends on October 22 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 50 hPa differs from the monthly climatology for October mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Temperature at 50 hPa values.", + "response": "Based on the provided data, no significant Temperature at 50 hPa anomalies were detected relative to the monthly climatology for October baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "temperature", + 50 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for October", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "1e501d8598cca1ed", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78589:78615:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31385:31396:1'} The data starts from June 25 06:00 and ends on June 27 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, there is no extreme weather event occuring.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "c6532c86349080f1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31385:31396:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92555:92567:1'} The data starts from May 08 18:00 and ends on May 11 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Surface temperature values? An exceedance is defined as a period of at least 72 consecutive hours where the Surface temperature values exceed the 90th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Surface temperature: Indonesia(average 0.5278 K)\nIndia(average 1.035 K)\nSouth Sudan(average 0.4309 K)\nMorocco(average 0.4454 K)\nWestern Sahara(average 0.8097 K)\nSudan(average 0.4309 K)\nGuatemala(average 0.7954 K)\nEast Timor(average 0.484 K)\nHonduras(average 0.102 K)\nColombia(average 0.1129 K)\nGabon(average 0.06607 K)\nCanada(average 0.05066 K)\nMexico(average 0.5568 K)\nBelize(average 0.9151 K)\nPanama(average 0.242 K)\nPapua New Guinea(average 0.7558 K)\nYemen(average 0.9347 K)\nEquatorial Guinea(average 0.1262 K)\nAustralia(average 0.5584 K)\nPhilippines(average 0.6323 K)\nSri Lanka(average 0.5432 K)\nFrench Polynesia(average 0.3651 K)\nSeychelles(average 0.1131 K)\nSaint Helena(average 0.4643 K)\nIndian Ocean Territories(average 1.011 K)\nBritish Indian Ocean Territory(average 0.09677 K)\nTuvalu(average 0.03116 K)\nMaldives(average 0.7088 K)\nFederated States of Micronesia(average 0.4414 K)\nSpratly Islands(average 0.5052 K)\nAshmore and Cartier Islands(average 0.01511 K)\nScarborough Reef(average 0.8625 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.9", + "min_duration_days": 3, + "true_value": [ + "Indonesia", + "India", + "South Sudan", + "Morocco", + "Western Sahara", + "Sudan", + "Guatemala", + "East Timor", + "Honduras", + "Colombia", + "Gabon", + "Canada", + "Mexico", + "Belize", + "Panama", + "Papua New Guinea", + "Yemen", + "Equatorial Guinea", + "Australia", + "Philippines", + "Sri Lanka", + "French Polynesia", + "Seychelles", + "Saint Helena", + "Indian Ocean Territories", + "British Indian Ocean Territory", + "Tuvalu", + "Maldives", + "Federated States of Micronesia", + "Spratly Islands", + "Ashmore and Cartier Islands", + "Scarborough Reef" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "f228b173ffd002ec", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92555:92567:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83668:83687:1'} The data starts from April 08 00:00 and ends on April 12 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Geopotential at 250 hPa lies outside the climatological 10th–99th percentile envelope for the monthly climatology for April. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–99th percentile envelope for Geopotential at 250 hPa during monthly climatology for April: SOUTHERN OCEAN(average -199.7 m²/s²)\nNorth Pacific Ocean(average 47.04 m²/s²)\nSouth Pacific Ocean(average 60.63 m²/s²)\nINDIAN OCEAN(average 3.048 m²/s²)\nSouth Atlantic Ocean(average 217.6 m²/s²)\nPhilippine Sea(average 52.37 m²/s²)\nBay of Bengal(average 30.13 m²/s²)\nSouth China Sea(average 1.681 m²/s²)\nSea of Okhotsk(average -20.55 m²/s²)\nGreenland Sea(average 66.07 m²/s²)\nAndaman Sea(average 32.09 m²/s²)\nArafura Sea(average 13.88 m²/s²)\nTimor Sea(average 18.62 m²/s²)\nGulf of Thailand(average 15.41 m²/s²)\nJames Bay(average -58.4 m²/s²)\nGulf of Carpentaria(average 16.45 m²/s²)\nSolomon Sea(average 13.6 m²/s²)\nMcMurdo Sound(average -122 m²/s²)\nLa Pérouse Strait(average -29.08 m²/s²)\nPrydz Bay(average -26.52 m²/s²)\nSt. Helena Bay(average 7.664 m²/s²)\nJoseph Bonaparte Gulf(average 44.5 m²/s²)\nGulf of Martaban(average 34.7 m²/s²)\nSavu Sea(average 0.9219 m²/s²)\nRoss Sea(average -82.22 m²/s²)\nCoral Sea(average 21.85 m²/s²)\nSea of Japan(average -82.17 m²/s²)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "geopotential", + 250 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for April", + "lower_quantile": "0.1", + "upper_quantile": "0.99", + "true_value": [ + "SOUTHERN OCEAN", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Bay of Bengal", + "South China Sea", + "Sea of Okhotsk", + "Greenland Sea", + "Andaman Sea", + "Arafura Sea", + "Timor Sea", + "Gulf of Thailand", + "James Bay", + "Gulf of Carpentaria", + "Solomon Sea", + "McMurdo Sound", + "La Pérouse Strait", + "Prydz Bay", + "St. Helena Bay", + "Joseph Bonaparte Gulf", + "Gulf of Martaban", + "Savu Sea", + "Ross Sea", + "Coral Sea", + "Sea of Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "4826e9992426592a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83668:83687:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90605:90632:1'} The data starts from January 06 06:00 and ends on January 12 18:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Blizzard/Winter Storm currently happening? Specify the affected countries or regions, or respond 'No Blizzard/Winter Storm detected.'", + "response": "Based on the provided data, the Blizzard/Winter Storm is affecting: Spain; Taiwan (Province of China); Japan", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Spain", + "Taiwan (Province of China)", + "Japan" + ], + "target_disaster": "Blizzard/Winter Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "8287124721b4fa76", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90605:90632:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50380:50402:1'} The data starts from June 26 00:00 and ends on July 01 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Philippines. Specifically the region(s) being affected are: Cordillera autonomous region (including Bagio city), Regions I-IV (including Metro Manilla)\nA Tropical cyclone is occuring in the country of Mexico. Specifically the region(s) being affected are: Acapulco\nA Tropical cyclone is occuring in the country of China, Hong Kong Special Administrative Region.\nA Tropical cyclone is occuring in the country of China, Macao Special Administrative Region.\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Philippines", + "Mexico", + "China, Hong Kong Special Administrative Region", + "China, Macao Special Administrative Region" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "1768903521e35694", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50380:50402:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78868:78870:1'} The data starts from December 25 00:00 and ends on December 25 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Philippines", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Philippines" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "3194a28824414e8e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78868:78870:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63973:63978:1'} The data starts from October 15 06:00 and ends on October 16 06:00. Based on the above data, answer the following question:", + "question": "What will the minimum Temperature at 250 hPa be in Asia, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum Temperature at 250 hPa in Asia 12 hours after the given time window will be 211.3 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "211.30687", + "location": "Asia", + "target_variable": "temperature_250", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9167c7935e5f4410", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63973:63978:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80817:80840:1'} The data starts from April 26 06:00 and ends on May 01 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Temperature at 50 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the Temperature at 50 hPa values exceed the 99th percentile climatology for the MAM seasonal climatology.", + "response": "Based on the provided data, no significant Temperature at 50 hPa anomalies were detected relative to the MAM seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "temperature", + 50 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "MAM seasonal climatology", + "quantile": "0.99", + "min_duration_days": 3, + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "a2b93466cb86d4b7", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80817:80840:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87089:87095:1'} The data starts from August 11 06:00 and ends on August 12 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) 10-meter U component of wind values running above the 95th percentile climatology for the monthly climatology for August? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show 10-meter U component of wind values above the 95th percentile climatology for monthly climatology for August: Indonesia(average 0.07387 m/s)\nPeru(average 0.249 m/s)\nIndia(average 0.1311 m/s)\nChina(average 1.001 m/s)\nOman(average 0.374 m/s)\nUzbekistan(average 0.331 m/s)\nKazakhstan(average 0.539 m/s)\nTajikistan(average 0.4189 m/s)\nBrazil(average 0.4612 m/s)\nRussia(average 0.7444 m/s)\nNorway(average 0.3758 m/s)\nSweden(average 0.4672 m/s)\nFinland(average 0.5234 m/s)\nVietnam(average 0.2957 m/s)\nCambodia(average 0.0398 m/s)\nSaudi Arabia(average 0.313 m/s)\nPakistan(average 0.164 m/s)\nThailand(average 0.0398 m/s)\nAfghanistan(average 0.3216 m/s)\nTurkmenistan(average 0.2431 m/s)\nUnited States of America(average 0.1646 m/s)\nCanada(average 1.205 m/s)\nMexico(average 0.214 m/s)\nPapua New Guinea(average 0.6036 m/s)\nYemen(average 0.2293 m/s)\nFiji(average 0.1874 m/s)\nPhilippines(average 1.036 m/s)\nFrench Polynesia(average 0.8451 m/s)\nUnited States Minor Outlying Islands(average 5.221 m/s)\nCook Islands(average 0.6517 m/s)\nTonga(average 0.7413 m/s)\nWallis and Futuna(average 0.9671 m/s)\nTuvalu(average 0.9211 m/s)\nFederated States of Micronesia(average 0.444 m/s)\nScarborough Reef(average 1.45 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for August", + "quantile": "0.95", + "threshold_direction": "above", + "true_value": [ + "Indonesia", + "Peru", + "India", + "China", + "Oman", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Brazil", + "Russia", + "Norway", + "Sweden", + "Finland", + "Vietnam", + "Cambodia", + "Saudi Arabia", + "Pakistan", + "Thailand", + "Afghanistan", + "Turkmenistan", + "United States of America", + "Canada", + "Mexico", + "Papua New Guinea", + "Yemen", + "Fiji", + "Philippines", + "French Polynesia", + "United States Minor Outlying Islands", + "Cook Islands", + "Tonga", + "Wallis and Futuna", + "Tuvalu", + "Federated States of Micronesia", + "Scarborough Reef" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "1630f830bf357435", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87089:87095:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87232:87236:1'} The data starts from September 16 00:00 and ends on September 16 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter U component of wind differs from the daily climatology for day 259 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below 10-meter U component of wind values. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) exceed the ±3σ anomaly threshold for 10-meter U component of wind relative to the daily climatology for day 259 mean: China(average -4.342 m/s)\nUnited States of America(average -5.823 m/s)\nCanada(average -1.833 m/s)\nHong Kong S.A.R.(average -7.456 m/s)\nTaiwan(average -5.706 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 259", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "China", + "United States of America", + "Canada", + "Hong Kong S.A.R.", + "Taiwan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "8498f1588e02ee9e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87232:87236:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89955:89971:1'} The data starts from July 27 18:00 and ends on July 31 12:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Viet Nam; Cuba; Dominican Republic; United States of America", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Viet Nam", + "Cuba", + "Dominican Republic", + "United States of America" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "1667cd3578963e6f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89955:89971:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87882:87901:1'} The data starts from February 25 12:00 and ends on March 02 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Temperature at 925 hPa values running below the 1st percentile climatology for the monthly climatology for February? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Temperature at 925 hPa anomalies were detected relative to the monthly climatology for February baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "temperature", + 925 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for February", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "228b6335aad494df", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87882:87901:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32950:32977:1'} The data starts from July 21 12:00 and ends on July 28 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, there is no extreme weather event occuring.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "db8ead2a75791220", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32950:32977:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75948:75959:1'} The data starts from December 26 00:00 and ends on December 28 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Mean sea level pressure differs from the monthly climatology for December mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Mean sea level pressure values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Mean sea level pressure anomalies were detected relative to the monthly climatology for December baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for December", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "822b3c1b24764d9d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75948:75959:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84749:84763:1'} The data starts from January 03 06:00 and ends on January 06 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Mean sea level pressure differs from the daily climatology for day 3 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Mean sea level pressure values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for Mean sea level pressure relative to the daily climatology for day 3 mean: INDIAN OCEAN(average -579.1 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 3", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "INDIAN OCEAN" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "077b8776e0ca56cf", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84749:84763:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71449:71473:1'} The data starts from November 27 06:00 and ends on December 03 00:00. Based on the above data, answer the following question:", + "question": "What will the maximum 10-meter U component of wind be in Asia, 18 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum 10-meter U component of wind in Asia 18 hours after the given time window will be 10.27 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "10.266631", + "location": "Asia", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cbdf5abd0c2f911f", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71449:71473:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88995:89005:1'} The data starts from November 30 18:00 and ends on December 03 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Surface temperature values running below the 5th percentile climatology for the daily climatology for day 334? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Surface temperature values below the 5th percentile climatology for daily climatology for day 334: India(average -0.2541 K)\nChina(average -0.2541 K)\nMorocco(average -0.271 K)\nWestern Sahara(average -0.2083 K)\nRussia(average -1.614 K)\nCanada(average -0.3108 K)\nMauritania(average -0.2083 K)\nAustralia(average -0.2447 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 334", + "quantile": "0.05", + "threshold_direction": "below", + "true_value": [ + "India", + "China", + "Morocco", + "Western Sahara", + "Russia", + "Canada", + "Mauritania", + "Australia" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "67ac3412894939dc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88995:89005:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93073:93074:1'} The data corresponds to corresponds to a snapshot on September 15 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Sand/Dust Storm currently happening? Specify the affected countries or regions, or respond 'No Sand/Dust Storm detected.'", + "response": "No Sand/Dust Storm detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Sand/Dust Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "ddddb62da995d039", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93073:93074:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68630:68647:1'} The data starts from December 22 12:00 and ends on December 26 12:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Severe Winter Conditions currently happening? Specify the affected countries or regions, or respond 'No Severe Winter Conditions detected.'", + "response": "Based on the provided data, the Severe Winter Conditions is affecting: Türkiye", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Türkiye" + ], + "target_disaster": "Severe Winter Conditions", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "033838eb38f0d3bc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68630:68647:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36503:36508:1'} The data starts from December 26 18:00 and ends on December 27 18:00. Based on the above data, answer the following question:", + "question": "What will the average 10-meter U component of wind be in Africa, 42 hours after the end of the given time window?", + "response": "Based on the provided data, the average 10-meter U component of wind in Africa 42 hours after the given time window will be -1.538 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-1.5375578", + "location": "Africa", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7c620b7d76e68c1c", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36503:36508:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88570:88579:1'} The data starts from August 16 12:00 and ends on August 18 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Specific humidity at 500 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the Specific humidity at 500 hPa values exceed the 99th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Specific humidity at 500 hPa: China(average 0.0003379 kg/kg)\nFrance(average 0.000292 kg/kg)\nMorocco(average 0.000221 kg/kg)\nWestern Sahara(average 0.0001183 kg/kg)\nRussia(average 0.0003216 kg/kg)\nGeorgia(average 0.0001861 kg/kg)\nAzerbaijan(average 0.0001689 kg/kg)\nTurkey(average 0.0001689 kg/kg)\nArmenia(average 0.0001911 kg/kg)\nSudan(average 0.0005193 kg/kg)\nIran(average 0.0001689 kg/kg)\nNigeria(average 2.624e-05 kg/kg)\nSaudi Arabia(average 0.0002921 kg/kg)\nAlgeria(average 0.000207 kg/kg)\nUnited States of America(average 0.0004072 kg/kg)\nMexico(average 2.091e-05 kg/kg)\nEgypt(average 0.0004971 kg/kg)\nBir Tawil(average 0.0003521 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 500 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.99", + "min_duration_days": 2, + "true_value": [ + "China", + "France", + "Morocco", + "Western Sahara", + "Russia", + "Georgia", + "Azerbaijan", + "Turkey", + "Armenia", + "Sudan", + "Iran", + "Nigeria", + "Saudi Arabia", + "Algeria", + "United States of America", + "Mexico", + "Egypt", + "Bir Tawil" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "144b728f8419a4b2", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88570:88579:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32022:32024:1'} The data starts from December 01 12:00 and ends on December 01 18:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Severe Weather currently happening? Specify the affected countries or regions, or respond 'No Severe Weather detected.'", + "response": "No Severe Weather detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Severe Weather", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "741125bda85797a3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32022:32024:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57616:57625:1'} The data starts from June 09 00:00 and ends on June 11 00:00. Based on the above data, answer the following question:", + "question": "In the 6 hours after the end of the given time window, when will Democratic Republic of the Congo experience its lowest 10-meter U component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Democratic Republic of the Congo will experience its lowest 10-meter U component of wind of -3.199 m/s 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "Democratic Republic of the Congo", + "extremum_value": "-3.1994305", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "966be7aa7c8eea0c", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57616:57625:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71590:71617:1'} The data starts from January 01 12:00 and ends on January 08 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Severe winter conditions is occuring in the country of Afghanistan. Specifically the region(s) being affected are: Nangarhar, Laghman (Eastern), Paktika, Paktya (Central), Ghor, Badghis, Hirat (Western), Sar-e-Pul, Kunduz, Balkh, Jawzjan, Takhar (Northern), Hilmand (SouthWest), Farah provinces\nA Extra-tropical storm is occuring in the country of United States of America. Specifically the region(s) being affected are: Arkansas, California, Colorado, Illinois, Indiana, Kansas, Michigan, Missouri, New York, Ohio, Oklahoma, Oregon, Washington, Wisconsin provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Afghanistan", + "United States of America" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "a2851b39d19ed12e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71590:71617:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79047:79058:1'} The data starts from February 07 18:00 and ends on February 10 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter U component of wind differs from the daily climatology for day 38 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above 10-meter U component of wind values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant 10-meter U component of wind anomalies were detected relative to the daily climatology for day 38 baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 38", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "22e9a3ae2df1b7f3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79047:79058:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70294:70310:1'} The data starts from February 11 12:00 and ends on February 15 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface temperature lies outside the climatological 1st–90th percentile envelope for the DJF seasonal climatology. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–90th percentile envelope for Surface temperature during DJF seasonal climatology: Arctic Ocean(average 1.698 K)\nSOUTHERN OCEAN(average 0.2646 K)\nNorth Atlantic Ocean(average 0.2663 K)\nNorth Pacific Ocean(average 0.1958 K)\nSouth Pacific Ocean(average 0.3121 K)\nINDIAN OCEAN(average 0.4534 K)\nSouth Atlantic Ocean(average 0.2828 K)\nBlack Sea(average 1.268 K)\nPhilippine Sea(average 0.2024 K)\nBay of Bengal(average 0.1265 K)\nSouth China Sea(average 0.2173 K)\nArabian Sea(average 0.2122 K)\nCaribbean Sea(average 0.1117 K)\nBaffin Bay(average 1.926 K)\nBanda Sea(average 0.06736 K)\nLuzon Strait(average 0.2173 K)\nMozambique Channel(average 0.2503 K)\nGulf of Guinea(average 0.1034 K)\nScotia Sea(average 0.3171 K)\nBarents Sea(average 0.9738 K)\nJava Sea(average 0.1898 K)\nAndaman Sea(average 0.009949 K)\nArafura Sea(average 0.1415 K)\nTimor Sea(average 0.05556 K)\nLaccadive Sea(average 0.07597 K)\nDrake Passage(average 0.1586 K)\nGreat Australian Bight(average 0.1362 K)\nSea of Azov(average 0.525 K)\nCeram Sea(average 0.02371 K)\nTaiwan Strait(average 0.263 K)\nBalearic Sea(average 0.2519 K)\nEast Siberian Sea(average 1.155 K)\nBransfield Strait(average 0.2607 K)\nGulf of Masira(average 0.05261 K)\nAlboran Sea(average 0.1605 K)\nBight of Benin(average 0.121 K)\nBight of Biafra(average 0.07977 K)\nDavis Sea(average 0.3218 K)\nBali Sea(average 0.1726 K)\nSelat Bali(average 0.2379 K)\nFlores Sea(average 0.191 K)\nSavu Sea(average 0.03915 K)\nMurchison Sound(average 1.32 K)\nMediterranean Sea(average 0.1341 K)\nCoral Sea(average 0.1207 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "DJF seasonal climatology", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Black Sea", + "Philippine Sea", + "Bay of Bengal", + "South China Sea", + "Arabian Sea", + "Caribbean Sea", + "Baffin Bay", + "Banda Sea", + "Luzon Strait", + "Mozambique Channel", + "Gulf of Guinea", + "Scotia Sea", + "Barents Sea", + "Java Sea", + "Andaman Sea", + "Arafura Sea", + "Timor Sea", + "Laccadive Sea", + "Drake Passage", + "Great Australian Bight", + "Sea of Azov", + "Ceram Sea", + "Taiwan Strait", + "Balearic Sea", + "East Siberian Sea", + "Bransfield Strait", + "Gulf of Masira", + "Alboran Sea", + "Bight of Benin", + "Bight of Biafra", + "Davis Sea", + "Bali Sea", + "Selat Bali", + "Flores Sea", + "Savu Sea", + "Murchison Sound", + "Mediterranean Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0c66d7e262052ff2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70294:70310:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90902:90929:1'} The data starts from March 21 12:00 and ends on March 28 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Severe Weather currently happening? Specify the affected countries or regions, or respond 'No Severe Weather detected.'", + "response": "Based on the provided data, the Severe Weather is affecting: Thailand; Viet Nam", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Thailand", + "Viet Nam" + ], + "target_disaster": "Severe Weather", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "58709621716098d9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90902:90929:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75781:75805:1'} The data starts from November 14 06:00 and ends on November 20 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Surface temperature values running below the 5th percentile climatology for the SON seasonal climatology? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Surface temperature values below the 5th percentile climatology for SON seasonal climatology: Norway(average -0.4389 K)\nSweden(average -0.4709 K)\nFinland(average -0.3086 K)\nUnited States of America(average -0.03351 K)\nCanada(average -0.7377 K)\nMexico(average -0.4101 K)\nPanama(average -0.07501 K)\nGreenland(average -1.103 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "SON seasonal climatology", + "quantile": "0.05", + "threshold_direction": "below", + "true_value": [ + "Norway", + "Sweden", + "Finland", + "United States of America", + "Canada", + "Mexico", + "Panama", + "Greenland" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ecbdb7494f754179", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75781:75805:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92062:92089:1'} The data starts from January 05 12:00 and ends on January 12 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) V (meridional) component of wind at 50 hPa values running above the 99th percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant V (meridional) component of wind at 50 hPa anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "v_component_of_wind", + 50 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.99", + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "dccc5bd4c5fbab88", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92062:92089:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43598:43606:1'} The data starts from November 03 12:00 and ends on November 05 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Philippines. Specifically the region(s) being affected are: Manilla, Tagalog, Bicol, western Visayas\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Philippines" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "c216d3f1e877cce6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43598:43606:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40607:40618:1'} The data starts from October 17 18:00 and ends on October 20 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Storm (General) is occuring in the country of United Kingdom of Great Britain and Northern Ireland.\nA Storm (General) is occuring in the country of Netherlands (Kingdom of the).\nA Storm (General) is occuring in the country of Switzerland.\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "United Kingdom of Great Britain and Northern Ireland", + "Netherlands (Kingdom of the)", + "Switzerland" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "afa1298dc4505092", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40607:40618:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80776:80785:1'} The data starts from April 16 00:00 and ends on April 18 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) U (zonal) component of wind at 850 hPa values running below the 5th percentile climatology for the daily climatology for day 106? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show U (zonal) component of wind at 850 hPa values below the 5th percentile climatology for daily climatology for day 106: China(average -1.86 m/s)\nSyria(average -1.573 m/s)\nMorocco(average -0.6262 m/s)\nUkraine(average -1.241 m/s)\nKazakhstan(average -0.233 m/s)\nMongolia(average -1.77 m/s)\nRussia(average -1.005 m/s)\nTurkey(average -1.397 m/s)\nSpain(average -0.6262 m/s)\nRomania(average -3.133 m/s)\nHungary(average -1.71 m/s)\nSlovakia(average -1.332 m/s)\nGreece(average -0.1181 m/s)\nSierra Leone(average -0.357 m/s)\nGuinea(average -0.4036 m/s)\nLiberia(average -0.02278 m/s)\nIraq(average -1.414 m/s)\nRepublic of Serbia(average -2.062 m/s)\nMali(average -0.3958 m/s)\nSenegal(average -1.238 m/s)\nCroatia(average -2.471 m/s)\nSaudi Arabia(average -0.2535 m/s)\nBulgaria(average -1.494 m/s)\nKuwait(average -0.8649 m/s)\nAlgeria(average -1.484 m/s)\nEcuador(average -0.5632 m/s)\nColombia(average -0.8377 m/s)\nMoldova(average -2.024 m/s)\nUnited States of America(average -0.9955 m/s)\nCanada(average -1.645 m/s)\nPapua New Guinea(average -0.5951 m/s)\nMauritania(average -1.238 m/s)\nBaykonur Cosmodrome(average -0.2962 m/s)\nGreenland(average -1.078 m/s)\nNew Zealand(average -10.21 m/s)\nMadagascar(average -0.1836 m/s)\nJapan(average -2.623 m/s)\nFrench Polynesia(average -0.5087 m/s)\nCabo Verde(average -0.9483 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "u_component_of_wind", + 850 + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 106", + "quantile": "0.05", + "threshold_direction": "below", + "true_value": [ + "China", + "Syria", + "Morocco", + "Ukraine", + "Kazakhstan", + "Mongolia", + "Russia", + "Turkey", + "Spain", + "Romania", + "Hungary", + "Slovakia", + "Greece", + "Sierra Leone", + "Guinea", + "Liberia", + "Iraq", + "Republic of Serbia", + "Mali", + "Senegal", + "Croatia", + "Saudi Arabia", + "Bulgaria", + "Kuwait", + "Algeria", + "Ecuador", + "Colombia", + "Moldova", + "United States of America", + "Canada", + "Papua New Guinea", + "Mauritania", + "Baykonur Cosmodrome", + "Greenland", + "New Zealand", + "Madagascar", + "Japan", + "French Polynesia", + "Cabo Verde" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "f67ef89b9876c31f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80776:80785:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79305:79314:1'} The data starts from April 13 06:00 and ends on April 15 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) Mean sea level pressure values running above the 99th percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show Mean sea level pressure values above the 99th percentile climatology for all-time climatology: North Pacific Ocean(average 11.98 Pa)\nSouth Atlantic Ocean(average 97.32 Pa)\nGulf of Alaska(average 62.33 Pa)\nBristol Bay(average 191.9 Pa)\nBering Sea(average 191.8 Pa)\nBaird Inlet(average 230.8 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.99", + "threshold_direction": "above", + "true_value": [ + "North Pacific Ocean", + "South Atlantic Ocean", + "Gulf of Alaska", + "Bristol Bay", + "Bering Sea", + "Baird Inlet" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "557c58bd330384fd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79305:79314:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69510:69533:1'} The data starts from July 30 12:00 and ends on August 05 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Mean sea level pressure values? An exceedance is defined as a period of at least 72 consecutive hours where the Mean sea level pressure values exceed the 99th percentile climatology for the six-hourly climatology for day 211 at 12 UTC.", + "response": "The following water body(s) are currently experiencing an exceedance in Mean sea level pressure: SOUTHERN OCEAN(average 545.5 Pa)\nNorth Pacific Ocean(average 56.73 Pa)\nSouth Pacific Ocean(average 295 Pa)\nINDIAN OCEAN(average 607.4 Pa)\nSouth Atlantic Ocean(average 280 Pa)\nPhilippine Sea(average 63.51 Pa)\nSea of Okhotsk(average 77.39 Pa)\nWeddell Sea(average 475.5 Pa)\nJava Sea(average 31.16 Pa)\nYellow Sea(average 122.1 Pa)\nEast China Sea(average 129.7 Pa)\nBellingshausen Sea(average 321.1 Pa)\nDrake Passage(average 184.5 Pa)\nInner Sea(average 85.99 Pa)\nRonne Entrance(average 463.7 Pa)\nUchiura Bay(average 28.71 Pa)\nTsugaru Strait(average 79.33 Pa)\nEast Korea Bay(average 95.54 Pa)\nEstrecho de Magellanes(average 439.9 Pa)\nBahía Grande(average 454 Pa)\nLützow-Holm Bay(average 721 Pa)\nHangzhou Bay(average 206.9 Pa)\nGeorge VI Sound(average 556.1 Pa)\nSeno de Skyring(average 356.5 Pa)\nSeno Otway(average 289.9 Pa)\nBay Inútil(average 512.2 Pa)\nBali Sea(average 17.1 Pa)\nSelat Bali(average 28.52 Pa)\nFlores Sea(average 2.812 Pa)\nSavu Sea(average 2.812 Pa)\nYangtze River(average 177.8 Pa)\nSea of Japan(average 87.58 Pa)\nKorea Strait(average 130.6 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 211 at 12 UTC", + "quantile": "0.99", + "min_duration_days": 3, + "true_value": [ + "SOUTHERN OCEAN", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Sea of Okhotsk", + "Weddell Sea", + "Java Sea", + "Yellow Sea", + "East China Sea", + "Bellingshausen Sea", + "Drake Passage", + "Inner Sea", + "Ronne Entrance", + "Uchiura Bay", + "Tsugaru Strait", + "East Korea Bay", + "Estrecho de Magellanes", + "Bahía Grande", + "Lützow-Holm Bay", + "Hangzhou Bay", + "George VI Sound", + "Seno de Skyring", + "Seno Otway", + "Bay Inútil", + "Bali Sea", + "Selat Bali", + "Flores Sea", + "Savu Sea", + "Yangtze River", + "Sea of Japan", + "Korea Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "bb32e9024f380b30", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69510:69533:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40609:40621:1'} The data starts from October 18 06:00 and ends on October 21 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 24 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 24 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 24 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 24, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "20d3e5ac51ee3798", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40609:40621:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87066:87087:1'} The data starts from August 05 12:00 and ends on August 10 12:00. Based on the above data, answer the following question:", + "question": "In the 12 hours after the end of the given time window, when will Aegean Sea experience its highest U (zonal) component of wind at 600 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Aegean Sea will experience its highest U (zonal) component of wind at 600 hPa of 1.069 m/s 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "Aegean Sea", + "extremum_value": "1.0688201", + "target_variable": "u_component_of_wind_600", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "872e36f844a20c1e", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87066:87087:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78217:78222:1'} The data starts from July 15 06:00 and ends on July 16 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Surface pressure values? An exceedance is defined as a period of at least 24 consecutive hours where the Surface pressure values exceed the 99th percentile climatology for the JJA seasonal climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Surface pressure: Namibia(average 75.4 Pa)\nSouth Africa(average 61.14 Pa)\nFrench Southern and Antarctic Lands(average 12.51 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "JJA seasonal climatology", + "quantile": "0.99", + "min_duration_days": 1, + "true_value": [ + "Namibia", + "South Africa", + "French Southern and Antarctic Lands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "9a50a531b02b0eee", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78217:78222:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38684:38692:1'} The data starts from June 24 00:00 and ends on June 25 18:00. Based on the above data, answer the following question:", + "question": "What will the minimum U (zonal) component of wind at 250 hPa be in Gulf of Mannar, 6 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum U (zonal) component of wind at 250 hPa in Gulf of Mannar 6 hours after the given time window will be -19.81 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-19.810026", + "location": "Gulf of Mannar", + "target_variable": "u_component_of_wind_250", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9e8d858fcee39620", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38684:38692:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52761:52789:1'} The data starts from February 11 06:00 and ends on February 18 00:00. Based on the above data, answer the following question:", + "question": "What will the minimum Temperature at 150 hPa be in Africa, 18 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum Temperature at 150 hPa in Africa 18 hours after the given time window will be 202.9 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "202.87134", + "location": "Africa", + "target_variable": "temperature_150", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b7b39e78327c1f35", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52761:52789:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70810:70820:1'} The data starts from June 20 12:00 and ends on June 22 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Temperature at 250 hPa lies outside the climatological 1st–95th percentile envelope for the JJA seasonal climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 1st–95th percentile envelope for Temperature at 250 hPa during JJA seasonal climatology: Indonesia(average 0.3987 K)\nMalaysia(average 0.1732 K)\nChile(average -0.02681 K)\nIndia(average 0.9462 K)\nRussia(average 0.5555 K)\nVietnam(average 0.117 K)\nEast Timor(average 0.639 K)\nBrunei(average 0.205 K)\nCanada(average 0.06198 K)\nPapua New Guinea(average 0.5759 K)\nYemen(average 0.09515 K)\nAustralia(average 0.4144 K)\nPhilippines(average 0.128 K)\nSri Lanka(average 1.238 K)\nKiribati(average 0.07384 K)\nSaint Helena(average 1.181 K)\nSolomon Islands(average 0.2029 K)\nMaldives(average 0.4097 K)\nFederated States of Micronesia(average 0.1385 K)\nSpratly Islands(average 0.1307 K)\nAshmore and Cartier Islands(average 0.2616 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "temperature", + 250 + ], + "geofeature": "country", + "climatology_timescale_desc": "JJA seasonal climatology", + "lower_quantile": "0.01", + "upper_quantile": "0.95", + "true_value": [ + "Indonesia", + "Malaysia", + "Chile", + "India", + "Russia", + "Vietnam", + "East Timor", + "Brunei", + "Canada", + "Papua New Guinea", + "Yemen", + "Australia", + "Philippines", + "Sri Lanka", + "Kiribati", + "Saint Helena", + "Solomon Islands", + "Maldives", + "Federated States of Micronesia", + "Spratly Islands", + "Ashmore and Cartier Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "914be808e4affa3e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70810:70820:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80880:80887:1'} The data starts from May 12 00:00 and ends on May 13 12:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Niue experience its lowest V (meridional) component of wind at 300 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Niue will experience its lowest V (meridional) component of wind at 300 hPa of 2.98 m/s 30 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 30, + "location": "Niue", + "extremum_value": "2.9798698", + "target_variable": "v_component_of_wind_300", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "3cdb580f12c4b215", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80880:80887:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49226:49241:1'} The data starts from September 10 12:00 and ends on September 14 00:00. Based on the above data, answer the following question:", + "question": "In the 24 hours after the end of the given time window, when will Sulzberger Bay experience its highest U (zonal) component of wind at 50 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Sulzberger Bay will experience its highest U (zonal) component of wind at 50 hPa of 25.96 m/s 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "Sulzberger Bay", + "extremum_value": "25.957802", + "target_variable": "u_component_of_wind_50", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "1a553ece80cdfbf5", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49226:49241:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83518:83546:1'} The data starts from March 01 12:00 and ends on March 08 06:00. Based on the above data, answer the following question:", + "question": "What will the minimum 10-meter V component of wind be in Europe, 42 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum 10-meter V component of wind in Europe 42 hours after the given time window will be -14.72 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-14.720667", + "location": "Europe", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5079075f2b51df41", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83518:83546:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83487:83508:1'} The data starts from February 22 18:00 and ends on February 27 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter U component of wind differs from the all-time climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above 10-meter U component of wind values. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) exceed the ±3σ anomaly threshold for 10-meter U component of wind relative to the all-time climatology mean: Kiribati(average 8.363 m/s)\nCook Islands(average 11.9 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [ + "Kiribati", + "Cook Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "3ec5327d405a689e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83487:83508:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76139:76152:1'} The data starts from February 11 18:00 and ends on February 14 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) V (meridional) component of wind at 150 hPa lies outside the climatological 1st–99th percentile envelope for the DJF seasonal climatology. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–99th percentile envelope for V (meridional) component of wind at 150 hPa during DJF seasonal climatology: SOUTHERN OCEAN(average -0.4565 m/s)\nNorth Pacific Ocean(average 2.413 m/s)\nSouth Pacific Ocean(average 0.2117 m/s)\nINDIAN OCEAN(average 0.9751 m/s)\nSouth Atlantic Ocean(average -0.18 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 150 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "DJF seasonal climatology", + "lower_quantile": "0.01", + "upper_quantile": "0.99", + "true_value": [ + "SOUTHERN OCEAN", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "7bbb605e06ce6bfd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76139:76152:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89684:89703:1'} The data starts from May 21 00:00 and ends on May 25 12:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will Haiti experience its highest Mean sea level pressure? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Haiti will experience its highest Mean sea level pressure of 1.015e+05 Pa 48 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 48, + "location": "Haiti", + "extremum_value": "101496.53", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5fa35811899d3d7c", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89684:89703:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93189:93201:1'} The data starts from October 14 06:00 and ends on October 17 00:00. Based on the above data, answer the following question:", + "question": "What will the median Geopotential at 600 hPa be in Flores Sea, 36 hours after the end of the given time window?", + "response": "Based on the provided data, the median Geopotential at 600 hPa in Flores Sea 36 hours after the given time window will be 4.328e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "43280.812", + "location": "Flores Sea", + "target_variable": "geopotential_600", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c2b59a2f284bdcc4", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93189:93201:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67663:67691:1'} The data starts from April 24 18:00 and ends on May 01 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Specific humidity at 925 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the Specific humidity at 925 hPa values exceed the 90th percentile climatology for the daily climatology for day 114. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Specific humidity at 925 hPa: Indonesia(average 0.0001337 kg/kg)\nIndia(average 0.000455 kg/kg)\nChina(average 0.0001938 kg/kg)\nSomalia(average 5.078e-05 kg/kg)\nSyria(average 0.0004993 kg/kg)\nSomaliland(average 3.931e-05 kg/kg)\nFrance(average 0.0004127 kg/kg)\nUzbekistan(average 0.000388 kg/kg)\nKazakhstan(average 0.0003701 kg/kg)\nMongolia(average 4.533e-05 kg/kg)\nRussia(average 0.000248 kg/kg)\nGermany(average 0.0001325 kg/kg)\nLuxembourg(average 9.565e-07 kg/kg)\nBelgium(average 0.000106 kg/kg)\nKyrgyzstan(average 0.000388 kg/kg)\nIreland(average 0.0001089 kg/kg)\nUnited Kingdom(average 0.0005816 kg/kg)\nSudan(average 0.0009476 kg/kg)\nIraq(average 0.0006919 kg/kg)\nItaly(average 3.877e-05 kg/kg)\nIran(average 0.0003999 kg/kg)\nNetherlands(average 0.0001521 kg/kg)\nQatar(average 0.0001084 kg/kg)\nSaudi Arabia(average 0.0006819 kg/kg)\nPakistan(average 0.0006955 kg/kg)\nKuwait(average 0.0005214 kg/kg)\nEl Salvador(average 0.0001444 kg/kg)\nGuatemala(average 0.0001444 kg/kg)\nAlgeria(average 0.0004039 kg/kg)\nPortugal(average 0.0001565 kg/kg)\nJordan(average 0.0006923 kg/kg)\nUnited States of America(average 0.0002142 kg/kg)\nCanada(average 0.0002519 kg/kg)\nYemen(average 2.323e-05 kg/kg)\nBaykonur Cosmodrome(average 0.0003512 kg/kg)\nAustralia(average 9.87e-05 kg/kg)\nGreenland(average 2.829e-05 kg/kg)\nFiji(average 7.522e-05 kg/kg)\nFrench Southern and Antarctic Lands(average 0.0003073 kg/kg)\nJersey(average 0.000724 kg/kg)\nGuernsey(average 0.000724 kg/kg)\nBritish Indian Ocean Territory(average 0.0001455 kg/kg)\nSamoa(average 9.053e-05 kg/kg)\nMaldives(average 0.0002731 kg/kg)\nBahrain(average 0.0001084 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 925 + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 114", + "quantile": "0.9", + "min_duration_days": 2, + "true_value": [ + "Indonesia", + "India", + "China", + "Somalia", + "Syria", + "Somaliland", + "France", + "Uzbekistan", + "Kazakhstan", + "Mongolia", + "Russia", + "Germany", + "Luxembourg", + "Belgium", + "Kyrgyzstan", + "Ireland", + "United Kingdom", + "Sudan", + "Iraq", + "Italy", + "Iran", + "Netherlands", + "Qatar", + "Saudi Arabia", + "Pakistan", + "Kuwait", + "El Salvador", + "Guatemala", + "Algeria", + "Portugal", + "Jordan", + "United States of America", + "Canada", + "Yemen", + "Baykonur Cosmodrome", + "Australia", + "Greenland", + "Fiji", + "French Southern and Antarctic Lands", + "Jersey", + "Guernsey", + "British Indian Ocean Territory", + "Samoa", + "Maldives", + "Bahrain" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "678bcd7e7088453b", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67663:67691:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69469:69486:1'} The data starts from July 20 06:00 and ends on July 24 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Mean sea level pressure differs from the JJA seasonal climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Mean sea level pressure values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Mean sea level pressure anomalies were detected relative to the JJA seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "JJA seasonal climatology", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "5760be3e2d707c92", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69469:69486:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82723:82737:1'} The data starts from August 15 18:00 and ends on August 19 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Surface temperature values running above the 99th percentile climatology for the monthly climatology for August? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Surface temperature values above the 99th percentile climatology for monthly climatology for August: Russia(average 0.3133 K)\nNorway(average 0.5311 K)\nUnited States of America(average 0.1013 K)\nCanada(average 0.1144 K)\nMexico(average 0.3966 K)\nGreenland(average 0.5303 K)\nPhilippines(average 0.09351 K)\nKiribati(average 0.01294 K)\nUnited States Minor Outlying Islands(average 0.04105 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for August", + "quantile": "0.99", + "threshold_direction": "above", + "true_value": [ + "Russia", + "Norway", + "United States of America", + "Canada", + "Mexico", + "Greenland", + "Philippines", + "Kiribati", + "United States Minor Outlying Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "3ccb28e0c37a1eb3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82723:82737:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88849:88875:1'} The data starts from October 25 06:00 and ends on October 31 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 150 hPa lies outside the climatological 5th–95th percentile envelope for the daily climatology for day 298. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 5th–95th percentile envelope for Temperature at 150 hPa during daily climatology for day 298: Arctic Ocean(average -0.2599 K)\nSOUTHERN OCEAN(average 3.195 K)\nNorth Atlantic Ocean(average -0.4404 K)\nNorth Pacific Ocean(average -2.43 K)\nSouth Pacific Ocean(average -0.4782 K)\nINDIAN OCEAN(average 0.2643 K)\nSouth Atlantic Ocean(average -0.1025 K)\nTasman Sea(average -0.3618 K)\nArabian Sea(average 0.1883 K)\nGulf of Mexico(average -0.06984 K)\nGulf of Alaska(average -2.419 K)\nWeddell Sea(average 0.0878 K)\nMozambique Channel(average -0.5607 K)\nLaccadive Sea(average 0.2896 K)\nAmundsen Sea(average 0.3795 K)\nKara Sea(average -0.1243 K)\nLaptev Sea(average -0.07818 K)\nGulf of Aden(average 0.06258 K)\nCook Inlet(average -0.2495 K)\nGulf of Maine(average -0.3503 K)\nChesapeake Bay(average -0.3248 K)\nWrigley Gulf(average 2.791 K)\nSulzberger Bay(average 6.876 K)\nMcMurdo Sound(average 12.06 K)\nGulf of Gabès(average 0.4575 K)\nPrince William Sound(average -1.239 K)\nPrydz Bay(average 8.05 K)\nVincennes Bay(average 8.828 K)\nPorpoise Bay(average 6.728 K)\nDavis Sea(average 5.251 K)\nLützow-Holm Bay(average 1.073 K)\nDelaware Bay(average -0.2556 K)\nLong Island Sound(average -0.0844 K)\nAlbemarle Sound(average -0.6935 K)\nPamlico Sound(average -0.8975 K)\nLake Pontchartrain(average -0.1439 K)\nHecate Strait(average -0.3666 K)\nCordova Bay(average -0.7735 K)\nSargasso Sea(average -0.2553 K)\nMediterranean Sea(average 0.4575 K)\nRoss Sea(average 7.427 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "temperature", + 150 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 298", + "lower_quantile": "0.05", + "upper_quantile": "0.95", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Tasman Sea", + "Arabian Sea", + "Gulf of Mexico", + "Gulf of Alaska", + "Weddell Sea", + "Mozambique Channel", + "Laccadive Sea", + "Amundsen Sea", + "Kara Sea", + "Laptev Sea", + "Gulf of Aden", + "Cook Inlet", + "Gulf of Maine", + "Chesapeake Bay", + "Wrigley Gulf", + "Sulzberger Bay", + "McMurdo Sound", + "Gulf of Gabès", + "Prince William Sound", + "Prydz Bay", + "Vincennes Bay", + "Porpoise Bay", + "Davis Sea", + "Lützow-Holm Bay", + "Delaware Bay", + "Long Island Sound", + "Albemarle Sound", + "Pamlico Sound", + "Lake Pontchartrain", + "Hecate Strait", + "Cordova Bay", + "Sargasso Sea", + "Mediterranean Sea", + "Ross Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "1d0575e356a2afbe", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88849:88875:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87538:87566:1'} The data starts from December 01 12:00 and ends on December 08 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) Temperature at 250 hPa values running below the 1st percentile climatology for the six-hourly climatology for day 335 at 12 UTC? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show Temperature at 250 hPa values below the 1st percentile climatology for six-hourly climatology for day 335 at 12 UTC: North Atlantic Ocean(average -0.5184 K)\nNorth Pacific Ocean(average -1.223 K)\nINDIAN OCEAN(average -0.2452 K)\nSouth Atlantic Ocean(average -0.4258 K)\nPhilippine Sea(average -0.3158 K)\nQueen Charlotte Sound(average -0.1242 K)\nHecate Strait(average -0.1242 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "temperature", + 250 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 335 at 12 UTC", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "North Atlantic Ocean", + "North Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Queen Charlotte Sound", + "Hecate Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "21043956130852f3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87538:87566:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59354:59364:1'} The data starts from August 17 12:00 and ends on August 19 18:00. Based on the above data, answer the following question:", + "question": "What will the average 10-meter U component of wind be in South Pacific Ocean, 18 hours after the end of the given time window?", + "response": "Based on the provided data, the average 10-meter U component of wind in South Pacific Ocean 18 hours after the given time window will be 1.099 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "1.0987197", + "location": "South Pacific Ocean", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cbd2bf1e212ceaba", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59354:59364:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66727:66748:1'} The data starts from September 02 18:00 and ends on September 07 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 30 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 30 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 30 hours:\nA Tropical cyclone is expected in the country of Barbados in approximately the next 6 hours. Specifically the region(s) that might get affected are: St. Andrew, St. George, St. James, St. John, St. Joseph, St. Lucy, St. Michael, St. Peter, St. Philip, St. Thomas, Christ Church provinces\nA Tropical cyclone is expected in the country of Dominican Republic in approximately the next 30 hours. Specifically the region(s) that might get affected are: Azua, Baoruco, Barahona, Dajabon, Distrito Nacional, Duarte, El Seibo, Elias Pina, Espaillat, Hato Mayor, Independencia, La Altagracia, La Romana, La Vega, Maria Trinidad Sanches, Monsenor Nouel, Monte Cristi, Monte Plata, Pedernales, Peravia, Puerto Plata, Salcedo, Samana, San Cristobal, San José de Ocoa, San Juan, San Pedro de Macoris, Sanchez Ramirez, Santiago, Santiago Rodriguez, Santo Domingo, Valverde provinces\nA Tropical cyclone is expected in the country of Grenada in approximately the next 6 hours. Specifically the region(s) that might get affected are: Name Unknown, St. Andrew's, St. George's, St. John's, St. Mark's, St. Patrick's provinces\nA Tropical cyclone is expected in the country of Saint Lucia in approximately the next 6 hours. Specifically the region(s) that might get affected are: Area Under National Administra, Region Number 1, Region Number 2, Region Number 3, Region Number 4, Region Number 5, Region Number 6, Region Number 7, Region Number 8 provinces\nA Tropical cyclone is expected in the country of Trinidad and Tobago in approximately the next 30 hours. Specifically the region(s) that might get affected are: Caparo, Tumpuna villages (Couva/Tabaquite/Talparo province), Caroni village (Tunapuna/Piarco province)\nA Tropical cyclone is expected in the country of Saint Vincent and the Grenadines in approximately the next 6 hours. Specifically the region(s) that might get affected are: Charlotte, Grenadines, Saint Andrew, Saint David, Saint George, Saint Patrick provinces\nA Tropical cyclone is expected in the country of Venezuela (Bolivarian Republic of) in approximately the next 6 hours. Specifically the region(s) that might get affected are: Aragua, Distrito Capital, Miranda, Vargas provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Barbados", + "Dominican Republic", + "Grenada", + "Saint Lucia", + "Trinidad and Tobago", + "Saint Vincent and the Grenadines", + "Venezuela (Bolivarian Republic of)" + ], + "extreme_event_hours": 30, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "ae261d9f44619008", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66727:66748:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85184:85212:1'} The data starts from April 22 00:00 and ends on April 28 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Specific humidity at 850 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the Specific humidity at 850 hPa values exceed the 95th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Specific humidity at 850 hPa anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 850 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.95", + "min_duration_days": 3, + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ee77b4e4fbd3793c", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85184:85212:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47989:48012:1'} The data starts from November 06 06:00 and ends on November 11 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 30 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 30 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 30 hours:\nA Tropical cyclone is expected in the country of India in approximately the next 6 to 174 hours. Specifically the region(s) that might get affected are: Andhra Pradesh, Tamil Nadu, Pondicherry\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "India" + ], + "extreme_event_hours": 30, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "51f08e5f7303d0bc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47989:48012:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90845:90848:1'} The data starts from March 07 06:00 and ends on March 07 18:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will Cyprus experience its highest Temperature at 500 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Cyprus will experience its highest Temperature at 500 hPa of 256.5 K 24 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 24, + "location": "Cyprus", + "extremum_value": "256.4686", + "target_variable": "temperature_500", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "3d37bb238bbdcb49", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90845:90848:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70247:70259:1'} The data starts from January 30 18:00 and ends on February 02 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) 10-meter V component of wind values running below the 1st percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show 10-meter V component of wind values below the 1st percentile climatology for all-time climatology: Indonesia(average -0.3967 m/s)\nMalaysia(average -0.3633 m/s)\nBrunei(average -0.6618 m/s)\nCanada(average -0.1467 m/s)\nPapua New Guinea(average -0.08325 m/s)\nAustralia(average -0.2149 m/s)\nPhilippines(average -0.5882 m/s)\nSingapore(average -0.0145 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "Indonesia", + "Malaysia", + "Brunei", + "Canada", + "Papua New Guinea", + "Australia", + "Philippines", + "Singapore" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "2426ddb7cfa17675", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70247:70259:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82182:82200:1'} The data starts from April 02 12:00 and ends on April 06 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Geopotential at 1000 hPa differs from the daily climatology for day 92 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Geopotential at 1000 hPa values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Geopotential at 1000 hPa anomalies were detected relative to the daily climatology for day 92 baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "geopotential", + 1000 + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 92", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "de430d09753a173f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82182:82200:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91524:91533:1'} The data starts from August 24 00:00 and ends on August 26 00:00. Based on the above data, answer the following question:", + "question": "What will the average Mean sea level pressure be in Ashmore and Cartier Islands, 30 hours after the end of the given time window?", + "response": "Based on the provided data, the average Mean sea level pressure in Ashmore and Cartier Islands 30 hours after the given time window will be 1.01e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "101037.31", + "location": "Ashmore and Cartier Islands", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7faad244ba9a3c4f", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91524:91533:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35155:35160:1'} The data starts from January 23 18:00 and ends on January 24 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, there is no extreme weather event occuring.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "db96ed43b39413e0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35155:35160:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76292:76312:1'} The data starts from March 22 00:00 and ends on March 26 18:00. Based on the above data, answer the following question:", + "question": "What will the minimum Mean sea level pressure be in Clipperton Island, 6 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum Mean sea level pressure in Clipperton Island 6 hours after the given time window will be 1.008e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "100838.195", + "location": "Clipperton Island", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b25bac546d1efa7d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76292:76312:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83832:83846:1'} The data starts from May 19 00:00 and ends on May 22 06:00. Based on the above data, answer the following question:", + "question": "In the 36 hours after the end of the given time window, when will Faroe Islands experience its lowest Temperature at 50 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Faroe Islands will experience its lowest Temperature at 50 hPa of 221.8 K 36 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 36, + "location": "Faroe Islands", + "extremum_value": "221.82228", + "target_variable": "temperature_50", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "c3d53fdef0359c79", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83832:83846:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68564:68592:1'} The data starts from December 06 00:00 and ends on December 12 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Surface pressure values? An exceedance is defined as a period of at least 48 consecutive hours where the Surface pressure values exceed the 99th percentile climatology for the monthly climatology for December.", + "response": "The following water body(s) are currently experiencing an exceedance in Surface pressure: Gulf of Ob(average 61.11 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for December", + "quantile": "0.99", + "min_duration_days": 2, + "true_value": [ + "Gulf of Ob" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ffbc661e3fda41a5", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68564:68592:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77282:77301:1'} The data starts from November 24 12:00 and ends on November 29 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Specific humidity at 850 hPa values? An exceedance is defined as a period of at least 96 consecutive hours where the Specific humidity at 850 hPa values exceed the 95th percentile climatology for the daily climatology for day 328. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Specific humidity at 850 hPa: Indonesia(average 0.0002143 kg/kg)\nMalaysia(average 0.0002595 kg/kg)\nIndia(average 0.0002992 kg/kg)\nChina(average 0.0002603 kg/kg)\nEthiopia(average 0.0003292 kg/kg)\nKenya(average 0.0002153 kg/kg)\nSomaliland(average 8.949e-06 kg/kg)\nNamibia(average 0.001657 kg/kg)\nKazakhstan(average 0.000298 kg/kg)\nTajikistan(average 5.389e-05 kg/kg)\nMongolia(average 0.0001748 kg/kg)\nRussia(average 0.0002938 kg/kg)\nKyrgyzstan(average 4.812e-05 kg/kg)\nTunisia(average 0.0002279 kg/kg)\nZambia(average 8.111e-05 kg/kg)\nDjibouti(average 8.949e-06 kg/kg)\nEritrea(average 0.0005466 kg/kg)\nIran(average 0.0002949 kg/kg)\nAngola(average 0.001947 kg/kg)\nSaudi Arabia(average 0.0003838 kg/kg)\nZimbabwe(average 0.000156 kg/kg)\nPakistan(average 0.0001514 kg/kg)\nAlgeria(average 0.0001666 kg/kg)\nAfghanistan(average 0.0001602 kg/kg)\nYemen(average 0.0003037 kg/kg)\nAustralia(average 0.0003003 kg/kg)\nMadagascar(average 0.0003056 kg/kg)\nSeychelles(average 0.0006847 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 850 + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 328", + "quantile": "0.95", + "min_duration_days": 4, + "true_value": [ + "Indonesia", + "Malaysia", + "India", + "China", + "Ethiopia", + "Kenya", + "Somaliland", + "Namibia", + "Kazakhstan", + "Tajikistan", + "Mongolia", + "Russia", + "Kyrgyzstan", + "Tunisia", + "Zambia", + "Djibouti", + "Eritrea", + "Iran", + "Angola", + "Saudi Arabia", + "Zimbabwe", + "Pakistan", + "Algeria", + "Afghanistan", + "Yemen", + "Australia", + "Madagascar", + "Seychelles" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "79414ce86c5988c7", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77282:77301:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78197:78217:1'} The data starts from July 10 06:00 and ends on July 15 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) U (zonal) component of wind at 600 hPa lies outside the climatological 10th–95th percentile envelope for the all-time climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–95th percentile envelope for U (zonal) component of wind at 600 hPa during all-time climatology: Indonesia(average -0.5494 m/s)\nChile(average -1.15 m/s)\nBolivia(average -0.1035 m/s)\nPeru(average -0.3819 m/s)\nArgentina(average 0.308 m/s)\nChina(average -2.106 m/s)\nEthiopia(average -0.1504 m/s)\nFrance(average 0.4444 m/s)\nMorocco(average -1.87 m/s)\nWestern Sahara(average -2.322 m/s)\nNamibia(average 0.728 m/s)\nOman(average -0.3512 m/s)\nKazakhstan(average -2.362 m/s)\nTajikistan(average 0.1188 m/s)\nBrazil(average 0.7018 m/s)\nMongolia(average -1.545 m/s)\nRussia(average -0.4674 m/s)\nUnited Arab Emirates(average -0.02134 m/s)\nSpain(average 1.407 m/s)\nKyrgyzstan(average 0.1188 m/s)\nLibya(average -4.229 m/s)\nTunisia(average -4.474 m/s)\nZambia(average 0.7345 m/s)\nItaly(average 0.2793 m/s)\nIran(average -0.3641 m/s)\nMali(average -1.471 m/s)\nAngola(average 0.2763 m/s)\nSaudi Arabia(average -0.08722 m/s)\nBotswana(average 0.7794 m/s)\nZimbabwe(average 0.9447 m/s)\nPakistan(average -0.4275 m/s)\nChad(average -1.541 m/s)\nAlgeria(average -4.937 m/s)\nMozambique(average 0.6962 m/s)\nAndorra(average 0.4318 m/s)\nParaguay(average 0.3229 m/s)\nNiger(average -3.321 m/s)\nUnited States of America(average -1.152 m/s)\nMexico(average -1.701 m/s)\nPapua New Guinea(average -2.414 m/s)\nEgypt(average -2.119 m/s)\nMauritania(average -1.774 m/s)\nVatican(average 0.1599 m/s)\nAustralia(average 1.473 m/s)\nGreenland(average -0.1273 m/s)\nMadagascar(average -0.1217 m/s)\nThe Bahamas(average -1.631 m/s)\nJapan(average -0.4682 m/s)\nPitcairn Islands(average 1.432 m/s)\nFrench Polynesia(average -2.359 m/s)\nFrench Southern and Antarctic Lands(average -0.1526 m/s)\nSeychelles(average -0.1436 m/s)\nKiribati(average -0.9327 m/s)\nUnited States Minor Outlying Islands(average -1.108 m/s)\nBermuda(average -2.289 m/s)\nComoros(average -0.3001 m/s)\nCabo Verde(average -0.7114 m/s)\nSolomon Islands(average -3.665 m/s)\nNauru(average -0.7406 m/s)\nFederated States of Micronesia(average -1.446 m/s)\nFalkland Islands(average -0.1916 m/s)\nVanuatu(average -0.826 m/s)\nNiue(average 0.7304 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "u_component_of_wind", + 600 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "Indonesia", + "Chile", + "Bolivia", + "Peru", + "Argentina", + "China", + "Ethiopia", + "France", + "Morocco", + "Western Sahara", + "Namibia", + "Oman", + "Kazakhstan", + "Tajikistan", + "Brazil", + "Mongolia", + "Russia", + "United Arab Emirates", + "Spain", + "Kyrgyzstan", + "Libya", + "Tunisia", + "Zambia", + "Italy", + "Iran", + "Mali", + "Angola", + "Saudi Arabia", + "Botswana", + "Zimbabwe", + "Pakistan", + "Chad", + "Algeria", + "Mozambique", + "Andorra", + "Paraguay", + "Niger", + "United States of America", + "Mexico", + "Papua New Guinea", + "Egypt", + "Mauritania", + "Vatican", + "Australia", + "Greenland", + "Madagascar", + "The Bahamas", + "Japan", + "Pitcairn Islands", + "French Polynesia", + "French Southern and Antarctic Lands", + "Seychelles", + "Kiribati", + "United States Minor Outlying Islands", + "Bermuda", + "Comoros", + "Cabo Verde", + "Solomon Islands", + "Nauru", + "Federated States of Micronesia", + "Falkland Islands", + "Vanuatu", + "Niue" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "3044923c6db0c418", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78197:78217:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91518:91542:1'} The data starts from August 22 12:00 and ends on August 28 06:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Caspian Sea experience its highest Specific humidity at 700 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Caspian Sea will experience its highest Specific humidity at 700 hPa of 0.006847 kg/kg 30 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 30, + "location": "Caspian Sea", + "extremum_value": "0.0068466016", + "target_variable": "specific_humidity_700", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "27631ec306d62bf9", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91518:91542:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75649:75667:1'} The data starts from October 12 06:00 and ends on October 16 12:00. Based on the above data, answer the following question:", + "question": "What will the maximum 10-meter V component of wind be in Benin, 30 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum 10-meter V component of wind in Benin 30 hours after the given time window will be 3.257 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "3.257071", + "location": "Benin", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bb77b0b0ab5fa6c5", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75649:75667:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65254:65266:1'} The data starts from August 31 12:00 and ends on September 03 06:00. Based on the above data, answer the following question:", + "question": "In the 42 hours after the end of the given time window, when will Antarctica experience its highest Temperature at 200 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Antarctica will experience its highest Temperature at 200 hPa of 217.3 K 30 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 30, + "location": "Antarctica", + "extremum_value": "217.29764", + "target_variable": "temperature_200", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "dc1b340255b3f364", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65254:65266:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33474:33492:1'} The data starts from November 29 12:00 and ends on December 03 18:00. Based on the above data, answer the following question:", + "question": "What will the average U (zonal) component of wind at 400 hPa be in Asia, 48 hours after the end of the given time window?", + "response": "Based on the provided data, the average U (zonal) component of wind at 400 hPa in Asia 48 hours after the given time window will be 13.73 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "13.732878", + "location": "Asia", + "target_variable": "u_component_of_wind_400", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9ff49d2300ac6eb1", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33474:33492:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72026:72032:1'} The data starts from April 19 12:00 and ends on April 20 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 1000 hPa differs from the six-hourly climatology for day 110 at 12 UTC mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Temperature at 1000 hPa values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for Temperature at 1000 hPa relative to the six-hourly climatology for day 110 at 12 UTC mean: SOUTHERN OCEAN(average -11.16 K)\nNorth Pacific Ocean(average -4.728 K)\nINDIAN OCEAN(average -2.87 K)\nSouth Atlantic Ocean(average -7.213 K)\nGulf of Alaska(average -5.013 K)\nJava Sea(average -1.571 K)\nQueen Charlotte Sound(average -5.447 K)\nPrince William Sound(average -4.135 K)\nSmith Sound(average -6.933 K)\nQueen Charlotte Strait(average -5.351 K)\nHecate Strait(average -5.131 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "temperature", + 1000 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 110 at 12 UTC", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "SOUTHERN OCEAN", + "North Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Gulf of Alaska", + "Java Sea", + "Queen Charlotte Sound", + "Prince William Sound", + "Smith Sound", + "Queen Charlotte Strait", + "Hecate Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "a9cdc0f6b3d619cf", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72026:72032:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89505:89533:1'} The data starts from April 06 06:00 and ends on April 13 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Fiji. Specifically the region(s) being affected are: Kadavvu, Southern Lau, Matuku, Vatulele\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Fiji" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "5a504bbb9d8039cb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89505:89533:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54309:54314:1'} The data starts from March 04 06:00 and ends on March 05 06:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will St. Helena Bay experience its lowest Temperature at 1000 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, St. Helena Bay will experience its lowest Temperature at 1000 hPa of 290.9 K 24 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 24, + "location": "St. Helena Bay", + "extremum_value": "290.9464", + "target_variable": "temperature_1000", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a84bb93f6b23a5dd", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54309:54314:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73122:73127:1'} The data starts from January 18 12:00 and ends on January 19 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) 10-meter U component of wind values running below the 10th percentile climatology for the daily climatology for day 18? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show 10-meter U component of wind values below the 10th percentile climatology for daily climatology for day 18: Arctic Ocean(average -0.6802 m/s)\nSOUTHERN OCEAN(average -1.803 m/s)\nNorth Atlantic Ocean(average -1.533 m/s)\nNorth Pacific Ocean(average -0.7865 m/s)\nSouth Pacific Ocean(average -1.657 m/s)\nINDIAN OCEAN(average -1.015 m/s)\nSouth Atlantic Ocean(average -1.906 m/s)\nPhilippine Sea(average -0.7079 m/s)\nGreat Barrier Reef(average -0.1689 m/s)\nBay of Bengal(average -0.2735 m/s)\nSouth China Sea(average -0.1257 m/s)\nArabian Sea(average -0.2387 m/s)\nSea of Okhotsk(average -1.126 m/s)\nPersian Gulf(average -0.2627 m/s)\nNorwegian Sea(average -1.319 m/s)\nGreenland Sea(average -0.7456 m/s)\nBanda Sea(average -1.622 m/s)\nMozambique Channel(average -1.528 m/s)\nBaltic Sea(average -0.9494 m/s)\nBarents Sea(average -0.8318 m/s)\nNorth Sea(average -0.5477 m/s)\nAndaman Sea(average -0.3064 m/s)\nEast China Sea(average -0.1814 m/s)\nArafura Sea(average -1.482 m/s)\nLaccadive Sea(average -0.2292 m/s)\nJames Bay(average -0.3237 m/s)\nGulf of Finland(average -1.137 m/s)\nGulf of Bothnia(average -0.9276 m/s)\nThe North Western Passages(average -0.4672 m/s)\nGulf of Saint Lawrence(average -0.7844 m/s)\nBismarck Sea(average -0.1048 m/s)\nBay of Fundy(average -0.8054 m/s)\nStrait of Malacca(average -0.1931 m/s)\nStrait of Singapore(average -0.1222 m/s)\nCeram Sea(average -1.501 m/s)\nGulf of Maine(average -0.05369 m/s)\nBering Sea(average -0.2183 m/s)\nLincoln Sea(average -0.0541 m/s)\nVestfjorden(average -1.827 m/s)\nSkagerrak(average -1.887 m/s)\nSognefjorden(average -1.034 m/s)\nTrondheimsfjorden(average -1.376 m/s)\nUda Bay(average -0.7446 m/s)\nTatar Strait(average -0.2469 m/s)\nGulf of Papua(average -0.2179 m/s)\nGulf of Riga(average -0.8168 m/s)\nPorpoise Bay(average -1.146 m/s)\nLützow-Holm Bay(average -1.088 m/s)\nSt. Helena Bay(average -0.2152 m/s)\nBoknafjorden(average -0.5477 m/s)\nGulf of Martaban(average -0.4683 m/s)\nGulf of Sakhalin(average -0.4449 m/s)\nKangertittivaq(average -0.2242 m/s)\nSaint Lawrence River(average -0.5685 m/s)\nDenmark Strait(average -0.9775 m/s)\nHall Basin(average -0.04984 m/s)\nBras d'Or Lake(average -0.06439 m/s)\nKaliningrad(average -0.8044 m/s)\nMurchison Sound(average -0.2157 m/s)\nRobeson Channel(average -0.04239 m/s)\nSargasso Sea(average -0.3063 m/s)\nSan Francisco Bay(average -0.7211 m/s)\nMonterey Bay(average -0.7211 m/s)\nGulf of Anadyr'(average -0.2183 m/s)\nRoss Sea(average -0.2789 m/s)\nCoral Sea(average -0.7015 m/s)\nSea of Japan(average -0.2799 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 18", + "quantile": "0.1", + "threshold_direction": "below", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Great Barrier Reef", + "Bay of Bengal", + "South China Sea", + "Arabian Sea", + "Sea of Okhotsk", + "Persian Gulf", + "Norwegian Sea", + "Greenland Sea", + "Banda Sea", + "Mozambique Channel", + "Baltic Sea", + "Barents Sea", + "North Sea", + "Andaman Sea", + "East China Sea", + "Arafura Sea", + "Laccadive Sea", + "James Bay", + "Gulf of Finland", + "Gulf of Bothnia", + "The North Western Passages", + "Gulf of Saint Lawrence", + "Bismarck Sea", + "Bay of Fundy", + "Strait of Malacca", + "Strait of Singapore", + "Ceram Sea", + "Gulf of Maine", + "Bering Sea", + "Lincoln Sea", + "Vestfjorden", + "Skagerrak", + "Sognefjorden", + "Trondheimsfjorden", + "Uda Bay", + "Tatar Strait", + "Gulf of Papua", + "Gulf of Riga", + "Porpoise Bay", + "Lützow-Holm Bay", + "St. Helena Bay", + "Boknafjorden", + "Gulf of Martaban", + "Gulf of Sakhalin", + "Kangertittivaq", + "Saint Lawrence River", + "Denmark Strait", + "Hall Basin", + "Bras d'Or Lake", + "Kaliningrad", + "Murchison Sound", + "Robeson Channel", + "Sargasso Sea", + "San Francisco Bay", + "Monterey Bay", + "Gulf of Anadyr'", + "Ross Sea", + "Coral Sea", + "Sea of Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d05850c58d37bfb1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73122:73127:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81108:81121:1'} The data starts from July 08 00:00 and ends on July 11 00:00. Based on the above data, answer the following question:", + "question": "What will the minimum U (zonal) component of wind at 700 hPa be in Tunisia, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum U (zonal) component of wind at 700 hPa in Tunisia 12 hours after the given time window will be 8.682 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "8.682377", + "location": "Tunisia", + "target_variable": "u_component_of_wind_700", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3ea4219baec5d14f", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81108:81121:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72196:72203:1'} The data starts from June 01 00:00 and ends on June 02 12:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Queen Charlotte Sound experience its lowest Geopotential at 600 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Queen Charlotte Sound will experience its lowest Geopotential at 600 hPa of 4.024e+04 m²/s² 24 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 24, + "location": "Queen Charlotte Sound", + "extremum_value": "40238.035", + "target_variable": "geopotential_600", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "1898022035a2dcc5", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72196:72203:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68010:68017:1'} The data starts from July 20 12:00 and ends on July 22 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Mean sea level pressure values running below the 1st percentile climatology for the daily climatology for day 201? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Mean sea level pressure values below the 1st percentile climatology for daily climatology for day 201: Russia(average -30.23 Pa)\nPoland(average -32.31 Pa)\nSudan(average -7.195 Pa)\nUnited States of America(average -70.08 Pa)\nCanada(average -138 Pa)\nMexico(average -62.79 Pa)\nNorthern Mariana Islands(average -45.02 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 201", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "Russia", + "Poland", + "Sudan", + "United States of America", + "Canada", + "Mexico", + "Northern Mariana Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "baa053d1eff18eed", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68010:68017:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50116:50122:1'} The data starts from April 21 00:00 and ends on April 22 06:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Ukraine experience its lowest 10-meter V component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Ukraine will experience its lowest 10-meter V component of wind of -4.737 m/s 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "Ukraine", + "extremum_value": "-4.736962", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "46bfaa5621a840d1", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50116:50122:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74009:74019:1'} The data starts from August 28 06:00 and ends on August 30 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) U (zonal) component of wind at 700 hPa differs from the monthly climatology for August mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below U (zonal) component of wind at 700 hPa values.", + "response": "Based on the provided data, no significant U (zonal) component of wind at 700 hPa anomalies were detected relative to the monthly climatology for August baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "u_component_of_wind", + 700 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for August", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "cfb94c6f44ab3098", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74009:74019:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33039:33057:1'} The data starts from August 12 18:00 and ends on August 17 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Philippines.\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Philippines" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "a0c7190167fb046c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33039:33057:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69215:69238:1'} The data starts from May 17 18:00 and ends on May 23 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in 10-meter V component of wind values? An exceedance is defined as a period of at least 96 consecutive hours where the 10-meter V component of wind values exceed the 95th percentile climatology for the monthly climatology for May. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in 10-meter V component of wind: France(average 0.05582 m/s)\nOman(average 0.4508 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for May", + "quantile": "0.95", + "min_duration_days": 4, + "true_value": [ + "France", + "Oman" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "dc876749ef3a978a", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69215:69238:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86384:86396:1'} The data starts from February 16 00:00 and ends on February 18 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Surface pressure lies outside the climatological 1st–90th percentile envelope for the DJF seasonal climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 1st–90th percentile envelope for Surface pressure during DJF seasonal climatology: Chile(average 11.91 Pa)\nBolivia(average 8.779 Pa)\nPeru(average 9.577 Pa)\nArgentina(average 72.89 Pa)\nFrance(average 73.74 Pa)\nGuyana(average 18.09 Pa)\nSaint Martin(average 103.6 Pa)\nSint Maarten(average 103.6 Pa)\nUzbekistan(average 90.41 Pa)\nKazakhstan(average 162.2 Pa)\nTajikistan(average 22.68 Pa)\nBrazil(average 69.32 Pa)\nRussia(average 191.1 Pa)\nNorway(average 518.3 Pa)\nKyrgyzstan(average 40.99 Pa)\nNetherlands(average 95.51 Pa)\nHaiti(average 118 Pa)\nDominican Republic(average 128.2 Pa)\nUS Naval Base Guantanamo Bay(average 107.2 Pa)\nCuba(average 72.53 Pa)\nHonduras(average 19 Pa)\nEcuador(average 3.688 Pa)\nColombia(average 18.34 Pa)\nParaguay(average 41.86 Pa)\nTurkmenistan(average 61.44 Pa)\nUnited States of America(average 399.9 Pa)\nCanada(average 212.2 Pa)\nVenezuela(average 42.69 Pa)\nBaykonur Cosmodrome(average 265.8 Pa)\nGreenland(average 137.6 Pa)\nCuraçao(average 85.05 Pa)\nAruba(average 75.92 Pa)\nThe Bahamas(average 84.24 Pa)\nTurks and Caicos Islands(average 145.5 Pa)\nTrinidad and Tobago(average 55.48 Pa)\nGrenada(average 65.87 Pa)\nSaint Vincent and the Grenadines(average 63.23 Pa)\nBarbados(average 50.12 Pa)\nSaint Lucia(average 66.07 Pa)\nDominica(average 64.18 Pa)\nUnited States Minor Outlying Islands(average 111.8 Pa)\nMontserrat(average 89.51 Pa)\nAntigua and Barbuda(average 89.11 Pa)\nSaint Kitts and Nevis(average 89.11 Pa)\nUnited States Virgin Islands(average 112.8 Pa)\nSaint Barthelemy(average 88.71 Pa)\nPuerto Rico(average 121.4 Pa)\nAnguilla(average 96.17 Pa)\nBritish Virgin Islands(average 108.2 Pa)\nJamaica(average 101 Pa)\nCayman Islands(average 61.37 Pa)\nBajo Nuevo Bank (Petrel Is.)(average 62.82 Pa)\nSerranilla Bank(average 73.27 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "DJF seasonal climatology", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "Chile", + "Bolivia", + "Peru", + "Argentina", + "France", + "Guyana", + "Saint Martin", + "Sint Maarten", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Brazil", + "Russia", + "Norway", + "Kyrgyzstan", + "Netherlands", + "Haiti", + "Dominican Republic", + "US Naval Base Guantanamo Bay", + "Cuba", + "Honduras", + "Ecuador", + "Colombia", + "Paraguay", + "Turkmenistan", + "United States of America", + "Canada", + "Venezuela", + "Baykonur Cosmodrome", + "Greenland", + "Curaçao", + "Aruba", + "The Bahamas", + "Turks and Caicos Islands", + "Trinidad and Tobago", + "Grenada", + "Saint Vincent and the Grenadines", + "Barbados", + "Saint Lucia", + "Dominica", + "United States Minor Outlying Islands", + "Montserrat", + "Antigua and Barbuda", + "Saint Kitts and Nevis", + "United States Virgin Islands", + "Saint Barthelemy", + "Puerto Rico", + "Anguilla", + "British Virgin Islands", + "Jamaica", + "Cayman Islands", + "Bajo Nuevo Bank (Petrel Is.)", + "Serranilla Bank" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "be8ad08b6f67d44b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86384:86396:1" + } + } +] \ No newline at end of file diff --git a/level2a_part2.json b/level2a_part2.json new file mode 100644 index 0000000000000000000000000000000000000000..a97bf0d8b0e18be7b38fc6e5917d587cf72338cf --- /dev/null +++ b/level2a_part2.json @@ -0,0 +1,4668 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54496:54509:1'} The data starts from April 20 00:00 and ends on April 23 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Storm (General) currently happening? Specify the affected countries or regions, or respond 'No Storm (General) detected.'", + "response": "Based on the provided data, the Storm (General) is affecting: Bangladesh", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Bangladesh" + ], + "target_disaster": "Storm (General)", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "59b96b468b2f1956", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54496:54509:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71885:71894:1'} The data starts from March 15 06:00 and ends on March 17 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Temperature at 850 hPa differs from the monthly climatology for March mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Temperature at 850 hPa values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Temperature at 850 hPa anomalies were detected relative to the monthly climatology for March baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "temperature", + 850 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for March", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d7fa3b26670d47f1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71885:71894:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45518:45537:1'} The data starts from February 26 12:00 and ends on March 03 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Storm (General) is occuring in the country of Austria.\nA Storm (General) is occuring in the country of Belgium.\nA Storm (General) is occuring in the country of Switzerland.\nA Storm (General) is occuring in the country of Germany.\nA Storm (General) is occuring in the country of Denmark.\nA Storm (General) is occuring in the country of France.\nA Storm (General) is occuring in the country of United Kingdom of Great Britain and Northern Ireland.\nA Storm (General) is occuring in the country of Greece.\nA Storm (General) is occuring in the country of Italy.\nA Storm (General) is occuring in the country of Luxembourg.\nA Storm (General) is occuring in the country of Netherlands (Kingdom of the).\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Austria", + "Belgium", + "Switzerland", + "Germany", + "Denmark", + "France", + "United Kingdom of Great Britain and Northern Ireland", + "Greece", + "Italy", + "Luxembourg", + "Netherlands (Kingdom of the)" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "9d877f2fbf151d80", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45518:45537:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33703:33731:1'} The data starts from January 25 18:00 and ends on February 01 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Madagascar. Specifically the region(s) being affected are: Sambava, Antalaha, Tulear, Morondova, Miandrivazo, Morombe\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Madagascar" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "7ee349f02e60acdc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33703:33731:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36644:36659:1'} The data starts from January 31 00:00 and ends on February 03 12:00. Based on the above data, answer the following question:", + "question": "In the 42 hours after the end of the given time window, when will Antarctica experience its highest Surface temperature? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Antarctica will experience its highest Surface temperature of 274.9 K 36 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 36, + "location": "Antarctica", + "extremum_value": "274.85846", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "41d1b6c7820dbe00", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36644:36659:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92210:92235:1'} The data starts from February 11 12:00 and ends on February 17 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 12 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 12 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 12 hours:\nA Tropical cyclone is expected in the country of Mozambique in approximately the next 12 hours. Specifically the region(s) that might get affected are: Zambezia, Nampula, Niassa,Tete, Sofala, and Manica provinces\nA Extra-tropical storm is expected in the country of Belgium in approximately the next 12 hours\nA Extra-tropical storm is expected in the country of Germany in approximately the next 12 hours\nA Extra-tropical storm is expected in the country of France in approximately the next 12 hours. Specifically the region(s) that might get affected are: Pas-de-Calais, Nord, Seine-Maritime, Manche departments\nA Extra-tropical storm is expected in the country of United Kingdom of Great Britain and Northern Ireland in approximately the next 12 hours\nA Extra-tropical storm is expected in the country of Ireland in approximately the next 12 hours\nA Extra-tropical storm is expected in the country of Netherlands (Kingdom of the) in approximately the next 12 hours\nA Extra-tropical storm is expected in the country of Poland in approximately the next 12 hours\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Mozambique", + "Belgium", + "Germany", + "France", + "United Kingdom of Great Britain and Northern Ireland", + "Ireland", + "Netherlands (Kingdom of the)", + "Poland" + ], + "extreme_event_hours": 12, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "706eed46617c418b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92210:92235:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61674:61701:1'} The data starts from March 19 12:00 and ends on March 26 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Storm (General) currently happening? Specify the affected countries or regions, or respond 'No Storm (General) detected.'", + "response": "Based on the provided data, the Storm (General) is affecting: United States of America", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "United States of America" + ], + "target_disaster": "Storm (General)", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "bb71b3c476ecb057", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61674:61701:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74903:74926:1'} The data starts from April 08 18:00 and ends on April 14 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Bangladesh. Specifically the region(s) being affected are: Rangpur, Dinajpur, Nilphamari, Lalmonirhat, Kurigram, Gaibandha districts (Rangpur province), Sirajganj, Bogra districts (Rajshahi province)\nA Tropical cyclone is occuring in the country of India. Specifically the region(s) being affected are: Bihar, West Bengal, Assam provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Bangladesh", + "India" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "e0382c7f69ec0523", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74903:74926:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65024:65040:1'} The data starts from July 05 00:00 and ends on July 08 18:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Chile experience its lowest U (zonal) component of wind at 200 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Chile will experience its lowest U (zonal) component of wind at 200 hPa of 9.501 m/s 30 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 30, + "location": "Chile", + "extremum_value": "9.501005", + "target_variable": "u_component_of_wind_200", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "36eb8c21f2067c74", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65024:65040:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88308:88319:1'} The data starts from June 12 00:00 and ends on June 14 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, there is no extreme weather event occuring.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "a27c7af3b0f25624", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88308:88319:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63604:63608:1'} The data starts from July 15 00:00 and ends on July 15 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Japan. Specifically the region(s) being affected are: Okinawa, Kagosima, Isikawa provinces\nA Blizzard/Winter storm is occuring in the country of South Africa. Specifically the region(s) being affected are: Eastern Cape, KwaZulu-Natal provinces\nA Storm (General) is occuring in the country of Philippines. Specifically the region(s) being affected are: Region IV (Southern Tagalog), Region VI (Western Visayas) provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Japan", + "South Africa", + "Philippines" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "dc59011d494c4204", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63604:63608:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78463:78477:1'} The data starts from September 14 18:00 and ends on September 18 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in V (meridional) component of wind at 400 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the V (meridional) component of wind at 400 hPa values exceed the 99th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in V (meridional) component of wind at 400 hPa: China(average 1.085 m/s)\nNorth Korea(average 1.085 m/s)\nRussia(average 1.085 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "v_component_of_wind", + 400 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.99", + "min_duration_days": 3, + "true_value": [ + "China", + "North Korea", + "Russia" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "060d2ffd4cb7de02", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78463:78477:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85735:85736:1'} The data corresponds to corresponds to a snapshot on September 06 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 36 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 36 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 36 hours:\nA Tropical cyclone is expected in the country of Bahamas in approximately the next 30 to 54 hours. Specifically the region(s) that might get affected are: Inagua, Mayaguana, Crooked Island, Acklins, Long Cay, Ragged Island, San Salvador, Bimini\nA Tropical cyclone is expected in the country of Saint Barthélemy in approximately the next 30 to 54 hours\nA Tropical cyclone is expected in the country of Barbados in approximately the next 30 to 54 hours. Specifically the region(s) that might get affected are: St. David's Christ Church, Weston St. James, Cattlewash St Joseph\nA Tropical cyclone is expected in the country of Cuba in approximately the next 30 to 78 hours. Specifically the region(s) that might get affected are: Habana del Este, Habana Vieja, Centro Habana, Plaza, Playa municipalities (Habana province); Sierra de Cúbitas, Florida, Nuevitas, Esmeralda municipalities (Camagüey province); Martí, Cárdenas, Matanzas, Los Arabos Unión de Reyes municipalities (Matanzas province); Jobabo, Manatí, Jesús Mendez, Puerto Padre municipalities (Las Tunas province); Gibara, Frank Paí, Banes, Mayarí, Rafael Freyre municipalities (Holguin province); Encrucijada Caibaríen, Sagüa la Grande, Santo Domingo, Santa Clara municipalities (Villa Clara province); Bolivia, Moron, Chambas, Venezuela municipalities (Ciego Avila province), Pinar del Rio, Matanzas, Artemisa, Mayabeque, Cienfuegos, Sancti Spiritus, Granma, Guantamo\nA Tropical cyclone is expected in the country of Haiti in approximately the next 6 to 30 hours. Specifically the region(s) that might get affected are: Caracol (Trou du Nord), Feroer, Malfety (Fort Liberte), Ouanaminthe, Cap Haitien; Port de Paix, St. Louis de Nord, Anse-a-Foleur, Jean Rabel, Baie-de-Henne, Detipotpe, Ile de la Tortue, Bassin Blue, Chansolme, Mole St. Nicolas which were supported\nA Tropical cyclone is expected in the country of Saint Martin (French Part) in approximately the next 30 to 54 hours\nA Tropical cyclone is expected in the country of Sint Maarten (Dutch part) in approximately the next 30 to 54 hours\nA Tropical cyclone is expected in the country of Turks and Caicos Islands in approximately the next 30 to 54 hours. Specifically the region(s) that might get affected are: South Caicos, Salt Cay, Grand Turks, Provo (Five keys)\nA Tropical cyclone is expected in the country of British Virgin Islands in approximately the next 30 to 54 hours. Specifically the region(s) that might get affected are: Anagoda, Tortola, Necker Island (Virgin Gorda)\nA Tropical cyclone is expected in the country of United States Virgin Islands in approximately the next 6 hours. Specifically the region(s) that might get affected are: St. John, St. Thomas, most of St. Croix\nA Tropical cyclone is expected in the country of Mexico in approximately the next 6 to 30 hours. Specifically the region(s) that might get affected are: Tecolutla, Xalapa, Jalcomulco\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Bahamas", + "Saint Barthélemy", + "Barbados", + "Cuba", + "Haiti", + "Saint Martin (French Part)", + "Sint Maarten (Dutch part)", + "Turks and Caicos Islands", + "British Virgin Islands", + "United States Virgin Islands", + "Mexico" + ], + "extreme_event_hours": 36, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "3d8582f5392cd112", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85735:85736:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81821:81822:1'} The data corresponds to corresponds to a snapshot on January 02 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Blizzard/Winter Storm currently happening? Specify the affected countries or regions, or respond 'No Blizzard/Winter Storm detected.'", + "response": "No Blizzard/Winter Storm detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Blizzard/Winter Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "954636050598ccba", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81821:81822:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33551:33560:1'} The data starts from December 18 18:00 and ends on December 20 18:00. Based on the above data, answer the following question:", + "question": "In the 42 hours after the end of the given time window, when will Oceania experience its lowest Specific humidity at 700 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Oceania will experience its lowest Specific humidity at 700 hPa of 0.0002712 kg/kg 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "Oceania", + "extremum_value": "0.00027124814", + "target_variable": "specific_humidity_700", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "724373fabb2f479d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33551:33560:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75100:75102:1'} The data starts from May 28 00:00 and ends on May 28 06:00. Based on the above data, answer the following question:", + "question": "In the 24 hours after the end of the given time window, when will Long Island Sound experience its lowest Temperature at 250 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Long Island Sound will experience its lowest Temperature at 250 hPa of 220.1 K 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Long Island Sound", + "extremum_value": "220.13191", + "target_variable": "temperature_250", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "0e24a6673c6080e2", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75100:75102:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73657:73682:1'} The data starts from June 01 06:00 and ends on June 07 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Temperature at 500 hPa values? An exceedance is defined as a period of at least 120 consecutive hours where the Temperature at 500 hPa values exceed the 90th percentile climatology for the six-hourly climatology for day 152 at 06 UTC.", + "response": "The following water body(s) are currently experiencing an exceedance in Temperature at 500 hPa: Arctic Ocean(average 0.6886 K)\nSOUTHERN OCEAN(average 1.074 K)\nNorth Atlantic Ocean(average 0.9323 K)\nNorth Pacific Ocean(average 1.09 K)\nSouth Pacific Ocean(average 0.4014 K)\nINDIAN OCEAN(average 1.022 K)\nSouth Atlantic Ocean(average 0.4344 K)\nPhilippine Sea(average 0.4163 K)\nSouth China Sea(average 0.1446 K)\nGulf of Alaska(average 2.598 K)\nSea of Okhotsk(average 1.814 K)\nSulu Sea(average 0.2199 K)\nGreenland Sea(average 1.004 K)\nDavis Strait(average 0.5572 K)\nCook Inlet(average 1.642 K)\nShelikhova Gulf(average 1.403 K)\nBering Sea(average 0.3128 K)\nDixon Entrance(average 1.642 K)\nGolfo Corcovado(average 1.076 K)\nCumberland Sound(average 0.3975 K)\nDisko Bay(average 0.1916 K)\nKronotskiy Gulf(average 1.349 K)\nUda Bay(average 0.4265 K)\nTatar Strait(average 0.3256 K)\nGulf of Yana(average 0.9589 K)\nPrince William Sound(average 3.433 K)\nLützow-Holm Bay(average 0.6395 K)\nKaraginskiy Gulf(average 0.2949 K)\nGulf of Kamchatka(average 0.639 K)\nGulf of Sakhalin(average 0.6796 K)\nDenmark Strait(average 1.264 K)\nSibuyan Sea(average 0.2199 K)\nTayabas Bay(average 0.2199 K)\nHecate Strait(average 1.734 K)\nCordova Bay(average 1.648 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "temperature", + 500 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 152 at 06 UTC", + "quantile": "0.9", + "min_duration_days": 5, + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "South China Sea", + "Gulf of Alaska", + "Sea of Okhotsk", + "Sulu Sea", + "Greenland Sea", + "Davis Strait", + "Cook Inlet", + "Shelikhova Gulf", + "Bering Sea", + "Dixon Entrance", + "Golfo Corcovado", + "Cumberland Sound", + "Disko Bay", + "Kronotskiy Gulf", + "Uda Bay", + "Tatar Strait", + "Gulf of Yana", + "Prince William Sound", + "Lützow-Holm Bay", + "Karaginskiy Gulf", + "Gulf of Kamchatka", + "Gulf of Sakhalin", + "Denmark Strait", + "Sibuyan Sea", + "Tayabas Bay", + "Hecate Strait", + "Cordova Bay" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "5d4a464555f5481d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73657:73682:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75997:76015:1'} The data starts from January 07 06:00 and ends on January 11 12:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will Saint Lawrence River experience its highest Geopotential at 50 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Saint Lawrence River will experience its highest Geopotential at 50 hPa of 1.983e+05 m²/s² 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "Saint Lawrence River", + "extremum_value": "198349.86", + "target_variable": "geopotential_50", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a3824b9f23ecec5b", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75997:76015:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35834:35849:1'} The data starts from July 12 12:00 and ends on July 16 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Philippines", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Philippines" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "7cab12813a83b97d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35834:35849:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73808:73828:1'} The data starts from July 09 00:00 and ends on July 13 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Specific humidity at 300 hPa values? An exceedance is defined as a period of at least 120 consecutive hours where the Specific humidity at 300 hPa values exceed the 99th percentile climatology for the all-time climatology.", + "response": "Based on the provided data, no significant Specific humidity at 300 hPa anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 300 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.99", + "min_duration_days": 5, + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "69cf1de32c06ad6a", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73808:73828:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88929:88941:1'} The data starts from November 14 06:00 and ends on November 17 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Philippines", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Philippines" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "378e4bce7cf5d38e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88929:88941:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85576:85578:1'} The data starts from July 29 00:00 and ends on July 29 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Taiwan (Province of China). Specifically the region(s) being affected are: Kaohsiung City, New Taipei City\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Taiwan (Province of China)" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "b7342bf3d3172e3f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85576:85578:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40280:40281:1'} The data corresponds to corresponds to a snapshot on July 28 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 18 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 18 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 18 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 18, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "70e8282cb15a052d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40280:40281:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90885:90890:1'} The data starts from March 17 06:00 and ends on March 18 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter V component of wind differs from the monthly climatology for March mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above 10-meter V component of wind values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant 10-meter V component of wind anomalies were detected relative to the monthly climatology for March baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for March", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "068c39eb22d7bdab", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90885:90890:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84648:84667:1'} The data starts from December 09 00:00 and ends on December 13 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) V (meridional) component of wind at 150 hPa lies outside the climatological 5th–95th percentile envelope for the monthly climatology for December. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 5th–95th percentile envelope for V (meridional) component of wind at 150 hPa during monthly climatology for December: Arctic Ocean(average -0.01746 m/s)\nNorth Pacific Ocean(average 0.1987 m/s)\nSouth Pacific Ocean(average -1.633 m/s)\nINDIAN OCEAN(average -0.1751 m/s)\nSouth Atlantic Ocean(average 0.8337 m/s)\nPhilippine Sea(average -1.763 m/s)\nSouth China Sea(average -2.482 m/s)\nSulu Sea(average -0.1421 m/s)\nNorwegian Sea(average -0.8656 m/s)\nGreenland Sea(average -0.5231 m/s)\nLuzon Strait(average -2.132 m/s)\nBaltic Sea(average -0.1827 m/s)\nBarents Sea(average -0.6523 m/s)\nYellow Sea(average -0.02772 m/s)\nEast China Sea(average -1.331 m/s)\nTimor Sea(average 0.2272 m/s)\nWhite Sea(average -0.06489 m/s)\nGulf of Finland(average -0.2156 m/s)\nGulf of Bothnia(average -0.5387 m/s)\nGulf of Tonkin(average -1.063 m/s)\nTaiwan Strait(average -2.278 m/s)\nStorfjorden(average -0.697 m/s)\nVestfjorden(average -0.2584 m/s)\nQiongzhou Strait(average -1.639 m/s)\nGulf of Riga(average -0.2282 m/s)\nHangzhou Bay(average -0.09642 m/s)\nJoseph Bonaparte Gulf(average 0.3846 m/s)\nSibuyan Sea(average -0.2403 m/s)\nRagay Gulf(average -0.2319 m/s)\nSamar Sea(average -0.3068 m/s)\nTayabas Bay(average -0.2265 m/s)\nVisayan Sea(average -0.2767 m/s)\nYangtze River(average -0.02772 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 150 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for December", + "lower_quantile": "0.05", + "upper_quantile": "0.95", + "true_value": [ + "Arctic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "South China Sea", + "Sulu Sea", + "Norwegian Sea", + "Greenland Sea", + "Luzon Strait", + "Baltic Sea", + "Barents Sea", + "Yellow Sea", + "East China Sea", + "Timor Sea", + "White Sea", + "Gulf of Finland", + "Gulf of Bothnia", + "Gulf of Tonkin", + "Taiwan Strait", + "Storfjorden", + "Vestfjorden", + "Qiongzhou Strait", + "Gulf of Riga", + "Hangzhou Bay", + "Joseph Bonaparte Gulf", + "Sibuyan Sea", + "Ragay Gulf", + "Samar Sea", + "Tayabas Bay", + "Visayan Sea", + "Yangtze River" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "40f9211faa76a5b7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84648:84667:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50743:50749:1'} The data starts from September 24 18:00 and ends on September 26 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of China, Macao Special Administrative Region.\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "China, Macao Special Administrative Region" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "fc9f0911b139ea62", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50743:50749:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85284:85288:1'} The data starts from May 17 00:00 and ends on May 17 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 30 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 30 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 30 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 30, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "3d756137eea85b3f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85284:85288:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90511:90524:1'} The data starts from December 13 18:00 and ends on December 16 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) 10-meter V component of wind differs from the DJF seasonal climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above 10-meter V component of wind values.", + "response": "Based on the provided data, no significant 10-meter V component of wind anomalies were detected relative to the DJF seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "DJF seasonal climatology", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "050c8b9717e79df5", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90511:90524:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72388:72403:1'} The data starts from July 19 00:00 and ends on July 22 12:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Guatemala; Mexico", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Guatemala", + "Mexico" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "19fe34bc72234732", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72388:72403:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70438:70466:1'} The data starts from March 19 12:00 and ends on March 26 06:00. Based on the above data, answer the following question:", + "question": "In the 24 hours after the end of the given time window, when will Timor Sea experience its highest V (meridional) component of wind at 850 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Timor Sea will experience its highest V (meridional) component of wind at 850 hPa of 3.364 m/s 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Timor Sea", + "extremum_value": "3.3642392", + "target_variable": "v_component_of_wind_850", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "49db426c2de0359a", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70438:70466:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88447:88475:1'} The data starts from July 16 18:00 and ends on July 23 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) V (meridional) component of wind at 50 hPa values running below the 1st percentile climatology for the JJA seasonal climatology? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant V (meridional) component of wind at 50 hPa anomalies were detected relative to the JJA seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "v_component_of_wind", + 50 + ], + "geofeature": "country", + "climatology_timescale_desc": "JJA seasonal climatology", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ca2a9a8de82dc5db", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88447:88475:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78282:78290:1'} The data starts from July 31 12:00 and ends on August 02 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 925 hPa lies outside the climatological 1st–99th percentile envelope for the daily climatology for day 213. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–99th percentile envelope for Temperature at 925 hPa during daily climatology for day 213: Arctic Ocean(average 2.275 K)\nSOUTHERN OCEAN(average -1.296 K)\nNorth Atlantic Ocean(average 0.4019 K)\nNorth Pacific Ocean(average -0.4996 K)\nSouth Pacific Ocean(average 0.5984 K)\nINDIAN OCEAN(average 0.2887 K)\nSouth Atlantic Ocean(average -0.1883 K)\nPhilippine Sea(average 0.2217 K)\nTasman Sea(average 0.7732 K)\nBay of Bengal(average -0.1958 K)\nSouth China Sea(average 0.3669 K)\nBeaufort Sea(average 3.037 K)\nCaribbean Sea(average 0.1369 K)\nLabrador Sea(average 0.3621 K)\nBaffin Bay(average 0.1558 K)\nPersian Gulf(average 0.2626 K)\nCelebes Sea(average 0.04086 K)\nSulu Sea(average 0.04086 K)\nGreenland Sea(average 0.1173 K)\nMozambique Channel(average 0.1292 K)\nDavis Strait(average 0.5502 K)\nThe North Western Passages(average 1.692 K)\nBalearic Sea(average 0.5052 K)\nBo Hai(average -0.2637 K)\nAmundsen Gulf(average 0.839 K)\nViscount Melville Sound(average 0.7582 K)\nM'Clure Strait(average 2.576 K)\nAlboran Sea(average 0.7332 K)\nPrydz Bay(average -0.4721 K)\nDavis Sea(average -2.592 K)\nLützow-Holm Bay(average -2.378 K)\nKangertittivaq(average 0.6846 K)\nDenmark Strait(average 0.2673 K)\nDarnley Bay(average 0.02902 K)\nPrince of Wales Strait(average 0.8412 K)\nRichard Collinson Inlet(average 0.4764 K)\nLiddon Gulf(average 0.9862 K)\nMediterranean Sea(average 0.6951 K)\nSea of Japan(average 0.3043 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "temperature", + 925 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 213", + "lower_quantile": "0.01", + "upper_quantile": "0.99", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Tasman Sea", + "Bay of Bengal", + "South China Sea", + "Beaufort Sea", + "Caribbean Sea", + "Labrador Sea", + "Baffin Bay", + "Persian Gulf", + "Celebes Sea", + "Sulu Sea", + "Greenland Sea", + "Mozambique Channel", + "Davis Strait", + "The North Western Passages", + "Balearic Sea", + "Bo Hai", + "Amundsen Gulf", + "Viscount Melville Sound", + "M'Clure Strait", + "Alboran Sea", + "Prydz Bay", + "Davis Sea", + "Lützow-Holm Bay", + "Kangertittivaq", + "Denmark Strait", + "Darnley Bay", + "Prince of Wales Strait", + "Richard Collinson Inlet", + "Liddon Gulf", + "Mediterranean Sea", + "Sea of Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "feb4dac71bf2ed71", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78282:78290:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73530:73547:1'} The data starts from April 30 12:00 and ends on May 04 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Surface temperature values running above the 95th percentile climatology for the daily climatology for day 120? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Surface temperature values above the 95th percentile climatology for daily climatology for day 120: Indonesia(average 0.07128 K)\nFrance(average 0.105 K)\nSouth Korea(average 0.2102 K)\nMongolia(average 0.2849 K)\nRussia(average 0.3381 K)\nNorway(average 0.7193 K)\nSweden(average 1.742 K)\nFinland(average 1.144 K)\nItaly(average 0.2861 K)\nSenegal(average 0.1499 K)\nUnited States of America(average 0.6331 K)\nCanada(average 0.4847 K)\nPapua New Guinea(average 0.03726 K)\nMauritania(average 0.1499 K)\nAustralia(average 0.0144 K)\nGreenland(average 1.332 K)\nPhilippines(average 0.07986 K)\nJapan(average 1.11 K)\nIceland(average 0.353 K)\nFrench Polynesia(average 0.1422 K)\nUnited States Minor Outlying Islands(average 0.03574 K)\nSaint Helena(average 0.986 K)\nFederated States of Micronesia(average 0.3408 K)\nPalau(average 0.1867 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 120", + "quantile": "0.95", + "threshold_direction": "above", + "true_value": [ + "Indonesia", + "France", + "South Korea", + "Mongolia", + "Russia", + "Norway", + "Sweden", + "Finland", + "Italy", + "Senegal", + "United States of America", + "Canada", + "Papua New Guinea", + "Mauritania", + "Australia", + "Greenland", + "Philippines", + "Japan", + "Iceland", + "French Polynesia", + "United States Minor Outlying Islands", + "Saint Helena", + "Federated States of Micronesia", + "Palau" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ab2f0512bca9a514", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73530:73547:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40993:40999:1'} The data starts from January 22 06:00 and ends on January 23 12:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will South America experience its lowest Surface pressure? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, South America will experience its lowest Surface pressure of 6.006e+04 Pa 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "South America", + "extremum_value": "60056.59", + "target_variable": "surface_pressure", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "8bb6c1f18853c800", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40993:40999:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55057:55064:1'} The data starts from September 07 06:00 and ends on September 08 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Canada.\nA Tropical cyclone is occuring in the country of Martinique.\nA Tropical cyclone is occuring in the country of China. Specifically the region(s) being affected are: Zhanjiang, Maoming, Yangjiang (Guangdong province), Beihei, Qinzhou, Yulin (Guangxi province)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Canada", + "Martinique", + "China" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "b2399a0727f0f9ed", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55057:55064:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70102:70125:1'} The data starts from December 25 12:00 and ends on December 31 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter V component of wind lies outside the climatological 10th–90th percentile envelope for the six-hourly climatology for day 359 at 12 UTC. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–90th percentile envelope for 10-meter V component of wind during six-hourly climatology for day 359 at 12 UTC: Indonesia(average 0.08439 m/s)\nMalaysia(average 0.09793 m/s)\nChile(average 0.9981 m/s)\nBolivia(average 0.1265 m/s)\nPeru(average 0.2567 m/s)\nArgentina(average 0.3976 m/s)\nDhekelia Sovereign Base Area(average -0.1181 m/s)\nCyprus(average -0.2751 m/s)\nIndia(average -0.04249 m/s)\nChina(average -0.0433 m/s)\nEthiopia(average -0.4495 m/s)\nSouth Sudan(average -0.2768 m/s)\nSomalia(average 0.2891 m/s)\nKenya(average -0.1454 m/s)\nMalawi(average -0.2878 m/s)\nUnited Republic of Tanzania(average -0.0842 m/s)\nSyria(average -0.2327 m/s)\nSomaliland(average 0.7127 m/s)\nFrance(average 1.148 m/s)\nNorth Korea(average -0.1057 m/s)\nMorocco(average 0.02499 m/s)\nWestern Sahara(average 0.07217 m/s)\nCosta Rica(average 0.08999 m/s)\nRepublic of the Congo(average -0.01443 m/s)\nDemocratic Republic of the Congo(average -0.002575 m/s)\nUkraine(average -0.06067 m/s)\nBelarus(average -0.2628 m/s)\nNamibia(average 0.7029 m/s)\nSouth Africa(average 0.04311 m/s)\nOman(average -0.3939 m/s)\nUzbekistan(average 0.3934 m/s)\nKazakhstan(average 0.6559 m/s)\nTajikistan(average 0.1467 m/s)\nLithuania(average -0.378 m/s)\nBrazil(average -0.4404 m/s)\nMongolia(average 0.06098 m/s)\nRussia(average -0.3347 m/s)\nLatvia(average -0.4001 m/s)\nNorway(average 0.9619 m/s)\nVietnam(average -0.02387 m/s)\nGeorgia(average -0.05961 m/s)\nAzerbaijan(average 0.07032 m/s)\nTurkey(average -0.3647 m/s)\nSpain(average 2.573 m/s)\nLaos(average -0.02387 m/s)\nKyrgyzstan(average 0.08259 m/s)\nArmenia(average -0.1925 m/s)\nRomania(average -0.2382 m/s)\nPoland(average -0.072 m/s)\nIreland(average 0.6271 m/s)\nUnited Kingdom(average 2.088 m/s)\nGreece(average -0.03119 m/s)\nZambia(average -0.3109 m/s)\nSierra Leone(average 0.1482 m/s)\nGuinea(average 0.108 m/s)\nLiberia(average 0.2664 m/s)\nCentral African Republic(average -0.2031 m/s)\nSudan(average -0.2246 m/s)\nDjibouti(average -0.4452 m/s)\nEritrea(average -0.6912 m/s)\nIran(average -0.162 m/s)\nIvory Coast(average 0.2602 m/s)\nRepublic of Serbia(average -0.1892 m/s)\nNigeria(average -0.4739 m/s)\nBenin(average -0.09349 m/s)\nAngola(average -0.2847 m/s)\nSaudi Arabia(average -0.6363 m/s)\nBotswana(average 0.4618 m/s)\nPakistan(average -0.1737 m/s)\nHaiti(average 0.1746 m/s)\nDominican Republic(average 0.1746 m/s)\nChad(average -0.1731 m/s)\nAlgeria(average -0.02218 m/s)\nBurundi(average -0.07812 m/s)\nMyanmar(average 0.01115 m/s)\nAfghanistan(average 0.001221 m/s)\nUganda(average 0.01353 m/s)\nEcuador(average -0.0788 m/s)\nColombia(average -0.1305 m/s)\nPortugal(average 0.5267 m/s)\nTurkmenistan(average 0.3756 m/s)\nCameroon(average 0.1304 m/s)\nGabon(average 0.1035 m/s)\nGhana(average 0.06178 m/s)\nUnited States of America(average -0.02759 m/s)\nCanada(average -0.3207 m/s)\nMexico(average 0.3062 m/s)\nPanama(average 0.2359 m/s)\nVenezuela(average -0.07709 m/s)\nPapua New Guinea(average 0.7213 m/s)\nYemen(average -0.9352 m/s)\nMauritania(average 0.07217 m/s)\nNorthern Cyprus(average -0.3678 m/s)\nCyprus No Mans Area(average -0.2751 m/s)\nSiachen Glacier(average 0.007471 m/s)\nBaykonur Cosmodrome(average 0.9379 m/s)\nAkrotiri Sovereign Base Area(average -0.2751 m/s)\nAustralia(average 0.3336 m/s)\nGreenland(average 0.1828 m/s)\nNew Zealand(average 0.1373 m/s)\nMadagascar(average -1.213 m/s)\nPhilippines(average 0.1382 m/s)\nSri Lanka(average -0.1272 m/s)\nSeychelles(average 0.9898 m/s)\nKiribati(average 0.3364 m/s)\nSão Tomé and Principe(average -0.5538 m/s)\nTonga(average 0.734 m/s)\nSouth Georgia and the Islands(average -0.9309 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 359 at 12 UTC", + "lower_quantile": "0.1", + "upper_quantile": "0.9", + "true_value": [ + "Indonesia", + "Malaysia", + "Chile", + "Bolivia", + "Peru", + "Argentina", + "Dhekelia Sovereign Base Area", + "Cyprus", + "India", + "China", + "Ethiopia", + "South Sudan", + "Somalia", + "Kenya", + "Malawi", + "United Republic of Tanzania", + "Syria", + "Somaliland", + "France", + "North Korea", + "Morocco", + "Western Sahara", + "Costa Rica", + "Republic of the Congo", + "Democratic Republic of the Congo", + "Ukraine", + "Belarus", + "Namibia", + "South Africa", + "Oman", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Lithuania", + "Brazil", + "Mongolia", + "Russia", + "Latvia", + "Norway", + "Vietnam", + "Georgia", + "Azerbaijan", + "Turkey", + "Spain", + "Laos", + "Kyrgyzstan", + "Armenia", + "Romania", + "Poland", + "Ireland", + "United Kingdom", + "Greece", + "Zambia", + "Sierra Leone", + "Guinea", + "Liberia", + "Central African Republic", + "Sudan", + "Djibouti", + "Eritrea", + "Iran", + "Ivory Coast", + "Republic of Serbia", + "Nigeria", + "Benin", + "Angola", + "Saudi Arabia", + "Botswana", + "Pakistan", + "Haiti", + "Dominican Republic", + "Chad", + "Algeria", + "Burundi", + "Myanmar", + "Afghanistan", + "Uganda", + "Ecuador", + "Colombia", + "Portugal", + "Turkmenistan", + "Cameroon", + "Gabon", + "Ghana", + "United States of America", + "Canada", + "Mexico", + "Panama", + "Venezuela", + "Papua New Guinea", + "Yemen", + "Mauritania", + "Northern Cyprus", + "Cyprus No Mans Area", + "Siachen Glacier", + "Baykonur Cosmodrome", + "Akrotiri Sovereign Base Area", + "Australia", + "Greenland", + "New Zealand", + "Madagascar", + "Philippines", + "Sri Lanka", + "Seychelles", + "Kiribati", + "São Tomé and Principe", + "Tonga", + "South Georgia and the Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "577ee5318521c1f8", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70102:70125:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87296:87323:1'} The data starts from October 02 00:00 and ends on October 08 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Mean sea level pressure differs from the monthly climatology for October mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Mean sea level pressure values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Mean sea level pressure anomalies were detected relative to the monthly climatology for October baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for October", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "604de7d747bd5086", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87296:87323:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88327:88343:1'} The data starts from June 16 18:00 and ends on June 20 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) 10-meter V component of wind lies outside the climatological 1st–99th percentile envelope for the JJA seasonal climatology. Regions outside that envelope are anomalous.", + "response": "Based on the provided data, no significant 10-meter V component of wind anomalies were detected relative to the JJA seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "JJA seasonal climatology", + "lower_quantile": "0.01", + "upper_quantile": "0.99", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "730b89fd4ab7d99b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88327:88343:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81857:81861:1'} The data starts from January 11 06:00 and ends on January 12 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Temperature at 200 hPa values? An exceedance is defined as a period of at least 24 consecutive hours where the Temperature at 200 hPa values exceed the 95th percentile climatology for the DJF seasonal climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in Temperature at 200 hPa: Arctic Ocean(average 0.08109 K)\nNorth Pacific Ocean(average 0.5749 K)\nSouth Pacific Ocean(average 0.601 K)\nINDIAN OCEAN(average 0.7525 K)\nSouth Atlantic Ocean(average 0.08259 K)\nPhilippine Sea(average 0.117 K)\nRed Sea(average 0.3859 K)\nMozambique Channel(average 0.4878 K)\nLaptev Sea(average 0.4073 K)\nGreat Australian Bight(average 2.107 K)\nGulf of Aden(average 0.3267 K)\nGulf of Carpentaria(average 0.2034 K)\nSolomon Sea(average 0.4697 K)\nEast Siberian Sea(average 0.08311 K)\nUchiura Bay(average 0.5497 K)\nTsugaru Strait(average 0.4428 K)\nShark Bay(average 1.424 K)\nGulf of Yana(average 0.2192 K)\nDmitriy Laptev Strait(average 0.06471 K)\nEast Korea Bay(average 0.3698 K)\nGulf of Papua(average 0.2037 K)\nGulf of Olen‘k(average 0.1364 K)\nAntongila Bay(average 0.248 K)\nBab el Mandeb(average 0.3216 K)\nGulf St. Vincent(average 0.2007 K)\nCoral Sea(average 0.6641 K)\nSea of Japan(average 0.6834 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "temperature", + 200 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "DJF seasonal climatology", + "quantile": "0.95", + "min_duration_days": 1, + "true_value": [ + "Arctic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Red Sea", + "Mozambique Channel", + "Laptev Sea", + "Great Australian Bight", + "Gulf of Aden", + "Gulf of Carpentaria", + "Solomon Sea", + "East Siberian Sea", + "Uchiura Bay", + "Tsugaru Strait", + "Shark Bay", + "Gulf of Yana", + "Dmitriy Laptev Strait", + "East Korea Bay", + "Gulf of Papua", + "Gulf of Olen‘k", + "Antongila Bay", + "Bab el Mandeb", + "Gulf St. Vincent", + "Coral Sea", + "Sea of Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d64e9e446790ce17", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81857:81861:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76024:76042:1'} The data starts from January 14 00:00 and ends on January 18 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) 10-meter V component of wind values running above the 99th percentile climatology for the DJF seasonal climatology? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant 10-meter V component of wind anomalies were detected relative to the DJF seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "DJF seasonal climatology", + "quantile": "0.99", + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "1e1c4c9e720c36bd", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76024:76042:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66709:66722:1'} The data starts from August 29 06:00 and ends on September 01 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Guam. Specifically the region(s) being affected are: Guam province\nA Tropical cyclone is occuring in the country of Japan. Specifically the region(s) being affected are: Oosaka, Hyoogo, Okayama, Ehime, Kagawa, Miyazaki, Kagosima provinces\nA Tropical cyclone is occuring in the country of Puerto Rico. Specifically the region(s) being affected are: Aguadilla, Arecibo, Bayamon, Guayama, Humacao, Mayaguez, Ponce, San Juan provinces\nA Tropical cyclone is occuring in the country of Turks and Caicos Islands. Specifically the region(s) being affected are: Grand Turk, Providenciales and West Caicos provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Guam", + "Japan", + "Puerto Rico", + "Turks and Caicos Islands" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "062613b26b9ff24e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66709:66722:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76242:76263:1'} The data starts from March 09 12:00 and ends on March 14 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Specific humidity at 200 hPa values? An exceedance is defined as a period of at least 96 consecutive hours where the Specific humidity at 200 hPa values exceed the 95th percentile climatology for the daily climatology for day 68. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Specific humidity at 200 hPa: India(average 3.045e-07 kg/kg)\nRussia(average 6.464e-07 kg/kg)\nIran(average 2.209e-06 kg/kg)\nMyanmar(average 9.816e-08 kg/kg)\nAfghanistan(average 2.209e-06 kg/kg)\nTurkmenistan(average 2.209e-06 kg/kg)\nUnited States of America(average 1.439e-07 kg/kg)\nAustralia(average 3.549e-06 kg/kg)\nGreenland(average 1.735e-07 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 200 + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 68", + "quantile": "0.95", + "min_duration_days": 4, + "true_value": [ + "India", + "Russia", + "Iran", + "Myanmar", + "Afghanistan", + "Turkmenistan", + "United States of America", + "Australia", + "Greenland" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "bba6586207c64b56", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76242:76263:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88642:88653:1'} The data starts from September 03 12:00 and ends on September 06 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of China.\nA Tropical cyclone is occuring in the country of Japan. Specifically the region(s) being affected are: Nagano, Yamaguchi, Hiroshima, Tottori, Okayama, Okinawa and Miyazaki Prefectures\nA Tropical cyclone is occuring in the country of Republic of Korea. Specifically the region(s) being affected are: Jeju\nA Tropical cyclone is occuring in the country of Democratic People's Republic of Korea. Specifically the region(s) being affected are: Yonggwang, Yodok, Jangjin Counties; Tanchon City (South Hamgyong Province)\nA Tropical cyclone is occuring in the country of Mexico. Specifically the region(s) being affected are: Coahuila, Nuevo León, Tamaulipas, San Luis Potosí states\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "China", + "Japan", + "Republic of Korea", + "Democratic People's Republic of Korea", + "Mexico" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "0430820880b480d9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88642:88653:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68728:68729:1'} The data corresponds to corresponds to a snapshot on January 16 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Severe Winter Conditions currently happening? Specify the affected countries or regions, or respond 'No Severe Winter Conditions detected.'", + "response": "Based on the provided data, the Severe Winter Conditions is affecting: Ukraine", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Ukraine" + ], + "target_disaster": "Severe Winter Conditions", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "0ea9481e5ea0123c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68728:68729:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81666:81689:1'} The data starts from November 24 12:00 and ends on November 30 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in V (meridional) component of wind at 925 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the V (meridional) component of wind at 925 hPa values exceed the 90th percentile climatology for the SON seasonal climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in V (meridional) component of wind at 925 hPa: Arctic Ocean(average 1.743 m/s)\nNorth Atlantic Ocean(average 0.9849 m/s)\nNorth Pacific Ocean(average 1.111 m/s)\nSouth Pacific Ocean(average 0.5084 m/s)\nNorwegian Sea(average 0.6483 m/s)\nGreenland Sea(average 1.878 m/s)\nTyrrhenian Sea(average 1.918 m/s)\nGulf of Tonkin(average 0.516 m/s)\nStrait of Gibraltar(average 0.554 m/s)\nBalearic Sea(average 3.091 m/s)\nBering Sea(average 0.2732 m/s)\nGulf of Gabès(average 1.045 m/s)\nGulf of Sidra(average 1.244 m/s)\nAlboran Sea(average 1.676 m/s)\nDenmark Strait(average 1.211 m/s)\nMediterranean Sea(average 2.736 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "v_component_of_wind", + 925 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "SON seasonal climatology", + "quantile": "0.9", + "min_duration_days": 3, + "true_value": [ + "Arctic Ocean", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "Norwegian Sea", + "Greenland Sea", + "Tyrrhenian Sea", + "Gulf of Tonkin", + "Strait of Gibraltar", + "Balearic Sea", + "Bering Sea", + "Gulf of Gabès", + "Gulf of Sidra", + "Alboran Sea", + "Denmark Strait", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "80fd252275135bd6", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81666:81689:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66168:66179:1'} The data starts from April 16 00:00 and ends on April 18 12:00. Based on the above data, answer the following question:", + "question": "What will the maximum Mean sea level pressure be in Gulf of Mexico, 30 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum Mean sea level pressure in Gulf of Mexico 30 hours after the given time window will be 1.029e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "102856.484", + "location": "Gulf of Mexico", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "22fb5424a6a37b2c", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66168:66179:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81665:81679:1'} The data starts from November 24 06:00 and ends on November 27 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) U (zonal) component of wind at 300 hPa lies outside the climatological 10th–90th percentile envelope for the all-time climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–90th percentile envelope for U (zonal) component of wind at 300 hPa during all-time climatology: India(average -0.8323 m/s)\nChina(average 3.057 m/s)\nMalawi(average -0.3088 m/s)\nUnited Republic of Tanzania(average -1.1 m/s)\nNicaragua(average -0.6607 m/s)\nRepublic of the Congo(average -0.6107 m/s)\nDemocratic Republic of the Congo(average -1.811 m/s)\nUzbekistan(average 2.557 m/s)\nKazakhstan(average -1.284 m/s)\nTajikistan(average 2.143 m/s)\nBrazil(average -0.6239 m/s)\nMongolia(average 2.82 m/s)\nRussia(average -2.146 m/s)\nVietnam(average 0.4639 m/s)\nKyrgyzstan(average 4.186 m/s)\nZambia(average -1.42 m/s)\nSudan(average 1.863 m/s)\nAngola(average -1.558 m/s)\nSaudi Arabia(average 1.246 m/s)\nPakistan(average 0.7345 m/s)\nHaiti(average -2.853 m/s)\nDominican Republic(average -3.318 m/s)\nBurundi(average -1.16 m/s)\nRwanda(average -0.8327 m/s)\nAfghanistan(average 1.453 m/s)\nUganda(average -0.7547 m/s)\nCuba(average -0.288 m/s)\nHonduras(average -0.6607 m/s)\nColombia(average -1.091 m/s)\nUnited States of America(average 3.91 m/s)\nCanada(average 9.521 m/s)\nVenezuela(average -0.06531 m/s)\nPapua New Guinea(average 1.723 m/s)\nEgypt(average 2.376 m/s)\nBir Tawil(average 2.802 m/s)\nAustralia(average -0.6302 m/s)\nFiji(average 2.544 m/s)\nNew Zealand(average 4.284 m/s)\nNew Caledonia(average -8.55 m/s)\nTurks and Caicos Islands(average -0.6321 m/s)\nSaint Pierre and Miquelon(average 0.9596 m/s)\nKiribati(average 0.7063 m/s)\nUnited States Minor Outlying Islands(average -1.72 m/s)\nPuerto Rico(average -1.834 m/s)\nJamaica(average -1.066 m/s)\nTonga(average 3.532 m/s)\nWallis and Futuna(average 3.441 m/s)\nSolomon Islands(average 7.153 m/s)\nTuvalu(average 0.5148 m/s)\nVanuatu(average -1.307 m/s)\nCoral Sea Islands(average -2.569 m/s)\nBajo Nuevo Bank (Petrel Is.)(average -1.641 m/s)\nSerranilla Bank(average -2.304 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "u_component_of_wind", + 300 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.9", + "true_value": [ + "India", + "China", + "Malawi", + "United Republic of Tanzania", + "Nicaragua", + "Republic of the Congo", + "Democratic Republic of the Congo", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Brazil", + "Mongolia", + "Russia", + "Vietnam", + "Kyrgyzstan", + "Zambia", + "Sudan", + "Angola", + "Saudi Arabia", + "Pakistan", + "Haiti", + "Dominican Republic", + "Burundi", + "Rwanda", + "Afghanistan", + "Uganda", + "Cuba", + "Honduras", + "Colombia", + "United States of America", + "Canada", + "Venezuela", + "Papua New Guinea", + "Egypt", + "Bir Tawil", + "Australia", + "Fiji", + "New Zealand", + "New Caledonia", + "Turks and Caicos Islands", + "Saint Pierre and Miquelon", + "Kiribati", + "United States Minor Outlying Islands", + "Puerto Rico", + "Jamaica", + "Tonga", + "Wallis and Futuna", + "Solomon Islands", + "Tuvalu", + "Vanuatu", + "Coral Sea Islands", + "Bajo Nuevo Bank (Petrel Is.)", + "Serranilla Bank" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "8cb3303dd81668df", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81665:81679:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33559:33585:1'} The data starts from December 20 18:00 and ends on December 27 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 42 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 42 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 42 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 42, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "b7df8ab97727520f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33559:33585:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79350:79375:1'} The data starts from April 24 12:00 and ends on April 30 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) 10-meter V component of wind differs from the six-hourly climatology for day 114 at 12 UTC mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below 10-meter V component of wind values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for 10-meter V component of wind relative to the six-hourly climatology for day 114 at 12 UTC mean: Arabian Sea(average -3.493 m/s)\nCaribbean Sea(average -2.288 m/s)\nGulf of Aden(average -2.664 m/s)\nGulf of Khambhät(average -3.493 m/s)\nGulf of Martaban(average -1.574 m/s)\nMediterranean Sea(average -2.58 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 114 at 12 UTC", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "Arabian Sea", + "Caribbean Sea", + "Gulf of Aden", + "Gulf of Khambhät", + "Gulf of Martaban", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "a583ec57182cc69c", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79350:79375:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90326:90329:1'} The data starts from October 28 12:00 and ends on October 29 00:00. Based on the above data, answer the following question:", + "question": "In the 42 hours after the end of the given time window, when will Netherlands experience its lowest Temperature at 150 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Netherlands will experience its lowest Temperature at 150 hPa of 203.9 K 30 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 30, + "location": "Netherlands", + "extremum_value": "203.94241", + "target_variable": "temperature_150", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "9904d676f03cd4d1", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90326:90329:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75501:75506:1'} The data starts from September 05 06:00 and ends on September 06 06:00. Based on the above data, answer the following question:", + "question": "What will the minimum Specific humidity at 400 hPa be in Oceania, 30 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum Specific humidity at 400 hPa in Oceania 30 hours after the given time window will be 1.229e-05 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "1.2286525e-05", + "location": "Oceania", + "target_variable": "specific_humidity_400", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d6a13a2d5a0812a8", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75501:75506:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83900:83920:1'} The data starts from June 05 00:00 and ends on June 09 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter V component of wind lies outside the climatological 1st–90th percentile envelope for the all-time climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 1st–90th percentile envelope for 10-meter V component of wind during all-time climatology: Chile(average 0.1217 m/s)\nIndia(average 0.4766 m/s)\nEthiopia(average 0.1279 m/s)\nSouth Sudan(average 0.1485 m/s)\nSomalia(average 0.2248 m/s)\nKenya(average 0.335 m/s)\nMalawi(average 0.09656 m/s)\nUnited Republic of Tanzania(average 0.2159 m/s)\nSyria(average 0.06147 m/s)\nNicaragua(average 0.3725 m/s)\nDemocratic Republic of the Congo(average 0.2163 m/s)\nRussia(average 0.05872 m/s)\nTurkey(average 0.2043 m/s)\nZambia(average 0.1304 m/s)\nCentral African Republic(average 0.1011 m/s)\nSudan(average 0.1715 m/s)\nIraq(average 0.2516 m/s)\nIran(average 0.4253 m/s)\nNigeria(average 0.0117 m/s)\nHaiti(average 0.2905 m/s)\nDominican Republic(average 0.149 m/s)\nChad(average 0.1019 m/s)\nGuatemala(average 0.1872 m/s)\nMozambique(average 0.2799 m/s)\nMyanmar(average 0.2565 m/s)\nUganda(average 0.338 m/s)\nUS Naval Base Guantanamo Bay(average 0.4918 m/s)\nCuba(average 1.952 m/s)\nHonduras(average 0.6724 m/s)\nColombia(average 0.4636 m/s)\nCameroon(average 0.07986 m/s)\nGabon(average 0.001225 m/s)\nNiger(average 0.0117 m/s)\nUnited States of America(average 1.506 m/s)\nCanada(average 2.231 m/s)\nMexico(average 0.5685 m/s)\nAustralia(average 0.1235 m/s)\nGreenland(average 0.7306 m/s)\nMadagascar(average 0.2949 m/s)\nSri Lanka(average 0.6165 m/s)\nThe Bahamas(average 0.8223 m/s)\nFrench Southern and Antarctic Lands(average 0.3655 m/s)\nSeychelles(average 1.134 m/s)\nUnited States Minor Outlying Islands(average 0.9042 m/s)\nJamaica(average 1.287 m/s)\nCayman Islands(average 3.439 m/s)\nMauritius(average 1.377 m/s)\nComoros(average 0.6545 m/s)\nSão Tomé and Principe(average 0.08835 m/s)\nIndian Ocean Territories(average 0.7665 m/s)\nAmerican Samoa(average 0.3387 m/s)\nBajo Nuevo Bank (Petrel Is.)(average 1.778 m/s)\nSerranilla Bank(average 1.363 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "Chile", + "India", + "Ethiopia", + "South Sudan", + "Somalia", + "Kenya", + "Malawi", + "United Republic of Tanzania", + "Syria", + "Nicaragua", + "Democratic Republic of the Congo", + "Russia", + "Turkey", + "Zambia", + "Central African Republic", + "Sudan", + "Iraq", + "Iran", + "Nigeria", + "Haiti", + "Dominican Republic", + "Chad", + "Guatemala", + "Mozambique", + "Myanmar", + "Uganda", + "US Naval Base Guantanamo Bay", + "Cuba", + "Honduras", + "Colombia", + "Cameroon", + "Gabon", + "Niger", + "United States of America", + "Canada", + "Mexico", + "Australia", + "Greenland", + "Madagascar", + "Sri Lanka", + "The Bahamas", + "French Southern and Antarctic Lands", + "Seychelles", + "United States Minor Outlying Islands", + "Jamaica", + "Cayman Islands", + "Mauritius", + "Comoros", + "São Tomé and Principe", + "Indian Ocean Territories", + "American Samoa", + "Bajo Nuevo Bank (Petrel Is.)", + "Serranilla Bank" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "9c7efe812cba7b0d", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83900:83920:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32779:32781:1'} The data starts from June 08 18:00 and ends on June 09 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Sand/Dust Storm currently happening? Specify the affected countries or regions, or respond 'No Sand/Dust Storm detected.'", + "response": "No Sand/Dust Storm detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Sand/Dust Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "e48260eb19e71f0b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32779:32781:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80511:80525:1'} The data starts from February 08 18:00 and ends on February 12 00:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) 10-meter V component of wind lies outside the climatological 10th–95th percentile envelope for the daily climatology for day 39. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–95th percentile envelope for 10-meter V component of wind during daily climatology for day 39: SOUTHERN OCEAN(average 0.02146 m/s)\nNorth Atlantic Ocean(average -0.564 m/s)\nNorth Pacific Ocean(average -1.96 m/s)\nSouth Pacific Ocean(average -0.7666 m/s)\nINDIAN OCEAN(average -1.349 m/s)\nSouth Atlantic Ocean(average -0.8456 m/s)\nPhilippine Sea(average -1.046 m/s)\nSouth China Sea(average -0.6145 m/s)\nArabian Sea(average -0.5974 m/s)\nLabrador Sea(average -0.3859 m/s)\nGulf of Alaska(average -0.2069 m/s)\nWeddell Sea(average -0.4578 m/s)\nNorwegian Sea(average -0.3127 m/s)\nGreenland Sea(average -1.674 m/s)\nBanda Sea(average -0.4316 m/s)\nLuzon Strait(average -0.2355 m/s)\nBaltic Sea(average 0.363 m/s)\nBarents Sea(average 0.528 m/s)\nNorth Sea(average 1.616 m/s)\nYellow Sea(average -0.7441 m/s)\nEast China Sea(average -0.7182 m/s)\nArafura Sea(average -0.2398 m/s)\nTimor Sea(average -1.016 m/s)\nLaccadive Sea(average -0.6361 m/s)\nDavis Strait(average -0.3859 m/s)\nGolfo de California(average -0.6129 m/s)\nEnglish Channel(average 0.1446 m/s)\nGulf of Bothnia(average 0.7168 m/s)\nAdriatic Sea(average 0.7936 m/s)\nMolucca Sea(average 0.1492 m/s)\nBismarck Sea(average 0.2164 m/s)\nGulf of Tonkin(average -0.4175 m/s)\nStrait of Malacca(average -0.4768 m/s)\nCeram Sea(average 0.2348 m/s)\nInner Sea(average -0.859 m/s)\nTaiwan Strait(average -0.3183 m/s)\nCook Inlet(average -0.2669 m/s)\nBristol Bay(average -1.783 m/s)\nBering Sea(average -2.786 m/s)\nSkagerrak(average 1.591 m/s)\nTrondheimsfjorden(average 0.594 m/s)\nKattegat(average 1.078 m/s)\nNorton Sound(average -1.045 m/s)\nQiongzhou Strait(average -0.4786 m/s)\nWaddenzee(average 0.6433 m/s)\nPrince William Sound(average -0.2228 m/s)\nPorpoise Bay(average -0.562 m/s)\nHangzhou Bay(average -1.543 m/s)\nBoknafjorden(average 1.167 m/s)\nJoseph Bonaparte Gulf(average -1.16 m/s)\nDenmark Strait(average -1.737 m/s)\nBaird Inlet(average -1.806 m/s)\nHalmahera Sea(average 0.1492 m/s)\nFlores Sea(average -0.09034 m/s)\nGulf of Buli(average 0.06351 m/s)\nSavu Sea(average -0.5008 m/s)\nYangtze River(average -1.72 m/s)\nRoss Sea(average -0.1514 m/s)\nSea of Japan(average -0.6497 m/s)\nKorea Strait(average -0.4728 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 39", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "South China Sea", + "Arabian Sea", + "Labrador Sea", + "Gulf of Alaska", + "Weddell Sea", + "Norwegian Sea", + "Greenland Sea", + "Banda Sea", + "Luzon Strait", + "Baltic Sea", + "Barents Sea", + "North Sea", + "Yellow Sea", + "East China Sea", + "Arafura Sea", + "Timor Sea", + "Laccadive Sea", + "Davis Strait", + "Golfo de California", + "English Channel", + "Gulf of Bothnia", + "Adriatic Sea", + "Molucca Sea", + "Bismarck Sea", + "Gulf of Tonkin", + "Strait of Malacca", + "Ceram Sea", + "Inner Sea", + "Taiwan Strait", + "Cook Inlet", + "Bristol Bay", + "Bering Sea", + "Skagerrak", + "Trondheimsfjorden", + "Kattegat", + "Norton Sound", + "Qiongzhou Strait", + "Waddenzee", + "Prince William Sound", + "Porpoise Bay", + "Hangzhou Bay", + "Boknafjorden", + "Joseph Bonaparte Gulf", + "Denmark Strait", + "Baird Inlet", + "Halmahera Sea", + "Flores Sea", + "Gulf of Buli", + "Savu Sea", + "Yangtze River", + "Ross Sea", + "Sea of Japan", + "Korea Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "010e63a0a6b2f0e0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80511:80525:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91027:91044:1'} The data starts from April 21 18:00 and ends on April 25 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface pressure lies outside the climatological 5th–95th percentile envelope for the daily climatology for day 111. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 5th–95th percentile envelope for Surface pressure during daily climatology for day 111: SOUTHERN OCEAN(average -318.7 Pa)\nNorth Atlantic Ocean(average -186.7 Pa)\nNorth Pacific Ocean(average -136.5 Pa)\nSouth Pacific Ocean(average -16.85 Pa)\nINDIAN OCEAN(average -69.17 Pa)\nSouth Atlantic Ocean(average -1.713 Pa)\nPhilippine Sea(average -209.9 Pa)\nTasman Sea(average -61.78 Pa)\nBeaufort Sea(average 116.7 Pa)\nLabrador Sea(average -32.45 Pa)\nHudson Bay(average -130.3 Pa)\nWeddell Sea(average -490.7 Pa)\nNorwegian Sea(average 186.4 Pa)\nNorth Sea(average 206.4 Pa)\nInner Seas(average 106.7 Pa)\nIrish Sea(average 165.3 Pa)\nYellow Sea(average 191.8 Pa)\nChukchi Sea(average 64.61 Pa)\nEnglish Channel(average 7.023 Pa)\nThe North Western Passages(average -221.9 Pa)\nGulf of Saint Lawrence(average -333.2 Pa)\nBay of Fundy(average -311.9 Pa)\nCook Inlet(average 35.74 Pa)\nGulf of Maine(average -136.8 Pa)\nBo Hai(average 77.22 Pa)\nSkagerrak(average 144.4 Pa)\nSognefjorden(average 102.8 Pa)\nTrondheimsfjorden(average 21.33 Pa)\nMackenzie Bay(average 143.6 Pa)\nKotzebue Sound(average 88.97 Pa)\nGulf of Boothia(average -193.9 Pa)\nFoxe Basin(average -104.4 Pa)\nEast Korea Bay(average 193 Pa)\nWaddenzee(average 203.1 Pa)\nStrait of Belle Isle(average -53.48 Pa)\nPrince William Sound(average 15.38 Pa)\nBoknafjorden(average 122.7 Pa)\nSaint Lawrence River(average -368.3 Pa)\nHusky Lakes(average 16.18 Pa)\nSherman Basin(average -216.1 Pa)\nWager Bay(average -261.9 Pa)\nRoss Sea(average -28.05 Pa)\nSea of Japan(average 131.4 Pa)\nKorea Strait(average 21.13 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 111", + "lower_quantile": "0.05", + "upper_quantile": "0.95", + "true_value": [ + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Tasman Sea", + "Beaufort Sea", + "Labrador Sea", + "Hudson Bay", + "Weddell Sea", + "Norwegian Sea", + "North Sea", + "Inner Seas", + "Irish Sea", + "Yellow Sea", + "Chukchi Sea", + "English Channel", + "The North Western Passages", + "Gulf of Saint Lawrence", + "Bay of Fundy", + "Cook Inlet", + "Gulf of Maine", + "Bo Hai", + "Skagerrak", + "Sognefjorden", + "Trondheimsfjorden", + "Mackenzie Bay", + "Kotzebue Sound", + "Gulf of Boothia", + "Foxe Basin", + "East Korea Bay", + "Waddenzee", + "Strait of Belle Isle", + "Prince William Sound", + "Boknafjorden", + "Saint Lawrence River", + "Husky Lakes", + "Sherman Basin", + "Wager Bay", + "Ross Sea", + "Sea of Japan", + "Korea Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "05869d53025eff04", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91027:91044:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91825:91853:1'} The data starts from November 07 06:00 and ends on November 14 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in U (zonal) component of wind at 150 hPa values? An exceedance is defined as a period of at least 96 consecutive hours where the U (zonal) component of wind at 150 hPa values exceed the 99th percentile climatology for the SON seasonal climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in U (zonal) component of wind at 150 hPa: North Pacific Ocean(average 3.501 m/s)\nSouth Pacific Ocean(average 3.623 m/s)\nPhilippine Sea(average 1.963 m/s)\nSouth China Sea(average 0.8655 m/s)\nEast China Sea(average 0.9627 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "u_component_of_wind", + 150 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "SON seasonal climatology", + "quantile": "0.99", + "min_duration_days": 4, + "true_value": [ + "North Pacific Ocean", + "South Pacific Ocean", + "Philippine Sea", + "South China Sea", + "East China Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "3fb017732130c752", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91825:91853:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43586:43587:1'} The data corresponds to corresponds to a snapshot on October 31 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 24 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 24 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 24 hours:\nA Tropical cyclone is expected in the country of Philippines in approximately the next 12 hours\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Philippines" + ], + "extreme_event_hours": 24, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "8dcb4982b0e343f2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43586:43587:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90089:90113:1'} The data starts from August 30 06:00 and ends on September 05 00:00. Based on the above data, answer the following question:", + "question": "In the 36 hours after the end of the given time window, when will South America experience its highest Geopotential at 250 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, South America will experience its highest Geopotential at 250 hPa of 1.079e+05 m²/s² 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "South America", + "extremum_value": "107906.68", + "target_variable": "geopotential_250", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a66bfdd700b0bf7a", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90089:90113:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76136:76148:1'} The data starts from February 11 00:00 and ends on February 13 18:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will South America experience its highest 10-meter U component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, South America will experience its highest 10-meter U component of wind of 17.45 m/s 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "South America", + "extremum_value": "17.44865", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "500a4afcfb3c70ea", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76136:76148:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45044:45069:1'} The data starts from October 31 00:00 and ends on November 06 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Thailand. Specifically the region(s) being affected are: Chumphon, Nakkhon Si Thammarat, Phetchaburi, Prachuap Khiri Khan, Ranong, Songkhla, Surat Thani provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Thailand" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "ec6166492b73a603", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45044:45069:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48969:48985:1'} The data starts from July 08 06:00 and ends on July 12 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Philippines; Lao People's Democratic Republic", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Philippines", + "Lao People's Democratic Republic" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "e71717b4dffeb502", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48969:48985:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67648:67671:1'} The data starts from April 21 00:00 and ends on April 26 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Geopotential at 1000 hPa differs from the daily climatology for day 111 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Geopotential at 1000 hPa values.", + "response": "Based on the provided data, no significant Geopotential at 1000 hPa anomalies were detected relative to the daily climatology for day 111 baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "geopotential", + 1000 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 111", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "e01b40fdece77c93", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67648:67671:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58098:58114:1'} The data starts from October 07 12:00 and ends on October 11 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 42 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 42 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 42 hours:\nA Tropical cyclone is expected in the country of Philippines in approximately the next 42 to 162 hours. Specifically the region(s) that might get affected are: Town Of Tabuk Level 2 = Kalinga-Apayao (from GENAME)\nA Tropical cyclone is expected in the country of Taiwan (Province of China) in approximately the next 42 to 162 hours. Specifically the region(s) that might get affected are: Southern of Yakushuma Island (location not found)\nA Tropical cyclone is expected in the country of Japan in approximately the next 42 to 162 hours. Specifically the region(s) that might get affected are: Hiroshima Prefecture\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Philippines", + "Taiwan (Province of China)", + "Japan" + ], + "extreme_event_hours": 42, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "408043eb1c29e44a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58098:58114:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35310:35311:1'} The data corresponds to corresponds to a snapshot on March 03 12:00. Based on the above data, answer the following question:", + "question": "What will the average U (zonal) component of wind at 850 hPa be in Uganda, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the average U (zonal) component of wind at 850 hPa in Uganda 12 hours after the given time window will be 0.04729 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "0.0472878", + "location": "Uganda", + "target_variable": "u_component_of_wind_850", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "eeabc832e78fc11b", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35310:35311:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31711:31714:1'} The data starts from September 14 18:00 and ends on September 15 06:00. Based on the above data, answer the following question:", + "question": "What will the average Temperature at 150 hPa be in Caribbean Sea, 48 hours after the end of the given time window?", + "response": "Based on the provided data, the average Temperature at 150 hPa in Caribbean Sea 48 hours after the given time window will be 204.1 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "204.11409", + "location": "Caribbean Sea", + "target_variable": "temperature_150", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fcc125030b9b9384", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31711:31714:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72821:72845:1'} The data starts from November 04 06:00 and ends on November 10 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Cuba. Specifically the region(s) being affected are: Santa Cruz del Sur, Najasa, Guáimaro (Camaguey province), Amancio Rodriguez (Las Tunas province), Sancti Spirtitus, Ciego de Avila, Granma provinces\nA Tropical cyclone is occuring in the country of Cayman Islands. Specifically the region(s) being affected are: Cayman Brac, Little Cayman provinces\nA Severe weather is occuring in the country of Bolivia (Plurinational State of). Specifically the region(s) being affected are: San Lorenzo town (Mendez district, Tarija province), Uriondo town (Avilez district, Tarija province), Cercado district (Tarija province)\nA Tropical cyclone is occuring in the country of Philippines. Specifically the region(s) being affected are: Region IV (Southern Tagalog), Region V (Bicol region), Region VI (Western Visayas), Region VII (Central Visayas), Region VIII (Eastern Visayas) provinces\nA Tropical cyclone is occuring in the country of Viet Nam. Specifically the region(s) being affected are: Ho Chi Minh City province\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Cuba", + "Cayman Islands", + "Bolivia (Plurinational State of)", + "Philippines", + "Viet Nam" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "51e3a9f111305fd4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72821:72845:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81873:81882:1'} The data starts from January 15 06:00 and ends on January 17 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Madagascar. Specifically the region(s) being affected are: Ambatondrazaka district (Alaotra Mangoro province), Ambatofinandrahana district (Amoron I Mania province), Ambohidratrimo, Andramasina, Ankazobe, Antananarivo Avaradrano, Antananarivo Atsimondrano, Antananarivo I, Antananarivo II, Antananarivo III, Antananarivo IV, Antananarivo V; Antananarivo VI districts (Analamanga province), Maevatanana district (Betsiboka province), Ambato Boeni, Mahajanga II districts (Boeny province), Tsiroanomandidy district (Bongolava province), Ambanja district (Diana province), Ambalavao, Ambohimahasoa, Fianarantsoa I, Vohibato, Lalangina districts (Haute Matsiatra province), Soavinandriana district (Itasy province), Besalampy district (Melaky province), Belo Sur Tsiribihina, Mahabo, Miandrivazo, Morondava districts (Menabe province), Mampikony district (Sofia province) Farafangana, Vangaindrano, Vondrozo districts (Atsimo Atsinanana province), Antanifotsy distrcit (Vakinankaratra province), Ikongo, Manakara Atsimo, Mananjary, Nosy-Varika, Vohipeno districts (Vatovavy Fitovianny)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Madagascar" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "f70d1512ef2030fb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81873:81882:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80703:80731:1'} The data starts from March 28 18:00 and ends on April 04 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) V (meridional) component of wind at 50 hPa lies outside the climatological 10th–95th percentile envelope for the all-time climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–95th percentile envelope for V (meridional) component of wind at 50 hPa during all-time climatology: India(average -0.3025 m/s)\nUkraine(average -8.432 m/s)\nBelarus(average -14.66 m/s)\nUzbekistan(average 1.251 m/s)\nKazakhstan(average 2.445 m/s)\nTajikistan(average 0.3781 m/s)\nLithuania(average -15.86 m/s)\nRussia(average -5.4 m/s)\nCzechia(average -3.046 m/s)\nGermany(average -2.169 m/s)\nEstonia(average -21.3 m/s)\nLatvia(average -18.06 m/s)\nNorway(average -20.14 m/s)\nSweden(average -17.25 m/s)\nFinland(average -24.19 m/s)\nNorth Macedonia(average -1.295 m/s)\nAlbania(average -0.7072 m/s)\nKosovo(average -1.741 m/s)\nTurkey(average -1.376 m/s)\nKyrgyzstan(average 0.4892 m/s)\nDenmark(average -4.503 m/s)\nRomania(average -4.841 m/s)\nHungary(average -3.624 m/s)\nSlovakia(average -5.365 m/s)\nPoland(average -7.608 m/s)\nGreece(average -1.11 m/s)\nAustria(average -2.108 m/s)\nItaly(average -0.2672 m/s)\nIran(average 0.4168 m/s)\nRepublic of Serbia(average -2.499 m/s)\nCroatia(average -1.124 m/s)\nSlovenia(average -0.9974 m/s)\nBulgaria(average -2.655 m/s)\nAfghanistan(average 0.1164 m/s)\nMontenegro(average -1.234 m/s)\nBosnia and Herzegovina(average -1.035 m/s)\nMoldova(average -6.68 m/s)\nTurkmenistan(average 0.8835 m/s)\nUnited States of America(average -1.162 m/s)\nCanada(average 4.195 m/s)\nMexico(average 0.0704 m/s)\nBaykonur Cosmodrome(average 2.327 m/s)\nGreenland(average -2.511 m/s)\nAland(average -19.21 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 50 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "India", + "Ukraine", + "Belarus", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Lithuania", + "Russia", + "Czechia", + "Germany", + "Estonia", + "Latvia", + "Norway", + "Sweden", + "Finland", + "North Macedonia", + "Albania", + "Kosovo", + "Turkey", + "Kyrgyzstan", + "Denmark", + "Romania", + "Hungary", + "Slovakia", + "Poland", + "Greece", + "Austria", + "Italy", + "Iran", + "Republic of Serbia", + "Croatia", + "Slovenia", + "Bulgaria", + "Afghanistan", + "Montenegro", + "Bosnia and Herzegovina", + "Moldova", + "Turkmenistan", + "United States of America", + "Canada", + "Mexico", + "Baykonur Cosmodrome", + "Greenland", + "Aland" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "7346281b9de06316", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80703:80731:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67436:67440:1'} The data starts from February 27 00:00 and ends on February 27 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in 10-meter V component of wind values? An exceedance is defined as a period of at least 24 consecutive hours where the 10-meter V component of wind values exceed the 90th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in 10-meter V component of wind: Chile(average 1.632 m/s)\nArgentina(average 1.496 m/s)\nIndia(average 0.1094 m/s)\nSouth Sudan(average 0.4932 m/s)\nKenya(average 0.1478 m/s)\nSyria(average 1.57 m/s)\nMorocco(average 4.454 m/s)\nWestern Sahara(average 1.773 m/s)\nNicaragua(average 0.1671 m/s)\nDemocratic Republic of the Congo(average 0.4536 m/s)\nUkraine(average 0.8825 m/s)\nSouth Africa(average 0.1575 m/s)\nKazakhstan(average 1.677 m/s)\nRussia(average 0.6406 m/s)\nCambodia(average 0.4646 m/s)\nGeorgia(average 0.2215 m/s)\nAzerbaijan(average 0.2152 m/s)\nTurkey(average 1.962 m/s)\nSpain(average 4.797 m/s)\nLaos(average 0.4646 m/s)\nArmenia(average 0.2656 m/s)\nGreece(average 1.495 m/s)\nGuinea(average 0.02337 m/s)\nCentral African Republic(average 0.2566 m/s)\nIraq(average 1.793 m/s)\nMali(average 1.624 m/s)\nSenegal(average 0.02337 m/s)\nSaudi Arabia(average 1.004 m/s)\nZimbabwe(average 0.1202 m/s)\nBulgaria(average 1.607 m/s)\nThailand(average 0.4646 m/s)\nGuatemala(average 0.05666 m/s)\nAlgeria(average 0.9926 m/s)\nMozambique(average 0.1202 m/s)\nUganda(average 0.5428 m/s)\nCuba(average 3.065 m/s)\nHonduras(average 0.2305 m/s)\nPortugal(average 4.114 m/s)\nTurkmenistan(average 3.111 m/s)\nJordan(average 1.17 m/s)\nCameroon(average 0.05442 m/s)\nUnited States of America(average 2.602 m/s)\nCanada(average 1.202 m/s)\nMexico(average 0.4839 m/s)\nBelize(average 0.03049 m/s)\nEgypt(average 0.4583 m/s)\nMauritania(average 1.848 m/s)\nSouthern Patagonian Ice Field(average 1.205 m/s)\nAustralia(average 0.5667 m/s)\nGreenland(average 3.693 m/s)\nTonga(average 0.1933 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.9", + "min_duration_days": 1, + "true_value": [ + "Chile", + "Argentina", + "India", + "South Sudan", + "Kenya", + "Syria", + "Morocco", + "Western Sahara", + "Nicaragua", + "Democratic Republic of the Congo", + "Ukraine", + "South Africa", + "Kazakhstan", + "Russia", + "Cambodia", + "Georgia", + "Azerbaijan", + "Turkey", + "Spain", + "Laos", + "Armenia", + "Greece", + "Guinea", + "Central African Republic", + "Iraq", + "Mali", + "Senegal", + "Saudi Arabia", + "Zimbabwe", + "Bulgaria", + "Thailand", + "Guatemala", + "Algeria", + "Mozambique", + "Uganda", + "Cuba", + "Honduras", + "Portugal", + "Turkmenistan", + "Jordan", + "Cameroon", + "United States of America", + "Canada", + "Mexico", + "Belize", + "Egypt", + "Mauritania", + "Southern Patagonian Ice Field", + "Australia", + "Greenland", + "Tonga" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "f810fbc9b898fc2c", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67436:67440:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82333:82342:1'} The data starts from May 10 06:00 and ends on May 12 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Surface pressure lies outside the climatological 10th–95th percentile envelope for the all-time climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–95th percentile envelope for Surface pressure during all-time climatology: Peru(average -30.67 Pa)\nChina(average -117.9 Pa)\nNorth Korea(average -67.68 Pa)\nMorocco(average -48.64 Pa)\nWestern Sahara(average -49.32 Pa)\nCosta Rica(average -39.74 Pa)\nNicaragua(average -23.46 Pa)\nSouth Africa(average 309.8 Pa)\nMongolia(average -96.17 Pa)\nRussia(average -400.6 Pa)\nEcuador(average -68.14 Pa)\nColombia(average -38.7 Pa)\nMexico(average -2.32 Pa)\nPanama(average -44.37 Pa)\nMauritania(average -42.36 Pa)\nAustralia(average -60.26 Pa)\nNew Zealand(average -355 Pa)\nFrench Polynesia(average -242.4 Pa)\nBermuda(average 84.24 Pa)\nFederated States of Micronesia(average -117.5 Pa)\nSouth Georgia and the Islands(average 111.9 Pa)\nFalkland Islands(average 181.4 Pa)\nClipperton Island(average -78.48 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "Peru", + "China", + "North Korea", + "Morocco", + "Western Sahara", + "Costa Rica", + "Nicaragua", + "South Africa", + "Mongolia", + "Russia", + "Ecuador", + "Colombia", + "Mexico", + "Panama", + "Mauritania", + "Australia", + "New Zealand", + "French Polynesia", + "Bermuda", + "Federated States of Micronesia", + "South Georgia and the Islands", + "Falkland Islands", + "Clipperton Island" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "9b77e9884b7677dc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82333:82342:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93105:93111:1'} The data starts from September 23 06:00 and ends on September 24 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Canada. Specifically the region(s) being affected are: Nova Scotia, Prince Edward Island, Newfoundland, and Quebec\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Canada" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "daf7879df2120eae", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93105:93111:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55294:55305:1'} The data starts from November 05 12:00 and ends on November 08 00:00. Based on the above data, answer the following question:", + "question": "What will the average Specific humidity at 250 hPa be in Europe, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the average Specific humidity at 250 hPa in Europe 12 hours after the given time window will be 2.03e-05 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "2.030362e-05", + "location": "Europe", + "target_variable": "specific_humidity_250", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1a2f8f4d0b2e29ca", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55294:55305:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81839:81846:1'} The data starts from January 06 18:00 and ends on January 08 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in 10-meter V component of wind values? An exceedance is defined as a period of at least 24 consecutive hours where the 10-meter V component of wind values exceed the 90th percentile climatology for the six-hourly climatology for day 6 at 18 UTC.", + "response": "The following water body(s) are currently experiencing an exceedance in 10-meter V component of wind: Arctic Ocean(average 2.824 m/s)\nSOUTHERN OCEAN(average 1.699 m/s)\nNorth Atlantic Ocean(average 2.035 m/s)\nNorth Pacific Ocean(average 1.595 m/s)\nSouth Pacific Ocean(average 1.248 m/s)\nINDIAN OCEAN(average 1.466 m/s)\nSouth Atlantic Ocean(average 1.013 m/s)\nTasman Sea(average 0.7312 m/s)\nBay of Bengal(average 0.5844 m/s)\nSouth China Sea(average 1.337 m/s)\nArabian Sea(average 0.7742 m/s)\nCaspian Sea(average 0.5589 m/s)\nSea of Okhotsk(average 2.736 m/s)\nWeddell Sea(average 0.3008 m/s)\nPersian Gulf(average 0.321 m/s)\nNorwegian Sea(average 3.51 m/s)\nGreenland Sea(average 2.036 m/s)\nMozambique Channel(average 0.848 m/s)\nBaltic Sea(average 1.138 m/s)\nBarents Sea(average 1.134 m/s)\nNorth Sea(average 0.9678 m/s)\nIrish Sea(average 1.265 m/s)\nJava Sea(average 0.368 m/s)\nChukchi Sea(average 0.7532 m/s)\nArafura Sea(average 0.03666 m/s)\nLaccadive Sea(average 0.4976 m/s)\nBellingshausen Sea(average 1.061 m/s)\nDavis Strait(average 0.8006 m/s)\nKara Sea(average 0.6295 m/s)\nLaptev Sea(average 3.193 m/s)\nGreat Australian Bight(average 0.7123 m/s)\nGulf of Oman(average 0.151 m/s)\nGulf of Carpentaria(average 0.03666 m/s)\nGulf of Finland(average 0.7047 m/s)\nGulf of Bothnia(average 0.5031 m/s)\nStrait of Malacca(average 0.5007 m/s)\nStrait of Singapore(average 0.5007 m/s)\nGulf of Mannar(average 0.6813 m/s)\nBering Sea(average 0.337 m/s)\nEast Siberian Sea(average 1.193 m/s)\nStorfjorden(average 0.4485 m/s)\nPalk Strait(average 0.7018 m/s)\nShark Bay(average 0.2849 m/s)\nGolfo de Guayaquil(average 1.062 m/s)\nCook Strait(average 1.179 m/s)\nGeographe Bay(average 0.8307 m/s)\nGulf of Olen‘k(average 2.76 m/s)\nGulf of Riga(average 1.154 m/s)\nEstrecho de Magellanes(average 1.588 m/s)\nPrydz Bay(average 1.79 m/s)\nBaia de Maputo(average 0.2158 m/s)\nChaun Bay(average 1.815 m/s)\nKhatanga Gulf(average 1.139 m/s)\nGulf of Aqaba(average 1.175 m/s)\nDenmark Strait(average 1.874 m/s)\nGeorge VI Sound(average 0.4359 m/s)\nSeno de Skyring(average 0.1695 m/s)\nSeno Otway(average 1.861 m/s)\nBay Inútil(average 1.412 m/s)\nKaliningrad(average 3.037 m/s)\nSargasso Sea(average 0.6046 m/s)\nMediterranean Sea(average 1.612 m/s)\nRoss Sea(average 0.548 m/s)\nCoral Sea(average 0.3436 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 6 at 18 UTC", + "quantile": "0.9", + "min_duration_days": 1, + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Tasman Sea", + "Bay of Bengal", + "South China Sea", + "Arabian Sea", + "Caspian Sea", + "Sea of Okhotsk", + "Weddell Sea", + "Persian Gulf", + "Norwegian Sea", + "Greenland Sea", + "Mozambique Channel", + "Baltic Sea", + "Barents Sea", + "North Sea", + "Irish Sea", + "Java Sea", + "Chukchi Sea", + "Arafura Sea", + "Laccadive Sea", + "Bellingshausen Sea", + "Davis Strait", + "Kara Sea", + "Laptev Sea", + "Great Australian Bight", + "Gulf of Oman", + "Gulf of Carpentaria", + "Gulf of Finland", + "Gulf of Bothnia", + "Strait of Malacca", + "Strait of Singapore", + "Gulf of Mannar", + "Bering Sea", + "East Siberian Sea", + "Storfjorden", + "Palk Strait", + "Shark Bay", + "Golfo de Guayaquil", + "Cook Strait", + "Geographe Bay", + "Gulf of Olen‘k", + "Gulf of Riga", + "Estrecho de Magellanes", + "Prydz Bay", + "Baia de Maputo", + "Chaun Bay", + "Khatanga Gulf", + "Gulf of Aqaba", + "Denmark Strait", + "George VI Sound", + "Seno de Skyring", + "Seno Otway", + "Bay Inútil", + "Kaliningrad", + "Sargasso Sea", + "Mediterranean Sea", + "Ross Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "b778081bef360f8d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81839:81846:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45371:45389:1'} The data starts from January 20 18:00 and ends on January 25 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Storm (General) is occuring in the country of Belgium.\nA Storm (General) is occuring in the country of Germany.\nA Storm (General) is occuring in the country of Denmark. Specifically the region(s) being affected are: Sydjylland, Vestjylland, Nordjylland\nA Storm (General) is occuring in the country of Finland.\nA Storm (General) is occuring in the country of France.\nA Storm (General) is occuring in the country of United Kingdom of Great Britain and Northern Ireland.\nA Storm (General) is occuring in the country of Ireland.\nA Storm (General) is occuring in the country of Luxembourg.\nA Storm (General) is occuring in the country of Netherlands (Kingdom of the).\nA Storm (General) is occuring in the country of Norway.\nA Storm (General) is occuring in the country of Poland.\nA Storm (General) is occuring in the country of Sweden.\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Belgium", + "Germany", + "Denmark", + "Finland", + "France", + "United Kingdom of Great Britain and Northern Ireland", + "Ireland", + "Luxembourg", + "Netherlands (Kingdom of the)", + "Norway", + "Poland", + "Sweden" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "a58eaab1bb5200d9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45371:45389:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69101:69128:1'} The data starts from April 19 06:00 and ends on April 25 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in 10-meter V component of wind values? An exceedance is defined as a period of at least 72 consecutive hours where the 10-meter V component of wind values exceed the 90th percentile climatology for the daily climatology for day 109. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in 10-meter V component of wind: Indonesia(average 0.6329 m/s)\nIndia(average 0.2747 m/s)\nRussia(average 1.141 m/s)\nNorway(average 1.325 m/s)\nMali(average 0.1793 m/s)\nPakistan(average 0.2747 m/s)\nThailand(average 0.3194 m/s)\nEast Timor(average 0.7604 m/s)\nAlgeria(average 0.3398 m/s)\nColombia(average 1.031 m/s)\nNiger(average 0.1448 m/s)\nAustralia(average 0.4462 m/s)\nFrench Polynesia(average 1.598 m/s)\nCook Islands(average 0.1574 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 109", + "quantile": "0.9", + "min_duration_days": 3, + "true_value": [ + "Indonesia", + "India", + "Russia", + "Norway", + "Mali", + "Pakistan", + "Thailand", + "East Timor", + "Algeria", + "Colombia", + "Niger", + "Australia", + "French Polynesia", + "Cook Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0df3266702c6f8e2", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69101:69128:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47203:47209:1'} The data starts from April 23 18:00 and ends on April 25 00:00. Based on the above data, answer the following question:", + "question": "What will the average U (zonal) component of wind at 700 hPa be in Canal do Norte, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the average U (zonal) component of wind at 700 hPa in Canal do Norte 12 hours after the given time window will be -6.438 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-6.4382234", + "location": "Canal do Norte", + "target_variable": "u_component_of_wind_700", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "70f89807a07b8305", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47203:47209:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82277:82280:1'} The data starts from April 26 06:00 and ends on April 26 18:00. Based on the above data, answer the following question:", + "question": "What will the maximum Geopotential at 500 hPa be in North America, 42 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum Geopotential at 500 hPa in North America 42 hours after the given time window will be 5.772e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "57715.64", + "location": "North America", + "target_variable": "geopotential_500", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b91ae1d1ec924c2a", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82277:82280:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38257:38284:1'} The data starts from March 09 06:00 and ends on March 15 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 30 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 30 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 30 hours:\nA Tropical cyclone is expected in the country of Fiji in approximately the next 30 hours. Specifically the region(s) that might get affected are: Western, Central divisions\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Fiji" + ], + "extreme_event_hours": 30, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "b473adc05e53a342", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38257:38284:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84648:84654:1'} The data starts from December 09 00:00 and ends on December 10 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) V (meridional) component of wind at 250 hPa values running above the 90th percentile climatology for the DJF seasonal climatology? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show V (meridional) component of wind at 250 hPa values above the 90th percentile climatology for DJF seasonal climatology: Arctic Ocean(average 10.5 m/s)\nSOUTHERN OCEAN(average 2.355 m/s)\nNorth Atlantic Ocean(average 9.204 m/s)\nNorth Pacific Ocean(average 5.26 m/s)\nSouth Pacific Ocean(average 5.256 m/s)\nINDIAN OCEAN(average 2.005 m/s)\nSouth Atlantic Ocean(average 5.389 m/s)\nPhilippine Sea(average 3.031 m/s)\nSea of Okhotsk(average 0.4576 m/s)\nBristol Channel(average 2.098 m/s)\nInner Seas(average 2.602 m/s)\nIrish Sea(average 2.863 m/s)\nTimor Sea(average 0.4467 m/s)\nLaptev Sea(average 2.385 m/s)\nDrake Passage(average 3.729 m/s)\nGulf of Aden(average 0.2596 m/s)\nBismarck Sea(average 0.6311 m/s)\nGolfo San Jorge(average 1.125 m/s)\nShelikhova Gulf(average 3.819 m/s)\nBering Sea(average 9.737 m/s)\nEast Siberian Sea(average 7.757 m/s)\nGolfo Corcovado(average 4.741 m/s)\nBransfield Strait(average 2.712 m/s)\nKronotskiy Gulf(average 3.326 m/s)\nGulf of Yana(average 2.008 m/s)\nDmitriy Laptev Strait(average 1.512 m/s)\nGulf of Sidra(average 0.3211 m/s)\nGolfo de Penas(average 2 m/s)\nKaraginskiy Gulf(average 10.77 m/s)\nGulf of Kamchatka(average 9.159 m/s)\nJoseph Bonaparte Gulf(average 0.7187 m/s)\nChaun Bay(average 1.078 m/s)\nOzero Mogotoyevo(average 0.9415 m/s)\nGuba Gusinaya(average 1.759 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "v_component_of_wind", + 250 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "DJF seasonal climatology", + "quantile": "0.9", + "threshold_direction": "above", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Sea of Okhotsk", + "Bristol Channel", + "Inner Seas", + "Irish Sea", + "Timor Sea", + "Laptev Sea", + "Drake Passage", + "Gulf of Aden", + "Bismarck Sea", + "Golfo San Jorge", + "Shelikhova Gulf", + "Bering Sea", + "East Siberian Sea", + "Golfo Corcovado", + "Bransfield Strait", + "Kronotskiy Gulf", + "Gulf of Yana", + "Dmitriy Laptev Strait", + "Gulf of Sidra", + "Golfo de Penas", + "Karaginskiy Gulf", + "Gulf of Kamchatka", + "Joseph Bonaparte Gulf", + "Chaun Bay", + "Ozero Mogotoyevo", + "Guba Gusinaya" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "44d4ac8f6b6ecfe3", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84648:84654:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81820:81827:1'} The data starts from January 02 00:00 and ends on January 03 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) Geopotential at 600 hPa values running below the 5th percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show Geopotential at 600 hPa values below the 5th percentile climatology for all-time climatology: North Pacific Ocean(average -305.8 m²/s²)\nINDIAN OCEAN(average -13.33 m²/s²)\nPhilippine Sea(average -70.61 m²/s²)\nArabian Sea(average -32.77 m²/s²)\nLabrador Sea(average -263.4 m²/s²)\nHudson Bay(average -342.6 m²/s²)\nSea of Okhotsk(average -299.2 m²/s²)\nNorwegian Sea(average -201.6 m²/s²)\nGreenland Sea(average -138.5 m²/s²)\nBaltic Sea(average -57.47 m²/s²)\nTimor Sea(average -11.23 m²/s²)\nDavis Strait(average -184.7 m²/s²)\nLaptev Sea(average -197.9 m²/s²)\nWhite Sea(average -50.36 m²/s²)\nJames Bay(average -302.8 m²/s²)\nGolfo de California(average -181.8 m²/s²)\nHudson Strait(average -195.4 m²/s²)\nGulf of Finland(average -114.3 m²/s²)\nGulf of Bothnia(average -249.6 m²/s²)\nGulf of Saint Lawrence(average -157.1 m²/s²)\nShelikhova Gulf(average -41.86 m²/s²)\nGulf of Kutch(average -7.902 m²/s²)\nUngava Bay(average -394.3 m²/s²)\nBering Sea(average -223 m²/s²)\nFrobisher Bay(average -91.16 m²/s²)\nHamilton Inlet(average -299.1 m²/s²)\nVestfjorden(average -221.4 m²/s²)\nTrondheimsfjorden(average -27.56 m²/s²)\nGulf of Khambhät(average -50.79 m²/s²)\nKronotskiy Gulf(average -404.5 m²/s²)\nUchiura Bay(average -203.8 m²/s²)\nTsugaru Strait(average -142.1 m²/s²)\nTatar Strait(average -38.57 m²/s²)\nLa Pérouse Strait(average -236.2 m²/s²)\nStrait of Belle Isle(average -50.88 m²/s²)\nKaraginskiy Gulf(average -117.1 m²/s²)\nGulf of Kamchatka(average -363.4 m²/s²)\nJoseph Bonaparte Gulf(average -27.24 m²/s²)\nGulf of Sakhalin(average -48.48 m²/s²)\nSaint Lawrence River(average -201.2 m²/s²)\nKhatanga Gulf(average -8.215 m²/s²)\nGulf of Ob(average -134.5 m²/s²)\nYenisey Gulf(average -3.828 m²/s²)\nSea of Japan(average -128.3 m²/s²)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "geopotential", + 600 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.05", + "threshold_direction": "below", + "true_value": [ + "North Pacific Ocean", + "INDIAN OCEAN", + "Philippine Sea", + "Arabian Sea", + "Labrador Sea", + "Hudson Bay", + "Sea of Okhotsk", + "Norwegian Sea", + "Greenland Sea", + "Baltic Sea", + "Timor Sea", + "Davis Strait", + "Laptev Sea", + "White Sea", + "James Bay", + "Golfo de California", + "Hudson Strait", + "Gulf of Finland", + "Gulf of Bothnia", + "Gulf of Saint Lawrence", + "Shelikhova Gulf", + "Gulf of Kutch", + "Ungava Bay", + "Bering Sea", + "Frobisher Bay", + "Hamilton Inlet", + "Vestfjorden", + "Trondheimsfjorden", + "Gulf of Khambhät", + "Kronotskiy Gulf", + "Uchiura Bay", + "Tsugaru Strait", + "Tatar Strait", + "La Pérouse Strait", + "Strait of Belle Isle", + "Karaginskiy Gulf", + "Gulf of Kamchatka", + "Joseph Bonaparte Gulf", + "Gulf of Sakhalin", + "Saint Lawrence River", + "Khatanga Gulf", + "Gulf of Ob", + "Yenisey Gulf", + "Sea of Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "2547f395fc7e7250", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81820:81827:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85692:85698:1'} The data starts from August 27 00:00 and ends on August 28 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: China; China, Hong Kong Special Administrative Region; China, Macao Special Administrative Region", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "China", + "China, Hong Kong Special Administrative Region", + "China, Macao Special Administrative Region" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "5d99d00784fac39e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85692:85698:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77814:77825:1'} The data starts from April 05 12:00 and ends on April 08 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Surface pressure values? An exceedance is defined as a period of at least 48 consecutive hours where the Surface pressure values exceed the 95th percentile climatology for the all-time climatology. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Surface pressure anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.95", + "min_duration_days": 2, + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ef30ec78a32d1b6b", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77814:77825:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87627:87651:1'} The data starts from December 23 18:00 and ends on December 29 12:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Blizzard/Winter Storm currently happening? Specify the affected countries or regions, or respond 'No Blizzard/Winter Storm detected.'", + "response": "Based on the provided data, the Blizzard/Winter Storm is affecting: United States of America; China", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "United States of America", + "China" + ], + "target_disaster": "Blizzard/Winter Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "c8e39a24842eba99", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87627:87651:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71253:71273:1'} The data starts from October 09 06:00 and ends on October 14 00:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) U (zonal) component of wind at 200 hPa differs from the SON seasonal climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below U (zonal) component of wind at 200 hPa values.", + "response": "Based on the provided data, no significant U (zonal) component of wind at 200 hPa anomalies were detected relative to the SON seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "u_component_of_wind", + 200 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "SON seasonal climatology", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "31f91fea4c157f15", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71253:71273:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74545:74550:1'} The data starts from January 09 06:00 and ends on January 10 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) Surface pressure values running below the 1st percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show Surface pressure values below the 1st percentile climatology for all-time climatology: North Pacific Ocean(average -570.7 Pa)\nSouth Pacific Ocean(average -85.02 Pa)\nINDIAN OCEAN(average -348.7 Pa)\nSouth Atlantic Ocean(average -37.78 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "8f45f497d08ffb80", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74545:74550:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56788:56806:1'} The data starts from November 14 00:00 and ends on November 18 06:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Blizzard/Winter Storm currently happening? Specify the affected countries or regions, or respond 'No Blizzard/Winter Storm detected.'", + "response": "No Blizzard/Winter Storm detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Blizzard/Winter Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "04d4257ffe3d0f53", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56788:56806:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78796:78800:1'} The data starts from December 07 00:00 and ends on December 07 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Specific humidity at 300 hPa lies outside the climatological 5th–95th percentile envelope for the all-time climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 5th–95th percentile envelope for Specific humidity at 300 hPa during all-time climatology: Indonesia(average 3.065e-06 kg/kg)\nChile(average 4.028e-05 kg/kg)\nBolivia(average 6.995e-05 kg/kg)\nPeru(average 3.553e-05 kg/kg)\nArgentina(average 0.0001168 kg/kg)\nChina(average -1.477e-07 kg/kg)\nEthiopia(average -5.125e-06 kg/kg)\nSomalia(average -1.326e-05 kg/kg)\nKenya(average -8.608e-06 kg/kg)\nWestern Sahara(average -3.991e-07 kg/kg)\nUkraine(average -1.281e-06 kg/kg)\nBelarus(average -5.209e-07 kg/kg)\nBrazil(average 0.0001294 kg/kg)\nUruguay(average 0.0001363 kg/kg)\nRussia(average -8.587e-07 kg/kg)\nTurkey(average -1.94e-06 kg/kg)\nSpain(average 2.53e-05 kg/kg)\nLibya(average 1.368e-06 kg/kg)\nRomania(average -1.231e-06 kg/kg)\nHungary(average -3.414e-07 kg/kg)\nSlovakia(average -6.248e-07 kg/kg)\nPoland(average -4.465e-07 kg/kg)\nUnited Kingdom(average -1.786e-06 kg/kg)\nGreece(average -5.945e-07 kg/kg)\nGuinea(average -1.322e-05 kg/kg)\nNetherlands(average -1.774e-06 kg/kg)\nMali(average 4.584e-06 kg/kg)\nSenegal(average -2.379e-05 kg/kg)\nZimbabwe(average 1.319e-05 kg/kg)\nBulgaria(average -1.763e-06 kg/kg)\nGuatemala(average -1.794e-05 kg/kg)\nAlgeria(average 1.305e-05 kg/kg)\nMozambique(average 1.625e-05 kg/kg)\nCuba(average -9.871e-06 kg/kg)\nBrazilian Island(average 0.0001591 kg/kg)\nPortugal(average 1.895e-05 kg/kg)\nMoldova(average -1.961e-06 kg/kg)\nNiger(average 6.519e-06 kg/kg)\nGuinea-Bissau(average -2.281e-05 kg/kg)\nUnited States of America(average -1.153e-06 kg/kg)\nCanada(average -8.663e-07 kg/kg)\nMexico(average -1.219e-05 kg/kg)\nBelize(average -2.006e-05 kg/kg)\nPapua New Guinea(average 1.7e-05 kg/kg)\nMauritania(average -1.632e-05 kg/kg)\nGambia(average -3.237e-05 kg/kg)\nThe Bahamas(average -5.731e-06 kg/kg)\nJapan(average -1.347e-06 kg/kg)\nFrench Polynesia(average 4.052e-05 kg/kg)\nKiribati(average -7.586e-06 kg/kg)\nCayman Islands(average -3.828e-06 kg/kg)\nBritish Indian Ocean Territory(average 3.646e-05 kg/kg)\nCook Islands(average 2.922e-06 kg/kg)\nSolomon Islands(average 7.441e-06 kg/kg)\nFederated States of Micronesia(average -7.564e-06 kg/kg)\nVanuatu(average 1.733e-05 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "specific_humidity", + 300 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "lower_quantile": "0.05", + "upper_quantile": "0.95", + "true_value": [ + "Indonesia", + "Chile", + "Bolivia", + "Peru", + "Argentina", + "China", + "Ethiopia", + "Somalia", + "Kenya", + "Western Sahara", + "Ukraine", + "Belarus", + "Brazil", + "Uruguay", + "Russia", + "Turkey", + "Spain", + "Libya", + "Romania", + "Hungary", + "Slovakia", + "Poland", + "United Kingdom", + "Greece", + "Guinea", + "Netherlands", + "Mali", + "Senegal", + "Zimbabwe", + "Bulgaria", + "Guatemala", + "Algeria", + "Mozambique", + "Cuba", + "Brazilian Island", + "Portugal", + "Moldova", + "Niger", + "Guinea-Bissau", + "United States of America", + "Canada", + "Mexico", + "Belize", + "Papua New Guinea", + "Mauritania", + "Gambia", + "The Bahamas", + "Japan", + "French Polynesia", + "Kiribati", + "Cayman Islands", + "British Indian Ocean Territory", + "Cook Islands", + "Solomon Islands", + "Federated States of Micronesia", + "Vanuatu" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "5ab7f759f9b03ad0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78796:78800:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51762:51769:1'} The data starts from June 06 12:00 and ends on June 08 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: China", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "China" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "c1ce4f3dc951eb64", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51762:51769:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72344:72348:1'} The data starts from July 08 00:00 and ends on July 08 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in 10-meter U component of wind values? An exceedance is defined as a period of at least 24 consecutive hours where the 10-meter U component of wind values exceed the 99th percentile climatology for the all-time climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in 10-meter U component of wind: SOUTHERN OCEAN(average 2.196 m/s)\nNorth Pacific Ocean(average 0.3816 m/s)\nWeddell Sea(average 3.782 m/s)\nBering Sea(average 0.2771 m/s)\nWrigley Gulf(average 0.202 m/s)\nGulf of Anadyr'(average 0.04478 m/s)\nRoss Sea(average 0.5539 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.99", + "min_duration_days": 1, + "true_value": [ + "SOUTHERN OCEAN", + "North Pacific Ocean", + "Weddell Sea", + "Bering Sea", + "Wrigley Gulf", + "Gulf of Anadyr'", + "Ross Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "c936e043f3e7c6bb", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72344:72348:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84348:84376:1'} The data starts from September 25 00:00 and ends on October 01 18:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: China; Taiwan (Province of China); Bahamas; Cuba; Haiti; Jamaica; Saint Lucia; Saint Vincent and the Grenadines; Philippines", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "China", + "Taiwan (Province of China)", + "Bahamas", + "Cuba", + "Haiti", + "Jamaica", + "Saint Lucia", + "Saint Vincent and the Grenadines", + "Philippines" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "2c790fe11386b0ea", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84348:84376:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35147:35153:1'} The data starts from January 21 18:00 and ends on January 23 00:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will Vatican experience its lowest Mean sea level pressure? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Vatican will experience its lowest Mean sea level pressure of 1.032e+05 Pa 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "Vatican", + "extremum_value": "103206.71", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5a6b20621f1c7eda", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35147:35153:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85831:85845:1'} The data starts from September 30 18:00 and ends on October 04 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Surface temperature values running below the 1st percentile climatology for the six-hourly climatology for day 273 at 18 UTC? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Surface temperature values below the 1st percentile climatology for six-hourly climatology for day 273 at 18 UTC: Chile(average -1.805 K)\nBolivia(average -3.2 K)\nPeru(average -1.551 K)\nArgentina(average -2.427 K)\nSuriname(average -0.5117 K)\nGuyana(average -1.198 K)\nMorocco(average -0.6884 K)\nWestern Sahara(average -0.8111 K)\nCosta Rica(average -0.5262 K)\nNicaragua(average -0.4299 K)\nNamibia(average -0.2299 K)\nKazakhstan(average -0.136 K)\nBrazil(average -1.295 K)\nUruguay(average -0.1368 K)\nRussia(average -0.3076 K)\nTurkey(average -0.5992 K)\nLibya(average -1.209 K)\nTunisia(average -1.282 K)\nGreece(average -0.4226 K)\nSudan(average -0.6237 K)\nItaly(average -0.1543 K)\nMali(average -1.223 K)\nHaiti(average -0.6585 K)\nDominican Republic(average -0.6513 K)\nChad(average -0.1031 K)\nGuatemala(average -0.2506 K)\nAlgeria(average -1.158 K)\nCuba(average -0.9082 K)\nHonduras(average -0.7073 K)\nEcuador(average -0.8004 K)\nColombia(average -0.4598 K)\nNiger(average -0.1769 K)\nUnited States of America(average -0.7942 K)\nCanada(average -0.6269 K)\nMexico(average -0.5623 K)\nBelize(average -0.4038 K)\nVenezuela(average -0.921 K)\nMauritania(average -1.021 K)\nPitcairn Islands(average -0.5326 K)\nFrench Polynesia(average -0.2703 K)\nTrinidad and Tobago(average -0.321 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 273 at 18 UTC", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "Chile", + "Bolivia", + "Peru", + "Argentina", + "Suriname", + "Guyana", + "Morocco", + "Western Sahara", + "Costa Rica", + "Nicaragua", + "Namibia", + "Kazakhstan", + "Brazil", + "Uruguay", + "Russia", + "Turkey", + "Libya", + "Tunisia", + "Greece", + "Sudan", + "Italy", + "Mali", + "Haiti", + "Dominican Republic", + "Chad", + "Guatemala", + "Algeria", + "Cuba", + "Honduras", + "Ecuador", + "Colombia", + "Niger", + "United States of America", + "Canada", + "Mexico", + "Belize", + "Venezuela", + "Mauritania", + "Pitcairn Islands", + "French Polynesia", + "Trinidad and Tobago" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "4ef9ee2035f9cc11", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85831:85845:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78469:78479:1'} The data starts from September 16 06:00 and ends on September 18 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Specific humidity at 250 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the Specific humidity at 250 hPa values exceed the 90th percentile climatology for the six-hourly climatology for day 260 at 06 UTC.", + "response": "The following water body(s) are currently experiencing an exceedance in Specific humidity at 250 hPa: Arctic Ocean(average 3.494e-06 kg/kg)\nSOUTHERN OCEAN(average 2.294e-06 kg/kg)\nNorth Atlantic Ocean(average 3.252e-05 kg/kg)\nNorth Pacific Ocean(average 2.511e-05 kg/kg)\nSouth Pacific Ocean(average 2.684e-05 kg/kg)\nINDIAN OCEAN(average 1.05e-05 kg/kg)\nSouth Atlantic Ocean(average 2.739e-05 kg/kg)\nBlack Sea(average 3.743e-06 kg/kg)\nPhilippine Sea(average 2.979e-05 kg/kg)\nBay of Bengal(average 2.33e-05 kg/kg)\nSouth China Sea(average 2.657e-05 kg/kg)\nArabian Sea(average 2.113e-05 kg/kg)\nBeaufort Sea(average 5.228e-06 kg/kg)\nCaribbean Sea(average 3.104e-05 kg/kg)\nLabrador Sea(average 6.263e-06 kg/kg)\nRed Sea(average 3.479e-06 kg/kg)\nSea of Okhotsk(average 7.044e-05 kg/kg)\nWeddell Sea(average 3.188e-07 kg/kg)\nPersian Gulf(average 5.387e-06 kg/kg)\nGreenland Sea(average 6.814e-06 kg/kg)\nBay of Biscay(average 1.069e-05 kg/kg)\nBarents Sea(average 5.4e-07 kg/kg)\nYellow Sea(average 1.28e-05 kg/kg)\nEast China Sea(average 3.592e-05 kg/kg)\nKara Sea(average 4.78e-07 kg/kg)\nLaptev Sea(average 1.412e-06 kg/kg)\nGulf of Oman(average 1.12e-06 kg/kg)\nGulf of Finland(average 5.793e-06 kg/kg)\nGolfe du Lion(average 2.936e-05 kg/kg)\nGulf of Bothnia(average 9.111e-07 kg/kg)\nBismarck Sea(average 3.255e-05 kg/kg)\nSolomon Sea(average 3.255e-05 kg/kg)\nRío de la Plata(average 3.239e-06 kg/kg)\nBalearic Sea(average 4.162e-05 kg/kg)\nEast Siberian Sea(average 8.62e-07 kg/kg)\nLincoln Sea(average 5.575e-06 kg/kg)\nBaía de Marajó(average 3.49e-05 kg/kg)\nBaía de São Marcos(average 6.19e-05 kg/kg)\nLa Pérouse Strait(average 9.575e-05 kg/kg)\nGulf of Suez(average 8.046e-06 kg/kg)\nGulf of Aqaba(average 5.19e-06 kg/kg)\nLagoa dos Patos(average 5.666e-05 kg/kg)\nAmazon River(average 2.937e-05 kg/kg)\nSibuyan Sea(average 4.04e-06 kg/kg)\nRagay Gulf(average 4.04e-06 kg/kg)\nSamar Sea(average 4.04e-06 kg/kg)\nCanal Perigoso(average 2.937e-05 kg/kg)\nCanal do Sul(average 3.208e-05 kg/kg)\nCanal do Norte(average 2.731e-05 kg/kg)\nMediterranean Sea(average 2.486e-05 kg/kg)\nRoss Sea(average 4.254e-07 kg/kg)\nCoral Sea(average 2.973e-05 kg/kg)\nSea of Japan(average 6.532e-05 kg/kg)\nKorea Strait(average 2.204e-05 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 250 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 260 at 06 UTC", + "quantile": "0.9", + "min_duration_days": 2, + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Black Sea", + "Philippine Sea", + "Bay of Bengal", + "South China Sea", + "Arabian Sea", + "Beaufort Sea", + "Caribbean Sea", + "Labrador Sea", + "Red Sea", + "Sea of Okhotsk", + "Weddell Sea", + "Persian Gulf", + "Greenland Sea", + "Bay of Biscay", + "Barents Sea", + "Yellow Sea", + "East China Sea", + "Kara Sea", + "Laptev Sea", + "Gulf of Oman", + "Gulf of Finland", + "Golfe du Lion", + "Gulf of Bothnia", + "Bismarck Sea", + "Solomon Sea", + "Río de la Plata", + "Balearic Sea", + "East Siberian Sea", + "Lincoln Sea", + "Baía de Marajó", + "Baía de São Marcos", + "La Pérouse Strait", + "Gulf of Suez", + "Gulf of Aqaba", + "Lagoa dos Patos", + "Amazon River", + "Sibuyan Sea", + "Ragay Gulf", + "Samar Sea", + "Canal Perigoso", + "Canal do Sul", + "Canal do Norte", + "Mediterranean Sea", + "Ross Sea", + "Coral Sea", + "Sea of Japan", + "Korea Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ac8e29566d167e88", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78469:78479:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81024:81045:1'} The data starts from June 17 00:00 and ends on June 22 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Surface pressure differs from the JJA seasonal climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Surface pressure values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Surface pressure anomalies were detected relative to the JJA seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "JJA seasonal climatology", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "23880ab8d7bb6e81", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81024:81045:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92776:92792:1'} The data starts from July 03 00:00 and ends on July 06 18:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Japan", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Japan" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "351d713027ba0ea1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92776:92792:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87783:87797:1'} The data starts from January 31 18:00 and ends on February 04 00:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will South Africa experience its lowest Surface temperature? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, South Africa will experience its lowest Surface temperature of 280.1 K 48 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 48, + "location": "South Africa", + "extremum_value": "280.1399", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "e045d8b0cc49d888", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87783:87797:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89267:89294:1'} The data starts from February 06 18:00 and ends on February 13 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Geopotential at 300 hPa values? An exceedance is defined as a period of at least 120 consecutive hours where the Geopotential at 300 hPa values exceed the 99th percentile climatology for the monthly climatology for February. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Geopotential at 300 hPa: Oman(average 9.31 m²/s²)\nSudan(average 2.934 m²/s²)\nSaudi Arabia(average 8.109 m²/s²)\nCuba(average 56.19 m²/s²)\nPhilippines(average 29.17 m²/s²)\nThe Bahamas(average 5.125 m²/s²)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "geopotential", + 300 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for February", + "quantile": "0.99", + "min_duration_days": 5, + "true_value": [ + "Oman", + "Sudan", + "Saudi Arabia", + "Cuba", + "Philippines", + "The Bahamas" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d44fe7b8249b3bb6", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89267:89294:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81951:81960:1'} The data starts from February 03 18:00 and ends on February 05 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) 10-meter U component of wind values running below the 1st percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous.", + "response": "Based on the provided data, no significant 10-meter U component of wind anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "20d6b87b69446942", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81951:81960:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88654:88674:1'} The data starts from September 06 12:00 and ends on September 11 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Specific humidity at 1000 hPa values? An exceedance is defined as a period of at least 96 consecutive hours where the Specific humidity at 1000 hPa values exceed the 99th percentile climatology for the six-hourly climatology for day 249 at 12 UTC.", + "response": "The following water body(s) are currently experiencing an exceedance in Specific humidity at 1000 hPa: Arctic Ocean(average 0.0003468 kg/kg)\nNorth Atlantic Ocean(average 0.0002705 kg/kg)\nNorth Pacific Ocean(average 0.0006467 kg/kg)\nSouth Pacific Ocean(average 0.0001391 kg/kg)\nINDIAN OCEAN(average 0.0001072 kg/kg)\nSouth Atlantic Ocean(average 0.0004102 kg/kg)\nPhilippine Sea(average 0.000943 kg/kg)\nSouth China Sea(average 0.0002151 kg/kg)\nArabian Sea(average 0.0003449 kg/kg)\nBeaufort Sea(average 0.0003728 kg/kg)\nCaribbean Sea(average 0.0003906 kg/kg)\nPersian Gulf(average 0.0009566 kg/kg)\nCelebes Sea(average 6.909e-05 kg/kg)\nSulu Sea(average 6.909e-05 kg/kg)\nGulf of Guinea(average 0.0002312 kg/kg)\nAndaman Sea(average 3.015e-05 kg/kg)\nChukchi Sea(average 0.0005995 kg/kg)\nLaccadive Sea(average 0.0003392 kg/kg)\nGulf of Oman(average 0.0003586 kg/kg)\nAdriatic Sea(average 0.0001255 kg/kg)\nIonian Sea(average 0.0001255 kg/kg)\nInner Sea(average 0.0005007 kg/kg)\nGulf of Kutch(average 0.0005664 kg/kg)\nGolfo de Panamá(average 0.0003108 kg/kg)\nBering Sea(average 9.713e-05 kg/kg)\nEast Siberian Sea(average 0.0002355 kg/kg)\nGulf of Khambhät(average 0.0002346 kg/kg)\nMackenzie Bay(average 0.0002614 kg/kg)\nNorton Sound(average 8.061e-05 kg/kg)\nKotzebue Sound(average 0.0008685 kg/kg)\nBight of Benin(average 0.0001604 kg/kg)\nBight of Biafra(average 0.0002631 kg/kg)\nGolfo de Urabá(average 0.0001932 kg/kg)\nHusky Lakes(average 0.0002325 kg/kg)\nSalish Sea(average 1.54e-05 kg/kg)\nMediterranean Sea(average 0.002371 kg/kg)\nSea of Japan(average 0.000417 kg/kg)\nKorea Strait(average 0.0006219 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 1000 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 249 at 12 UTC", + "quantile": "0.99", + "min_duration_days": 4, + "true_value": [ + "Arctic Ocean", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "South China Sea", + "Arabian Sea", + "Beaufort Sea", + "Caribbean Sea", + "Persian Gulf", + "Celebes Sea", + "Sulu Sea", + "Gulf of Guinea", + "Andaman Sea", + "Chukchi Sea", + "Laccadive Sea", + "Gulf of Oman", + "Adriatic Sea", + "Ionian Sea", + "Inner Sea", + "Gulf of Kutch", + "Golfo de Panamá", + "Bering Sea", + "East Siberian Sea", + "Gulf of Khambhät", + "Mackenzie Bay", + "Norton Sound", + "Kotzebue Sound", + "Bight of Benin", + "Bight of Biafra", + "Golfo de Urabá", + "Husky Lakes", + "Salish Sea", + "Mediterranean Sea", + "Sea of Japan", + "Korea Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "52f8c34992fd5773", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88654:88674:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35134:35160:1'} The data starts from January 18 12:00 and ends on January 24 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Severe weather is occuring in the country of Poland. Specifically the region(s) being affected are: Baltic coastline\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Poland" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "ee464ad5436a567e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35134:35160:1" + } + } +] \ No newline at end of file diff --git a/level2a_part3.json b/level2a_part3.json new file mode 100644 index 0000000000000000000000000000000000000000..0902dfa30972807205e60dce30fe843f92e87d1c --- /dev/null +++ b/level2a_part3.json @@ -0,0 +1,5082 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42320:42348:1'} The data starts from December 20 00:00 and ends on December 26 18:00. Based on the above data, answer the following question:", + "question": "In the 48 hours after the end of the given time window, when will Oceania experience its highest Surface temperature? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Oceania will experience its highest Surface temperature of 315.8 K 36 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 36, + "location": "Oceania", + "extremum_value": "315.8095", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "cbf537b8c210266c", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42320:42348:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87633:87658:1'} The data starts from December 25 06:00 and ends on December 31 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in 10-meter U component of wind values? An exceedance is defined as a period of at least 120 consecutive hours where the 10-meter U component of wind values exceed the 90th percentile climatology for the six-hourly climatology for day 359 at 06 UTC. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in 10-meter U component of wind: Indonesia(average 1.104 m/s)\nMalaysia(average 1.642 m/s)\nChile(average 0.9254 m/s)\nArgentina(average 0.7433 m/s)\nIndia(average 0.8652 m/s)\nChina(average 0.24 m/s)\nEthiopia(average 0.6204 m/s)\nSouth Sudan(average 0.7913 m/s)\nFrance(average 0.6793 m/s)\nRepublic of the Congo(average 0.3128 m/s)\nUkraine(average 0.57 m/s)\nNamibia(average 1.059 m/s)\nKazakhstan(average 0.2776 m/s)\nBrazil(average 0.3081 m/s)\nRussia(average 0.9339 m/s)\nCzechia(average 0.05775 m/s)\nGermany(average 0.2996 m/s)\nNorway(average 0.5303 m/s)\nSweden(average 0.423 m/s)\nVietnam(average 0.4384 m/s)\nCambodia(average 0.4478 m/s)\nNorth Macedonia(average 0.5922 m/s)\nKosovo(average 0.203 m/s)\nTurkey(average 0.4541 m/s)\nDenmark(average 0.36 m/s)\nRomania(average 0.6049 m/s)\nHungary(average 0.2891 m/s)\nSlovakia(average 0.05269 m/s)\nPoland(average 0.1201 m/s)\nGreece(average 0.3256 m/s)\nSudan(average 0.7615 m/s)\nIran(average 0.1289 m/s)\nRepublic of Serbia(average 0.4966 m/s)\nAngola(average 0.4174 m/s)\nBulgaria(average 0.5379 m/s)\nThailand(average 1.561 m/s)\nBrunei(average 1.653 m/s)\nMyanmar(average 1.375 m/s)\nBangladesh(average 0.1923 m/s)\nAfghanistan(average 0.1849 m/s)\nMoldova(average 0.9368 m/s)\nTurkmenistan(average 0.1549 m/s)\nNepal(average 0.08188 m/s)\nGabon(average 0.3343 m/s)\nUnited States of America(average 0.2865 m/s)\nCanada(average 0.7223 m/s)\nPapua New Guinea(average 1.16 m/s)\nGreenland(average 0.4302 m/s)\nMadagascar(average 0.6078 m/s)\nPhilippines(average 1.562 m/s)\nJapan(average 0.1852 m/s)\nFrench Southern and Antarctic Lands(average 0.24 m/s)\nMauritius(average 0.6991 m/s)\nIndian Ocean Territories(average 0.6566 m/s)\nSingapore(average 1.021 m/s)\nPalau(average 0.4792 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 359 at 06 UTC", + "quantile": "0.9", + "min_duration_days": 5, + "true_value": [ + "Indonesia", + "Malaysia", + "Chile", + "Argentina", + "India", + "China", + "Ethiopia", + "South Sudan", + "France", + "Republic of the Congo", + "Ukraine", + "Namibia", + "Kazakhstan", + "Brazil", + "Russia", + "Czechia", + "Germany", + "Norway", + "Sweden", + "Vietnam", + "Cambodia", + "North Macedonia", + "Kosovo", + "Turkey", + "Denmark", + "Romania", + "Hungary", + "Slovakia", + "Poland", + "Greece", + "Sudan", + "Iran", + "Republic of Serbia", + "Angola", + "Bulgaria", + "Thailand", + "Brunei", + "Myanmar", + "Bangladesh", + "Afghanistan", + "Moldova", + "Turkmenistan", + "Nepal", + "Gabon", + "United States of America", + "Canada", + "Papua New Guinea", + "Greenland", + "Madagascar", + "Philippines", + "Japan", + "French Southern and Antarctic Lands", + "Mauritius", + "Indian Ocean Territories", + "Singapore", + "Palau" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "53cb4b5354fd0c56", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87633:87658:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77668:77695:1'} The data starts from February 29 00:00 and ends on March 06 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) 10-meter U component of wind values running above the 95th percentile climatology for the six-hourly climatology for day 60 at 00 UTC? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show 10-meter U component of wind values above the 95th percentile climatology for six-hourly climatology for day 60 at 00 UTC: Arctic Ocean(average 1.262 m/s)\nSOUTHERN OCEAN(average 0.2481 m/s)\nNorth Atlantic Ocean(average 0.3883 m/s)\nNorth Pacific Ocean(average 0.7269 m/s)\nSouth Pacific Ocean(average 1.085 m/s)\nINDIAN OCEAN(average 0.4199 m/s)\nSouth Atlantic Ocean(average 0.5491 m/s)\nBlack Sea(average 0.4204 m/s)\nBay of Bengal(average 0.6016 m/s)\nSouth China Sea(average 0.2905 m/s)\nArabian Sea(average 0.1689 m/s)\nGulf of Mexico(average 0.427 m/s)\nCaspian Sea(average 0.8483 m/s)\nGulf of Alaska(average 0.7973 m/s)\nRed Sea(average 0.9373 m/s)\nSea of Okhotsk(average 0.7459 m/s)\nPersian Gulf(average 0.3964 m/s)\nMozambique Channel(average 0.3013 m/s)\nJava Sea(average 0.08513 m/s)\nAndaman Sea(average 0.4632 m/s)\nBahía de Campeche(average 0.04319 m/s)\nGulf of Thailand(average 0.3257 m/s)\nLaccadive Sea(average 0.5068 m/s)\nGulf of Aden(average 0.01034 m/s)\nGulf of Oman(average 0.2053 m/s)\nGulf of Honduras(average 0.1377 m/s)\nGulf of Tonkin(average 0.06711 m/s)\nStrait of Malacca(average 0.345 m/s)\nStrait of Singapore(average 0.5284 m/s)\nGolfo San Jorge(average 0.8623 m/s)\nGulf of Mannar(average 0.9661 m/s)\nGolfo San Matías(average 1.173 m/s)\nPalk Strait(average 1.339 m/s)\nQiongzhou Strait(average 0.09902 m/s)\nGulf of Martaban(average 0.4993 m/s)\nGulf of Suez(average 1.474 m/s)\nGulf of Aqaba(average 1.33 m/s)\nHecate Strait(average 0.2824 m/s)\nCordova Bay(average 0.3397 m/s)\nMediterranean Sea(average 0.3958 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 60 at 00 UTC", + "quantile": "0.95", + "threshold_direction": "above", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Black Sea", + "Bay of Bengal", + "South China Sea", + "Arabian Sea", + "Gulf of Mexico", + "Caspian Sea", + "Gulf of Alaska", + "Red Sea", + "Sea of Okhotsk", + "Persian Gulf", + "Mozambique Channel", + "Java Sea", + "Andaman Sea", + "Bahía de Campeche", + "Gulf of Thailand", + "Laccadive Sea", + "Gulf of Aden", + "Gulf of Oman", + "Gulf of Honduras", + "Gulf of Tonkin", + "Strait of Malacca", + "Strait of Singapore", + "Golfo San Jorge", + "Gulf of Mannar", + "Golfo San Matías", + "Palk Strait", + "Qiongzhou Strait", + "Gulf of Martaban", + "Gulf of Suez", + "Gulf of Aqaba", + "Hecate Strait", + "Cordova Bay", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0f1184ab532b6668", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77668:77695:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29776:29793:1'} The data starts from May 20 00:00 and ends on May 24 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 36 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 36 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 36 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 36, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "a081d8ef53b2da08", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29776:29793:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70860:70885:1'} The data starts from July 03 00:00 and ends on July 09 00:00. Based on the above data, answer the following question:", + "question": "What will the minimum Temperature at 925 hPa be in Antarctica, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum Temperature at 925 hPa in Antarctica 12 hours after the given time window will be 233.6 K.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "233.60031", + "location": "Antarctica", + "target_variable": "temperature_925", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cba2eee963cab4f5", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70860:70885:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46399:46418:1'} The data starts from October 04 18:00 and ends on October 09 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Thailand. Specifically the region(s) being affected are: Central and northeastern Provinces\nA Tropical cyclone is occuring in the country of Bangladesh. Specifically the region(s) being affected are: Chittagong\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Thailand", + "Bangladesh" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "49116f1a682e7745", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46399:46418:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91131:91149:1'} The data starts from May 17 18:00 and ends on May 22 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Mean sea level pressure values? An exceedance is defined as a period of at least 48 consecutive hours where the Mean sea level pressure values exceed the 90th percentile climatology for the daily climatology for day 137.", + "response": "The following water body(s) are currently experiencing an exceedance in Mean sea level pressure: Arctic Ocean(average 23.48 Pa)\nSOUTHERN OCEAN(average 319.2 Pa)\nNorth Atlantic Ocean(average 212.2 Pa)\nNorth Pacific Ocean(average 31.6 Pa)\nSouth Pacific Ocean(average 114 Pa)\nSouth Atlantic Ocean(average 421.1 Pa)\nBeaufort Sea(average 23.48 Pa)\nCaribbean Sea(average 26.64 Pa)\nGulf of Mexico(average 106.4 Pa)\nLabrador Sea(average 161.9 Pa)\nScotia Sea(average 359.8 Pa)\nKara Sea(average 12.66 Pa)\nDrake Passage(average 45.78 Pa)\nTyrrhenian Sea(average 35.95 Pa)\nStraits of Florida(average 100.4 Pa)\nGulf of Saint Lawrence(average 144.7 Pa)\nBay of Fundy(average 19.91 Pa)\nGulf of Maine(average 33.15 Pa)\nChesapeake Bay(average 374.9 Pa)\nBransfield Strait(average 326.7 Pa)\nStrait of Belle Isle(average 110.7 Pa)\nSaint Lawrence River(average 114.8 Pa)\nMassachusetts Bay(average 47.7 Pa)\nDelaware Bay(average 326.7 Pa)\nLong Island Sound(average 200 Pa)\nAlbemarle Sound(average 445 Pa)\nPamlico Sound(average 454.7 Pa)\nBras d'Or Lake(average 84.94 Pa)\nSargasso Sea(average 183.7 Pa)\nMediterranean Sea(average 31.62 Pa)\nCoral Sea(average 1.023 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 137", + "quantile": "0.9", + "min_duration_days": 2, + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "South Atlantic Ocean", + "Beaufort Sea", + "Caribbean Sea", + "Gulf of Mexico", + "Labrador Sea", + "Scotia Sea", + "Kara Sea", + "Drake Passage", + "Tyrrhenian Sea", + "Straits of Florida", + "Gulf of Saint Lawrence", + "Bay of Fundy", + "Gulf of Maine", + "Chesapeake Bay", + "Bransfield Strait", + "Strait of Belle Isle", + "Saint Lawrence River", + "Massachusetts Bay", + "Delaware Bay", + "Long Island Sound", + "Albemarle Sound", + "Pamlico Sound", + "Bras d'Or Lake", + "Sargasso Sea", + "Mediterranean Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "662d54189c1c1f72", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91131:91149:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91718:91723:1'} The data starts from October 11 12:00 and ends on October 12 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface pressure differs from the monthly climatology for October mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Surface pressure values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for Surface pressure relative to the monthly climatology for October mean: North Pacific Ocean(average -864.1 Pa)\nPhilippine Sea(average -1354 Pa)\nSouth China Sea(average -1330 Pa)\nSulu Sea(average -730.8 Pa)\nLuzon Strait(average -1489 Pa)\nEast China Sea(average -983.9 Pa)\nTaiwan Strait(average -1334 Pa)\nSibuyan Sea(average -808.9 Pa)\nRagay Gulf(average -883.8 Pa)\nTayabas Bay(average -897.4 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for October", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "North Pacific Ocean", + "Philippine Sea", + "South China Sea", + "Sulu Sea", + "Luzon Strait", + "East China Sea", + "Taiwan Strait", + "Sibuyan Sea", + "Ragay Gulf", + "Tayabas Bay" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "045787bdd4fff786", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91718:91723:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87360:87381:1'} The data starts from October 18 00:00 and ends on October 23 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Mexico; Mexico", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Mexico", + "Mexico" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "00908129c1f94954", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87360:87381:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92454:92468:1'} The data starts from April 13 12:00 and ends on April 16 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 850 hPa lies outside the climatological 5th–95th percentile envelope for the daily climatology for day 103. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 5th–95th percentile envelope for Temperature at 850 hPa during daily climatology for day 103: Arctic Ocean(average 1.37 K)\nNorth Atlantic Ocean(average 0.6984 K)\nNorth Pacific Ocean(average -0.522 K)\nSouth Pacific Ocean(average -0.2771 K)\nINDIAN OCEAN(average 0.1923 K)\nSouth Atlantic Ocean(average -0.1953 K)\nBlack Sea(average -0.1552 K)\nPhilippine Sea(average 0.105 K)\nSouth China Sea(average -0.1422 K)\nArabian Sea(average 0.7556 K)\nBaffin Bay(average -0.1427 K)\nWeddell Sea(average -0.6284 K)\nPersian Gulf(average 1.125 K)\nNorwegian Sea(average 0.7856 K)\nGreenland Sea(average 1.811 K)\nBay of Biscay(average 0.3871 K)\nMozambique Channel(average 0.311 K)\nNorth Sea(average 0.5459 K)\nInner Seas(average 0.4789 K)\nIrish Sea(average 0.2441 K)\nJava Sea(average 0.08745 K)\nChukchi Sea(average 1.215 K)\nLaccadive Sea(average -0.008433 K)\nGreat Australian Bight(average 0.2451 K)\nGulf of Aden(average 0.9277 K)\nGulf of Oman(average 1.396 K)\nTyrrhenian Sea(average 1.523 K)\nGulf of Carpentaria(average 0.02271 K)\nEnglish Channel(average 0.2243 K)\nGolfe du Lion(average 2.383 K)\nAdriatic Sea(average 1.808 K)\nThe North Western Passages(average -0.9682 K)\nQueen Charlotte Sound(average -0.3686 K)\nIonian Sea(average 0.8455 K)\nBismarck Sea(average 0.1107 K)\nMakassar Strait(average 0.1028 K)\nBalearic Sea(average 1.666 K)\nViscount Melville Sound(average -0.06298 K)\nEast Siberian Sea(average 1.915 K)\nDixon Entrance(average -0.3398 K)\nSognefjorden(average 0.677 K)\nTrondheimsfjorden(average 0.2853 K)\nGulf of Khambhät(average 0.2357 K)\nGulf of Masira(average 1.932 K)\nGulf of Sidra(average 0.519 K)\nLigurian Sea(average 3.661 K)\nPrydz Bay(average -0.8405 K)\nBab el Mandeb(average 1.042 K)\nChaun Bay(average 1.896 K)\nDenmark Strait(average 1.146 K)\nWynniatt Bay(average -0.00206 K)\nHadley Bay(average -0.07244 K)\nGoldsmith Channel(average -0.06808 K)\nJones Sound(average -0.2317 K)\nEclipse Sound(average -0.4128 K)\nLagoa dos Patos(average -1.288 K)\nHecate Strait(average -0.4821 K)\nCordova Bay(average -0.3398 K)\nSargasso Sea(average 0.4834 K)\nColumbia River(average -1.799 K)\nSalish Sea(average -1.18 K)\nMediterranean Sea(average 2.32 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "temperature", + 850 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 103", + "lower_quantile": "0.05", + "upper_quantile": "0.95", + "true_value": [ + "Arctic Ocean", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Black Sea", + "Philippine Sea", + "South China Sea", + "Arabian Sea", + "Baffin Bay", + "Weddell Sea", + "Persian Gulf", + "Norwegian Sea", + "Greenland Sea", + "Bay of Biscay", + "Mozambique Channel", + "North Sea", + "Inner Seas", + "Irish Sea", + "Java Sea", + "Chukchi Sea", + "Laccadive Sea", + "Great Australian Bight", + "Gulf of Aden", + "Gulf of Oman", + "Tyrrhenian Sea", + "Gulf of Carpentaria", + "English Channel", + "Golfe du Lion", + "Adriatic Sea", + "The North Western Passages", + "Queen Charlotte Sound", + "Ionian Sea", + "Bismarck Sea", + "Makassar Strait", + "Balearic Sea", + "Viscount Melville Sound", + "East Siberian Sea", + "Dixon Entrance", + "Sognefjorden", + "Trondheimsfjorden", + "Gulf of Khambhät", + "Gulf of Masira", + "Gulf of Sidra", + "Ligurian Sea", + "Prydz Bay", + "Bab el Mandeb", + "Chaun Bay", + "Denmark Strait", + "Wynniatt Bay", + "Hadley Bay", + "Goldsmith Channel", + "Jones Sound", + "Eclipse Sound", + "Lagoa dos Patos", + "Hecate Strait", + "Cordova Bay", + "Sargasso Sea", + "Columbia River", + "Salish Sea", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "8aca2b59c8b384f0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92454:92468:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42492:42520:1'} The data starts from February 01 00:00 and ends on February 07 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, there is no extreme weather event occuring.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "e82418340f2642e6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42492:42520:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68559:68584:1'} The data starts from December 04 18:00 and ends on December 10 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Geopotential at 400 hPa values? An exceedance is defined as a period of at least 120 consecutive hours where the Geopotential at 400 hPa values exceed the 99th percentile climatology for the DJF seasonal climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in Geopotential at 400 hPa: Kara Sea(average 191.5 m²/s²)\nLaptev Sea(average 45.04 m²/s²)\nKhatanga Gulf(average 132.5 m²/s²)\nYenisey Gulf(average 58.23 m²/s²)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "geopotential", + 400 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "DJF seasonal climatology", + "quantile": "0.99", + "min_duration_days": 5, + "true_value": [ + "Kara Sea", + "Laptev Sea", + "Khatanga Gulf", + "Yenisey Gulf" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "75e497b201a61beb", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68559:68584:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42937:42955:1'} The data starts from May 22 06:00 and ends on May 26 12:00. Based on the above data, answer the following question:", + "question": "In the 24 hours after the end of the given time window, when will Guba Gusinaya experience its lowest Surface temperature? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Guba Gusinaya will experience its lowest Surface temperature of 267.2 K 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "Guba Gusinaya", + "extremum_value": "267.23502", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "79188850af40e9dd", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42937:42955:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72581:72605:1'} The data starts from September 05 06:00 and ends on September 11 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Bahamas; Cuba; Haiti; Turks and Caicos Islands", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Bahamas", + "Cuba", + "Haiti", + "Turks and Caicos Islands" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "d2b2548a582aad9b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72581:72605:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53471:53493:1'} The data starts from August 07 18:00 and ends on August 13 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Storm (General) is occuring in the country of Liberia. Specifically the region(s) being affected are: Monrovia\nA Tropical cyclone is occuring in the country of Mexico. Specifically the region(s) being affected are: Chiapas & Oaxaca\nA Tropical cyclone is occuring in the country of Thailand. Specifically the region(s) being affected are: Bangkok\nA Storm (General) is occuring in the country of China. Specifically the region(s) being affected are: Guangdong\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Liberia", + "Mexico", + "Thailand", + "China" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "933fd2bb04976aaa", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53471:53493:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84346:84369:1'} The data starts from September 24 12:00 and ends on September 30 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) Specific humidity at 850 hPa values running above the 90th percentile climatology for the monthly climatology for September? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show Specific humidity at 850 hPa values above the 90th percentile climatology for monthly climatology for September: Arctic Ocean(average 0.0001293 kg/kg)\nNorth Atlantic Ocean(average 0.0001687 kg/kg)\nNorth Pacific Ocean(average 0.0001678 kg/kg)\nSouth Pacific Ocean(average 0.0002724 kg/kg)\nINDIAN OCEAN(average 0.001191 kg/kg)\nSouth Atlantic Ocean(average 0.0002443 kg/kg)\nPhilippine Sea(average 5.155e-05 kg/kg)\nTasman Sea(average 0.0003129 kg/kg)\nBay of Bengal(average 0.0002334 kg/kg)\nSouth China Sea(average 0.0001048 kg/kg)\nWeddell Sea(average 2.087e-05 kg/kg)\nGreenland Sea(average 0.0001118 kg/kg)\nBanda Sea(average 0.0001584 kg/kg)\nGulf of Guinea(average 0.0001078 kg/kg)\nJava Sea(average 0.0002098 kg/kg)\nAndaman Sea(average 0.0001166 kg/kg)\nEast China Sea(average 2.867e-05 kg/kg)\nArafura Sea(average 0.0001987 kg/kg)\nTimor Sea(average 0.0004872 kg/kg)\nGulf of Carpentaria(average 6.521e-05 kg/kg)\nBay of Plenty(average 0.0004373 kg/kg)\nThe North Western Passages(average 8.293e-05 kg/kg)\nBismarck Sea(average 7.885e-05 kg/kg)\nSolomon Sea(average 6.288e-05 kg/kg)\nCeram Sea(average 0.0002026 kg/kg)\nBaía de Marajó(average 0.0001579 kg/kg)\nBaía de São Marcos(average 5.524e-05 kg/kg)\nKane Basin(average 6.295e-05 kg/kg)\nCook Strait(average 0.0001363 kg/kg)\nGulf of Papua(average 1.662e-05 kg/kg)\nBight of Biafra(average 1.827e-05 kg/kg)\nGulf of Martaban(average 0.0001166 kg/kg)\nHall Basin(average 2.086e-05 kg/kg)\nAmazon River(average 0.0001057 kg/kg)\nBali Sea(average 0.000213 kg/kg)\nHalmahera Sea(average 0.0001097 kg/kg)\nSelat Bali(average 0.0001757 kg/kg)\nFlores Sea(average 0.000141 kg/kg)\nSelat Dampier(average 6.963e-05 kg/kg)\nSavu Sea(average 0.0004373 kg/kg)\nKennedy Channel(average 2.086e-05 kg/kg)\nSargasso Sea(average 0.000197 kg/kg)\nMediterranean Sea(average 0.0003297 kg/kg)\nCoral Sea(average 4.835e-05 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "specific_humidity", + 850 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for September", + "quantile": "0.9", + "threshold_direction": "above", + "true_value": [ + "Arctic Ocean", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Tasman Sea", + "Bay of Bengal", + "South China Sea", + "Weddell Sea", + "Greenland Sea", + "Banda Sea", + "Gulf of Guinea", + "Java Sea", + "Andaman Sea", + "East China Sea", + "Arafura Sea", + "Timor Sea", + "Gulf of Carpentaria", + "Bay of Plenty", + "The North Western Passages", + "Bismarck Sea", + "Solomon Sea", + "Ceram Sea", + "Baía de Marajó", + "Baía de São Marcos", + "Kane Basin", + "Cook Strait", + "Gulf of Papua", + "Bight of Biafra", + "Gulf of Martaban", + "Hall Basin", + "Amazon River", + "Bali Sea", + "Halmahera Sea", + "Selat Bali", + "Flores Sea", + "Selat Dampier", + "Savu Sea", + "Kennedy Channel", + "Sargasso Sea", + "Mediterranean Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "b8f7d290d6645a38", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84346:84369:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33052:33067:1'} The data starts from August 16 00:00 and ends on August 19 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Philippines.\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Philippines" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "1e32f006d0cb9bd6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33052:33067:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49576:49577:1'} The data corresponds to corresponds to a snapshot on December 07 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Storm (General) currently happening? Specify the affected countries or regions, or respond 'No Storm (General) detected.'", + "response": "Based on the provided data, the Storm (General) is affecting: United States of America", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "United States of America" + ], + "target_disaster": "Storm (General)", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "0c551665ae8bc62a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49576:49577:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87793:87817:1'} The data starts from February 03 06:00 and ends on February 09 00:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 100 hPa lies outside the climatological 10th–95th percentile envelope for the all-time climatology. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–95th percentile envelope for Temperature at 100 hPa during all-time climatology: North Pacific Ocean(average -0.8681 K)\nSouth Pacific Ocean(average -1.015 K)\nINDIAN OCEAN(average -0.4486 K)\nSouth Atlantic Ocean(average -1.291 K)\nTasman Sea(average -1.213 K)\nGulf of Alaska(average -0.1473 K)\nMozambique Channel(average -0.1166 K)\nArafura Sea(average -0.1278 K)\nSea of Azov(average -0.0511 K)\nGulf of Carpentaria(average -0.1373 K)\nSolomon Sea(average -0.8858 K)\nCook Inlet(average -0.08822 K)\nBass Strait(average -0.2272 K)\nCook Strait(average -0.5595 K)\nPrince William Sound(average -0.1431 K)\nAntongila Bay(average -0.1192 K)\nSt. Helena Bay(average -0.7154 K)\nCoral Sea(average -0.5412 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "temperature", + 100 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.95", + "true_value": [ + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Tasman Sea", + "Gulf of Alaska", + "Mozambique Channel", + "Arafura Sea", + "Sea of Azov", + "Gulf of Carpentaria", + "Solomon Sea", + "Cook Inlet", + "Bass Strait", + "Cook Strait", + "Prince William Sound", + "Antongila Bay", + "St. Helena Bay", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "78d0ef44a178a950", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87793:87817:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33201:33217:1'} The data starts from September 22 06:00 and ends on September 26 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, there is no extreme weather event occuring.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "1e4e726fff81cfc1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33201:33217:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92181:92188:1'} The data starts from February 04 06:00 and ends on February 05 18:00. Based on the above data, answer the following question:", + "question": "In the 24 hours after the end of the given time window, when will Sint Maarten experience its lowest Geopotential at 925 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Sint Maarten will experience its lowest Geopotential at 925 hPa of 7925 m²/s² 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "Sint Maarten", + "extremum_value": "7924.9927", + "target_variable": "geopotential_925", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f6e94186c3e03cfb", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92181:92188:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81662:81670:1'} The data starts from November 23 12:00 and ends on November 25 06:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) 10-meter V component of wind values running above the 95th percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show 10-meter V component of wind values above the 95th percentile climatology for all-time climatology: Arctic Ocean(average 0.4481 m/s)\nNorth Atlantic Ocean(average 0.5046 m/s)\nNorth Pacific Ocean(average 1.077 m/s)\nSouth Pacific Ocean(average 0.459 m/s)\nINDIAN OCEAN(average 0.3276 m/s)\nSouth Atlantic Ocean(average 0.1595 m/s)\nBeaufort Sea(average 0.7751 m/s)\nGulf of Mexico(average 0.3966 m/s)\nWeddell Sea(average 0.1342 m/s)\nGreenland Sea(average 0.3447 m/s)\nBaltic Sea(average 0.5801 m/s)\nKara Sea(average 1.151 m/s)\nGulf of Finland(average 0.3178 m/s)\nGulf of Bothnia(average 0.505 m/s)\nStraits of Florida(average 0.07973 m/s)\nChesapeake Bay(average 0.1389 m/s)\nBalearic Sea(average 0.8945 m/s)\nMackenzie Bay(average 0.5984 m/s)\nGulf of Riga(average 0.6819 m/s)\nAlboran Sea(average 0.05982 m/s)\nSaint Lawrence River(average 0.1168 m/s)\nGulf of Ob(average 0.4314 m/s)\nDelaware Bay(average 0.3133 m/s)\nLong Island Sound(average 0.1582 m/s)\nKaliningrad(average 0.2063 m/s)\nMediterranean Sea(average 1.115 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.95", + "threshold_direction": "above", + "true_value": [ + "Arctic Ocean", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Beaufort Sea", + "Gulf of Mexico", + "Weddell Sea", + "Greenland Sea", + "Baltic Sea", + "Kara Sea", + "Gulf of Finland", + "Gulf of Bothnia", + "Straits of Florida", + "Chesapeake Bay", + "Balearic Sea", + "Mackenzie Bay", + "Gulf of Riga", + "Alboran Sea", + "Saint Lawrence River", + "Gulf of Ob", + "Delaware Bay", + "Long Island Sound", + "Kaliningrad", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "86390b49ede03bc1", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81662:81670:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86010:86031:1'} The data starts from November 14 12:00 and ends on November 19 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter V component of wind lies outside the climatological 10th–90th percentile envelope for the SON seasonal climatology. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–90th percentile envelope for 10-meter V component of wind during SON seasonal climatology: Indonesia(average -0.3601 m/s)\nChile(average 0.03337 m/s)\nPeru(average -0.06259 m/s)\nArgentina(average 0.05147 m/s)\nIndia(average -0.645 m/s)\nChina(average 0.5416 m/s)\nEthiopia(average -0.2006 m/s)\nSouth Sudan(average 0.3099 m/s)\nSomalia(average -0.5015 m/s)\nKenya(average -0.4502 m/s)\nUnited Republic of Tanzania(average -0.4491 m/s)\nSomaliland(average -0.2299 m/s)\nFrance(average 0.2884 m/s)\nMorocco(average 0.06537 m/s)\nWestern Sahara(average 0.06537 m/s)\nNicaragua(average -0.3642 m/s)\nSaint Martin(average 1.295 m/s)\nSint Maarten(average 1.295 m/s)\nRussia(average -1.129 m/s)\nTunisia(average -1.045 m/s)\nGreece(average 0.2539 m/s)\nAustria(average -0.1058 m/s)\nItaly(average -0.6528 m/s)\nNetherlands(average 1.169 m/s)\nMali(average -0.1718 m/s)\nSenegal(average -0.02009 m/s)\nBenin(average -0.000946 m/s)\nCroatia(average -0.4282 m/s)\nSlovenia(average -0.3642 m/s)\nSaudi Arabia(average 0.006177 m/s)\nPakistan(average -0.581 m/s)\nSan Marino(average -0.826 m/s)\nDominican Republic(average 0.04957 m/s)\nAlgeria(average -0.8523 m/s)\nMyanmar(average -0.07258 m/s)\nAfghanistan(average -0.03143 m/s)\nBosnia and Herzegovina(average -0.1902 m/s)\nCuba(average -0.5138 m/s)\nHonduras(average -0.7525 m/s)\nEcuador(average -0.03696 m/s)\nColombia(average 0.04901 m/s)\nNepal(average 0.8197 m/s)\nNiger(average -0.141 m/s)\nBurkina Faso(average -0.2126 m/s)\nUnited States of America(average -0.1611 m/s)\nCanada(average 0.07021 m/s)\nPanama(average 0.2498 m/s)\nVenezuela(average 0.5881 m/s)\nPapua New Guinea(average -0.07583 m/s)\nYemen(average -0.1096 m/s)\nMauritania(average 0.1378 m/s)\nVatican(average -1.277 m/s)\nAustralia(average -0.1837 m/s)\nGreenland(average -0.08631 m/s)\nFiji(average -0.6025 m/s)\nThe Bahamas(average -0.1713 m/s)\nSeychelles(average -0.493 m/s)\nMarshall Islands(average -0.4357 m/s)\nDominica(average 0.3416 m/s)\nUnited States Minor Outlying Islands(average -0.1705 m/s)\nMontserrat(average 1.128 m/s)\nAntigua and Barbuda(average 1.086 m/s)\nSaint Kitts and Nevis(average 1.086 m/s)\nUnited States Virgin Islands(average 0.922 m/s)\nSaint Barthelemy(average 1.043 m/s)\nPuerto Rico(average 0.7836 m/s)\nAnguilla(average 1.169 m/s)\nBritish Virgin Islands(average 1.108 m/s)\nJamaica(average -0.2668 m/s)\nCayman Islands(average -1.301 m/s)\nTonga(average -0.4148 m/s)\nWallis and Futuna(average -0.4764 m/s)\nPalau(average -0.05119 m/s)\nBajo Nuevo Bank (Petrel Is.)(average -1.12 m/s)\nSerranilla Bank(average -0.5074 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "SON seasonal climatology", + "lower_quantile": "0.1", + "upper_quantile": "0.9", + "true_value": [ + "Indonesia", + "Chile", + "Peru", + "Argentina", + "India", + "China", + "Ethiopia", + "South Sudan", + "Somalia", + "Kenya", + "United Republic of Tanzania", + "Somaliland", + "France", + "Morocco", + "Western Sahara", + "Nicaragua", + "Saint Martin", + "Sint Maarten", + "Russia", + "Tunisia", + "Greece", + "Austria", + "Italy", + "Netherlands", + "Mali", + "Senegal", + "Benin", + "Croatia", + "Slovenia", + "Saudi Arabia", + "Pakistan", + "San Marino", + "Dominican Republic", + "Algeria", + "Myanmar", + "Afghanistan", + "Bosnia and Herzegovina", + "Cuba", + "Honduras", + "Ecuador", + "Colombia", + "Nepal", + "Niger", + "Burkina Faso", + "United States of America", + "Canada", + "Panama", + "Venezuela", + "Papua New Guinea", + "Yemen", + "Mauritania", + "Vatican", + "Australia", + "Greenland", + "Fiji", + "The Bahamas", + "Seychelles", + "Marshall Islands", + "Dominica", + "United States Minor Outlying Islands", + "Montserrat", + "Antigua and Barbuda", + "Saint Kitts and Nevis", + "United States Virgin Islands", + "Saint Barthelemy", + "Puerto Rico", + "Anguilla", + "British Virgin Islands", + "Jamaica", + "Cayman Islands", + "Tonga", + "Wallis and Futuna", + "Palau", + "Bajo Nuevo Bank (Petrel Is.)", + "Serranilla Bank" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "224840d8e7e90686", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86010:86031:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58486:58503:1'} The data starts from January 12 12:00 and ends on January 16 12:00. Based on the above data, answer the following question:", + "question": "What will the median Specific humidity at 500 hPa be in Fiji, 18 hours after the end of the given time window?", + "response": "Based on the provided data, the median Specific humidity at 500 hPa in Fiji 18 hours after the given time window will be 0.004232 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "0.004231564", + "location": "Fiji", + "target_variable": "specific_humidity_500", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d534becd0727244f", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58486:58503:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74703:74722:1'} The data starts from February 17 18:00 and ends on February 22 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 30 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 30 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 30 hours:\nA Blizzard/Winter storm is expected in the country of United States of America in approximately the next 18 to 138 hours. Specifically the region(s) that might get affected are: New York, District of Columbia, New Jersey, Pennsylvania, New Hampshire, Massachusetts, Maine, Connecticut, Rhode Island, Vermont provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "United States of America" + ], + "extreme_event_hours": 30, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "020fe0c27b263536", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74703:74722:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89707:89733:1'} The data starts from May 26 18:00 and ends on June 02 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 24 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 24 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 24 hours:\nA Tropical cyclone is expected in the country of India in approximately the next 24 hours. Specifically the region(s) that might get affected are: Raigad, Pune Districts (Maharashtra State)\nA Tropical cyclone is expected in the country of Mexico in approximately the next 24 hours. Specifically the region(s) that might get affected are: Ciudad del Carmen (Campeche State); Quintana Roo, Yucatan, Tabasco, Chiapas Oaxaca, Veracruz states\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "India", + "Mexico" + ], + "extreme_event_hours": 24, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "29a8f004b3c4bc55", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89707:89733:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67945:67955:1'} The data starts from July 04 06:00 and ends on July 06 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Specific humidity at 300 hPa lies outside the climatological 10th–90th percentile envelope for the monthly climatology for July. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 10th–90th percentile envelope for Specific humidity at 300 hPa during monthly climatology for July: Indonesia(average 3.467e-05 kg/kg)\nIndia(average -6.683e-05 kg/kg)\nChina(average 9.655e-06 kg/kg)\nLebanon(average -4.276e-07 kg/kg)\nSomalia(average 6.363e-06 kg/kg)\nSyria(average -2.789e-07 kg/kg)\nFrance(average 5.753e-05 kg/kg)\nSuriname(average 3.356e-05 kg/kg)\nGuyana(average 2.436e-05 kg/kg)\nMorocco(average 3.801e-05 kg/kg)\nWestern Sahara(average 3.738e-05 kg/kg)\nUkraine(average 4.536e-06 kg/kg)\nBrazil(average 4.087e-05 kg/kg)\nRussia(average -1.136e-05 kg/kg)\nNorway(average 2.282e-06 kg/kg)\nVietnam(average -3.098e-05 kg/kg)\nGeorgia(average 2.271e-06 kg/kg)\nAzerbaijan(average -2.648e-06 kg/kg)\nTurkey(average 1.202e-05 kg/kg)\nLaos(average -8.084e-06 kg/kg)\nArmenia(average -2.648e-06 kg/kg)\nLibya(average -6.056e-06 kg/kg)\nUnited Kingdom(average -4.369e-06 kg/kg)\nSudan(average 9.376e-05 kg/kg)\nIraq(average 9.634e-05 kg/kg)\nIran(average 7.124e-05 kg/kg)\nNetherlands(average 5.333e-05 kg/kg)\nMali(average -1.342e-05 kg/kg)\nSaudi Arabia(average 7.956e-05 kg/kg)\nChad(average 2.388e-05 kg/kg)\nKuwait(average 7.886e-05 kg/kg)\nEast Timor(average 5.343e-05 kg/kg)\nAlgeria(average -5.142e-07 kg/kg)\nMyanmar(average -5.918e-05 kg/kg)\nNiger(average -6.463e-06 kg/kg)\nUnited States of America(average -2.025e-05 kg/kg)\nCanada(average -5.507e-06 kg/kg)\nMexico(average -1.372e-05 kg/kg)\nVenezuela(average 4.435e-05 kg/kg)\nPapua New Guinea(average 1.375e-05 kg/kg)\nEgypt(average 4.739e-05 kg/kg)\nMauritania(average 3.027e-05 kg/kg)\nBir Tawil(average 7.722e-05 kg/kg)\nAustralia(average -2.545e-06 kg/kg)\nGreenland(average 8.849e-06 kg/kg)\nNew Zealand(average 1.049e-05 kg/kg)\nPhilippines(average -7.541e-06 kg/kg)\nCuraçao(average 5.333e-05 kg/kg)\nJapan(average 6.068e-05 kg/kg)\nIceland(average -1.419e-05 kg/kg)\nFrench Polynesia(average -3.213e-06 kg/kg)\nFrench Southern and Antarctic Lands(average 1.594e-05 kg/kg)\nSeychelles(average 2.584e-05 kg/kg)\nKiribati(average 7.631e-06 kg/kg)\nMarshall Islands(average 2.007e-05 kg/kg)\nTrinidad and Tobago(average 8.362e-05 kg/kg)\nGrenada(average 0.0001319 kg/kg)\nSaint Vincent and the Grenadines(average 0.0001357 kg/kg)\nBarbados(average 0.0001487 kg/kg)\nSaint Lucia(average 0.0001369 kg/kg)\nDominica(average 0.0001009 kg/kg)\nMontserrat(average 1.435e-05 kg/kg)\nAntigua and Barbuda(average 1.435e-05 kg/kg)\nSaint Kitts and Nevis(average 1.435e-05 kg/kg)\nJamaica(average -1.258e-05 kg/kg)\nSaint Helena(average 7.768e-06 kg/kg)\nMauritius(average 3.548e-05 kg/kg)\nIsle of Man(average -6.878e-06 kg/kg)\nBajo Nuevo Bank (Petrel Is.)(average -1.624e-05 kg/kg)\nSerranilla Bank(average -2.507e-05 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "specific_humidity", + 300 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for July", + "lower_quantile": "0.1", + "upper_quantile": "0.9", + "true_value": [ + "Indonesia", + "India", + "China", + "Lebanon", + "Somalia", + "Syria", + "France", + "Suriname", + "Guyana", + "Morocco", + "Western Sahara", + "Ukraine", + "Brazil", + "Russia", + "Norway", + "Vietnam", + "Georgia", + "Azerbaijan", + "Turkey", + "Laos", + "Armenia", + "Libya", + "United Kingdom", + "Sudan", + "Iraq", + "Iran", + "Netherlands", + "Mali", + "Saudi Arabia", + "Chad", + "Kuwait", + "East Timor", + "Algeria", + "Myanmar", + "Niger", + "United States of America", + "Canada", + "Mexico", + "Venezuela", + "Papua New Guinea", + "Egypt", + "Mauritania", + "Bir Tawil", + "Australia", + "Greenland", + "New Zealand", + "Philippines", + "Curaçao", + "Japan", + "Iceland", + "French Polynesia", + "French Southern and Antarctic Lands", + "Seychelles", + "Kiribati", + "Marshall Islands", + "Trinidad and Tobago", + "Grenada", + "Saint Vincent and the Grenadines", + "Barbados", + "Saint Lucia", + "Dominica", + "Montserrat", + "Antigua and Barbuda", + "Saint Kitts and Nevis", + "Jamaica", + "Saint Helena", + "Mauritius", + "Isle of Man", + "Bajo Nuevo Bank (Petrel Is.)", + "Serranilla Bank" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ec9737313feb8676", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67945:67955:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34668:34673:1'} The data starts from September 24 00:00 and ends on September 25 00:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will North America experience its highest 10-meter V component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, North America will experience its highest 10-meter V component of wind of 15.13 m/s 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "North America", + "extremum_value": "15.131251", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "a3e6b9fb7a2b07a6", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34668:34673:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91556:91573:1'} The data starts from September 01 00:00 and ends on September 05 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Geopotential at 600 hPa differs from the monthly climatology for September mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Geopotential at 600 hPa values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Geopotential at 600 hPa anomalies were detected relative to the monthly climatology for September baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "geopotential", + 600 + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for September", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "dce8c53cb6619dfc", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91556:91573:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79538:79551:1'} The data starts from June 10 12:00 and ends on June 13 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in U (zonal) component of wind at 500 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the U (zonal) component of wind at 500 hPa values exceed the 99th percentile climatology for the JJA seasonal climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in U (zonal) component of wind at 500 hPa: North Atlantic Ocean(average 4.188 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "u_component_of_wind", + 500 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "JJA seasonal climatology", + "quantile": "0.99", + "min_duration_days": 3, + "true_value": [ + "North Atlantic Ocean" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "9b145de802577800", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79538:79551:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90226:90241:1'} The data starts from October 03 12:00 and ends on October 07 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Surface pressure differs from the six-hourly climatology for day 277 at 12 UTC mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below Surface pressure values. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) exceed the ±3σ anomaly threshold for Surface pressure relative to the six-hourly climatology for day 277 at 12 UTC mean: Brazil(average -300.3 Pa)\nSerranilla Bank(average -382 Pa)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "surface_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 277 at 12 UTC", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "Brazil", + "Serranilla Bank" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "82c99c86c48f57a2", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90226:90241:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45309:45310:1'} The data corresponds to corresponds to a snapshot on January 05 06:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Asia experience its highest 10-meter V component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Asia will experience its highest 10-meter V component of wind of 8.15 m/s 30 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 30, + "location": "Asia", + "extremum_value": "8.149884", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "69d28f489b4eff5f", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45309:45310:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90397:90411:1'} The data starts from November 15 06:00 and ends on November 18 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter U component of wind differs from the six-hourly climatology for day 320 at 06 UTC mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below 10-meter U component of wind values. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) exceed the ±3σ anomaly threshold for 10-meter U component of wind relative to the six-hourly climatology for day 320 at 06 UTC mean: India(average -0.5121 m/s)\nChina(average -0.5665 m/s)\nLibya(average -2.157 m/s)\nSierra Leone(average -1.341 m/s)\nGuinea(average -1.955 m/s)\nSudan(average -1.14 m/s)\nIvory Coast(average -1.926 m/s)\nMali(average -2.323 m/s)\nSenegal(average -2.33 m/s)\nNigeria(average -1.551 m/s)\nBenin(average -1.954 m/s)\nChad(average -2.008 m/s)\nMyanmar(average -0.5665 m/s)\nCameroon(average -0.8977 m/s)\nBurkina Faso(average -1.895 m/s)\nGhana(average -1.717 m/s)\nAustralia(average -4.057 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 320 at 06 UTC", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [ + "India", + "China", + "Libya", + "Sierra Leone", + "Guinea", + "Sudan", + "Ivory Coast", + "Mali", + "Senegal", + "Nigeria", + "Benin", + "Chad", + "Myanmar", + "Cameroon", + "Burkina Faso", + "Ghana", + "Australia" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "9979da7a7f541c0f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90397:90411:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82443:82471:1'} The data starts from June 06 18:00 and ends on June 13 12:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter U component of wind lies outside the climatological 5th–90th percentile envelope for the six-hourly climatology for day 157 at 18 UTC. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 5th–90th percentile envelope for 10-meter U component of wind during six-hourly climatology for day 157 at 18 UTC: Indonesia(average -0.09961 m/s)\nMalaysia(average -0.2442 m/s)\nChile(average -0.7697 m/s)\nBolivia(average -0.6016 m/s)\nPeru(average -0.1582 m/s)\nArgentina(average -0.09336 m/s)\nIndia(average 0.1277 m/s)\nChina(average 0.4348 m/s)\nEthiopia(average 0.5423 m/s)\nSouth Sudan(average 0.3111 m/s)\nSomalia(average 0.8014 m/s)\nKenya(average 0.2156 m/s)\nMalawi(average -0.3244 m/s)\nUnited Republic of Tanzania(average 0.2491 m/s)\nSyria(average 0.07737 m/s)\nSomaliland(average 0.716 m/s)\nFrance(average -0.827 m/s)\nGuyana(average 0.139 m/s)\nMorocco(average -0.8709 m/s)\nWestern Sahara(average -0.8709 m/s)\nCosta Rica(average -0.1435 m/s)\nRepublic of the Congo(average -0.2937 m/s)\nDemocratic Republic of the Congo(average -0.1542 m/s)\nBhutan(average 0.3649 m/s)\nBelarus(average 0.04897 m/s)\nOman(average -0.3307 m/s)\nUzbekistan(average 0.1934 m/s)\nKazakhstan(average 0.1834 m/s)\nTajikistan(average 0.1081 m/s)\nLithuania(average 0.2596 m/s)\nBrazil(average 0.2446 m/s)\nMongolia(average 0.1885 m/s)\nRussia(average 0.4949 m/s)\nGermany(average -0.2257 m/s)\nEstonia(average 1.218 m/s)\nLatvia(average 0.5224 m/s)\nNorway(average 1.1 m/s)\nSweden(average 0.9675 m/s)\nFinland(average 0.8113 m/s)\nLuxembourg(average -0.08326 m/s)\nUnited Arab Emirates(average -0.5188 m/s)\nBelgium(average -0.123 m/s)\nNorth Macedonia(average -0.3435 m/s)\nAlbania(average -0.1574 m/s)\nAzerbaijan(average -0.01454 m/s)\nKosovo(average -0.3241 m/s)\nTurkey(average 0.07737 m/s)\nSpain(average 0.1131 m/s)\nLaos(average 0.2045 m/s)\nKyrgyzstan(average 0.1188 m/s)\nLibya(average 0.2714 m/s)\nTunisia(average 1.583 m/s)\nIreland(average -0.5664 m/s)\nUnited Kingdom(average -0.5052 m/s)\nGreece(average -0.2001 m/s)\nZambia(average -0.3021 m/s)\nSierra Leone(average -0.3323 m/s)\nGuinea(average -0.1613 m/s)\nCentral African Republic(average 0.1797 m/s)\nSudan(average 0.1244 m/s)\nDjibouti(average 0.4102 m/s)\nEritrea(average 0.2856 m/s)\nIraq(average 0.1337 m/s)\nItaly(average -0.3674 m/s)\nSwitzerland(average -0.2297 m/s)\nIran(average -0.2957 m/s)\nNetherlands(average -0.01842 m/s)\nIvory Coast(average 0.145 m/s)\nRepublic of Serbia(average -0.3241 m/s)\nMali(average 0.2405 m/s)\nSenegal(average 0.179 m/s)\nNigeria(average 0.1334 m/s)\nBenin(average 0.04146 m/s)\nAngola(average -0.4868 m/s)\nSaudi Arabia(average 0.08242 m/s)\nZimbabwe(average -0.4088 m/s)\nPakistan(average -0.4251 m/s)\nBulgaria(average -0.5296 m/s)\nThailand(average 0.2229 m/s)\nHaiti(average -0.5802 m/s)\nChad(average -0.3013 m/s)\nEl Salvador(average 0.4003 m/s)\nGuatemala(average 0.09272 m/s)\nMonaco(average -1.095 m/s)\nMozambique(average 0.2261 m/s)\nMyanmar(average 0.1639 m/s)\nAfghanistan(average 0.06755 m/s)\nMontenegro(average -0.1185 m/s)\nBosnia and Herzegovina(average -0.1185 m/s)\nUganda(average 0.01267 m/s)\nHonduras(average 0.2022 m/s)\nEcuador(average -0.2936 m/s)\nColombia(average -0.129 m/s)\nNepal(average 0.5066 m/s)\nCameroon(average 0.1909 m/s)\nGabon(average -0.4424 m/s)\nNiger(average -0.02948 m/s)\nBurkina Faso(average 0.1473 m/s)\nTogo(average 0.1098 m/s)\nGhana(average 0.1584 m/s)\nUnited States of America(average -0.04606 m/s)\nCanada(average 0.2591 m/s)\nMexico(average 0.1197 m/s)\nPanama(average -0.2069 m/s)\nVenezuela(average 0.2199 m/s)\nPapua New Guinea(average -0.25 m/s)\nEgypt(average -0.2593 m/s)\nYemen(average 0.4526 m/s)\nMauritania(average -0.8709 m/s)\nBir Tawil(average -0.5368 m/s)\nAustralia(average -0.4946 m/s)\nGreenland(average 0.1526 m/s)\nNew Zealand(average 1.787 m/s)\nMadagascar(average 0.07083 m/s)\nJapan(average 0.4146 m/s)\nIceland(average 0.3482 m/s)\nPitcairn Islands(average -0.282 m/s)\nFrench Southern and Antarctic Lands(average 0.0407 m/s)\nSeychelles(average 0.9842 m/s)\nUnited States Minor Outlying Islands(average -0.1137 m/s)\nJersey(average -0.5264 m/s)\nGuernsey(average -0.5264 m/s)\nAland(average 1.051 m/s)\nFaroe Islands(average 1.224 m/s)\nSolomon Islands(average -0.156 m/s)\nMaldives(average -0.3953 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 157 at 18 UTC", + "lower_quantile": "0.05", + "upper_quantile": "0.9", + "true_value": [ + "Indonesia", + "Malaysia", + "Chile", + "Bolivia", + "Peru", + "Argentina", + "India", + "China", + "Ethiopia", + "South Sudan", + "Somalia", + "Kenya", + "Malawi", + "United Republic of Tanzania", + "Syria", + "Somaliland", + "France", + "Guyana", + "Morocco", + "Western Sahara", + "Costa Rica", + "Republic of the Congo", + "Democratic Republic of the Congo", + "Bhutan", + "Belarus", + "Oman", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Lithuania", + "Brazil", + "Mongolia", + "Russia", + "Germany", + "Estonia", + "Latvia", + "Norway", + "Sweden", + "Finland", + "Luxembourg", + "United Arab Emirates", + "Belgium", + "North Macedonia", + "Albania", + "Azerbaijan", + "Kosovo", + "Turkey", + "Spain", + "Laos", + "Kyrgyzstan", + "Libya", + "Tunisia", + "Ireland", + "United Kingdom", + "Greece", + "Zambia", + "Sierra Leone", + "Guinea", + "Central African Republic", + "Sudan", + "Djibouti", + "Eritrea", + "Iraq", + "Italy", + "Switzerland", + "Iran", + "Netherlands", + "Ivory Coast", + "Republic of Serbia", + "Mali", + "Senegal", + "Nigeria", + "Benin", + "Angola", + "Saudi Arabia", + "Zimbabwe", + "Pakistan", + "Bulgaria", + "Thailand", + "Haiti", + "Chad", + "El Salvador", + "Guatemala", + "Monaco", + "Mozambique", + "Myanmar", + "Afghanistan", + "Montenegro", + "Bosnia and Herzegovina", + "Uganda", + "Honduras", + "Ecuador", + "Colombia", + "Nepal", + "Cameroon", + "Gabon", + "Niger", + "Burkina Faso", + "Togo", + "Ghana", + "United States of America", + "Canada", + "Mexico", + "Panama", + "Venezuela", + "Papua New Guinea", + "Egypt", + "Yemen", + "Mauritania", + "Bir Tawil", + "Australia", + "Greenland", + "New Zealand", + "Madagascar", + "Japan", + "Iceland", + "Pitcairn Islands", + "French Southern and Antarctic Lands", + "Seychelles", + "United States Minor Outlying Islands", + "Jersey", + "Guernsey", + "Aland", + "Faroe Islands", + "Solomon Islands", + "Maldives" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0b1397c9ab865e2a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82443:82471:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75536:75551:1'} The data starts from September 14 00:00 and ends on September 17 12:00. Based on the above data, answer the following question:", + "question": "What will the average Specific humidity at 400 hPa be in Oceania, 18 hours after the end of the given time window?", + "response": "Based on the provided data, the average Specific humidity at 400 hPa in Oceania 18 hours after the given time window will be 0.000563 kg/kg.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "0.0005630464", + "location": "Oceania", + "target_variable": "specific_humidity_400", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "652bb767caeed812", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75536:75551:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78105:78114:1'} The data starts from June 17 06:00 and ends on June 19 06:00. Based on the above data, answer the following question:", + "question": "What will the minimum U (zonal) component of wind at 850 hPa be in Grenada, 48 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum U (zonal) component of wind at 850 hPa in Grenada 48 hours after the given time window will be -9.387 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-9.387292", + "location": "Grenada", + "target_variable": "u_component_of_wind_850", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "75deed032dccc56b", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78105:78114:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74260:74276:1'} The data starts from October 30 00:00 and ends on November 02 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) V (meridional) component of wind at 300 hPa lies outside the climatological 1st–90th percentile envelope for the six-hourly climatology for day 303 at 00 UTC. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–90th percentile envelope for V (meridional) component of wind at 300 hPa during six-hourly climatology for day 303 at 00 UTC: Arctic Ocean(average 2.032 m/s)\nSOUTHERN OCEAN(average 2.477 m/s)\nNorth Atlantic Ocean(average 0.4618 m/s)\nNorth Pacific Ocean(average 1.515 m/s)\nSouth Pacific Ocean(average 3.605 m/s)\nINDIAN OCEAN(average 1.857 m/s)\nSouth Atlantic Ocean(average -0.1101 m/s)\nBlack Sea(average 2.645 m/s)\nPhilippine Sea(average 0.9071 m/s)\nGreat Barrier Reef(average -0.937 m/s)\nTasman Sea(average 1.207 m/s)\nBay of Bengal(average 2.456 m/s)\nSouth China Sea(average 0.6323 m/s)\nArabian Sea(average -2.722 m/s)\nGulf of Mexico(average 1.011 m/s)\nCaspian Sea(average 3.805 m/s)\nBaffin Bay(average 7.631 m/s)\nSea of Okhotsk(average 2.87 m/s)\nWeddell Sea(average 0.2206 m/s)\nNorwegian Sea(average 1.954 m/s)\nGreenland Sea(average 1.504 m/s)\nBanda Sea(average 2.048 m/s)\nGulf of Guinea(average 0.9114 m/s)\nBarents Sea(average -1.592 m/s)\nNorth Sea(average 4.344 m/s)\nJava Sea(average 0.697 m/s)\nChukchi Sea(average 3.777 m/s)\nArafura Sea(average -1.689 m/s)\nTimor Sea(average 4.159 m/s)\nAmundsen Sea(average 0.2653 m/s)\nDavis Strait(average 1.021 m/s)\nKara Sea(average -1.273 m/s)\nLaptev Sea(average 2.974 m/s)\nSea of Azov(average 2.413 m/s)\nAdriatic Sea(average -1.079 m/s)\nBay of Plenty(average 0.8271 m/s)\nIonian Sea(average -1.239 m/s)\nBismarck Sea(average -1.45 m/s)\nSolomon Sea(average -1.316 m/s)\nBristol Bay(average -1.269 m/s)\nMelville Bay(average 13.82 m/s)\nChesapeake Bay(average 0.2959 m/s)\nShelikhova Gulf(average 2.475 m/s)\nBering Sea(average 4.099 m/s)\nEast Siberian Sea(average 1.603 m/s)\nLincoln Sea(average 1.526 m/s)\nMcMurdo Sound(average 2.245 m/s)\nDisko Bay(average 1.741 m/s)\nSkagerrak(average 2.865 m/s)\nTrondheimsfjorden(average 0.2502 m/s)\nKane Basin(average 3.371 m/s)\nGulf of Yana(average 2.437 m/s)\nDmitriy Laptev Strait(average 1.799 m/s)\nCook Strait(average 1.45 m/s)\nTorres Strait(average -1.35 m/s)\nGulf of Papua(average -2.397 m/s)\nWaddenzee(average 3.35 m/s)\nBight of Benin(average 1.275 m/s)\nPrydz Bay(average 2.438 m/s)\nKaraginskiy Gulf(average 3.661 m/s)\nBoknafjorden(average 5.462 m/s)\nJoseph Bonaparte Gulf(average 4.943 m/s)\nGarabogaz Bay(average 0.9325 m/s)\nDelaware Bay(average 0.8548 m/s)\nHall Basin(average 0.3312 m/s)\nUummannaq Fjord(average 4.338 m/s)\nOzero Mogotoyevo(average 0.4862 m/s)\nGuba Gusinaya(average 0.6784 m/s)\nBali Sea(average 0.5374 m/s)\nDavao Gulf(average 0.4424 m/s)\nFlores Sea(average 1.411 m/s)\nSavu Sea(average 2.635 m/s)\nMurchison Sound(average 4.513 m/s)\nKennedy Channel(average 0.3312 m/s)\nSargasso Sea(average -4.042 m/s)\nGulf of Anadyr'(average 2.839 m/s)\nRoss Sea(average 1.373 m/s)\nCoral Sea(average -1.872 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "v_component_of_wind", + 300 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 303 at 00 UTC", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Black Sea", + "Philippine Sea", + "Great Barrier Reef", + "Tasman Sea", + "Bay of Bengal", + "South China Sea", + "Arabian Sea", + "Gulf of Mexico", + "Caspian Sea", + "Baffin Bay", + "Sea of Okhotsk", + "Weddell Sea", + "Norwegian Sea", + "Greenland Sea", + "Banda Sea", + "Gulf of Guinea", + "Barents Sea", + "North Sea", + "Java Sea", + "Chukchi Sea", + "Arafura Sea", + "Timor Sea", + "Amundsen Sea", + "Davis Strait", + "Kara Sea", + "Laptev Sea", + "Sea of Azov", + "Adriatic Sea", + "Bay of Plenty", + "Ionian Sea", + "Bismarck Sea", + "Solomon Sea", + "Bristol Bay", + "Melville Bay", + "Chesapeake Bay", + "Shelikhova Gulf", + "Bering Sea", + "East Siberian Sea", + "Lincoln Sea", + "McMurdo Sound", + "Disko Bay", + "Skagerrak", + "Trondheimsfjorden", + "Kane Basin", + "Gulf of Yana", + "Dmitriy Laptev Strait", + "Cook Strait", + "Torres Strait", + "Gulf of Papua", + "Waddenzee", + "Bight of Benin", + "Prydz Bay", + "Karaginskiy Gulf", + "Boknafjorden", + "Joseph Bonaparte Gulf", + "Garabogaz Bay", + "Delaware Bay", + "Hall Basin", + "Uummannaq Fjord", + "Ozero Mogotoyevo", + "Guba Gusinaya", + "Bali Sea", + "Davao Gulf", + "Flores Sea", + "Savu Sea", + "Murchison Sound", + "Kennedy Channel", + "Sargasso Sea", + "Gulf of Anadyr'", + "Ross Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "ca2d6414618e94d7", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74260:74276:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92769:92783:1'} The data starts from July 01 06:00 and ends on July 04 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Specific humidity at 50 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the Specific humidity at 50 hPa values exceed the 90th percentile climatology for the six-hourly climatology for day 182 at 06 UTC. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Specific humidity at 50 hPa: Indonesia(average 4.582e-08 kg/kg)\nMalaysia(average 6.408e-08 kg/kg)\nChile(average 2.267e-08 kg/kg)\nPeru(average 4.628e-08 kg/kg)\nArgentina(average 2.148e-08 kg/kg)\nDhekelia Sovereign Base Area(average 5.884e-08 kg/kg)\nCyprus(average 5.85e-08 kg/kg)\nIndia(average 5.918e-08 kg/kg)\nChina(average 5.169e-08 kg/kg)\nIsrael(average 4.893e-08 kg/kg)\nPalestine(average 5.065e-08 kg/kg)\nLebanon(average 5.513e-08 kg/kg)\nEthiopia(average 5.609e-08 kg/kg)\nSouth Sudan(average 5.87e-08 kg/kg)\nSomalia(average 5.534e-08 kg/kg)\nKenya(average 4.543e-08 kg/kg)\nSyria(average 6.25e-08 kg/kg)\nSomaliland(average 5.3e-08 kg/kg)\nFrance(average 1.105e-07 kg/kg)\nSuriname(average 8.162e-08 kg/kg)\nGuyana(average 7.681e-08 kg/kg)\nSouth Korea(average 3.936e-08 kg/kg)\nNorth Korea(average 4.992e-08 kg/kg)\nMorocco(average 7.46e-08 kg/kg)\nWestern Sahara(average 8.276e-08 kg/kg)\nCosta Rica(average 7.985e-08 kg/kg)\nNicaragua(average 1.126e-07 kg/kg)\nRepublic of the Congo(average 8.982e-09 kg/kg)\nDemocratic Republic of the Congo(average 4.064e-08 kg/kg)\nBhutan(average 2.608e-08 kg/kg)\nUkraine(average 1.331e-07 kg/kg)\nBelarus(average 1.072e-07 kg/kg)\nSaint Martin(average 8.623e-08 kg/kg)\nSint Maarten(average 8.623e-08 kg/kg)\nOman(average 6.787e-08 kg/kg)\nUzbekistan(average 6.373e-08 kg/kg)\nKazakhstan(average 1.08e-07 kg/kg)\nTajikistan(average 5.857e-08 kg/kg)\nLithuania(average 8.046e-08 kg/kg)\nBrazil(average 4.125e-08 kg/kg)\nMongolia(average 3.711e-08 kg/kg)\nRussia(average 7.631e-08 kg/kg)\nCzechia(average 1.505e-07 kg/kg)\nGermany(average 1.411e-07 kg/kg)\nEstonia(average 6.764e-08 kg/kg)\nLatvia(average 7.193e-08 kg/kg)\nNorway(average 7.935e-08 kg/kg)\nSweden(average 8.195e-08 kg/kg)\nFinland(average 6.376e-08 kg/kg)\nVietnam(average 1.035e-07 kg/kg)\nCambodia(average 1.163e-07 kg/kg)\nLuxembourg(average 1.091e-07 kg/kg)\nUnited Arab Emirates(average 5.186e-08 kg/kg)\nBelgium(average 1.076e-07 kg/kg)\nGeorgia(average 9.389e-08 kg/kg)\nNorth Macedonia(average 4.365e-08 kg/kg)\nAlbania(average 3.837e-08 kg/kg)\nAzerbaijan(average 8.794e-08 kg/kg)\nKosovo(average 4.827e-08 kg/kg)\nTurkey(average 6.955e-08 kg/kg)\nSpain(average 1.084e-07 kg/kg)\nLaos(average 1.128e-07 kg/kg)\nKyrgyzstan(average 7.556e-08 kg/kg)\nArmenia(average 8.763e-08 kg/kg)\nDenmark(average 1.337e-07 kg/kg)\nLibya(average 6.29e-08 kg/kg)\nTunisia(average 6.78e-08 kg/kg)\nRomania(average 9.09e-08 kg/kg)\nHungary(average 8.871e-08 kg/kg)\nSlovakia(average 1.268e-07 kg/kg)\nPoland(average 1.339e-07 kg/kg)\nIreland(average 8.863e-08 kg/kg)\nUnited Kingdom(average 1.025e-07 kg/kg)\nGreece(average 4.578e-08 kg/kg)\nSierra Leone(average 8.458e-08 kg/kg)\nGuinea(average 7.448e-08 kg/kg)\nLiberia(average 6.189e-08 kg/kg)\nCentral African Republic(average 5.376e-08 kg/kg)\nSudan(average 8.187e-08 kg/kg)\nDjibouti(average 6.547e-08 kg/kg)\nEritrea(average 8.514e-08 kg/kg)\nAustria(average 1.199e-07 kg/kg)\nIraq(average 5.753e-08 kg/kg)\nItaly(average 8.361e-08 kg/kg)\nSwitzerland(average 1.202e-07 kg/kg)\nIran(average 6.434e-08 kg/kg)\nNetherlands(average 1.137e-07 kg/kg)\nLiechtenstein(average 1.259e-07 kg/kg)\nIvory Coast(average 6.471e-08 kg/kg)\nRepublic of Serbia(average 5.483e-08 kg/kg)\nMali(average 7.831e-08 kg/kg)\nSenegal(average 6.059e-08 kg/kg)\nNigeria(average 5.909e-08 kg/kg)\nBenin(average 7.108e-08 kg/kg)\nCroatia(average 6.105e-08 kg/kg)\nSlovenia(average 8.052e-08 kg/kg)\nQatar(average 6.8e-08 kg/kg)\nSaudi Arabia(average 7.163e-08 kg/kg)\nPakistan(average 5.172e-08 kg/kg)\nBulgaria(average 6.521e-08 kg/kg)\nThailand(average 1.123e-07 kg/kg)\nSan Marino(average 8.898e-08 kg/kg)\nHaiti(average 9.429e-08 kg/kg)\nDominican Republic(average 9.47e-08 kg/kg)\nChad(average 6.624e-08 kg/kg)\nKuwait(average 6.483e-08 kg/kg)\nEl Salvador(average 1.18e-07 kg/kg)\nGuatemala(average 1.015e-07 kg/kg)\nBrunei(average 9.24e-08 kg/kg)\nMonaco(average 1.38e-07 kg/kg)\nAlgeria(average 7.978e-08 kg/kg)\nMyanmar(average 8.714e-08 kg/kg)\nBangladesh(average 4.189e-08 kg/kg)\nAndorra(average 1.33e-07 kg/kg)\nAfghanistan(average 5.589e-08 kg/kg)\nMontenegro(average 4.443e-08 kg/kg)\nBosnia and Herzegovina(average 5.127e-08 kg/kg)\nUganda(average 4.197e-08 kg/kg)\nUS Naval Base Guantanamo Bay(average 8.833e-08 kg/kg)\nCuba(average 7.95e-08 kg/kg)\nHonduras(average 1.103e-07 kg/kg)\nEcuador(average 6.961e-08 kg/kg)\nColombia(average 7.75e-08 kg/kg)\nPortugal(average 1.01e-07 kg/kg)\nMoldova(average 1.155e-07 kg/kg)\nTurkmenistan(average 6.488e-08 kg/kg)\nJordan(average 4.806e-08 kg/kg)\nNepal(average 4.148e-08 kg/kg)\nCameroon(average 4.421e-08 kg/kg)\nGabon(average 1.884e-09 kg/kg)\nNiger(average 7.588e-08 kg/kg)\nBurkina Faso(average 7.034e-08 kg/kg)\nTogo(average 7.501e-08 kg/kg)\nGhana(average 6.716e-08 kg/kg)\nGuinea-Bissau(average 6.578e-08 kg/kg)\nGibraltar(average 9.733e-08 kg/kg)\nUnited States of America(average 5.934e-08 kg/kg)\nCanada(average 5.345e-08 kg/kg)\nMexico(average 8.218e-08 kg/kg)\nBelize(average 8.671e-08 kg/kg)\nPanama(average 7.787e-08 kg/kg)\nVenezuela(average 8.439e-08 kg/kg)\nPapua New Guinea(average 4.564e-08 kg/kg)\nEgypt(average 6.603e-08 kg/kg)\nYemen(average 8.862e-08 kg/kg)\nMauritania(average 7.498e-08 kg/kg)\nEquatorial Guinea(average 6.886e-09 kg/kg)\nGambia(average 6.746e-08 kg/kg)\nHong Kong S.A.R.(average 8.79e-08 kg/kg)\nVatican(average 6.935e-08 kg/kg)\nNorthern Cyprus(average 6.129e-08 kg/kg)\nCyprus No Mans Area(average 5.85e-08 kg/kg)\nSiachen Glacier(average 5.323e-08 kg/kg)\nBaykonur Cosmodrome(average 7.242e-08 kg/kg)\nAkrotiri Sovereign Base Area(average 5.85e-08 kg/kg)\nBir Tawil(average 1.116e-07 kg/kg)\nAustralia(average 7.71e-09 kg/kg)\nGreenland(average 8.137e-08 kg/kg)\nPhilippines(average 1.071e-07 kg/kg)\nSri Lanka(average 7.696e-08 kg/kg)\nCuraçao(average 1.059e-07 kg/kg)\nAruba(average 1.072e-07 kg/kg)\nThe Bahamas(average 6.868e-08 kg/kg)\nTurks and Caicos Islands(average 9.219e-08 kg/kg)\nTaiwan(average 8.111e-08 kg/kg)\nJapan(average 5.934e-08 kg/kg)\nSaint Pierre and Miquelon(average 6.584e-08 kg/kg)\nIceland(average 1.09e-07 kg/kg)\nKiribati(average 5.32e-08 kg/kg)\nMarshall Islands(average 1.123e-07 kg/kg)\nTrinidad and Tobago(average 1.088e-07 kg/kg)\nGrenada(average 1.205e-07 kg/kg)\nSaint Vincent and the Grenadines(average 1.208e-07 kg/kg)\nBarbados(average 1.334e-07 kg/kg)\nSaint Lucia(average 1.225e-07 kg/kg)\nDominica(average 8.459e-08 kg/kg)\nUnited States Minor Outlying Islands(average 7.11e-08 kg/kg)\nMontserrat(average 9.24e-08 kg/kg)\nAntigua and Barbuda(average 9.183e-08 kg/kg)\nSaint Kitts and Nevis(average 9.183e-08 kg/kg)\nUnited States Virgin Islands(average 8.588e-08 kg/kg)\nSaint Barthelemy(average 9.125e-08 kg/kg)\nPuerto Rico(average 8.874e-08 kg/kg)\nAnguilla(average 8.874e-08 kg/kg)\nBritish Virgin Islands(average 8.606e-08 kg/kg)\nJamaica(average 8.778e-08 kg/kg)\nCayman Islands(average 8.308e-08 kg/kg)\nBermuda(average 8.861e-08 kg/kg)\nSão Tomé and Principe(average 4.611e-09 kg/kg)\nCabo Verde(average 7.666e-08 kg/kg)\nMalta(average 6.305e-08 kg/kg)\nJersey(average 1.047e-07 kg/kg)\nGuernsey(average 1.047e-07 kg/kg)\nIsle of Man(average 8.108e-08 kg/kg)\nAland(average 6.619e-08 kg/kg)\nFaroe Islands(average 1.054e-07 kg/kg)\nSingapore(average 6.653e-08 kg/kg)\nMaldives(average 9.738e-08 kg/kg)\nNauru(average 8.915e-08 kg/kg)\nFederated States of Micronesia(average 1.175e-07 kg/kg)\nFalkland Islands(average 8.09e-10 kg/kg)\nPalau(average 8.141e-08 kg/kg)\nGuam(average 9.855e-08 kg/kg)\nNorthern Mariana Islands(average 1.003e-07 kg/kg)\nBahrain(average 8.106e-08 kg/kg)\nSpratly Islands(average 1.036e-07 kg/kg)\nClipperton Island(average 1.043e-07 kg/kg)\nMacao S.A.R(average 8.518e-08 kg/kg)\nBajo Nuevo Bank (Petrel Is.)(average 9.472e-08 kg/kg)\nSerranilla Bank(average 9.19e-08 kg/kg)\nScarborough Reef(average 1.476e-07 kg/kg)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "specific_humidity", + 50 + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 182 at 06 UTC", + "quantile": "0.9", + "min_duration_days": 3, + "true_value": [ + "Indonesia", + "Malaysia", + "Chile", + "Peru", + "Argentina", + "Dhekelia Sovereign Base Area", + "Cyprus", + "India", + "China", + "Israel", + "Palestine", + "Lebanon", + "Ethiopia", + "South Sudan", + "Somalia", + "Kenya", + "Syria", + "Somaliland", + "France", + "Suriname", + "Guyana", + "South Korea", + "North Korea", + "Morocco", + "Western Sahara", + "Costa Rica", + "Nicaragua", + "Republic of the Congo", + "Democratic Republic of the Congo", + "Bhutan", + "Ukraine", + "Belarus", + "Saint Martin", + "Sint Maarten", + "Oman", + "Uzbekistan", + "Kazakhstan", + "Tajikistan", + "Lithuania", + "Brazil", + "Mongolia", + "Russia", + "Czechia", + "Germany", + "Estonia", + "Latvia", + "Norway", + "Sweden", + "Finland", + "Vietnam", + "Cambodia", + "Luxembourg", + "United Arab Emirates", + "Belgium", + "Georgia", + "North Macedonia", + "Albania", + "Azerbaijan", + "Kosovo", + "Turkey", + "Spain", + "Laos", + "Kyrgyzstan", + "Armenia", + "Denmark", + "Libya", + "Tunisia", + "Romania", + "Hungary", + "Slovakia", + "Poland", + "Ireland", + "United Kingdom", + "Greece", + "Sierra Leone", + "Guinea", + "Liberia", + "Central African Republic", + "Sudan", + "Djibouti", + "Eritrea", + "Austria", + "Iraq", + "Italy", + "Switzerland", + "Iran", + "Netherlands", + "Liechtenstein", + "Ivory Coast", + "Republic of Serbia", + "Mali", + "Senegal", + "Nigeria", + "Benin", + "Croatia", + "Slovenia", + "Qatar", + "Saudi Arabia", + "Pakistan", + "Bulgaria", + "Thailand", + "San Marino", + "Haiti", + "Dominican Republic", + "Chad", + "Kuwait", + "El Salvador", + "Guatemala", + "Brunei", + "Monaco", + "Algeria", + "Myanmar", + "Bangladesh", + "Andorra", + "Afghanistan", + "Montenegro", + "Bosnia and Herzegovina", + "Uganda", + "US Naval Base Guantanamo Bay", + "Cuba", + "Honduras", + "Ecuador", + "Colombia", + "Portugal", + "Moldova", + "Turkmenistan", + "Jordan", + "Nepal", + "Cameroon", + "Gabon", + "Niger", + "Burkina Faso", + "Togo", + "Ghana", + "Guinea-Bissau", + "Gibraltar", + "United States of America", + "Canada", + "Mexico", + "Belize", + "Panama", + "Venezuela", + "Papua New Guinea", + "Egypt", + "Yemen", + "Mauritania", + "Equatorial Guinea", + "Gambia", + "Hong Kong S.A.R.", + "Vatican", + "Northern Cyprus", + "Cyprus No Mans Area", + "Siachen Glacier", + "Baykonur Cosmodrome", + "Akrotiri Sovereign Base Area", + "Bir Tawil", + "Australia", + "Greenland", + "Philippines", + "Sri Lanka", + "Curaçao", + "Aruba", + "The Bahamas", + "Turks and Caicos Islands", + "Taiwan", + "Japan", + "Saint Pierre and Miquelon", + "Iceland", + "Kiribati", + "Marshall Islands", + "Trinidad and Tobago", + "Grenada", + "Saint Vincent and the Grenadines", + "Barbados", + "Saint Lucia", + "Dominica", + "United States Minor Outlying Islands", + "Montserrat", + "Antigua and Barbuda", + "Saint Kitts and Nevis", + "United States Virgin Islands", + "Saint Barthelemy", + "Puerto Rico", + "Anguilla", + "British Virgin Islands", + "Jamaica", + "Cayman Islands", + "Bermuda", + "São Tomé and Principe", + "Cabo Verde", + "Malta", + "Jersey", + "Guernsey", + "Isle of Man", + "Aland", + "Faroe Islands", + "Singapore", + "Maldives", + "Nauru", + "Federated States of Micronesia", + "Falkland Islands", + "Palau", + "Guam", + "Northern Mariana Islands", + "Bahrain", + "Spratly Islands", + "Clipperton Island", + "Macao S.A.R", + "Bajo Nuevo Bank (Petrel Is.)", + "Serranilla Bank", + "Scarborough Reef" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "98fceb625f8dc0e1", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92769:92783:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76248:76264:1'} The data starts from March 11 00:00 and ends on March 14 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Geopotential at 850 hPa values running below the 10th percentile climatology for the all-time climatology? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) show Geopotential at 850 hPa values below the 10th percentile climatology for all-time climatology: Indonesia(average -26.3 m²/s²)\nEthiopia(average -7.322 m²/s²)\nSouth Sudan(average -17.34 m²/s²)\nKenya(average -16.43 m²/s²)\nUnited Republic of Tanzania(average -11.03 m²/s²)\nFrance(average -99.56 m²/s²)\nMorocco(average -464.1 m²/s²)\nWestern Sahara(average -304.4 m²/s²)\nRepublic of the Congo(average -49.49 m²/s²)\nDemocratic Republic of the Congo(average -24.75 m²/s²)\nNamibia(average -36.04 m²/s²)\nBrazil(average -88.61 m²/s²)\nRussia(average -470 m²/s²)\nNorway(average -274.1 m²/s²)\nSpain(average -422 m²/s²)\nSierra Leone(average -7.886 m²/s²)\nGuinea(average -9.75 m²/s²)\nLiberia(average -14.58 m²/s²)\nCentral African Republic(average -36.23 m²/s²)\nIvory Coast(average -25.97 m²/s²)\nMali(average -58.48 m²/s²)\nSenegal(average -19.56 m²/s²)\nNigeria(average -52.11 m²/s²)\nBenin(average -35.93 m²/s²)\nAngola(average -30.59 m²/s²)\nChad(average -13.95 m²/s²)\nEast Timor(average -28.47 m²/s²)\nAlgeria(average -215.2 m²/s²)\nBurundi(average -6.311 m²/s²)\nAndorra(average -51.82 m²/s²)\nUganda(average -22.73 m²/s²)\nPortugal(average -619.5 m²/s²)\nCameroon(average -61.44 m²/s²)\nGabon(average -61.27 m²/s²)\nNiger(average -11.48 m²/s²)\nBurkina Faso(average -26.42 m²/s²)\nTogo(average -47.29 m²/s²)\nGhana(average -39.61 m²/s²)\nGibraltar(average -627.7 m²/s²)\nUnited States of America(average -193.7 m²/s²)\nCanada(average -192.3 m²/s²)\nMauritania(average -145.9 m²/s²)\nEquatorial Guinea(average -68.8 m²/s²)\nAustralia(average -204.3 m²/s²)\nSaint Helena(average -26.64 m²/s²)\nSão Tomé and Principe(average -67.95 m²/s²)\nIndian Ocean Territories(average -102.5 m²/s²)\nAshmore and Cartier Islands(average -102.4 m²/s²)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "geopotential", + 850 + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.1", + "threshold_direction": "below", + "true_value": [ + "Indonesia", + "Ethiopia", + "South Sudan", + "Kenya", + "United Republic of Tanzania", + "France", + "Morocco", + "Western Sahara", + "Republic of the Congo", + "Democratic Republic of the Congo", + "Namibia", + "Brazil", + "Russia", + "Norway", + "Spain", + "Sierra Leone", + "Guinea", + "Liberia", + "Central African Republic", + "Ivory Coast", + "Mali", + "Senegal", + "Nigeria", + "Benin", + "Angola", + "Chad", + "East Timor", + "Algeria", + "Burundi", + "Andorra", + "Uganda", + "Portugal", + "Cameroon", + "Gabon", + "Niger", + "Burkina Faso", + "Togo", + "Ghana", + "Gibraltar", + "United States of America", + "Canada", + "Mauritania", + "Equatorial Guinea", + "Australia", + "Saint Helena", + "São Tomé and Principe", + "Indian Ocean Territories", + "Ashmore and Cartier Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "c65daf2e77392f17", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76248:76264:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89951:89970:1'} The data starts from July 26 18:00 and ends on July 31 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 42 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 42 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 42 hours:\nA Tropical cyclone is expected in the country of Lao People's Democratic Republic in approximately the next 18 to 66 hours. Specifically the region(s) that might get affected are: Xayaboury Province\nA Tropical cyclone is expected in the country of Thailand in approximately the next 18 to 66 hours. Specifically the region(s) that might get affected are: Loei Province\nA Tropical cyclone is expected in the country of Puerto Rico in approximately the next 42 hours. Specifically the region(s) that might get affected are: Yauco, Jayuya, Mayagüez cities\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Lao People's Democratic Republic", + "Thailand", + "Puerto Rico" + ], + "extreme_event_hours": 42, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "ba1e78b08acf8241", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89951:89970:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49183:49205:1'} The data starts from August 30 18:00 and ends on September 05 00:00. Based on the above data, answer the following question:", + "question": "What will the maximum U (zonal) component of wind at 1000 hPa be in Marguerite Bay, 30 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum U (zonal) component of wind at 1000 hPa in Marguerite Bay 30 hours after the given time window will be 0.8768 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "0.87680155", + "location": "Marguerite Bay", + "target_variable": "u_component_of_wind_1000", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0f7aeba5d08babc0", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49183:49205:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89948:89963:1'} The data starts from July 26 00:00 and ends on July 29 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in V (meridional) component of wind at 50 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the V (meridional) component of wind at 50 hPa values exceed the 95th percentile climatology for the monthly climatology for July.", + "response": "The following water body(s) are currently experiencing an exceedance in V (meridional) component of wind at 50 hPa: Arctic Ocean(average 0.1597 m/s)\nSOUTHERN OCEAN(average 1.213 m/s)\nSouth Pacific Ocean(average 1.685 m/s)\nSouth Atlantic Ocean(average 1.932 m/s)\nBeaufort Sea(average 0.1683 m/s)\nDrake Passage(average 3.307 m/s)\nThe North Western Passages(average 0.05389 m/s)\nAmundsen Gulf(average 0.08844 m/s)\nViscount Melville Sound(average 0.07483 m/s)\nM'Clure Strait(average 0.1298 m/s)\nEstrecho de Magellanes(average 1.524 m/s)\nPrince of Wales Strait(average 0.1395 m/s)\nMinto Inlet(average 0.1856 m/s)\nRichard Collinson Inlet(average 0.1508 m/s)\nPrince ALbert Sound(average 0.1845 m/s)\nLiddon Gulf(average 0.02194 m/s)\nWynniatt Bay(average 0.1155 m/s)\nSeno de Skyring(average 1.231 m/s)\nSeno Otway(average 1.524 m/s)\nBay Inútil(average 2.48 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "v_component_of_wind", + 50 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for July", + "quantile": "0.95", + "min_duration_days": 3, + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "South Pacific Ocean", + "South Atlantic Ocean", + "Beaufort Sea", + "Drake Passage", + "The North Western Passages", + "Amundsen Gulf", + "Viscount Melville Sound", + "M'Clure Strait", + "Estrecho de Magellanes", + "Prince of Wales Strait", + "Minto Inlet", + "Richard Collinson Inlet", + "Prince ALbert Sound", + "Liddon Gulf", + "Wynniatt Bay", + "Seno de Skyring", + "Seno Otway", + "Bay Inútil" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "3200195c2a68792d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89948:89963:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82860:82872:1'} The data starts from September 19 00:00 and ends on September 21 18:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Temperature at 600 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the Temperature at 600 hPa values exceed the 95th percentile climatology for the all-time climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in Temperature at 600 hPa: North Atlantic Ocean(average 0.3842 K)\nNorth Pacific Ocean(average 0.4369 K)\nSouth Pacific Ocean(average 0.2128 K)\nINDIAN OCEAN(average 0.386 K)\nSouth Atlantic Ocean(average 0.1032 K)\nPhilippine Sea(average 1.13 K)\nArabian Sea(average 0.1567 K)\nGulf of Mexico(average 1.587 K)\nNorwegian Sea(average 1.196 K)\nGreenland Sea(average 0.05423 K)\nBarents Sea(average 0.6942 K)\nLaccadive Sea(average 0.1935 K)\nSea of Azov(average 0.3496 K)\nSolomon Sea(average 0.1336 K)\nLake Pontchartrain(average 0.661 K)\nSargasso Sea(average 0.4029 K)\nCoral Sea(average 0.08857 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "temperature", + 600 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "all-time climatology", + "quantile": "0.95", + "min_duration_days": 3, + "true_value": [ + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Arabian Sea", + "Gulf of Mexico", + "Norwegian Sea", + "Greenland Sea", + "Barents Sea", + "Laccadive Sea", + "Sea of Azov", + "Solomon Sea", + "Lake Pontchartrain", + "Sargasso Sea", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "63e5384dd87b3d64", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82860:82872:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45497:45518:1'} The data starts from February 21 06:00 and ends on February 26 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 42 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 42 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 42 hours:\nA Storm (General) is expected in the country of Austria in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of Belgium in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of Switzerland in approximately the next 18 to 66 hours\nA Storm (General) is expected in the country of Germany in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of Denmark in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of France in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of United Kingdom of Great Britain and Northern Ireland in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of Greece in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of Italy in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of Luxembourg in approximately the next 42 to 66 hours\nA Storm (General) is expected in the country of Netherlands (Kingdom of the) in approximately the next 42 to 66 hours\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Austria", + "Belgium", + "Switzerland", + "Germany", + "Denmark", + "France", + "United Kingdom of Great Britain and Northern Ireland", + "Greece", + "Italy", + "Luxembourg", + "Netherlands (Kingdom of the)" + ], + "extreme_event_hours": 42, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "6a99ae6bea586800", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45497:45518:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58603:58616:1'} The data starts from February 10 18:00 and ends on February 13 18:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "No Tropical Cyclone detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "16a176ee50b56468", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58603:58616:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41533:41553:1'} The data starts from June 06 06:00 and ends on June 11 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 18 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 18 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 18 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 18, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "4a67b32df1c4066e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41533:41553:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75255:75267:1'} The data starts from July 05 18:00 and ends on July 08 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) V (meridional) component of wind at 100 hPa values running above the 90th percentile climatology for the monthly climatology for July? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show V (meridional) component of wind at 100 hPa values above the 90th percentile climatology for monthly climatology for July: Arctic Ocean(average 0.445 m/s)\nSOUTHERN OCEAN(average 2.195 m/s)\nNorth Atlantic Ocean(average 0.5522 m/s)\nNorth Pacific Ocean(average 2.846 m/s)\nSouth Pacific Ocean(average 0.7341 m/s)\nINDIAN OCEAN(average 0.5339 m/s)\nSouth Atlantic Ocean(average 1.646 m/s)\nPhilippine Sea(average 2.451 m/s)\nBay of Bengal(average 1.735 m/s)\nSouth China Sea(average 1.022 m/s)\nHudson Bay(average 2.344 m/s)\nGulf of Alaska(average 3.396 m/s)\nSea of Okhotsk(average 3.113 m/s)\nWeddell Sea(average 3.372 m/s)\nNorwegian Sea(average 0.7601 m/s)\nBaltic Sea(average 0.5179 m/s)\nNorth Sea(average 0.4375 m/s)\nInner Seas(average 0.222 m/s)\nAndaman Sea(average 1.276 m/s)\nEast China Sea(average 0.5186 m/s)\nGulf of Thailand(average 1.061 m/s)\nLaptev Sea(average 1.11 m/s)\nJames Bay(average 3.432 m/s)\nHudson Strait(average 0.6454 m/s)\nGulf of Bothnia(average 0.9644 m/s)\nInner Sea(average 1.757 m/s)\nCook Inlet(average 1.997 m/s)\nBristol Bay(average 0.6077 m/s)\nEast Siberian Sea(average 0.6551 m/s)\nVestfjorden(average 0.1182 m/s)\nSkagerrak(average 0.5618 m/s)\nSognefjorden(average 0.592 m/s)\nTrondheimsfjorden(average 0.7167 m/s)\nKattegat(average 0.4792 m/s)\nUda Bay(average 2.988 m/s)\nUchiura Bay(average 6.363 m/s)\nTsugaru Strait(average 6.381 m/s)\nTatar Strait(average 2.583 m/s)\nFoxe Basin(average 0.5742 m/s)\nGulf of Yana(average 1.733 m/s)\nDmitriy Laptev Strait(average 1.486 m/s)\nLa Pérouse Strait(average 4.928 m/s)\nGulf of Olen‘k(average 0.9554 m/s)\nPrince William Sound(average 2.827 m/s)\nBoknafjorden(average 0.3554 m/s)\nGulf of Martaban(average 1.069 m/s)\nGulf of Sakhalin(average 2.611 m/s)\nWager Bay(average 0.2599 m/s)\nOzero Mogotoyevo(average 0.7896 m/s)\nGuba Gusinaya(average 0.5481 m/s)\nSea of Japan(average 3.625 m/s)\nKorea Strait(average 0.4689 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "v_component_of_wind", + 100 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for July", + "quantile": "0.9", + "threshold_direction": "above", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Bay of Bengal", + "South China Sea", + "Hudson Bay", + "Gulf of Alaska", + "Sea of Okhotsk", + "Weddell Sea", + "Norwegian Sea", + "Baltic Sea", + "North Sea", + "Inner Seas", + "Andaman Sea", + "East China Sea", + "Gulf of Thailand", + "Laptev Sea", + "James Bay", + "Hudson Strait", + "Gulf of Bothnia", + "Inner Sea", + "Cook Inlet", + "Bristol Bay", + "East Siberian Sea", + "Vestfjorden", + "Skagerrak", + "Sognefjorden", + "Trondheimsfjorden", + "Kattegat", + "Uda Bay", + "Uchiura Bay", + "Tsugaru Strait", + "Tatar Strait", + "Foxe Basin", + "Gulf of Yana", + "Dmitriy Laptev Strait", + "La Pérouse Strait", + "Gulf of Olen‘k", + "Prince William Sound", + "Boknafjorden", + "Gulf of Martaban", + "Gulf of Sakhalin", + "Wager Bay", + "Ozero Mogotoyevo", + "Guba Gusinaya", + "Sea of Japan", + "Korea Strait" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "0c2e9b29d42d109e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75255:75267:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48906:48923:1'} The data starts from June 22 12:00 and ends on June 26 12:00. Based on the above data, answer the following question:", + "question": "What will the average 10-meter U component of wind be in Aland, 36 hours after the end of the given time window?", + "response": "Based on the provided data, the average 10-meter U component of wind in Aland 36 hours after the given time window will be 5.253 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "5.253295", + "location": "Aland", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8d9d05d0a744a9cf", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48906:48923:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46102:46123:1'} The data starts from July 22 12:00 and ends on July 27 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 36 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 36 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 36 hours:\nA Storm (General) is expected in the country of Switzerland in approximately the next 36 hours\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Switzerland" + ], + "extreme_event_hours": 36, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "7db3e4b87f12274a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46102:46123:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82822:82836:1'} The data starts from September 09 12:00 and ends on September 12 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 30 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 30 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 30 hours:\nA Tropical cyclone is expected in the country of Viet Nam in approximately the next 30 hours. Specifically the region(s) that might get affected are: Da Nang, Quang Nam (Duy Xuyen, Nong Son districts), Quand Ngai (Ly Son District), Thanh Hoa\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Viet Nam" + ], + "extreme_event_hours": 30, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "74903e70c2ac28cc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82822:82836:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72007:72023:1'} The data starts from April 14 18:00 and ends on April 18 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in 10-meter U component of wind values? An exceedance is defined as a period of at least 96 consecutive hours where the 10-meter U component of wind values exceed the 90th percentile climatology for the monthly climatology for April.", + "response": "The following water body(s) are currently experiencing an exceedance in 10-meter U component of wind: SOUTHERN OCEAN(average 0.601 m/s)\nNorth Atlantic Ocean(average 1.383 m/s)\nNorth Pacific Ocean(average 1.942 m/s)\nSouth Pacific Ocean(average 0.5503 m/s)\nINDIAN OCEAN(average 0.3459 m/s)\nSouth Atlantic Ocean(average 1.193 m/s)\nArabian Sea(average 0.3694 m/s)\nLabrador Sea(average 1.007 m/s)\nGulf of Alaska(average 1.832 m/s)\nNorwegian Sea(average 0.1145 m/s)\nGreenland Sea(average 0.1844 m/s)\nGulf of Aden(average 0.4095 m/s)\nGulf of Saint Lawrence(average 0.6428 m/s)\nCook Inlet(average 0.5826 m/s)\nHamilton Inlet(average 0.3403 m/s)\nNorton Sound(average 0.1499 m/s)\nStrait of Belle Isle(average 1.291 m/s)\nPrince William Sound(average 0.5027 m/s)\nDenmark Strait(average 0.1714 m/s)\nSmith Sound(average 0.02638 m/s)\nQueen Charlotte Strait(average 0.2623 m/s)\nHecate Strait(average 0.5501 m/s)\nSargasso Sea(average 0.6287 m/s)\nSalish Sea(average 0.2444 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for April", + "quantile": "0.9", + "min_duration_days": 4, + "true_value": [ + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Arabian Sea", + "Labrador Sea", + "Gulf of Alaska", + "Norwegian Sea", + "Greenland Sea", + "Gulf of Aden", + "Gulf of Saint Lawrence", + "Cook Inlet", + "Hamilton Inlet", + "Norton Sound", + "Strait of Belle Isle", + "Prince William Sound", + "Denmark Strait", + "Smith Sound", + "Queen Charlotte Strait", + "Hecate Strait", + "Sargasso Sea", + "Salish Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "bf8ae2384bae9542", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72007:72023:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30184:30200:1'} The data starts from August 30 00:00 and ends on September 02 18:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Trondheimsfjorden experience its highest Surface temperature? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Trondheimsfjorden will experience its highest Surface temperature of 286.1 K 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Trondheimsfjorden", + "extremum_value": "286.07257", + "target_variable": "2m_temperature", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "b21f12e850fd1523", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30184:30200:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85773:85788:1'} The data starts from September 16 06:00 and ends on September 19 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) V (meridional) component of wind at 400 hPa differs from the monthly climatology for September mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below V (meridional) component of wind at 400 hPa values.", + "response": "Based on the provided data, no significant V (meridional) component of wind at 400 hPa anomalies were detected relative to the monthly climatology for September baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "v_component_of_wind", + 400 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for September", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "06b8ba049de21b3e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85773:85788:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68563:68576:1'} The data starts from December 05 18:00 and ends on December 08 18:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in U (zonal) component of wind at 100 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the U (zonal) component of wind at 100 hPa values exceed the 99th percentile climatology for the six-hourly climatology for day 339 at 18 UTC. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in U (zonal) component of wind at 100 hPa: Ethiopia(average 0.05456 m/s)\nSomalia(average 0.04664 m/s)\nRussia(average 2.018 m/s)\nUnited States of America(average 3.82 m/s)\nAustralia(average 1.65 m/s)\nJapan(average 2.43 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "u_component_of_wind", + 100 + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 339 at 18 UTC", + "quantile": "0.99", + "min_duration_days": 3, + "true_value": [ + "Ethiopia", + "Somalia", + "Russia", + "United States of America", + "Australia", + "Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "652396b39e5cb45d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68563:68576:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67227:67233:1'} The data starts from January 05 18:00 and ends on January 07 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Extra-tropical storm is occuring in the country of Estonia. Specifically the region(s) being affected are: Pärnu (Pärnu linn district, Pärnu Maakond province); Lääne-Eesti\nA Extra-tropical storm is occuring in the country of United Kingdom of Great Britain and Northern Ireland. Specifically the region(s) being affected are: Appleby, Longtown, Shap,Carlisle towns (Cumbria district, England province), West Yorkshire district (England province), North Yorkshire district (England province), Haydon Brige town (Northumberland district, England province), Peebles, Jedburgh towns (Borders district, Scotland province), Grampian district (Scotland province), Wales province\nA Extra-tropical storm is occuring in the country of Ireland. Specifically the region(s) being affected are: Dublin, Kildare, Laois, Longford, Meath, Offaly, Westmeath, Wicklow provinces\nA Extra-tropical storm is occuring in the country of Poland. Specifically the region(s) being affected are: Gdansk district (Pomorskie province)\nA Extra-tropical storm is occuring in the country of Russian Federation. Specifically the region(s) being affected are: Sankt-peterburg province\nA Extra-tropical storm is occuring in the country of Sweden. Specifically the region(s) being affected are: Blekinge Laen, Dalarnas Laen, Gaevleborgs Laen, Gotlands Laen, Hallands Laen, Jaemtlands Laen, Joenkoepings Laen, Kalmar Laen, Kronobergs Laen, Norrbottens Laen, Oerebro Laen, Oestergoetlands Laen, Skaane Laen, Soedermanlands Laen, Stockholms Laen, Uppsala Laen, Vaermlands Laen, Vaesterbottens Laen, Vaesternorrlands Laen, Vaestmanlands Laen, Vaestra Goetalands Laen provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Estonia", + "United Kingdom of Great Britain and Northern Ireland", + "Ireland", + "Poland", + "Russian Federation", + "Sweden" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "a71bd91f5457898a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67227:67233:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86506:86528:1'} The data starts from March 18 12:00 and ends on March 23 18:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) U (zonal) component of wind at 1000 hPa differs from the six-hourly climatology for day 77 at 12 UTC mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above U (zonal) component of wind at 1000 hPa values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for U (zonal) component of wind at 1000 hPa relative to the six-hourly climatology for day 77 at 12 UTC mean: North Pacific Ocean(average 5.699 m/s)\nSouth Pacific Ocean(average 1.148 m/s)\nBay of Bengal(average 2.088 m/s)\nMolucca Sea(average 3.501 m/s)\nGulf of Buli(average 3.501 m/s)\nGulf of Kau(average 3.501 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "u_component_of_wind", + 1000 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "six-hourly climatology for day 77 at 12 UTC", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [ + "North Pacific Ocean", + "South Pacific Ocean", + "Bay of Bengal", + "Molucca Sea", + "Gulf of Buli", + "Gulf of Kau" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "b1aabf88e2064078", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86506:86528:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43108:43130:1'} The data starts from July 04 00:00 and ends on July 09 06:00. Based on the above data, answer the following question:", + "question": "What will the average Mean sea level pressure be in Antarctica, 12 hours after the end of the given time window?", + "response": "Based on the provided data, the average Mean sea level pressure in Antarctica 12 hours after the given time window will be 1.002e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "100187.28", + "location": "Antarctica", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fd76353dd513d7b5", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43108:43130:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79979:79981:1'} The data starts from September 28 18:00 and ends on September 29 00:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 24 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 24 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 24 hours:\nA Tropical cyclone is expected in the country of China in approximately the next 24 hours. Specifically the region(s) that might get affected are: Hainan Sheng, Guangdong Sheng provinces\nA Tropical cyclone is expected in the country of Viet Nam in approximately the next 24 to 384 hours. Specifically the region(s) that might get affected are: Binh Dinh, Quang Binh, Quang Nam, Quang Ngai, Ha Tinh, Quang Tri, Thua Thien - Hue, Khanh Hoa, Nghe An, Da Nang City, Phu Yen, Thanh Hoa provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "China", + "Viet Nam" + ], + "extreme_event_hours": 24, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "a80f08c8e4309f14", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79979:79981:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66516:66527:1'} The data starts from July 12 00:00 and ends on July 14 12:00. Based on the above data, answer the following question:", + "question": "In the 24 hours after the end of the given time window, when will Asia experience its lowest 10-meter U component of wind? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Asia will experience its lowest 10-meter U component of wind of -10.84 m/s 18 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 18, + "location": "Asia", + "extremum_value": "-10.844136", + "target_variable": "10m_u_component_of_wind", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "ff67bdb70a89d6dd", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66516:66527:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84797:84813:1'} The data starts from January 15 06:00 and ends on January 19 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in U (zonal) component of wind at 250 hPa values? An exceedance is defined as a period of at least 72 consecutive hours where the U (zonal) component of wind at 250 hPa values exceed the 99th percentile climatology for the daily climatology for day 15. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in U (zonal) component of wind at 250 hPa: China(average 0.6151 m/s)\nCanada(average 2.602 m/s)\nAustralia(average 6.998 m/s)\nMadagascar(average 1.539 m/s)\nSeychelles(average 1.009 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "u_component_of_wind", + 250 + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 15", + "quantile": "0.99", + "min_duration_days": 3, + "true_value": [ + "China", + "Canada", + "Australia", + "Madagascar", + "Seychelles" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "cd7de04a0212c7cc", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84797:84813:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74456:74465:1'} The data starts from December 18 00:00 and ends on December 20 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in Geopotential at 50 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the Geopotential at 50 hPa values exceed the 90th percentile climatology for the monthly climatology for December.", + "response": "The following water body(s) are currently experiencing an exceedance in Geopotential at 50 hPa: North Atlantic Ocean(average 142.8 m²/s²)\nLabrador Sea(average 167.8 m²/s²)\nSea of Okhotsk(average 493.2 m²/s²)\nKara Sea(average 379.2 m²/s²)\nLaptev Sea(average 1081 m²/s²)\nGulf of Saint Lawrence(average 112.4 m²/s²)\nBay of Fundy(average 5.141 m²/s²)\nVil'kitskogo Strait(average 597.8 m²/s²)\nUda Bay(average 1095 m²/s²)\nTatar Strait(average 452.4 m²/s²)\nGulf of Yana(average 790.9 m²/s²)\nGulf of Olen‘k(average 1614 m²/s²)\nStrait of Belle Isle(average 74.39 m²/s²)\nGulf of Sakhalin(average 591.3 m²/s²)\nKhatanga Gulf(average 1594 m²/s²)\nBras d'Or Lake(average 77.77 m²/s²)\nSea of Japan(average 203.1 m²/s²)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "geopotential", + 50 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for December", + "quantile": "0.9", + "min_duration_days": 2, + "true_value": [ + "North Atlantic Ocean", + "Labrador Sea", + "Sea of Okhotsk", + "Kara Sea", + "Laptev Sea", + "Gulf of Saint Lawrence", + "Bay of Fundy", + "Vil'kitskogo Strait", + "Uda Bay", + "Tatar Strait", + "Gulf of Yana", + "Gulf of Olen‘k", + "Strait of Belle Isle", + "Gulf of Sakhalin", + "Khatanga Gulf", + "Bras d'Or Lake", + "Sea of Japan" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "3a5fed2108f401cb", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74456:74465:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87365:87390:1'} The data starts from October 19 06:00 and ends on October 25 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) 10-meter U component of wind differs from the daily climatology for day 292 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously below 10-meter U component of wind values.", + "response": "Based on the provided data, no significant 10-meter U component of wind anomalies were detected relative to the daily climatology for day 292 baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 292", + "sigma_threshold": 3, + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "923014859ee15c77", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87365:87390:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87699:87709:1'} The data starts from January 10 18:00 and ends on January 13 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Severe Weather currently happening? Specify the affected countries or regions, or respond 'No Severe Weather detected.'", + "response": "Based on the provided data, the Severe Weather is affecting: Lebanon", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Lebanon" + ], + "target_disaster": "Severe Weather", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "4008a4ba8db45a3d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87699:87709:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34085:34098:1'} The data starts from May 01 06:00 and ends on May 04 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of Myanmar. Specifically the region(s) being affected are: South West\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "Myanmar" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "8d3c6635fd5c7591", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34085:34098:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51646:51674:1'} The data starts from May 08 12:00 and ends on May 15 06:00. Based on the above data, answer the following question:", + "question": "What will the maximum Mean sea level pressure be in Melville Bay, 24 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum Mean sea level pressure in Melville Bay 24 hours after the given time window will be 1.034e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "103374.26", + "location": "Melville Bay", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ab63b48271df3c31", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51646:51674:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82528:82550:1'} The data starts from June 28 00:00 and ends on July 03 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in 10-meter V component of wind values? An exceedance is defined as a period of at least 120 consecutive hours where the 10-meter V component of wind values exceed the 90th percentile climatology for the six-hourly climatology for day 179 at 00 UTC. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in 10-meter V component of wind: Indonesia(average 0.8596 m/s)\nChile(average 0.512 m/s)\nArgentina(average 0.5263 m/s)\nIndia(average 0.3468 m/s)\nChina(average 0.4046 m/s)\nEthiopia(average 0.2901 m/s)\nSomalia(average 0.2901 m/s)\nKenya(average 0.2901 m/s)\nSyria(average 0.1076 m/s)\nFrance(average 0.2585 m/s)\nMorocco(average 0.2565 m/s)\nBhutan(average 0.2748 m/s)\nUkraine(average 0.3109 m/s)\nKazakhstan(average 0.3273 m/s)\nBrazil(average 0.3362 m/s)\nMongolia(average 0.4345 m/s)\nRussia(average 0.2618 m/s)\nNorway(average 0.2616 m/s)\nSweden(average 0.272 m/s)\nGeorgia(average 0.2743 m/s)\nAzerbaijan(average 0.2743 m/s)\nTurkey(average 0.4758 m/s)\nSpain(average 0.2263 m/s)\nArmenia(average 0.2743 m/s)\nLibya(average 0.5457 m/s)\nIreland(average 1.245 m/s)\nUnited Kingdom(average 0.5499 m/s)\nSierra Leone(average 0.04498 m/s)\nGuinea(average 0.04498 m/s)\nSudan(average 0.3215 m/s)\nIraq(average 0.565 m/s)\nIran(average 1.068 m/s)\nAngola(average 0.05543 m/s)\nSaudi Arabia(average 0.953 m/s)\nPakistan(average 1.18 m/s)\nBangladesh(average 0.3693 m/s)\nAfghanistan(average 0.8475 m/s)\nEcuador(average 0.4123 m/s)\nColombia(average 0.2554 m/s)\nTurkmenistan(average 0.03197 m/s)\nJordan(average 0.8329 m/s)\nNepal(average 0.5229 m/s)\nCanada(average 0.678 m/s)\nVenezuela(average 0.326 m/s)\nPapua New Guinea(average 2.459 m/s)\nAustralia(average 0.1612 m/s)\nFiji(average 0.8016 m/s)\nThe Bahamas(average 0.2161 m/s)\nKiribati(average 0.3429 m/s)\nUnited States Minor Outlying Islands(average 0.3093 m/s)\nBermuda(average 1.537 m/s)\nTonga(average 0.8409 m/s)\nWallis and Futuna(average 1.719 m/s)\nSamoa(average 0.5774 m/s)\nSolomon Islands(average 3.066 m/s)\nFederated States of Micronesia(average 1.491 m/s)\nAmerican Samoa(average 0.4419 m/s)\nPalau(average 2.668 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 179 at 00 UTC", + "quantile": "0.9", + "min_duration_days": 5, + "true_value": [ + "Indonesia", + "Chile", + "Argentina", + "India", + "China", + "Ethiopia", + "Somalia", + "Kenya", + "Syria", + "France", + "Morocco", + "Bhutan", + "Ukraine", + "Kazakhstan", + "Brazil", + "Mongolia", + "Russia", + "Norway", + "Sweden", + "Georgia", + "Azerbaijan", + "Turkey", + "Spain", + "Armenia", + "Libya", + "Ireland", + "United Kingdom", + "Sierra Leone", + "Guinea", + "Sudan", + "Iraq", + "Iran", + "Angola", + "Saudi Arabia", + "Pakistan", + "Bangladesh", + "Afghanistan", + "Ecuador", + "Colombia", + "Turkmenistan", + "Jordan", + "Nepal", + "Canada", + "Venezuela", + "Papua New Guinea", + "Australia", + "Fiji", + "The Bahamas", + "Kiribati", + "United States Minor Outlying Islands", + "Bermuda", + "Tonga", + "Wallis and Futuna", + "Samoa", + "Solomon Islands", + "Federated States of Micronesia", + "American Samoa", + "Palau" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "c01982cb57da84fc", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82528:82550:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78635:78655:1'} The data starts from October 27 18:00 and ends on November 01 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of China. Specifically the region(s) being affected are: Hainan Sheng, Guangxi Zhuangzu Zizhiqu provinces\nA Tropical cyclone is occuring in the country of Viet Nam. Specifically the region(s) being affected are: Nghe An, Thanh Hoa, Ninh Binh, Nam Dinh, Thai Binh, Hai Phong City provinces\nA Tropical cyclone is occuring in the country of United States of America. Specifically the region(s) being affected are: New York, New Jersey, Pennsylvania, Connecticut, Ohio, Delaware, Rhode Island, Maryland, Massachusetts, Maine, New Hampshire, North Carolina, Vermont, Virginia, District of Columbia, West Virginia provinces\nA Severe weather is occuring in the country of Argentina. Specifically the region(s) being affected are: Buenos Aires, Buenos Aires D.f. provinces\nA Tropical cyclone is occuring in the country of Sri Lanka. Specifically the region(s) being affected are: Central, Eastern, North Central, North Western, Northern, Sabaragamuwa, Southern, Uva, Western provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "China", + "Viet Nam", + "United States of America", + "Argentina", + "Sri Lanka" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "1ecaea9cf09668ad", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78635:78655:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45833:45851:1'} The data starts from May 16 06:00 and ends on May 20 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 36 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 36 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 36 hours:\nA Severe weather is expected in the country of Austria in approximately the next 36 hours\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Austria" + ], + "extreme_event_hours": 36, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "471907b5804f0df7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45833:45851:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89128:89153:1'} The data starts from January 03 00:00 and ends on January 09 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) Surface temperature differs from the all-time climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Surface temperature values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Surface temperature anomalies were detected relative to the all-time climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "all-time climatology", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "a1a0542261e9b66f", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89128:89153:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86083:86100:1'} The data starts from December 02 18:00 and ends on December 06 18:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter U component of wind lies outside the climatological 1st–90th percentile envelope for the daily climatology for day 336. Regions outside that envelope are anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) fall outside the 1st–90th percentile envelope for 10-meter U component of wind during daily climatology for day 336: Indonesia(average 0.4548 m/s)\nBolivia(average 0.128 m/s)\nIndia(average 1.106 m/s)\nChina(average 0.417 m/s)\nSyria(average 0.3451 m/s)\nUkraine(average 0.4157 m/s)\nOman(average 0.2816 m/s)\nKazakhstan(average 0.02226 m/s)\nTajikistan(average 0.05729 m/s)\nBrazil(average 0.8911 m/s)\nRussia(average -0.9407 m/s)\nCzechia(average 0.1347 m/s)\nGermany(average 0.03762 m/s)\nNorway(average -1.952 m/s)\nNorth Macedonia(average 0.3888 m/s)\nAlbania(average 0.6356 m/s)\nTurkey(average 0.2648 m/s)\nKyrgyzstan(average 0.03393 m/s)\nRomania(average 0.42 m/s)\nHungary(average 0.478 m/s)\nSlovakia(average 0.3513 m/s)\nGreece(average 0.3535 m/s)\nAustria(average 0.1347 m/s)\nIraq(average 0.2723 m/s)\nItaly(average 0.5181 m/s)\nRepublic of Serbia(average 0.2419 m/s)\nNigeria(average 0.1305 m/s)\nCroatia(average 0.2798 m/s)\nSaudi Arabia(average 0.201 m/s)\nPakistan(average 0.1357 m/s)\nBulgaria(average 0.3309 m/s)\nAlgeria(average -0.2253 m/s)\nAfghanistan(average 0.05729 m/s)\nJordan(average 0.3451 m/s)\nCanada(average 0.3447 m/s)\nPapua New Guinea(average 1.114 m/s)\nYemen(average 0.2992 m/s)\nAustralia(average 1.027 m/s)\nGreenland(average 0.196 m/s)\nFiji(average 1.716 m/s)\nSeychelles(average 2.962 m/s)\nIndian Ocean Territories(average 2.033 m/s)\nCook Islands(average 0.5095 m/s)\nTonga(average 0.6871 m/s)\nSolomon Islands(average 0.8062 m/s)\nMaldives(average 1.002 m/s)\nNiue(average 0.8206 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 336", + "lower_quantile": "0.01", + "upper_quantile": "0.9", + "true_value": [ + "Indonesia", + "Bolivia", + "India", + "China", + "Syria", + "Ukraine", + "Oman", + "Kazakhstan", + "Tajikistan", + "Brazil", + "Russia", + "Czechia", + "Germany", + "Norway", + "North Macedonia", + "Albania", + "Turkey", + "Kyrgyzstan", + "Romania", + "Hungary", + "Slovakia", + "Greece", + "Austria", + "Iraq", + "Italy", + "Republic of Serbia", + "Nigeria", + "Croatia", + "Saudi Arabia", + "Pakistan", + "Bulgaria", + "Algeria", + "Afghanistan", + "Jordan", + "Canada", + "Papua New Guinea", + "Yemen", + "Australia", + "Greenland", + "Fiji", + "Seychelles", + "Indian Ocean Territories", + "Cook Islands", + "Tonga", + "Solomon Islands", + "Maldives", + "Niue" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "99a47a351d13b05e", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86083:86100:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71330:71350:1'} The data starts from October 28 12:00 and ends on November 02 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 150 hPa differs from the monthly climatology for October mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Temperature at 150 hPa values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for Temperature at 150 hPa relative to the monthly climatology for October mean: South Pacific Ocean(average 3.371 K)\nCoral Sea(average 3.348 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "temperature", + 150 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for October", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [ + "South Pacific Ocean", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "9e6609dc2e524357", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71330:71350:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56349:56370:1'} The data starts from July 27 06:00 and ends on August 01 06:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Dardanelles experience its highest Specific humidity at 925 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Dardanelles will experience its highest Specific humidity at 925 hPa of 0.01092 kg/kg 12 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 12, + "location": "Dardanelles", + "extremum_value": "0.010919136", + "target_variable": "specific_humidity_925", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "89222a293f79609d", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56349:56370:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82665:82675:1'} The data starts from August 01 06:00 and ends on August 03 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 600 hPa lies outside the climatological 1st–95th percentile envelope for the monthly climatology for August. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–95th percentile envelope for Temperature at 600 hPa during monthly climatology for August: Arctic Ocean(average 1.53 K)\nSOUTHERN OCEAN(average -0.2625 K)\nNorth Atlantic Ocean(average 0.4675 K)\nNorth Pacific Ocean(average 0.203 K)\nSouth Pacific Ocean(average 0.2984 K)\nINDIAN OCEAN(average 0.9185 K)\nSouth Atlantic Ocean(average 0.2661 K)\nBlack Sea(average 0.9 K)\nPhilippine Sea(average 0.1571 K)\nTasman Sea(average 0.07684 K)\nHudson Bay(average 0.5068 K)\nCaspian Sea(average 0.1805 K)\nGreenland Sea(average 1.264 K)\nBanda Sea(average 0.3545 K)\nGulf of Guinea(average 0.3056 K)\nBarents Sea(average 1.959 K)\nChukchi Sea(average 0.9064 K)\nArafura Sea(average 0.3535 K)\nTimor Sea(average 0.1753 K)\nDavis Strait(average 0.3403 K)\nKara Sea(average 1.178 K)\nSea of Azov(average 0.1835 K)\nHudson Strait(average 0.755 K)\nMolucca Sea(average 0.007843 K)\nBismarck Sea(average 0.1877 K)\nSolomon Sea(average 0.3231 K)\nCeram Sea(average 0.4013 K)\nBering Sea(average 0.7465 K)\nEast Siberian Sea(average 0.4142 K)\nCumberland Sound(average 0.6305 K)\nFrobisher Bay(average 0.6026 K)\nStorfjorden(average 0.8916 K)\nKotzebue Sound(average 0.6682 K)\nGulf of Boothia(average 0.4482 K)\nFoxe Basin(average 1.249 K)\nGulf of Papua(average 0.2475 K)\nAlboran Sea(average 0.1523 K)\nBight of Benin(average 0.2741 K)\nChaun Bay(average 0.1791 K)\nGulf of Ob(average 0.815 K)\nYenisey Gulf(average 0.835 K)\nWager Bay(average 0.3657 K)\nFury and Hecla Strait(average 0.8616 K)\nHalmahera Sea(average 0.1504 K)\nSelat Dampier(average 0.1699 K)\nGulf of Buli(average 0.01492 K)\nGulf of Anadyr'(average 1.263 K)\nMediterranean Sea(average 0.1153 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "temperature", + 600 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for August", + "lower_quantile": "0.01", + "upper_quantile": "0.95", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Black Sea", + "Philippine Sea", + "Tasman Sea", + "Hudson Bay", + "Caspian Sea", + "Greenland Sea", + "Banda Sea", + "Gulf of Guinea", + "Barents Sea", + "Chukchi Sea", + "Arafura Sea", + "Timor Sea", + "Davis Strait", + "Kara Sea", + "Sea of Azov", + "Hudson Strait", + "Molucca Sea", + "Bismarck Sea", + "Solomon Sea", + "Ceram Sea", + "Bering Sea", + "East Siberian Sea", + "Cumberland Sound", + "Frobisher Bay", + "Storfjorden", + "Kotzebue Sound", + "Gulf of Boothia", + "Foxe Basin", + "Gulf of Papua", + "Alboran Sea", + "Bight of Benin", + "Chaun Bay", + "Gulf of Ob", + "Yenisey Gulf", + "Wager Bay", + "Fury and Hecla Strait", + "Halmahera Sea", + "Selat Dampier", + "Gulf of Buli", + "Gulf of Anadyr'", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d4dd1acbe66ff147", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82665:82675:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75566:75578:1'} The data starts from September 21 12:00 and ends on September 24 06:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) V (meridional) component of wind at 400 hPa differs from the SON seasonal climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above V (meridional) component of wind at 400 hPa values. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant V (meridional) component of wind at 400 hPa anomalies were detected relative to the SON seasonal climatology baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "v_component_of_wind", + 400 + ], + "geofeature": "country", + "climatology_timescale_desc": "SON seasonal climatology", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "6726061d9e872593", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75566:75578:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78051:78074:1'} The data starts from June 03 18:00 and ends on June 09 06:00. Based on the above data, answer the following question:", + "question": "Which country(s) have regions with (time-averaged) Mean sea level pressure values running below the 1st percentile climatology for the monthly climatology for June? Treat any region beyond that percentile as anomalous. Please provide the name of the specific country(s) with the anomaly.", + "response": "Based on the provided data, no significant Mean sea level pressure anomalies were detected relative to the monthly climatology for June baseline.", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "mean_sea_level_pressure", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "monthly climatology for June", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "3c8fafabc71be5e9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78051:78074:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91681:91698:1'} The data starts from October 02 06:00 and ends on October 06 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) V (meridional) component of wind at 700 hPa differs from the SON seasonal climatology mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above V (meridional) component of wind at 700 hPa values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for V (meridional) component of wind at 700 hPa relative to the SON seasonal climatology mean: SOUTHERN OCEAN(average 18.22 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "v_component_of_wind", + 700 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "SON seasonal climatology", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [ + "SOUTHERN OCEAN" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "6bbda2a582e06e68", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91681:91698:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42121:42124:1'} The data starts from October 31 06:00 and ends on October 31 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 30 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 30 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 30 hours:\nA Tropical cyclone is expected in the country of India in approximately the next 30 hours. Specifically the region(s) that might get affected are: Andhra Pradesh, Tami Nadu\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "India" + ], + "extreme_event_hours": 30, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "fb3947a67aedd3d9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42121:42124:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88009:88014:1'} The data starts from March 29 06:00 and ends on March 30 06:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Temperature at 150 hPa lies outside the climatological 10th–99th percentile envelope for the monthly climatology for March. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 10th–99th percentile envelope for Temperature at 150 hPa during monthly climatology for March: SOUTHERN OCEAN(average -0.8807 K)\nNorth Atlantic Ocean(average 0.297 K)\nNorth Pacific Ocean(average -0.1665 K)\nSouth Pacific Ocean(average -0.6211 K)\nINDIAN OCEAN(average -0.6768 K)\nSouth Atlantic Ocean(average -3.206 K)\nPhilippine Sea(average -0.1375 K)\nWeddell Sea(average -0.3453 K)\nJava Sea(average 0.01523 K)\nBellingshausen Sea(average -0.6642 K)\nAmundsen Sea(average -1.679 K)\nGreat Australian Bight(average 0.3852 K)\nMakassar Strait(average 0.04208 K)\nGulf of Maine(average -0.4119 K)\nGolfo San Jorge(average -0.7561 K)\nBass Strait(average 0.3001 K)\nGolfo Corcovado(average -0.3237 K)\nWrigley Gulf(average -0.4253 K)\nSulzberger Bay(average -1.161 K)\nMcMurdo Sound(average -0.4747 K)\nBahía Grande(average -0.6341 K)\nPeacock Sound(average -1.359 K)\nRoss Sea(average -0.7693 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "temperature", + 150 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "monthly climatology for March", + "lower_quantile": "0.1", + "upper_quantile": "0.99", + "true_value": [ + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "Weddell Sea", + "Java Sea", + "Bellingshausen Sea", + "Amundsen Sea", + "Great Australian Bight", + "Makassar Strait", + "Gulf of Maine", + "Golfo San Jorge", + "Bass Strait", + "Golfo Corcovado", + "Wrigley Gulf", + "Sulzberger Bay", + "McMurdo Sound", + "Bahía Grande", + "Peacock Sound", + "Ross Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "c7a907875d9c6d8b", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88009:88014:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45916:45932:1'} The data starts from June 06 00:00 and ends on June 09 18:00. Based on the above data, answer the following question:", + "question": "What will the maximum Geopotential at 700 hPa be in Guba Gusinaya, 30 hours after the end of the given time window?", + "response": "Based on the provided data, the maximum Geopotential at 700 hPa in Guba Gusinaya 30 hours after the given time window will be 2.908e+04 m²/s².", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "29078.75", + "location": "Guba Gusinaya", + "target_variable": "geopotential_700", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8dfed129a6b81675", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45916:45932:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67514:67520:1'} The data starts from March 18 12:00 and ends on March 19 18:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 12 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 12 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 12 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 12, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "394e0a77b09f9e11", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67514:67520:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46152:46178:1'} The data starts from August 04 00:00 and ends on August 10 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 6 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 6 hours.'", + "response": "Based on the provided data, there is no extreme weather event expected within the next 6 hours.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [], + "extreme_event_hours": 6, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "c8d883dfa9c5eabe", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46152:46178:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89529:89549:1'} The data starts from April 12 06:00 and ends on April 17 00:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Sand/Dust Storm currently happening? Specify the affected countries or regions, or respond 'No Sand/Dust Storm detected.'", + "response": "No Sand/Dust Storm detected in the provided data.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [], + "target_disaster": "Sand/Dust Storm", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "cb03030c5aa86c17", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89529:89549:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71704:71706:1'} The data starts from January 30 00:00 and ends on January 30 06:00. Based on the above data, answer the following question:", + "question": "In the 30 hours after the end of the given time window, when will Ethiopia experience its highest U (zonal) component of wind at 200 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Ethiopia will experience its highest U (zonal) component of wind at 200 hPa of 13.54 m/s 30 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 30, + "location": "Ethiopia", + "extremum_value": "13.535465", + "target_variable": "u_component_of_wind_200", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "f0600b06fedee5fc", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71704:71706:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41176:41191:1'} The data starts from March 09 00:00 and ends on March 12 12:00. Based on the above data, answer the following question:", + "question": "What will the minimum U (zonal) component of wind at 700 hPa be in Saint Barthelemy, 18 hours after the end of the given time window?", + "response": "Based on the provided data, the minimum U (zonal) component of wind at 700 hPa in Saint Barthelemy 18 hours after the given time window will be 1.202 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "1.2015578", + "location": "Saint Barthelemy", + "target_variable": "u_component_of_wind_700", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6e19c12eefa42d45", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41176:41191:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29769:29775:1'} The data starts from May 18 06:00 and ends on May 19 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, there is no extreme weather event occuring.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": false, + "task_id": "0cba393d15327280", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29769:29775:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67544:67571:1'} The data starts from March 26 00:00 and ends on April 01 12:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in V (meridional) component of wind at 400 hPa values? An exceedance is defined as a period of at least 48 consecutive hours where the V (meridional) component of wind at 400 hPa values exceed the 95th percentile climatology for the six-hourly climatology for day 85 at 00 UTC. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in V (meridional) component of wind at 400 hPa: Ethiopia(average 0.2358 m/s)\nSouth Sudan(average 1.04 m/s)\nKazakhstan(average 2.574 m/s)\nBrazil(average 1.624 m/s)\nRussia(average 3.75 m/s)\nSudan(average 1.132 m/s)\nGuatemala(average 0.7021 m/s)\nHonduras(average 0.5542 m/s)\nUnited States of America(average 2.076 m/s)\nCanada(average 1.206 m/s)\nMexico(average 1.257 m/s)\nBelize(average 0.7693 m/s)\nAustralia(average 3.489 m/s)\nGreenland(average 1.508 m/s)\nFrench Polynesia(average 1.379 m/s)\nKiribati(average 2.434 m/s)\nCook Islands(average 2.998 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "v_component_of_wind", + 400 + ], + "geofeature": "country", + "climatology_timescale_desc": "six-hourly climatology for day 85 at 00 UTC", + "quantile": "0.95", + "min_duration_days": 2, + "true_value": [ + "Ethiopia", + "South Sudan", + "Kazakhstan", + "Brazil", + "Russia", + "Sudan", + "Guatemala", + "Honduras", + "United States of America", + "Canada", + "Mexico", + "Belize", + "Australia", + "Greenland", + "French Polynesia", + "Kiribati", + "Cook Islands" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "79e5c1928536ee6b", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67544:67571:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92839:92842:1'} The data starts from July 18 18:00 and ends on July 19 06:00. Based on the above data, answer the following question:", + "question": "What will the median Mean sea level pressure be in Madagascar, 24 hours after the end of the given time window?", + "response": "Based on the provided data, the median Mean sea level pressure in Madagascar 24 hours after the given time window will be 1.024e+05 Pa.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "102399.734", + "location": "Madagascar", + "target_variable": "mean_sea_level_pressure", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1e5adc406063a720", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92839:92842:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75403:75423:1'} The data starts from August 11 18:00 and ends on August 16 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) Surface temperature differs from the daily climatology for day 223 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above Surface temperature values.", + "response": "These water body(s) exceed the ±3σ anomaly threshold for Surface temperature relative to the daily climatology for day 223 mean: North Atlantic Ocean(average 3.461 K)\nNorth Pacific Ocean(average 3.527 K)\nSouth Atlantic Ocean(average 2.743 K)\nBlack Sea(average 4.898 K)\nHudson Bay(average 6.46 K)\nBaffin Bay(average 3.013 K)\nGreenland Sea(average 2.858 K)\nLaptev Sea(average 4.686 K)\nJames Bay(average 7.013 K)\nHudson Strait(average 4.992 K)\nEast Siberian Sea(average 3.688 K)\nGulf of Yana(average 4.405 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "2m_temperature", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 223", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [ + "North Atlantic Ocean", + "North Pacific Ocean", + "South Atlantic Ocean", + "Black Sea", + "Hudson Bay", + "Baffin Bay", + "Greenland Sea", + "Laptev Sea", + "James Bay", + "Hudson Strait", + "East Siberian Sea", + "Gulf of Yana" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "5f8468661d3ff2a9", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75403:75423:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74111:74121:1'} The data starts from September 22 18:00 and ends on September 25 00:00. Based on the above data, answer the following question:", + "question": "Identify country(s) which have regions where (time-averaged) 10-meter V component of wind differs from the daily climatology for day 265 mean by at least 3 standard deviations (|value − mean|/std ≥ 3). Report the regions with anomalously above 10-meter V component of wind values. Please provide the name of the specific country(s) with the anomaly.", + "response": "These country(s) exceed the ±3σ anomaly threshold for 10-meter V component of wind relative to the daily climatology for day 265 mean: Greenland(average 1.658 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "ifMUcY", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 265", + "sigma_threshold": 3, + "threshold_direction": "above", + "true_value": [ + "Greenland" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "847c8fc8a040d2b0", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74111:74121:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69690:69717:1'} The data starts from September 13 12:00 and ends on September 20 00:00. Based on the above data, answer the following question:", + "question": "What will the median U (zonal) component of wind at 250 hPa be in San Francisco Bay, 48 hours after the end of the given time window?", + "response": "Based on the provided data, the median U (zonal) component of wind at 250 hPa in San Francisco Bay 48 hours after the given time window will be 11.69 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "11.685053", + "location": "San Francisco Bay", + "target_variable": "u_component_of_wind_250", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d85b61cffd32e1ae", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69690:69717:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83522:83543:1'} The data starts from March 02 12:00 and ends on March 07 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) Temperature at 1000 hPa values running below the 1st percentile climatology for the daily climatology for day 62? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show Temperature at 1000 hPa values below the 1st percentile climatology for daily climatology for day 62: South Pacific Ocean(average -0.1638 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "temperature", + 1000 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 62", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "South Pacific Ocean" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "bf2a7a17810022ee", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83522:83543:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34415:34442:1'} The data starts from July 22 18:00 and ends on July 29 06:00. Based on the above data, answer the following question:", + "question": "In the 12 hours after the end of the given time window, when will Baía de Marajó experience its highest U (zonal) component of wind at 500 hPa? Provide your answer as the number of hours offset from the end of the given time window.", + "response": "Based on the provided data, Baía de Marajó will experience its highest U (zonal) component of wind at 500 hPa of -5.645 m/s 6 hours after the end of the given time window.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "pIzWBe", + "true_value": 6, + "location": "Baía de Marajó", + "extremum_value": "-5.645363", + "target_variable": "u_component_of_wind_500", + "level": "2a", + "eval_type": "time", + "forced_extreme_window": false, + "task_id": "5f56f8c9decd082b", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34415:34442:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75219:75235:1'} The data starts from June 26 18:00 and ends on June 30 12:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) have regions with (time-averaged) 10-meter V component of wind values running below the 1st percentile climatology for the daily climatology for day 177? Treat any region beyond that percentile as anomalous.", + "response": "The following water body(s) show 10-meter V component of wind values below the 1st percentile climatology for daily climatology for day 177: North Pacific Ocean(average -1.217 m/s)\nINDIAN OCEAN(average -0.3807 m/s)\nGulf of Mexico(average -0.486 m/s)\nBarents Sea(average -0.4887 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "PWioGV", + "variable": [ + "10m_v_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "daily climatology for day 177", + "quantile": "0.01", + "threshold_direction": "below", + "true_value": [ + "North Pacific Ocean", + "INDIAN OCEAN", + "Gulf of Mexico", + "Barents Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "05e7c4c70f0f2361", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75219:75235:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83222:83245:1'} The data starts from December 18 12:00 and ends on December 24 00:00. Based on the above data, answer the following question:", + "question": "Which country(s) are currently experiencing an exceedance in Temperature at 400 hPa values? An exceedance is defined as a period of at least 96 consecutive hours where the Temperature at 400 hPa values exceed the 90th percentile climatology for the daily climatology for day 352. Please provide the name of the specific country(s) with the anomaly.", + "response": "The following country(s) are currently experiencing an exceedance in Temperature at 400 hPa: Indonesia(average 0.07843 K)\nPeru(average 0.6125 K)\nIndia(average 1.12 K)\nChina(average 0.8357 K)\nEthiopia(average 0.3803 K)\nSouth Sudan(average 0.3233 K)\nSomalia(average 0.1581 K)\nSomaliland(average 0.1646 K)\nFrance(average 0.5992 K)\nSouth Korea(average 1.626 K)\nCosta Rica(average 0.7858 K)\nNicaragua(average 0.1613 K)\nRepublic of the Congo(average 0.3705 K)\nDemocratic Republic of the Congo(average 0.2652 K)\nUkraine(average 1.841 K)\nBelarus(average 1.806 K)\nNamibia(average 0.1518 K)\nSouth Africa(average 0.2256 K)\nOman(average 1.62 K)\nLithuania(average 1.196 K)\nRussia(average 0.8047 K)\nGermany(average 0.9492 K)\nEstonia(average 0.5244 K)\nLatvia(average 0.7301 K)\nNorway(average 0.269 K)\nSweden(average 0.3661 K)\nFinland(average 0.2892 K)\nVietnam(average 0.1592 K)\nLuxembourg(average 0.8001 K)\nUnited Arab Emirates(average 1.983 K)\nBelgium(average 0.9354 K)\nSpain(average 0.2556 K)\nLaos(average 0.02725 K)\nDenmark(average 0.3444 K)\nRomania(average 1.533 K)\nHungary(average 1.295 K)\nSlovakia(average 1.872 K)\nPoland(average 2.026 K)\nUnited Kingdom(average 0.4769 K)\nZambia(average 0.1598 K)\nLiberia(average 0.069 K)\nCentral African Republic(average 0.3655 K)\nSudan(average 0.3663 K)\nDjibouti(average 0.1938 K)\nEritrea(average 0.5406 K)\nAustria(average 0.8077 K)\nItaly(average 0.3676 K)\nSwitzerland(average 0.6397 K)\nIran(average 1.623 K)\nNetherlands(average 0.9741 K)\nIvory Coast(average 0.1048 K)\nRepublic of Serbia(average 0.8918 K)\nNigeria(average 0.4078 K)\nBenin(average 0.2857 K)\nAngola(average 0.1085 K)\nCroatia(average 0.7594 K)\nSlovenia(average 0.8077 K)\nQatar(average 1.711 K)\nSaudi Arabia(average 1.219 K)\nBotswana(average 0.1526 K)\nZimbabwe(average 0.1562 K)\nPakistan(average 2.188 K)\nThailand(average 0.1236 K)\nChad(average 0.3284 K)\nEl Salvador(average 0.793 K)\nGuatemala(average 1.058 K)\nMyanmar(average 0.2027 K)\nBangladesh(average 0.3755 K)\nAndorra(average 0.3997 K)\nAfghanistan(average 0.2369 K)\nBosnia and Herzegovina(average 0.4988 K)\nCuba(average 0.624 K)\nHonduras(average 0.4691 K)\nEcuador(average 0.9314 K)\nColombia(average 0.3663 K)\nMoldova(average 1.828 K)\nCameroon(average 0.3751 K)\nGabon(average 0.3794 K)\nNiger(average 0.3245 K)\nTogo(average 0.2448 K)\nGhana(average 0.2082 K)\nUnited States of America(average 0.5103 K)\nMexico(average 0.6655 K)\nBelize(average 0.8607 K)\nPapua New Guinea(average 0.2411 K)\nYemen(average 0.7688 K)\nEquatorial Guinea(average 0.5298 K)\nAustralia(average 0.4268 K)\nSri Lanka(average 0.03479 K)\nThe Bahamas(average 0.4796 K)\nTaiwan(average 0.2814 K)\nJapan(average 0.8696 K)\nKiribati(average 0.5546 K)\nMarshall Islands(average 0.7151 K)\nUnited States Minor Outlying Islands(average 0.4115 K)\nJamaica(average 0.1111 K)\nCayman Islands(average 0.2253 K)\nBermuda(average 0.7001 K)\nSão Tomé and Principe(average 0.2662 K)\nCook Islands(average 0.2726 K)\nFederated States of Micronesia(average 0.3428 K)\nNorthern Mariana Islands(average 0.1617 K)\nBahrain(average 1.214 K)\nClipperton Island(average 1.847 K)\nBajo Nuevo Bank (Petrel Is.)(average 0.02289 K)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "temperature", + 400 + ], + "geofeature": "country", + "climatology_timescale_desc": "daily climatology for day 352", + "quantile": "0.9", + "min_duration_days": 4, + "true_value": [ + "Indonesia", + "Peru", + "India", + "China", + "Ethiopia", + "South Sudan", + "Somalia", + "Somaliland", + "France", + "South Korea", + "Costa Rica", + "Nicaragua", + "Republic of the Congo", + "Democratic Republic of the Congo", + "Ukraine", + "Belarus", + "Namibia", + "South Africa", + "Oman", + "Lithuania", + "Russia", + "Germany", + "Estonia", + "Latvia", + "Norway", + "Sweden", + "Finland", + "Vietnam", + "Luxembourg", + "United Arab Emirates", + "Belgium", + "Spain", + "Laos", + "Denmark", + "Romania", + "Hungary", + "Slovakia", + "Poland", + "United Kingdom", + "Zambia", + "Liberia", + "Central African Republic", + "Sudan", + "Djibouti", + "Eritrea", + "Austria", + "Italy", + "Switzerland", + "Iran", + "Netherlands", + "Ivory Coast", + "Republic of Serbia", + "Nigeria", + "Benin", + "Angola", + "Croatia", + "Slovenia", + "Qatar", + "Saudi Arabia", + "Botswana", + "Zimbabwe", + "Pakistan", + "Thailand", + "Chad", + "El Salvador", + "Guatemala", + "Myanmar", + "Bangladesh", + "Andorra", + "Afghanistan", + "Bosnia and Herzegovina", + "Cuba", + "Honduras", + "Ecuador", + "Colombia", + "Moldova", + "Cameroon", + "Gabon", + "Niger", + "Togo", + "Ghana", + "United States of America", + "Mexico", + "Belize", + "Papua New Guinea", + "Yemen", + "Equatorial Guinea", + "Australia", + "Sri Lanka", + "The Bahamas", + "Taiwan", + "Japan", + "Kiribati", + "Marshall Islands", + "United States Minor Outlying Islands", + "Jamaica", + "Cayman Islands", + "Bermuda", + "São Tomé and Principe", + "Cook Islands", + "Federated States of Micronesia", + "Northern Mariana Islands", + "Bahrain", + "Clipperton Island", + "Bajo Nuevo Bank (Petrel Is.)" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "7a08d5bc0256c6be", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83222:83245:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68832:68857:1'} The data starts from February 11 00:00 and ends on February 17 00:00. Based on the above data, answer the following question:", + "question": "What will the average 10-meter V component of wind be in United Arab Emirates, 30 hours after the end of the given time window?", + "response": "Based on the provided data, the average 10-meter V component of wind in United Arab Emirates 30 hours after the given time window will be -6.693 m/s.", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "gExxyC", + "true_value": "-6.6926103", + "location": "United Arab Emirates", + "target_variable": "10m_v_component_of_wind", + "level": "2a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ce9cc489366e62be", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68832:68857:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90783:90795:1'} The data starts from February 19 18:00 and ends on February 22 12:00. Based on the above data, answer the following question:", + "question": "Identify water_body(s) which have regions where (time-averaged) 10-meter U component of wind lies outside the climatological 1st–95th percentile envelope for the DJF seasonal climatology. Regions outside that envelope are anomalous.", + "response": "These water body(s) fall outside the 1st–95th percentile envelope for 10-meter U component of wind during DJF seasonal climatology: Arctic Ocean(average 2.409 m/s)\nSOUTHERN OCEAN(average 0.4968 m/s)\nNorth Atlantic Ocean(average 0.07307 m/s)\nNorth Pacific Ocean(average 0.3333 m/s)\nSouth Pacific Ocean(average 0.9911 m/s)\nINDIAN OCEAN(average 1.589 m/s)\nSouth Atlantic Ocean(average 0.5436 m/s)\nPhilippine Sea(average 0.7489 m/s)\nSouth China Sea(average 0.218 m/s)\nCelebes Sea(average 0.3598 m/s)\nGreenland Sea(average -0.09789 m/s)\nBanda Sea(average 0.4809 m/s)\nMozambique Channel(average 1.101 m/s)\nBarents Sea(average 0.7001 m/s)\nEast China Sea(average 0.2897 m/s)\nTimor Sea(average 0.1782 m/s)\nThe North Western Passages(average -0.6113 m/s)\nQueen Charlotte Sound(average 0.1702 m/s)\nMolucca Sea(average 0.3305 m/s)\nTaiwan Strait(average 0.2062 m/s)\nHall Basin(average 0.1223 m/s)\nDavao Gulf(average 0.7381 m/s)\nGulf of Kau(average 0.1412 m/s)\nSavu Sea(average 0.2121 m/s)\nHecate Strait(average 0.1208 m/s)\nCordova Bay(average 0.1244 m/s)\nRobeson Channel(average 0.03548 m/s)\nKennedy Channel(average 0.05157 m/s)\nCoral Sea(average 0.6944 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "gRZFrm", + "variable": [ + "10m_u_component_of_wind", + null + ], + "geofeature": "water_body", + "climatology_timescale_desc": "DJF seasonal climatology", + "lower_quantile": "0.01", + "upper_quantile": "0.95", + "true_value": [ + "Arctic Ocean", + "SOUTHERN OCEAN", + "North Atlantic Ocean", + "North Pacific Ocean", + "South Pacific Ocean", + "INDIAN OCEAN", + "South Atlantic Ocean", + "Philippine Sea", + "South China Sea", + "Celebes Sea", + "Greenland Sea", + "Banda Sea", + "Mozambique Channel", + "Barents Sea", + "East China Sea", + "Timor Sea", + "The North Western Passages", + "Queen Charlotte Sound", + "Molucca Sea", + "Taiwan Strait", + "Hall Basin", + "Davao Gulf", + "Gulf of Kau", + "Savu Sea", + "Hecate Strait", + "Cordova Bay", + "Robeson Channel", + "Kennedy Channel", + "Coral Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "d4e3fd035e434f6a", + "difficulty": "easy" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90783:90795:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69523:69526:1'} The data starts from August 02 18:00 and ends on August 03 06:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench meteorological data for the given time window and identify if any extreme weather event is occurring. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is detected, specify the event type, and the specific countries and regions currently affected or in its path. If no extreme event is present, state 'No extreme weather event detected.'", + "response": "Based on the provided data, the following extreme weather event(s) are happening:\nA Tropical cyclone is occuring in the country of China. Specifically the region(s) being affected are: Guangdong Sheng, Guangxi Zhuangzu Zizhiqu, Hainan Sheng provinces\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "1cJ6lX", + "true_value": [ + "China" + ], + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "c01eaeebc13b635d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69523:69526:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68389:68407:1'} The data starts from October 23 06:00 and ends on October 27 12:00. Based on the above data, answer the following question:", + "question": "Analyze the provided WeatherBench data and forecast whether any extreme weather event will develop in the next 36 hours after the current time window. An extreme weather event is defined as a tropical cyclone, extra-tropical storm, blizzard/winter storm, derecho, severe weather, sand/dust storm, or general storm. If an event is forecasted, specify the event type, and the specific countries and regions that will be affected or in its path. If no extreme event is forecasted, state 'No extreme weather event forecasted in the next 36 hours.'", + "response": "Based on the provided data, the following extreme weather event(s) might happen in the next 36 hours:\nA Tropical cyclone is expected in the country of Colombia in approximately the next 36 hours. Specifically the region(s) that might get affected are: Providencia Isl. (Santa Catalina, Santa Catarina districts, San Andres y Providencia province), San Andres Isl. (San Andres y Providencia district, San Andres y Providencia province)\n", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "ry3OgP", + "true_value": [ + "Colombia" + ], + "extreme_event_hours": 36, + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "b6f0ab3ac4c776bd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68389:68407:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83937:83965:1'} The data starts from June 14 06:00 and ends on June 21 00:00. Based on the above data, answer the following question:", + "question": "Which water_body(s) are currently experiencing an exceedance in V (meridional) component of wind at 400 hPa values? An exceedance is defined as a period of at least 96 consecutive hours where the V (meridional) component of wind at 400 hPa values exceed the 95th percentile climatology for the JJA seasonal climatology.", + "response": "The following water body(s) are currently experiencing an exceedance in V (meridional) component of wind at 400 hPa: Arctic Ocean(average 2.092 m/s)\nNorth Pacific Ocean(average 0.3352 m/s)\nTyrrhenian Sea(average 1.912 m/s)\nAdriatic Sea(average 1.278 m/s)\nIonian Sea(average 1.662 m/s)\nMediterranean Sea(average 1.746 m/s)\n", + "metadata": { + "prompt_id": "70wPiM", + "question_id": "kjYH1S", + "variable": [ + "v_component_of_wind", + 400 + ], + "geofeature": "water_body", + "climatology_timescale_desc": "JJA seasonal climatology", + "quantile": "0.95", + "min_duration_days": 4, + "true_value": [ + "Arctic Ocean", + "North Pacific Ocean", + "Tyrrhenian Sea", + "Adriatic Sea", + "Ionian Sea", + "Mediterranean Sea" + ], + "level": "2a", + "eval_type": "anomaly", + "forced_extreme_window": false, + "task_id": "69d10c0a3792256f", + "difficulty": "medium" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83937:83965:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56992:57019:1'} The data starts from January 04 00:00 and ends on January 10 12:00. Based on the above data, answer the following question:", + "question": "Based on the provided meteorological data, where is the Tropical Cyclone currently happening? Specify the affected countries or regions, or respond 'No Tropical Cyclone detected.'", + "response": "Based on the provided data, the Tropical Cyclone is affecting: Tonga", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "a7d6Lm", + "true_value": [ + "Tonga" + ], + "target_disaster": "Tropical Cyclone", + "level": "2a", + "eval_type": "extreme_weather", + "forced_extreme_window": true, + "task_id": "2d9eab66ff933c9a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56992:57019:1" + } + } +] \ No newline at end of file diff --git a/level2b_boolean_part0.json b/level2b_boolean_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..37c0b77b16e0a29ecf9172b21c7b63dd39f709d4 --- /dev/null +++ b/level2b_boolean_part0.json @@ -0,0 +1,8016 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47106:47112:1'} The data starts from March 30 12:00 and ends on March 31 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 48 hours after the end of the given time window, does the area-averaged specific_humidity at 100 hPa within South West, Singapore exceed 0.0kg/kg and the area-averaged specific_humidity at 925 hPa within South West, Singapore remain below 0.52kg/kg?", + "response": "In 48 hours after the end of the given time window, the area-averaged specific_humidity at 100 hPa within South West, Singapore is 2.876835424103774e-06kg/kg relative to the threshold 0.0kg/kg, and the area-averaged specific_humidity at 925 hPa within South West, Singapore is 0.015146576799452305kg/kg relative to the threshold 0.52kg/kg; combined, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "2.876835424103774e-06", + "actualvalue_1": "0.015146576799452305", + "auxvariables_0": "0.0", + "auxvariables_1": "0.52", + "checks": [ + { + "name": "cond0", + "actual": "2.876835424103774e-06", + "op": ">", + "th": "0.0", + "ok": true + }, + { + "name": "gate0", + "actual": "0.015146576799452305", + "op": "<", + "th": "0.52", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area-averaged {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}} relative to the threshold {{auxvariables_0}}{{units_0}}, and the area-averaged {{wb2varnames_1}}{{levelsuffixes_1}} within {{regions_0}} is {{actualvalue_1}}{{units_1}} relative to the threshold {{auxvariables_1}}{{units_1}}; combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area-averaged {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0} and the area-averaged {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {auxvariables_1}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 100, + 925 + ], + "regions": [ + "South West, Singapore" + ], + "units": [ + "kg/kg", + "kg/kg" + ], + "duration": 48, + "auxvariables": [ + "0.0", + "0.52" + ], + "auxvariables_0": "0.0", + "auxvariables_1": "0.52" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "47112:47212:1" + }, + "rng_seed": null, + "justification": { + "text": "In 48 hours after the end of the given time window, the area-averaged specific_humidity at 100 hPa within South West, Singapore is 2.876835424103774e-06kg/kg relative to the threshold 0.0kg/kg, and the area-averaged specific_humidity at 925 hPa within South West, Singapore is 0.015146576799452305kg/kg relative to the threshold 0.52kg/kg; combined, this makes the statement True." + }, + "question_id": "Rdqpmf", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2c829915c09ede23" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47106:47112:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55255:55258:1'} The data starts from October 26 18:00 and ends on October 27 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 180 hours after the end of the given time window, does the maximum value of temperature at 50 hPa within Kazakhstan exceed the maximum value of temperature at 50 hPa within Samoa by more than 0.309K?", + "response": "In 180 hours after the end of the given time window, the maximum value of temperature at 50 hPa within Kazakhstan exceeds the maximum value within Samoa by 215.67137145996094 - 207.6278839111328, compared to the threshold 0.309K; therefore, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "207.6278839111328", + "actualvalue_1": "215.67137145996094", + "auxvariables_0": "0.309", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "8.043487548828125", + "op": ">", + "th": "0.309", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Samoa", + "Kazakhstan" + ], + "units": [ + "K" + ], + "duration": 180, + "auxvariables": [ + "0.309" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "55258:55358:1" + }, + "rng_seed": null, + "justification": { + "text": "In 180 hours after the end of the given time window, the maximum value of temperature at 50 hPa within Kazakhstan exceeds the maximum value within Samoa by 215.67137145996094 - 207.6278839111328, compared to the threshold 0.309K; therefore, the statement is True." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6a8457336fa18d69" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55255:55258:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88593:88604:1'} The data starts from August 22 06:00 and ends on August 24 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 174 hours after the end of the given time window, does the area within Georgia where temperature at 300 hPa exceeds 243.5399932861328K cover more than 66.951 percent of Georgia?", + "response": "In 174 hours after the end of the given time window, the area within Georgia where temperature at 300 hPa exceeds 243.5399932861328K covers 0.0 percent of Georgia, compared to the threshold 66.951 percent; the statement is thus False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0", + "actualvalue_1": "66.951", + "auxvariables_0": "243.5399932861328", + "auxvariables_1": "66.951", + "checks": [ + { + "name": "gating_exceeds", + "actual": "0.0", + "op": ">", + "th": "0.0", + "ok": false + }, + { + "name": "primary_percent", + "actual": "0.0", + "op": ">", + "th": "66.951", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area within {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} covers {{actualvalue_0}} percent of {{regions_0}}, compared to the threshold {{auxvariables_1}} percent; the statement is thus {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_023.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_023.py", + "template_id": "tmpl_023", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds {auxvariables_0}{units_0} cover more than {auxvariables_1} percent of {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Georgia" + ], + "units": [ + "K" + ], + "duration": 174, + "auxvariables": [ + "243.5399932861328", + "66.951" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "88604:88704:1" + }, + "rng_seed": null, + "justification": { + "text": "In 174 hours after the end of the given time window, the area within Georgia where temperature at 300 hPa exceeds 243.5399932861328K covers 0.0 percent of Georgia, compared to the threshold 66.951 percent; the statement is thus False." + }, + "question_id": "B7IciW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c0b9874ec9d2d579" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88593:88604:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59499:59516:1'} The data starts from September 22 18:00 and ends on September 26 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 84 hours after the end of the given time window, does the maximum value of temperature at 250 hPa within Saint Vincent and the Grenadines occur at a latitude that is at least 13.48 degrees farther north than its maximum within Ecuador?", + "response": "In 84 hours after the end of the given time window, the northward latitude difference between the maximum temperature at 250 hPa in Saint Vincent and the Grenadines (13.5) and Ecuador (-3.0000000000000044) is at least 13.48 degrees if and only if the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "13.5", + "actualvalue_1": "-3.0000000000000044", + "auxvariables_0": "13.48", + "checks": [ + { + "name": "north_lat_diff", + "actual": "16.500000000000004", + "op": ">=", + "th": "13.48", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Saint Vincent and the Grenadines", + "Ecuador" + ], + "duration": 84, + "auxvariables": [ + "13.48" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "59516:59616:1" + }, + "rng_seed": null, + "justification": { + "text": "In 84 hours after the end of the given time window, the northward latitude difference between the maximum temperature at 250 hPa in Saint Vincent and the Grenadines (13.5) and Ecuador (-3.0000000000000044) is at least 13.48 degrees if and only if the statement is True." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "bd1017a2ce167e06" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59499:59516:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45829:45832:1'} The data starts from May 15 06:00 and ends on May 15 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 126 hours after the end of the given time window, does the maximum value of v_component_of_wind at 50 hPa within Africa remain above 2.1510000228881836m/s and does the maximum value of v_component_of_wind at 200 hPa within Africa remain below -5.9029998779296875m/s?", + "response": "In Africa, the maximum v_component_of_wind at 50 hPa is 6.46160888671875m/s compared to the threshold 2.1510000228881836m/s, and the maximum v_component_of_wind at 200 hPa is 40.543540954589844m/s compared to the threshold -5.9029998779296875m/s. Combined, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "6.46160888671875", + "actualvalue_1": "40.543540954589844", + "auxvariables_0": "2.1510000228881836", + "auxvariables_1": "-5.9029998779296875", + "checks": [ + { + "name": "cond0", + "actual": "6.46160888671875", + "op": ">", + "th": "2.1510000228881836", + "ok": true + }, + { + "name": "cond1", + "actual": "40.543540954589844", + "op": "<", + "th": "-5.9029998779296875", + "ok": false + } + ], + "justification": "In {{regions_0}}, the maximum {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to the threshold {{auxvariables_1}}{{units_1}}. Combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} and does the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {{auxvariables_1}}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "levelsuffixes": [ + 50, + 200 + ], + "regions": [ + "Africa" + ], + "units": [ + "m/s", + "m/s" + ], + "duration": 126, + "auxvariables": [ + "2.1510000228881836", + "-5.9029998779296875" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "time_range": "45832:45932:1" + }, + "rng_seed": null, + "justification": { + "text": "In Africa, the maximum v_component_of_wind at 50 hPa is 6.46160888671875m/s compared to the threshold 2.1510000228881836m/s, and the maximum v_component_of_wind at 200 hPa is 40.543540954589844m/s compared to the threshold -5.9029998779296875m/s. Combined, this makes the statement False." + }, + "question_id": "5WVWUb", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4734dbc42dda6664" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45829:45832:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70815:70822:1'} The data starts from June 21 18:00 and ends on June 23 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, does the area within Cura\u00e7ao where v_component_of_wind at 925 hPa exceeds 2.2699999809265137m/s cover more than 66.951 percent of Cura\u00e7ao?", + "response": "In 30 hours after the end of the given time window, the area within Cura\u00e7ao where v_component_of_wind at 925 hPa exceeds 2.2699999809265137m/s covers 56.62095087294784 percent of Cura\u00e7ao, compared to the threshold 66.951 percent; the statement is thus False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "56.62095087294784", + "actualvalue_1": "66.951", + "auxvariables_0": "2.2699999809265137", + "auxvariables_1": "66.951", + "checks": [ + { + "name": "gating_exceeds", + "actual": "56.62095087294784", + "op": ">", + "th": "0.0", + "ok": true + }, + { + "name": "primary_percent", + "actual": "56.62095087294784", + "op": ">", + "th": "66.951", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area within {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} covers {{actualvalue_0}} percent of {{regions_0}}, compared to the threshold {{auxvariables_1}} percent; the statement is thus {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_023.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_023.py", + "template_id": "tmpl_023", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds {auxvariables_0}{units_0} cover more than {auxvariables_1} percent of {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Cura\u00e7ao" + ], + "units": [ + "m/s" + ], + "duration": 30, + "auxvariables": [ + "2.2699999809265137", + "66.951" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "70822:70922:1" + }, + "rng_seed": null, + "justification": { + "text": "In 30 hours after the end of the given time window, the area within Cura\u00e7ao where v_component_of_wind at 925 hPa exceeds 2.2699999809265137m/s covers 56.62095087294784 percent of Cura\u00e7ao, compared to the threshold 66.951 percent; the statement is thus False." + }, + "question_id": "B7IciW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "09b385edf862d280" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70815:70822:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43681:43686:1'} The data starts from November 24 06:00 and ends on November 25 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 120 hours after the end of the given time window, does the maximum value of u_component_of_wind at 50 hPa within Gasa, Bhutan exceed -0.6399999856948853m/s, while the maximum value of u_component_of_wind at 50 hPa within Anse aux Pins, Seychelles remains below 13.760000228881836m/s?", + "response": "In 120 hours after the end of the given time window, the maximum value of u_component_of_wind at 50 hPa within Gasa, Bhutan is -5.35197639465332m/s, relative to the threshold -0.6399999856948853m/s; while within Anse aux Pins, Seychelles the maximum is 2.664193630218506m/s, relative to the threshold 13.760000228881836m/s. Combined with the gating condition, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-5.35197639465332", + "actualvalue_1": "2.664193630218506", + "auxvariables_0": "-0.6399999856948853", + "auxvariables_1": "13.760000228881836", + "checks": [ + { + "name": "cond0", + "actual": "-5.35197639465332", + "op": ">", + "th": "-0.6399999856948853", + "ok": false + }, + { + "name": "cond1", + "actual": "2.664193630218506", + "op": "<", + "th": "13.760000228881836", + "ok": true + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Gasa, Bhutan", + "Anse aux Pins, Seychelles" + ], + "units": [ + "m/s" + ], + "duration": 120, + "auxvariables": [ + "-0.6399999856948853", + "13.760000228881836" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "43686:43786:1" + }, + "rng_seed": null, + "justification": { + "text": "In 120 hours after the end of the given time window, the maximum value of u_component_of_wind at 50 hPa within Gasa, Bhutan is -5.35197639465332m/s, relative to the threshold -0.6399999856948853m/s; while within Anse aux Pins, Seychelles the maximum is 2.664193630218506m/s, relative to the threshold 13.760000228881836m/s. Combined with the gating condition, this makes the statement False." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6ff56a460931a733" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43681:43686:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33458:33460:1'} The data starts from November 25 12:00 and ends on November 25 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 54 hours after the end of the given time window, does the maximum value of temperature at 1000 hPa within Ziguinchor, Senegal remain above 295.59K and does the maximum value of temperature at 925 hPa within Ziguinchor, Senegal remain below 303.99K?", + "response": "In Ziguinchor, Senegal, the maximum temperature at 1000 hPa is 301.62384033203125K compared to the threshold 295.59K, and the maximum temperature at 925 hPa is 298.0301208496094K compared to the threshold 303.99K. Combined, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "301.62384033203125", + "actualvalue_1": "298.0301208496094", + "auxvariables_0": "295.59", + "auxvariables_1": "303.99", + "checks": [ + { + "name": "cond0", + "actual": "301.62384033203125", + "op": ">", + "th": "295.59", + "ok": true + }, + { + "name": "cond1", + "actual": "298.0301208496094", + "op": "<", + "th": "303.99", + "ok": true + } + ], + "justification": "In {{regions_0}}, the maximum {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to the threshold {{auxvariables_1}}{{units_1}}. Combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} and does the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {{auxvariables_1}}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 1000, + 925 + ], + "regions": [ + "Ziguinchor, Senegal" + ], + "units": [ + "K", + "K" + ], + "duration": 54, + "auxvariables": [ + "295.59", + "303.99" + ], + "auxvariables_0": "295.59", + "auxvariables_1": "303.99" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "33460:33560:1" + }, + "rng_seed": null, + "justification": { + "text": "In Ziguinchor, Senegal, the maximum temperature at 1000 hPa is 301.62384033203125K compared to the threshold 295.59K, and the maximum temperature at 925 hPa is 298.0301208496094K compared to the threshold 303.99K. Combined, this makes the statement True." + }, + "question_id": "5WVWUb", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1bfcdf995e1fecf1" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33458:33460:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70451:70462:1'} The data starts from March 22 18:00 and ends on March 25 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 174 hours after the end of the given time window, does the maximum value of geopotential at 400 hPa within Marshall Islands exceed the maximum value of geopotential at 400 hPa within United Republic of Tanzania by more than 40.72999954223633m\u00b2/s\u00b2?", + "response": "In 174 hours after the end of the given time window, the maximum value of geopotential at 400 hPa within Marshall Islands exceeds the maximum value within United Republic of Tanzania by 74503.5390625 - 74428.484375, compared to the threshold 40.72999954223633m\u00b2/s\u00b2; therefore, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "74428.484375", + "actualvalue_1": "74503.5390625", + "auxvariables_0": "40.72999954223633", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "75.0546875", + "op": ">", + "th": "40.72999954223633", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "United Republic of Tanzania", + "Marshall Islands" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 174, + "auxvariables": [ + "40.72999954223633" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "70462:70562:1" + }, + "rng_seed": null, + "justification": { + "text": "In 174 hours after the end of the given time window, the maximum value of geopotential at 400 hPa within Marshall Islands exceeds the maximum value within United Republic of Tanzania by 74503.5390625 - 74428.484375, compared to the threshold 40.72999954223633m\u00b2/s\u00b2; therefore, the statement is True." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "748e236757ee825a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70451:70462:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35771:35776:1'} The data starts from June 26 18:00 and ends on June 27 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 24 hours after the end of the given time window, does the maximum value of temperature at 700 hPa within Europe occur at a latitude that is at least 33.57 degrees farther north than its maximum within Oceania?", + "response": "In 24 hours after the end of the given time window, the northward latitude difference between the maximum temperature at 700 hPa in Europe (34.49999999999999) and Oceania (-12.000000000000004) is at least 33.57 degrees if and only if the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "34.49999999999999", + "actualvalue_1": "-12.000000000000004", + "auxvariables_0": "33.57", + "checks": [ + { + "name": "north_lat_diff", + "actual": "46.5", + "op": ">=", + "th": "33.57", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Europe", + "Oceania" + ], + "duration": 24, + "auxvariables": [ + "33.57" + ], + "auxvariables_0": "33.57" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "35776:35876:1" + }, + "rng_seed": null, + "justification": { + "text": "In 24 hours after the end of the given time window, the northward latitude difference between the maximum temperature at 700 hPa in Europe (34.49999999999999) and Oceania (-12.000000000000004) is at least 33.57 degrees if and only if the statement is True." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b5cb4656be245cb6" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35771:35776:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57776:57798:1'} The data starts from July 19 00:00 and ends on July 24 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 186 hours after the end of the given time window, does the maximum value of geopotential at 850 hPa within Oceania exceed 15300.0m\u00b2/s\u00b2, while the maximum value of geopotential at 850 hPa within Africa remains below 14900.0m\u00b2/s\u00b2?", + "response": "In 186 hours after the end of the given time window, the maximum value of geopotential at 850 hPa within Oceania is 15874.7822265625m\u00b2/s\u00b2, relative to the threshold 15300.0m\u00b2/s\u00b2; while within Africa the maximum is 16072.498046875m\u00b2/s\u00b2, relative to the threshold 14900.0m\u00b2/s\u00b2. Combined with the gating condition, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "15874.7822265625", + "actualvalue_1": "16072.498046875", + "auxvariables_0": "15300.0", + "auxvariables_1": "14900.0", + "checks": [ + { + "name": "cond0", + "actual": "15874.7822265625", + "op": ">", + "th": "15300.0", + "ok": true + }, + { + "name": "cond1", + "actual": "16072.498046875", + "op": "<", + "th": "14900.0", + "ok": false + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Oceania", + "Africa" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 186, + "auxvariables": [ + "15300.0", + "14900.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "57798:57898:1" + }, + "rng_seed": null, + "justification": { + "text": "In 186 hours after the end of the given time window, the maximum value of geopotential at 850 hPa within Oceania is 15874.7822265625m\u00b2/s\u00b2, relative to the threshold 15300.0m\u00b2/s\u00b2; while within Africa the maximum is 16072.498046875m\u00b2/s\u00b2, relative to the threshold 14900.0m\u00b2/s\u00b2. Combined with the gating condition, this makes the statement False." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1ce764762ee86459" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57776:57798:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47455:47473:1'} The data starts from June 25 18:00 and ends on June 30 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 72 hours after the end of the given time window, does the maximum value of specific_humidity at 500 hPa within Turkey occur at a latitude that is at least 40.095 degrees farther north than its maximum within South Georgia and the Islands?", + "response": "In 72 hours after the end of the given time window, the northward latitude difference between the maximum specific_humidity at 500 hPa in Turkey (40.5) and South Georgia and the Islands (-54.00000000000001) is at least 40.095 degrees if and only if the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "40.5", + "actualvalue_1": "-54.00000000000001", + "auxvariables_0": "40.095", + "checks": [ + { + "name": "north_lat_diff", + "actual": "94.5", + "op": ">=", + "th": "40.095", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Turkey", + "South Georgia and the Islands" + ], + "duration": 72, + "auxvariables": [ + "40.095" + ], + "auxvariables_0": "40.095" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "47473:47573:1" + }, + "rng_seed": null, + "justification": { + "text": "In 72 hours after the end of the given time window, the northward latitude difference between the maximum specific_humidity at 500 hPa in Turkey (40.5) and South Georgia and the Islands (-54.00000000000001) is at least 40.095 degrees if and only if the statement is True." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "da693b065e7c0067" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47455:47473:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61617:61633:1'} The data starts from March 05 06:00 and ends on March 09 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 84 hours after the end of the given time window, does the maximum value of u_component_of_wind at 150 hPa within Europe exceed 24.360000610351562m/s, while the maximum value of u_component_of_wind at 150 hPa within Asia remains below 8.899999618530273m/s?", + "response": "In 84 hours after the end of the given time window, the maximum value of u_component_of_wind at 150 hPa within Europe is 39.7263298034668m/s, relative to the threshold 24.360000610351562m/s; while within Asia the maximum is 87.7833023071289m/s, relative to the threshold 8.899999618530273m/s. Combined with the gating condition, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "39.7263298034668", + "actualvalue_1": "87.7833023071289", + "auxvariables_0": "24.360000610351562", + "auxvariables_1": "8.899999618530273", + "checks": [ + { + "name": "cond0", + "actual": "39.7263298034668", + "op": ">", + "th": "24.360000610351562", + "ok": true + }, + { + "name": "cond1", + "actual": "87.7833023071289", + "op": "<", + "th": "8.899999618530273", + "ok": false + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Europe", + "Asia" + ], + "units": [ + "m/s" + ], + "duration": 84, + "auxvariables": [ + "24.360000610351562", + "8.899999618530273" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "61633:61733:1" + }, + "rng_seed": null, + "justification": { + "text": "In 84 hours after the end of the given time window, the maximum value of u_component_of_wind at 150 hPa within Europe is 39.7263298034668m/s, relative to the threshold 24.360000610351562m/s; while within Asia the maximum is 87.7833023071289m/s, relative to the threshold 8.899999618530273m/s. Combined with the gating condition, this makes the statement False." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f262cdf5a1b912a4" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61617:61633:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36571:36585:1'} The data starts from January 12 18:00 and ends on January 16 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 108 hours after the end of the given time window, does the area-averaged specific_humidity at 250 hPa within Kapisa, Afghanistan exceed 0.5kg/kg and the area-averaged specific_humidity at 500 hPa within Kapisa, Afghanistan remain below 0.0kg/kg?", + "response": "In 108 hours after the end of the given time window, the area-averaged specific_humidity at 250 hPa within Kapisa, Afghanistan is 1.0095624020323157e-05kg/kg relative to the threshold 0.5kg/kg, and the area-averaged specific_humidity at 500 hPa within Kapisa, Afghanistan is 0.0005740797496400774kg/kg relative to the threshold 0.0kg/kg; combined, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "1.0095624020323157e-05", + "actualvalue_1": "0.0005740797496400774", + "auxvariables_0": "0.5", + "auxvariables_1": "0.0", + "checks": [ + { + "name": "cond0", + "actual": "1.0095624020323157e-05", + "op": ">", + "th": "0.5", + "ok": false + }, + { + "name": "gate0", + "actual": "0.0005740797496400774", + "op": "<", + "th": "0.0", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area-averaged {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}} relative to the threshold {{auxvariables_0}}{{units_0}}, and the area-averaged {{wb2varnames_1}}{{levelsuffixes_1}} within {{regions_0}} is {{actualvalue_1}}{{units_1}} relative to the threshold {{auxvariables_1}}{{units_1}}; combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area-averaged {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0} and the area-averaged {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {auxvariables_1}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 250, + 500 + ], + "regions": [ + "Kapisa, Afghanistan" + ], + "units": [ + "kg/kg", + "kg/kg" + ], + "duration": 108, + "auxvariables": [ + "0.5", + "0.0" + ], + "auxvariables_0": "0.5", + "auxvariables_1": "0.0" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "36585:36685:1" + }, + "rng_seed": null, + "justification": { + "text": "In 108 hours after the end of the given time window, the area-averaged specific_humidity at 250 hPa within Kapisa, Afghanistan is 1.0095624020323157e-05kg/kg relative to the threshold 0.5kg/kg, and the area-averaged specific_humidity at 500 hPa within Kapisa, Afghanistan is 0.0005740797496400774kg/kg relative to the threshold 0.0kg/kg; combined, this makes the statement False." + }, + "question_id": "Rdqpmf", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ef6da6b0fdc7f0e3" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36571:36585:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76713:76729:1'} The data starts from July 05 06:00 and ends on July 09 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 162 hours after the end of the given time window, does the maximum value of v_component_of_wind at 400 hPa within Ukraine exceed 2.28m/s?", + "response": "In 162 hours after the end of the given time window, the maximum value of v_component_of_wind at 400 hPa within Ukraine is 17.517417907714844m/s, compared to the threshold 2.28m/s; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "17.517417907714844", + "auxvariables_0": "2.28", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "17.517417907714844", + "op": ">", + "th": "2.28", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "Ukraine" + ], + "units": [ + "m/s" + ], + "duration": 162, + "auxvariables": [ + "2.28" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "76729:76829:1" + }, + "rng_seed": null, + "justification": { + "text": "In 162 hours after the end of the given time window, the maximum value of v_component_of_wind at 400 hPa within Ukraine is 17.517417907714844m/s, compared to the threshold 2.28m/s; this makes the statement True." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "79f6dd5abcdeb9d9" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76713:76729:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31310:31328:1'} The data starts from June 06 12:00 and ends on June 10 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 90 hours after the end of the given time window, does the minimum value of specific_humidity at 600 hPa within Marshall Islands remain above 0.0010860000038519502kg/kg throughout Marshall Islands?", + "response": "In Marshall Islands, the minimum value of specific_humidity at 600 hPa over the 90 hours window is 0.0023675281554460526kg/kg, which is compared to the threshold 0.0010860000038519502kg/kg; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.0023675281554460526", + "auxvariables_0": "0.0010860000038519502", + "checks": [ + { + "name": "min_above_threshold", + "actual": "0.0023675281554460526", + "op": ">=", + "th": "0.0010860000038519502", + "ok": true + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Marshall Islands" + ], + "units": [ + "kg/kg" + ], + "duration": 90, + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P10 over window [0,90]" + ], + "auxvariables": [ + "0.0010860000038519502" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "31328:31428:1" + }, + "rng_seed": null, + "justification": { + "text": "In Marshall Islands, the minimum value of specific_humidity at 600 hPa over the 90 hours window is 0.0023675281554460526kg/kg, which is compared to the threshold 0.0010860000038519502kg/kg; this makes the statement True." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b746795a09d954a5" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31310:31328:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89348:89358:1'} The data starts from February 27 00:00 and ends on February 29 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, does the minimum value of specific_humidity at 400 hPa within Tasman Sea remain above 0.5kg/kg throughout Tasman Sea?", + "response": "In Tasman Sea, the minimum value of specific_humidity at 400 hPa over the 30 hours window is 0.00012038253044011071kg/kg, which is compared to the threshold 0.5kg/kg; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.00012038253044011071", + "auxvariables_0": "0.5", + "checks": [ + { + "name": "min_above_threshold", + "actual": "0.00012038253044011071", + "op": ">=", + "th": "0.5", + "ok": false + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "Tasman Sea" + ], + "units": [ + "kg/kg" + ], + "duration": 30, + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P10 over window [0,30]" + ], + "auxvariables": [ + "0.5" + ], + "auxvariables_0": "0.5" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "89358:89458:1" + }, + "rng_seed": null, + "justification": { + "text": "In Tasman Sea, the minimum value of specific_humidity at 400 hPa over the 30 hours window is 0.00012038253044011071kg/kg, which is compared to the threshold 0.5kg/kg; this makes the statement False." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0edd3587ce84d84a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89348:89358:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47278:47306:1'} The data starts from May 12 12:00 and ends on May 19 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 198 hours after the end of the given time window, does the minimum value of geopotential at 850 hPa within South America remain above 10540.0m\u00b2/s\u00b2 throughout South America?", + "response": "In South America, the minimum value of geopotential at 850 hPa over the 198 hours window is 10335.802734375m\u00b2/s\u00b2, which is compared to the threshold 10540.0m\u00b2/s\u00b2; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "10335.802734375", + "auxvariables_0": "10540.0", + "checks": [ + { + "name": "min_above_threshold", + "actual": "10335.802734375", + "op": ">=", + "th": "10540.0", + "ok": false + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "South America" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 198, + "auxvariables_0_provenance": [ + "var=geopotential, tail=P10 over window [0,198]" + ], + "auxvariables": [ + "10540.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "47306:47406:1" + }, + "rng_seed": null, + "justification": { + "text": "In South America, the minimum value of geopotential at 850 hPa over the 198 hours window is 10335.802734375m\u00b2/s\u00b2, which is compared to the threshold 10540.0m\u00b2/s\u00b2; this makes the statement False." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e61498ae9eee89a2" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47278:47306:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48840:48857:1'} The data starts from June 06 00:00 and ends on June 10 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 114 hours after the end of the given time window, does the minimum value of specific_humidity at 850 hPa within Bosnia and Herzegovina remain above 0.51kg/kg throughout Bosnia and Herzegovina?", + "response": "In Bosnia and Herzegovina, the minimum value of specific_humidity at 850 hPa over the 114 hours window is 0.00561749329790473kg/kg, which is compared to the threshold 0.51kg/kg; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.00561749329790473", + "auxvariables_0": "0.51", + "checks": [ + { + "name": "min_above_threshold", + "actual": "0.00561749329790473", + "op": ">=", + "th": "0.51", + "ok": false + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Bosnia and Herzegovina" + ], + "units": [ + "kg/kg" + ], + "duration": 114, + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P10 over window [0,114]" + ], + "auxvariables": [ + "0.51" + ], + "auxvariables_0": "0.51" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "48857:48957:1" + }, + "rng_seed": null, + "justification": { + "text": "In Bosnia and Herzegovina, the minimum value of specific_humidity at 850 hPa over the 114 hours window is 0.00561749329790473kg/kg, which is compared to the threshold 0.51kg/kg; this makes the statement False." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3841ea1c8b4d8f78" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48840:48857:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88535:88558:1'} The data starts from August 07 18:00 and ends on August 13 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 24 hours after the end of the given time window, does the maximum value of temperature at 300 hPa within Jendouba, Tunisia exceed 233.2K?", + "response": "In 24 hours after the end of the given time window, the maximum value of temperature at 300 hPa within Jendouba, Tunisia is 237.956787109375K, compared to the threshold 233.2K; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "237.956787109375", + "auxvariables_0": "233.2", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "237.956787109375", + "op": ">", + "th": "233.2", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Jendouba, Tunisia" + ], + "units": [ + "K" + ], + "duration": 24, + "auxvariables": [ + "233.2" + ], + "auxvariables_0": "233.2" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "88558:88658:1" + }, + "rng_seed": null, + "justification": { + "text": "In 24 hours after the end of the given time window, the maximum value of temperature at 300 hPa within Jendouba, Tunisia is 237.956787109375K, compared to the threshold 233.2K; this makes the statement True." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "796e53f0138bc46c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88535:88558:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59009:59037:1'} The data starts from May 23 06:00 and ends on May 30 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 174 hours after the end of the given time window, does the maximum value of specific_humidity at 300 hPa within Masall\u0131, Azerbaijan remain above 0.00018699999782256782kg/kg and does the maximum value of specific_humidity at 200 hPa within Masall\u0131, Azerbaijan remain below 1.1000000085914508e-05kg/kg?", + "response": "In Masall\u0131, Azerbaijan, the maximum specific_humidity at 300 hPa is 8.205910853575915e-05kg/kg compared to the threshold 0.00018699999782256782kg/kg, and the maximum specific_humidity at 200 hPa is 1.70626681210706e-05kg/kg compared to the threshold 1.1000000085914508e-05kg/kg. Combined, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "8.205910853575915e-05", + "actualvalue_1": "1.70626681210706e-05", + "auxvariables_0": "0.00018699999782256782", + "auxvariables_1": "1.1000000085914508e-05", + "checks": [ + { + "name": "cond0", + "actual": "8.205910853575915e-05", + "op": ">", + "th": "0.00018699999782256782", + "ok": false + }, + { + "name": "cond1", + "actual": "1.70626681210706e-05", + "op": "<", + "th": "1.1000000085914508e-05", + "ok": false + } + ], + "justification": "In {{regions_0}}, the maximum {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to the threshold {{auxvariables_1}}{{units_1}}. Combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} and does the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {{auxvariables_1}}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 300, + 200 + ], + "regions": [ + "Masall\u0131, Azerbaijan" + ], + "units": [ + "kg/kg", + "kg/kg" + ], + "duration": 174, + "auxvariables": [ + "0.00018699999782256782", + "1.1000000085914508e-05" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "59037:59137:1" + }, + "rng_seed": null, + "justification": { + "text": "In Masall\u0131, Azerbaijan, the maximum specific_humidity at 300 hPa is 8.205910853575915e-05kg/kg compared to the threshold 0.00018699999782256782kg/kg, and the maximum specific_humidity at 200 hPa is 1.70626681210706e-05kg/kg compared to the threshold 1.1000000085914508e-05kg/kg. Combined, this makes the statement False." + }, + "question_id": "5WVWUb", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "67091a8fab3a2972" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59009:59037:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35085:35088:1'} The data starts from January 06 06:00 and ends on January 06 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 168 hours after the end of the given time window, does the maximum value of u_component_of_wind at 850 hPa within Africa exceed 17.65m/s?", + "response": "In 168 hours after the end of the given time window, the maximum value of u_component_of_wind at 850 hPa within Africa is 16.721694946289062m/s, compared to the threshold 17.65m/s; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "16.721694946289062", + "auxvariables_0": "17.65", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "16.721694946289062", + "op": ">", + "th": "17.65", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Africa" + ], + "units": [ + "m/s" + ], + "duration": 168, + "auxvariables": [ + "17.65" + ], + "auxvariables_0": "17.65" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "35088:35188:1" + }, + "rng_seed": null, + "justification": { + "text": "In 168 hours after the end of the given time window, the maximum value of u_component_of_wind at 850 hPa within Africa is 16.721694946289062m/s, compared to the threshold 17.65m/s; this makes the statement False." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3111b75f2c7fe591" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35085:35088:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37733:37736:1'} The data starts from October 29 06:00 and ends on October 29 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 54 hours after the end of the given time window, does the area-averaged u_component_of_wind at 100 hPa within Asia exceed 10.2m/s and the area-averaged u_component_of_wind at 50 hPa within Asia remain below 1.3m/s?", + "response": "In 54 hours after the end of the given time window, the area-averaged u_component_of_wind at 100 hPa within Asia is 13.274433685096074m/s relative to the threshold 10.2m/s, and the area-averaged u_component_of_wind at 50 hPa within Asia is 3.2472681006952895m/s relative to the threshold 1.3m/s; combined, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "13.274433685096074", + "actualvalue_1": "3.2472681006952895", + "auxvariables_0": "10.2", + "auxvariables_1": "1.3", + "checks": [ + { + "name": "cond0", + "actual": "13.274433685096074", + "op": ">", + "th": "10.2", + "ok": true + }, + { + "name": "gate0", + "actual": "3.2472681006952895", + "op": "<", + "th": "1.3", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area-averaged {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}} relative to the threshold {{auxvariables_0}}{{units_0}}, and the area-averaged {{wb2varnames_1}}{{levelsuffixes_1}} within {{regions_0}} is {{actualvalue_1}}{{units_1}} relative to the threshold {{auxvariables_1}}{{units_1}}; combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area-averaged {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0} and the area-averaged {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {auxvariables_1}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 100, + 50 + ], + "regions": [ + "Asia" + ], + "units": [ + "m/s", + "m/s" + ], + "duration": 54, + "auxvariables": [ + "10.2", + "1.3" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "37736:37836:1" + }, + "rng_seed": null, + "justification": { + "text": "In 54 hours after the end of the given time window, the area-averaged u_component_of_wind at 100 hPa within Asia is 13.274433685096074m/s relative to the threshold 10.2m/s, and the area-averaged u_component_of_wind at 50 hPa within Asia is 3.2472681006952895m/s relative to the threshold 1.3m/s; combined, this makes the statement False." + }, + "question_id": "Rdqpmf", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "40f5dda0310021cf" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37733:37736:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38444:38460:1'} The data starts from April 25 00:00 and ends on April 28 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 36 hours after the end of the given time window, does the maximum value of temperature at 250 hPa within Oceania exceed the maximum value of temperature at 250 hPa within Asia by more than 0.103K?", + "response": "In 36 hours after the end of the given time window, the maximum value of temperature at 250 hPa within Oceania exceeds the maximum value within Asia by 234.1936798095703 - 234.40988159179688, compared to the threshold 0.103K; therefore, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "234.40988159179688", + "actualvalue_1": "234.1936798095703", + "auxvariables_0": "0.103", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "-0.2162017822265625", + "op": ">", + "th": "0.103", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Asia", + "Oceania" + ], + "units": [ + "K" + ], + "duration": 36, + "auxvariables": [ + "0.103" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "38460:38560:1" + }, + "rng_seed": null, + "justification": { + "text": "In 36 hours after the end of the given time window, the maximum value of temperature at 250 hPa within Oceania exceeds the maximum value within Asia by 234.1936798095703 - 234.40988159179688, compared to the threshold 0.103K; therefore, the statement is False." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "bbcc34b387705772" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38444:38460:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65265:65291:1'} The data starts from September 03 06:00 and ends on September 09 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, does the maximum value of specific_humidity at 150 hPa within Roga\u0161ka Slatina, Slovenia occur at a latitude that is at least 0.01 degrees farther north than its maximum within Galway, Ireland?", + "response": "In 30 hours after the end of the given time window, the northward latitude difference between the maximum specific_humidity at 150 hPa in Roga\u0161ka Slatina, Slovenia (46.499999999999986) and Galway, Ireland (54.00000000000001) is at least 0.01 degrees if and only if the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "46.499999999999986", + "actualvalue_1": "54.00000000000001", + "auxvariables_0": "0.01", + "checks": [ + { + "name": "north_lat_diff", + "actual": "-7.500000000000021", + "op": ">=", + "th": "0.01", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Roga\u0161ka Slatina, Slovenia", + "Galway, Ireland" + ], + "duration": 30, + "auxvariables": [ + "0.01" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "65291:65391:1" + }, + "rng_seed": null, + "justification": { + "text": "In 30 hours after the end of the given time window, the northward latitude difference between the maximum specific_humidity at 150 hPa in Roga\u0161ka Slatina, Slovenia (46.499999999999986) and Galway, Ireland (54.00000000000001) is at least 0.01 degrees if and only if the statement is False." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f360f12c6b62997f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65265:65291:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71898:71921:1'} The data starts from March 18 12:00 and ends on March 24 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 162 hours after the end of the given time window, does the minimum value of geopotential at 1000 hPa within Gulf of Finland remain above 1819.54m\u00b2/s\u00b2 throughout Gulf of Finland?", + "response": "In Gulf of Finland, the minimum value of geopotential at 1000 hPa over the 162 hours window is 1773.7431640625m\u00b2/s\u00b2, which is compared to the threshold 1819.54m\u00b2/s\u00b2; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "1773.7431640625", + "auxvariables_0": "1819.54", + "checks": [ + { + "name": "min_above_threshold", + "actual": "1773.7431640625", + "op": ">=", + "th": "1819.54", + "ok": false + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Gulf of Finland" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 162, + "auxvariables_0_provenance": [ + "var=geopotential, tail=P10 over window [0,162]" + ], + "auxvariables": [ + "1819.54" + ], + "auxvariables_0": "1819.54" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "71921:72021:1" + }, + "rng_seed": null, + "justification": { + "text": "In Gulf of Finland, the minimum value of geopotential at 1000 hPa over the 162 hours window is 1773.7431640625m\u00b2/s\u00b2, which is compared to the threshold 1819.54m\u00b2/s\u00b2; this makes the statement False." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9a6bc41f3b12fce0" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71898:71921:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87060:87074:1'} The data starts from August 04 00:00 and ends on August 07 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 150 hours after the end of the given time window, does the maximum value of specific_humidity at 700 hPa within Africa exceed the maximum value of specific_humidity at 700 hPa within Antarctica by more than 0.5kg/kg?", + "response": "In 150 hours after the end of the given time window, the maximum value of specific_humidity at 700 hPa within Africa exceeds the maximum value within Antarctica by 0.009835666045546532 - 0.0014497579541057348, compared to the threshold 0.5kg/kg; therefore, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0014497579541057348", + "actualvalue_1": "0.009835666045546532", + "auxvariables_0": "0.5", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "0.008385908091440797", + "op": ">", + "th": "0.5", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Antarctica", + "Africa" + ], + "units": [ + "kg/kg" + ], + "duration": 150, + "auxvariables": [ + "0.5" + ], + "auxvariables_0": "0.5" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "87074:87174:1" + }, + "rng_seed": null, + "justification": { + "text": "In 150 hours after the end of the given time window, the maximum value of specific_humidity at 700 hPa within Africa exceeds the maximum value within Antarctica by 0.009835666045546532 - 0.0014497579541057348, compared to the threshold 0.5kg/kg; therefore, the statement is False." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "dbcf1b8c08dbd526" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87060:87074:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46249:46256:1'} The data starts from August 28 06:00 and ends on August 29 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 102 hours after the end of the given time window, does the area within Avellino, Italy where geopotential at 250 hPa exceeds 105580.203125m\u00b2/s\u00b2 cover more than 66.951 percent of Avellino, Italy?", + "response": "In 102 hours after the end of the given time window, the area within Avellino, Italy where geopotential at 250 hPa exceeds 105580.203125m\u00b2/s\u00b2 covers 0.0 percent of Avellino, Italy, compared to the threshold 66.951 percent; the statement is thus False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0", + "actualvalue_1": "66.951", + "auxvariables_0": "105580.203125", + "auxvariables_1": "66.951", + "checks": [ + { + "name": "gating_exceeds", + "actual": "0.0", + "op": ">", + "th": "0.0", + "ok": false + }, + { + "name": "primary_percent", + "actual": "0.0", + "op": ">", + "th": "66.951", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area within {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} covers {{actualvalue_0}} percent of {{regions_0}}, compared to the threshold {{auxvariables_1}} percent; the statement is thus {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_023.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_023.py", + "template_id": "tmpl_023", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds {auxvariables_0}{units_0} cover more than {auxvariables_1} percent of {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Avellino, Italy" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 102, + "auxvariables": [ + "105580.203125", + "66.951" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "46256:46356:1" + }, + "rng_seed": null, + "justification": { + "text": "In 102 hours after the end of the given time window, the area within Avellino, Italy where geopotential at 250 hPa exceeds 105580.203125m\u00b2/s\u00b2 covers 0.0 percent of Avellino, Italy, compared to the threshold 66.951 percent; the statement is thus False." + }, + "question_id": "B7IciW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "64ae1697ee233068" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46249:46256:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56827:56845:1'} The data starts from November 23 18:00 and ends on November 28 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 114 hours after the end of the given time window, does the minimum value of specific_humidity at 50 hPa within Robeson Channel remain above 3.000000106112566e-06kg/kg throughout Robeson Channel?", + "response": "In Robeson Channel, the minimum value of specific_humidity at 50 hPa over the 114 hours window is 2.7349394713382935e-06kg/kg, which is compared to the threshold 3.000000106112566e-06kg/kg; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "2.7349394713382935e-06", + "auxvariables_0": "3.000000106112566e-06", + "checks": [ + { + "name": "min_above_threshold", + "actual": "2.7349394713382935e-06", + "op": ">=", + "th": "3.000000106112566e-06", + "ok": false + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Robeson Channel" + ], + "units": [ + "kg/kg" + ], + "duration": 114, + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P10 over window [0,114]" + ], + "auxvariables": [ + "3.000000106112566e-06" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "56845:56945:1" + }, + "rng_seed": null, + "justification": { + "text": "In Robeson Channel, the minimum value of specific_humidity at 50 hPa over the 114 hours window is 2.7349394713382935e-06kg/kg, which is compared to the threshold 3.000000106112566e-06kg/kg; this makes the statement False." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "76bde44e04e81d04" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56827:56845:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54394:54407:1'} The data starts from March 25 12:00 and ends on March 28 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 174 hours after the end of the given time window, does the area-averaged geopotential at 600 hPa within Malawi exceed 43231.61m\u00b2/s\u00b2 and the area-averaged geopotential at 100 hPa within Malawi remain below 162350.39m\u00b2/s\u00b2?", + "response": "In 174 hours after the end of the given time window, the area-averaged geopotential at 600 hPa within Malawi is 43344.22642212613m\u00b2/s\u00b2 relative to the threshold 43231.61m\u00b2/s\u00b2, and the area-averaged geopotential at 100 hPa within Malawi is 162449.4519332814m\u00b2/s\u00b2 relative to the threshold 162350.39m\u00b2/s\u00b2; combined, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "43344.22642212613", + "actualvalue_1": "162449.4519332814", + "auxvariables_0": "43231.61", + "auxvariables_1": "162350.39", + "checks": [ + { + "name": "cond0", + "actual": "43344.22642212613", + "op": ">", + "th": "43231.61", + "ok": true + }, + { + "name": "gate0", + "actual": "162449.4519332814", + "op": "<", + "th": "162350.39", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area-averaged {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}} relative to the threshold {{auxvariables_0}}{{units_0}}, and the area-averaged {{wb2varnames_1}}{{levelsuffixes_1}} within {{regions_0}} is {{actualvalue_1}}{{units_1}} relative to the threshold {{auxvariables_1}}{{units_1}}; combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area-averaged {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0} and the area-averaged {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {auxvariables_1}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 600, + 100 + ], + "regions": [ + "Malawi" + ], + "units": [ + "m\u00b2/s\u00b2", + "m\u00b2/s\u00b2" + ], + "duration": 174, + "auxvariables": [ + "43231.61", + "162350.39" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "54407:54507:1" + }, + "rng_seed": null, + "justification": { + "text": "In 174 hours after the end of the given time window, the area-averaged geopotential at 600 hPa within Malawi is 43344.22642212613m\u00b2/s\u00b2 relative to the threshold 43231.61m\u00b2/s\u00b2, and the area-averaged geopotential at 100 hPa within Malawi is 162449.4519332814m\u00b2/s\u00b2 relative to the threshold 162350.39m\u00b2/s\u00b2; combined, this makes the statement False." + }, + "question_id": "Rdqpmf", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "793fc551237cd052" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54394:54407:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44579:44601:1'} The data starts from July 06 18:00 and ends on July 12 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 132 hours after the end of the given time window, does the maximum value of temperature at 600 hPa within Canillo, Andorra occur at a latitude that is at least 40.89 degrees farther north than its maximum within Amambay, Paraguay?", + "response": "In 132 hours after the end of the given time window, the northward latitude difference between the maximum temperature at 600 hPa in Canillo, Andorra (41.999999999999986) and Amambay, Paraguay (-22.5) is at least 40.89 degrees if and only if the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "41.999999999999986", + "actualvalue_1": "-22.5", + "auxvariables_0": "40.89", + "checks": [ + { + "name": "north_lat_diff", + "actual": "64.49999999999999", + "op": ">=", + "th": "40.89", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Canillo, Andorra", + "Amambay, Paraguay" + ], + "duration": 132, + "auxvariables": [ + "40.89" + ], + "auxvariables_0": "40.89" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "44601:44701:1" + }, + "rng_seed": null, + "justification": { + "text": "In 132 hours after the end of the given time window, the northward latitude difference between the maximum temperature at 600 hPa in Canillo, Andorra (41.999999999999986) and Amambay, Paraguay (-22.5) is at least 40.89 degrees if and only if the statement is True." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4de45a7700839e35" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44579:44601:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71847:71852:1'} The data starts from March 05 18:00 and ends on March 06 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 108 hours after the end of the given time window, does the maximum value of temperature at 600 hPa within Saint James, Jamaica occur at a latitude that is at least 0.01 degrees farther north than its maximum within San Giljan, Malta?", + "response": "In 108 hours after the end of the given time window, the northward latitude difference between the maximum temperature at 600 hPa in Saint James, Jamaica (18.0) and San Giljan, Malta (36.0) is at least 0.01 degrees if and only if the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "18.0", + "actualvalue_1": "36.0", + "auxvariables_0": "0.01", + "checks": [ + { + "name": "north_lat_diff", + "actual": "-18.0", + "op": ">=", + "th": "0.01", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Saint James, Jamaica", + "San Giljan, Malta" + ], + "duration": 108, + "auxvariables": [ + "0.01" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "71852:71952:1" + }, + "rng_seed": null, + "justification": { + "text": "In 108 hours after the end of the given time window, the northward latitude difference between the maximum temperature at 600 hPa in Saint James, Jamaica (18.0) and San Giljan, Malta (36.0) is at least 0.01 degrees if and only if the statement is False." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4e5f21d708e720be" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71847:71852:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40541:40553:1'} The data starts from October 01 06:00 and ends on October 04 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 60 hours after the end of the given time window, does the maximum value of geopotential at 150 hPa within Africa exceed the maximum value of geopotential at 150 hPa within North America by more than 53.5m\u00b2/s\u00b2?", + "response": "In 60 hours after the end of the given time window, the maximum value of geopotential at 150 hPa within Africa exceeds the maximum value within North America by 139611.328125 - 139763.640625, compared to the threshold 53.5m\u00b2/s\u00b2; therefore, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "139763.640625", + "actualvalue_1": "139611.328125", + "auxvariables_0": "53.5", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "-152.3125", + "op": ">", + "th": "53.5", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "North America", + "Africa" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 60, + "auxvariables": [ + "53.5" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "40553:40653:1" + }, + "rng_seed": null, + "justification": { + "text": "In 60 hours after the end of the given time window, the maximum value of geopotential at 150 hPa within Africa exceeds the maximum value within North America by 139611.328125 - 139763.640625, compared to the threshold 53.5m\u00b2/s\u00b2; therefore, the statement is False." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a2c5233a433c92f2" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40541:40553:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85594:85606:1'} The data starts from August 02 12:00 and ends on August 05 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 156 hours after the end of the given time window, does the minimum value of specific_humidity at 150 hPa within Southern Patagonian Ice Field remain above 0.5kg/kg throughout Southern Patagonian Ice Field?", + "response": "In Southern Patagonian Ice Field, the minimum value of specific_humidity at 150 hPa over the 156 hours window is 2.960978008559323e-06kg/kg, which is compared to the threshold 0.5kg/kg; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "2.960978008559323e-06", + "auxvariables_0": "0.5", + "checks": [ + { + "name": "min_above_threshold", + "actual": "2.960978008559323e-06", + "op": ">=", + "th": "0.5", + "ok": false + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Southern Patagonian Ice Field" + ], + "units": [ + "kg/kg" + ], + "duration": 156, + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P10 over window [0,156]" + ], + "auxvariables": [ + "0.5" + ], + "auxvariables_0": "0.5" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "85606:85706:1" + }, + "rng_seed": null, + "justification": { + "text": "In Southern Patagonian Ice Field, the minimum value of specific_humidity at 150 hPa over the 156 hours window is 2.960978008559323e-06kg/kg, which is compared to the threshold 0.5kg/kg; this makes the statement False." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d99650f5b24334ea" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85594:85606:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42216:42220:1'} The data starts from November 24 00:00 and ends on November 24 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 96 hours after the end of the given time window, does the maximum value of specific_humidity at 150 hPa within Lagoa dos Patos exceed 0.5kg/kg?", + "response": "In 96 hours after the end of the given time window, the maximum value of specific_humidity at 150 hPa within Lagoa dos Patos is 1.056761720974464e-05kg/kg, compared to the threshold 0.5kg/kg; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "1.056761720974464e-05", + "auxvariables_0": "0.5", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "1.056761720974464e-05", + "op": ">", + "th": "0.5", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Lagoa dos Patos" + ], + "units": [ + "kg/kg" + ], + "duration": 96, + "auxvariables": [ + "0.5" + ], + "auxvariables_0": "0.5" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "42220:42320:1" + }, + "rng_seed": null, + "justification": { + "text": "In 96 hours after the end of the given time window, the maximum value of specific_humidity at 150 hPa within Lagoa dos Patos is 1.056761720974464e-05kg/kg, compared to the threshold 0.5kg/kg; this makes the statement False." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "65b111a69f9aeb97" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42216:42220:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92776:92784:1'} The data starts from July 03 00:00 and ends on July 04 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 126 hours after the end of the given time window, does the maximum value of v_component_of_wind at 50 hPa within Ti\u1ec1n Giang, Vietnam exceed the maximum value of v_component_of_wind at 50 hPa within Haute-Kotto, Central African Republic by more than 0.656m/s?", + "response": "In 126 hours after the end of the given time window, the maximum value of v_component_of_wind at 50 hPa within Ti\u1ec1n Giang, Vietnam exceeds the maximum value within Haute-Kotto, Central African Republic by -2.926792621612549 - 2.94535756111145, compared to the threshold 0.656m/s; therefore, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "2.94535756111145", + "actualvalue_1": "-2.926792621612549", + "auxvariables_0": "0.656", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "-5.872150182723999", + "op": ">", + "th": "0.656", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Haute-Kotto, Central African Republic", + "Ti\u1ec1n Giang, Vietnam" + ], + "units": [ + "m/s" + ], + "duration": 126, + "auxvariables": [ + "0.656" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "92784:92884:1" + }, + "rng_seed": null, + "justification": { + "text": "In 126 hours after the end of the given time window, the maximum value of v_component_of_wind at 50 hPa within Ti\u1ec1n Giang, Vietnam exceeds the maximum value within Haute-Kotto, Central African Republic by -2.926792621612549 - 2.94535756111145, compared to the threshold 0.656m/s; therefore, the statement is False." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5ddcd7304899d786" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92776:92784:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31517:31532:1'} The data starts from July 28 06:00 and ends on July 31 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 186 hours after the end of the given time window, does the maximum value of temperature at 850 hPa within Southern Patagonian Ice Field exceed 262.71K, while the maximum value of temperature at 850 hPa within eSwatini remains below 295.62K?", + "response": "In 186 hours after the end of the given time window, the maximum value of temperature at 850 hPa within Southern Patagonian Ice Field is 268.0753173828125K, relative to the threshold 262.71K; while within eSwatini the maximum is 289.8190612792969K, relative to the threshold 295.62K. Combined with the gating condition, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "268.0753173828125", + "actualvalue_1": "289.8190612792969", + "auxvariables_0": "262.71", + "auxvariables_1": "295.62", + "checks": [ + { + "name": "cond0", + "actual": "268.0753173828125", + "op": ">", + "th": "262.71", + "ok": true + }, + { + "name": "cond1", + "actual": "289.8190612792969", + "op": "<", + "th": "295.62", + "ok": true + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Southern Patagonian Ice Field", + "eSwatini" + ], + "units": [ + "K" + ], + "duration": 186, + "auxvariables": [ + "262.71", + "295.62" + ], + "auxvariables_0": "262.71", + "auxvariables_1": "295.62" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "31532:31632:1" + }, + "rng_seed": null, + "justification": { + "text": "In 186 hours after the end of the given time window, the maximum value of temperature at 850 hPa within Southern Patagonian Ice Field is 268.0753173828125K, relative to the threshold 262.71K; while within eSwatini the maximum is 289.8190612792969K, relative to the threshold 295.62K. Combined with the gating condition, this makes the statement True." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a12e5d096166e45e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31517:31532:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83880:83888:1'} The data starts from May 31 00:00 and ends on June 01 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 66 hours after the end of the given time window, does the maximum value of v_component_of_wind at 600 hPa within South America remain above 21.15m/s and does the maximum value of u_component_of_wind at 100 hPa within South America remain below 50.73m/s?", + "response": "In South America, the maximum v_component_of_wind at 600 hPa is 22.853416442871094m/s compared to the threshold 21.15m/s, and the maximum u_component_of_wind at 100 hPa is 49.73728942871094m/s compared to the threshold 50.73m/s. Combined, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "22.853416442871094", + "actualvalue_1": "49.73728942871094", + "auxvariables_0": "21.15", + "auxvariables_1": "50.73", + "checks": [ + { + "name": "cond0", + "actual": "22.853416442871094", + "op": ">", + "th": "21.15", + "ok": true + }, + { + "name": "cond1", + "actual": "49.73728942871094", + "op": "<", + "th": "50.73", + "ok": true + } + ], + "justification": "In {{regions_0}}, the maximum {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to the threshold {{auxvariables_1}}{{units_1}}. Combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} and does the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {{auxvariables_1}}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 600, + 100 + ], + "regions": [ + "South America" + ], + "units": [ + "m/s", + "m/s" + ], + "duration": 66, + "auxvariables": [ + "21.15", + "50.73" + ], + "auxvariables_0": "21.15", + "auxvariables_1": "50.73" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "u_component_of_wind" + ], + "time_range": "83888:83988:1" + }, + "rng_seed": null, + "justification": { + "text": "In South America, the maximum v_component_of_wind at 600 hPa is 22.853416442871094m/s compared to the threshold 21.15m/s, and the maximum u_component_of_wind at 100 hPa is 49.73728942871094m/s compared to the threshold 50.73m/s. Combined, this makes the statement True." + }, + "question_id": "5WVWUb", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "04dd5eecdc1c3c71" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83880:83888:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37527:37555:1'} The data starts from September 07 18:00 and ends on September 14 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 186 hours after the end of the given time window, does the minimum value of specific_humidity at 100 hPa within Burkina Faso remain above 1.9999999949504854e-06kg/kg throughout Burkina Faso?", + "response": "In Burkina Faso, the minimum value of specific_humidity at 100 hPa over the 186 hours window is 3.4172405776189407e-06kg/kg, which is compared to the threshold 1.9999999949504854e-06kg/kg; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "3.4172405776189407e-06", + "auxvariables_0": "1.9999999949504854e-06", + "checks": [ + { + "name": "min_above_threshold", + "actual": "3.4172405776189407e-06", + "op": ">=", + "th": "1.9999999949504854e-06", + "ok": true + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Burkina Faso" + ], + "units": [ + "kg/kg" + ], + "duration": 186, + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P10 over window [0,186]" + ], + "auxvariables": [ + "1.9999999949504854e-06" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "37555:37655:1" + }, + "rng_seed": null, + "justification": { + "text": "In Burkina Faso, the minimum value of specific_humidity at 100 hPa over the 186 hours window is 3.4172405776189407e-06kg/kg, which is compared to the threshold 1.9999999949504854e-06kg/kg; this makes the statement True." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d575ce4393c151f6" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37527:37555:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62263:62270:1'} The data starts from August 13 18:00 and ends on August 15 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 144 hours after the end of the given time window, does the maximum value of geopotential at 600 hPa within Gulf of Bothnia exceed 41557.85m\u00b2/s\u00b2, while the maximum value of geopotential at 600 hPa within Sargasso Sea remains below 45064.19m\u00b2/s\u00b2?", + "response": "In 144 hours after the end of the given time window, the maximum value of geopotential at 600 hPa within Gulf of Bothnia is 42405.96875m\u00b2/s\u00b2, relative to the threshold 41557.85m\u00b2/s\u00b2; while within Sargasso Sea the maximum is 44180.578125m\u00b2/s\u00b2, relative to the threshold 45064.19m\u00b2/s\u00b2. Combined with the gating condition, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "42405.96875", + "actualvalue_1": "44180.578125", + "auxvariables_0": "41557.85", + "auxvariables_1": "45064.19", + "checks": [ + { + "name": "cond0", + "actual": "42405.96875", + "op": ">", + "th": "41557.85", + "ok": true + }, + { + "name": "cond1", + "actual": "44180.578125", + "op": "<", + "th": "45064.19", + "ok": true + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Gulf of Bothnia", + "Sargasso Sea" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 144, + "auxvariables": [ + "41557.85", + "45064.19" + ], + "auxvariables_0": "41557.85", + "auxvariables_1": "45064.19" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "62270:62370:1" + }, + "rng_seed": null, + "justification": { + "text": "In 144 hours after the end of the given time window, the maximum value of geopotential at 600 hPa within Gulf of Bothnia is 42405.96875m\u00b2/s\u00b2, relative to the threshold 41557.85m\u00b2/s\u00b2; while within Sargasso Sea the maximum is 44180.578125m\u00b2/s\u00b2, relative to the threshold 45064.19m\u00b2/s\u00b2. Combined with the gating condition, this makes the statement True." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "67725be79dfaebb0" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62263:62270:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67001:67029:1'} The data starts from November 10 06:00 and ends on November 17 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 114 hours after the end of the given time window, does the maximum value of u_component_of_wind at 700 hPa within Brazilian Island exceed -2.25m/s, while the maximum value of u_component_of_wind at 700 hPa within Switzerland remains below 11.72m/s?", + "response": "In 114 hours after the end of the given time window, the maximum value of u_component_of_wind at 700 hPa within Brazilian Island is -1.7268506288528442m/s, relative to the threshold -2.25m/s; while within Switzerland the maximum is 10.53718090057373m/s, relative to the threshold 11.72m/s. Combined with the gating condition, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "-1.7268506288528442", + "actualvalue_1": "10.53718090057373", + "auxvariables_0": "-2.25", + "auxvariables_1": "11.72", + "checks": [ + { + "name": "cond0", + "actual": "-1.7268506288528442", + "op": ">", + "th": "-2.25", + "ok": true + }, + { + "name": "cond1", + "actual": "10.53718090057373", + "op": "<", + "th": "11.72", + "ok": true + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Brazilian Island", + "Switzerland" + ], + "units": [ + "m/s" + ], + "duration": 114, + "auxvariables": [ + "-2.25", + "11.72" + ], + "auxvariables_0": "-2.25", + "auxvariables_1": "11.72" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "67029:67129:1" + }, + "rng_seed": null, + "justification": { + "text": "In 114 hours after the end of the given time window, the maximum value of u_component_of_wind at 700 hPa within Brazilian Island is -1.7268506288528442m/s, relative to the threshold -2.25m/s; while within Switzerland the maximum is 10.53718090057373m/s, relative to the threshold 11.72m/s. Combined with the gating condition, this makes the statement True." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "def5948c3c969873" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67001:67029:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31770:31773:1'} The data starts from September 29 12:00 and ends on September 30 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 168 hours after the end of the given time window, does the maximum value of specific_humidity at 600 hPa within Hong Kong S.A.R. exceed 0.005088kg/kg?", + "response": "In 168 hours after the end of the given time window, the maximum value of specific_humidity at 600 hPa within Hong Kong S.A.R. is 0.005526767577975988kg/kg, compared to the threshold 0.005088kg/kg; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.005526767577975988", + "auxvariables_0": "0.005088", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "0.005526767577975988", + "op": ">", + "th": "0.005088", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Hong Kong S.A.R." + ], + "units": [ + "kg/kg" + ], + "duration": 168, + "auxvariables": [ + "0.005088" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "31773:31873:1" + }, + "rng_seed": null, + "justification": { + "text": "In 168 hours after the end of the given time window, the maximum value of specific_humidity at 600 hPa within Hong Kong S.A.R. is 0.005526767577975988kg/kg, compared to the threshold 0.005088kg/kg; this makes the statement True." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0c76aa7745b90148" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31770:31773:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33697:33698:1'} The data corresponds to corresponds to a snapshot on January 24 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 204 hours after the end of the given time window, does the maximum value of u_component_of_wind at 925 hPa within North Pacific Ocean exceed 19.329999923706055m/s, while the maximum value of u_component_of_wind at 925 hPa within Davao Gulf remains below -0.25m/s?", + "response": "In 204 hours after the end of the given time window, the maximum value of u_component_of_wind at 925 hPa within North Pacific Ocean is 25.951068878173828m/s, relative to the threshold 19.329999923706055m/s; while within Davao Gulf the maximum is -2.4095966815948486m/s, relative to the threshold -0.25m/s. Combined with the gating condition, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "25.951068878173828", + "actualvalue_1": "-2.4095966815948486", + "auxvariables_0": "19.329999923706055", + "auxvariables_1": "-0.25", + "checks": [ + { + "name": "cond0", + "actual": "25.951068878173828", + "op": ">", + "th": "19.329999923706055", + "ok": true + }, + { + "name": "cond1", + "actual": "-2.4095966815948486", + "op": "<", + "th": "-0.25", + "ok": true + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "North Pacific Ocean", + "Davao Gulf" + ], + "units": [ + "m/s" + ], + "duration": 204, + "auxvariables": [ + "19.329999923706055", + "-0.25" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "33698:33798:1" + }, + "rng_seed": null, + "justification": { + "text": "In 204 hours after the end of the given time window, the maximum value of u_component_of_wind at 925 hPa within North Pacific Ocean is 25.951068878173828m/s, relative to the threshold 19.329999923706055m/s; while within Davao Gulf the maximum is -2.4095966815948486m/s, relative to the threshold -0.25m/s. Combined with the gating condition, this makes the statement True." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c5924642f8d068c1" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33697:33698:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53069:53086:1'} The data starts from April 29 06:00 and ends on May 03 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 192 hours after the end of the given time window, does the maximum value of geopotential at 500 hPa within South Sudan occur at a latitude that is at least 0.01 degrees farther north than its maximum within Bahrain?", + "response": "In 192 hours after the end of the given time window, the northward latitude difference between the maximum geopotential at 500 hPa in South Sudan (10.499999999999996) and Bahrain (25.49999999999999) is at least 0.01 degrees if and only if the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "10.499999999999996", + "actualvalue_1": "25.49999999999999", + "auxvariables_0": "0.01", + "checks": [ + { + "name": "north_lat_diff", + "actual": "-14.999999999999993", + "op": ">=", + "th": "0.01", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "South Sudan", + "Bahrain" + ], + "duration": 192, + "auxvariables": [ + "0.01" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "53086:53186:1" + }, + "rng_seed": null, + "justification": { + "text": "In 192 hours after the end of the given time window, the northward latitude difference between the maximum geopotential at 500 hPa in South Sudan (10.499999999999996) and Bahrain (25.49999999999999) is at least 0.01 degrees if and only if the statement is False." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "21d6029cf90869af" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53069:53086:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49114:49115:1'} The data corresponds to corresponds to a snapshot on August 13 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 48 hours after the end of the given time window, does the maximum value of temperature at 250 hPa within Europe remain above 225.19900512695312K and does the maximum value of temperature at 700 hPa within Europe remain below 269.0539855957031K?", + "response": "In Europe, the maximum temperature at 250 hPa is 235.6223907470703K compared to the threshold 225.19900512695312K, and the maximum temperature at 700 hPa is 284.5505065917969K compared to the threshold 269.0539855957031K. Combined, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "235.6223907470703", + "actualvalue_1": "284.5505065917969", + "auxvariables_0": "225.19900512695312", + "auxvariables_1": "269.0539855957031", + "checks": [ + { + "name": "cond0", + "actual": "235.6223907470703", + "op": ">", + "th": "225.19900512695312", + "ok": true + }, + { + "name": "cond1", + "actual": "284.5505065917969", + "op": "<", + "th": "269.0539855957031", + "ok": false + } + ], + "justification": "In {{regions_0}}, the maximum {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to the threshold {{auxvariables_1}}{{units_1}}. Combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} and does the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {{auxvariables_1}}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 250, + 700 + ], + "regions": [ + "Europe" + ], + "units": [ + "K", + "K" + ], + "duration": 48, + "auxvariables": [ + "225.19900512695312", + "269.0539855957031" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "49115:49215:1" + }, + "rng_seed": null, + "justification": { + "text": "In Europe, the maximum temperature at 250 hPa is 235.6223907470703K compared to the threshold 225.19900512695312K, and the maximum temperature at 700 hPa is 284.5505065917969K compared to the threshold 269.0539855957031K. Combined, this makes the statement False." + }, + "question_id": "5WVWUb", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0b3b55bae85c1364" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49114:49115:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34126:34130:1'} The data starts from May 11 12:00 and ends on May 12 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 96 hours after the end of the given time window, does the maximum value of geopotential at 100 hPa within A\u00efn Defla, Algeria exceed 160684.0m\u00b2/s\u00b2?", + "response": "In 96 hours after the end of the given time window, the maximum value of geopotential at 100 hPa within A\u00efn Defla, Algeria is 159930.90625m\u00b2/s\u00b2, compared to the threshold 160684.0m\u00b2/s\u00b2; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "159930.90625", + "auxvariables_0": "160684.0", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "159930.90625", + "op": ">", + "th": "160684.0", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "A\u00efn Defla, Algeria" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 96, + "auxvariables": [ + "160684.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "34130:34230:1" + }, + "rng_seed": null, + "justification": { + "text": "In 96 hours after the end of the given time window, the maximum value of geopotential at 100 hPa within A\u00efn Defla, Algeria is 159930.90625m\u00b2/s\u00b2, compared to the threshold 160684.0m\u00b2/s\u00b2; this makes the statement False." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "18fdd3ce1c5aa21c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34126:34130:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34202:34218:1'} The data starts from May 30 12:00 and ends on June 03 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 138 hours after the end of the given time window, does the maximum value of specific_humidity at 50 hPa within Halton, United Kingdom exceed the maximum value of specific_humidity at 50 hPa within Butel, North Macedonia by more than 0.0kg/kg?", + "response": "In 138 hours after the end of the given time window, the maximum value of specific_humidity at 50 hPa within Halton, United Kingdom exceeds the maximum value within Butel, North Macedonia by 2.9197067306085955e-06 - 2.9692430416616844e-06, compared to the threshold 0.0kg/kg; therefore, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "2.9692430416616844e-06", + "actualvalue_1": "2.9197067306085955e-06", + "auxvariables_0": "0.0", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "-4.9536311053088866e-08", + "op": ">", + "th": "0.0", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Butel, North Macedonia", + "Halton, United Kingdom" + ], + "units": [ + "kg/kg" + ], + "duration": 138, + "auxvariables": [ + "0.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "34218:34318:1" + }, + "rng_seed": null, + "justification": { + "text": "In 138 hours after the end of the given time window, the maximum value of specific_humidity at 50 hPa within Halton, United Kingdom exceeds the maximum value within Butel, North Macedonia by 2.9197067306085955e-06 - 2.9692430416616844e-06, compared to the threshold 0.0kg/kg; therefore, the statement is False." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "56ce422aec9b300e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34202:34218:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92499:92508:1'} The data starts from April 24 18:00 and ends on April 26 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 132 hours after the end of the given time window, does the maximum value of specific_humidity at 600 hPa within Irish Sea remain above 0.0kg/kg and does the maximum value of specific_humidity at 850 hPa within Irish Sea remain below 0.5kg/kg?", + "response": "In Irish Sea, the maximum specific_humidity at 600 hPa is 0.0013287524925544858kg/kg compared to the threshold 0.0kg/kg, and the maximum specific_humidity at 850 hPa is 0.004941207822412252kg/kg compared to the threshold 0.5kg/kg. Combined, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.0013287524925544858", + "actualvalue_1": "0.004941207822412252", + "auxvariables_0": "0.0", + "auxvariables_1": "0.5", + "checks": [ + { + "name": "cond0", + "actual": "0.0013287524925544858", + "op": ">", + "th": "0.0", + "ok": true + }, + { + "name": "cond1", + "actual": "0.004941207822412252", + "op": "<", + "th": "0.5", + "ok": true + } + ], + "justification": "In {{regions_0}}, the maximum {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to the threshold {{auxvariables_1}}{{units_1}}. Combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} and does the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {{auxvariables_1}}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 600, + 850 + ], + "regions": [ + "Irish Sea" + ], + "units": [ + "kg/kg", + "kg/kg" + ], + "duration": 132, + "auxvariables": [ + "0.0", + "0.5" + ], + "auxvariables_0": "0.0", + "auxvariables_1": "0.5" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "92508:92608:1" + }, + "rng_seed": null, + "justification": { + "text": "In Irish Sea, the maximum specific_humidity at 600 hPa is 0.0013287524925544858kg/kg compared to the threshold 0.0kg/kg, and the maximum specific_humidity at 850 hPa is 0.004941207822412252kg/kg compared to the threshold 0.5kg/kg. Combined, this makes the statement True." + }, + "question_id": "5WVWUb", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "08edf28436fc9d09" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92499:92508:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51483:51509:1'} The data starts from March 28 18:00 and ends on April 04 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 48 hours after the end of the given time window, does the area within Tottori, Japan where specific_humidity at 400 hPa exceeds 0.0007229999755509198kg/kg cover more than 0.01 percent of Tottori, Japan?", + "response": "In 48 hours after the end of the given time window, the area within Tottori, Japan where specific_humidity at 400 hPa exceeds 0.0007229999755509198kg/kg covers 13.540906304392486 percent of Tottori, Japan, compared to the threshold 0.01 percent; the statement is thus True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "13.540906304392486", + "actualvalue_1": "0.01", + "auxvariables_0": "0.0007229999755509198", + "auxvariables_1": "0.01", + "checks": [ + { + "name": "gating_exceeds", + "actual": "13.540906304392486", + "op": ">", + "th": "0.0", + "ok": true + }, + { + "name": "primary_percent", + "actual": "13.540906304392486", + "op": ">", + "th": "0.01", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area within {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} covers {{actualvalue_0}} percent of {{regions_0}}, compared to the threshold {{auxvariables_1}} percent; the statement is thus {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_023.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_023.py", + "template_id": "tmpl_023", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds {auxvariables_0}{units_0} cover more than {auxvariables_1} percent of {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "Tottori, Japan" + ], + "units": [ + "kg/kg" + ], + "duration": 48, + "auxvariables": [ + "0.0007229999755509198", + "0.01" + ], + "auxvariables_1": "0.01" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "51509:51609:1" + }, + "rng_seed": null, + "justification": { + "text": "In 48 hours after the end of the given time window, the area within Tottori, Japan where specific_humidity at 400 hPa exceeds 0.0007229999755509198kg/kg covers 13.540906304392486 percent of Tottori, Japan, compared to the threshold 0.01 percent; the statement is thus True." + }, + "question_id": "B7IciW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "82ec1b36e5f62e36" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51483:51509:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62869:62878:1'} The data starts from January 12 06:00 and ends on January 14 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 186 hours after the end of the given time window, does the maximum value of specific_humidity at 200 hPa within Medio Campidano, Italy exceed 0.0kg/kg?", + "response": "In 186 hours after the end of the given time window, the maximum value of specific_humidity at 200 hPa within Medio Campidano, Italy is 6.863716407679021e-06kg/kg, compared to the threshold 0.0kg/kg; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "6.863716407679021e-06", + "auxvariables_0": "0.0", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "6.863716407679021e-06", + "op": ">", + "th": "0.0", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Medio Campidano, Italy" + ], + "units": [ + "kg/kg" + ], + "duration": 186, + "auxvariables": [ + "0.0" + ], + "auxvariables_0": "0.0" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "62878:62978:1" + }, + "rng_seed": null, + "justification": { + "text": "In 186 hours after the end of the given time window, the maximum value of specific_humidity at 200 hPa within Medio Campidano, Italy is 6.863716407679021e-06kg/kg, compared to the threshold 0.0kg/kg; this makes the statement True." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "34f9da17512e8261" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62869:62878:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60459:60463:1'} The data starts from May 19 18:00 and ends on May 20 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 114 hours after the end of the given time window, does the maximum value of temperature at 1000 hPa within South America remain above 296.36K and does the maximum value of temperature at 400 hPa within South America remain below 263.57K?", + "response": "In South America, the maximum temperature at 1000 hPa is 302.4112548828125K compared to the threshold 296.36K, and the maximum temperature at 400 hPa is 258.399169921875K compared to the threshold 263.57K. Combined, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "302.4112548828125", + "actualvalue_1": "258.399169921875", + "auxvariables_0": "296.36", + "auxvariables_1": "263.57", + "checks": [ + { + "name": "cond0", + "actual": "302.4112548828125", + "op": ">", + "th": "296.36", + "ok": true + }, + { + "name": "cond1", + "actual": "258.399169921875", + "op": "<", + "th": "263.57", + "ok": true + } + ], + "justification": "In {{regions_0}}, the maximum {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to the threshold {{auxvariables_1}}{{units_1}}. Combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} and does the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {{auxvariables_1}}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 1000, + 400 + ], + "regions": [ + "South America" + ], + "units": [ + "K", + "K" + ], + "duration": 114, + "auxvariables": [ + "296.36", + "263.57" + ], + "auxvariables_0": "296.36", + "auxvariables_1": "263.57" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "60463:60563:1" + }, + "rng_seed": null, + "justification": { + "text": "In South America, the maximum temperature at 1000 hPa is 302.4112548828125K compared to the threshold 296.36K, and the maximum temperature at 400 hPa is 258.399169921875K compared to the threshold 263.57K. Combined, this makes the statement True." + }, + "question_id": "5WVWUb", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "528adfae96a65567" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60459:60463:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38578:38598:1'} The data starts from May 28 12:00 and ends on June 02 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 114 hours after the end of the given time window, does the maximum value of u_component_of_wind at 500 hPa within Novo Selo, North Macedonia remain above 2.29m/s and does the maximum value of u_component_of_wind at 400 hPa within Novo Selo, North Macedonia remain below 5.06m/s?", + "response": "In Novo Selo, North Macedonia, the maximum u_component_of_wind at 500 hPa is 3.9577062129974365m/s compared to the threshold 2.29m/s, and the maximum u_component_of_wind at 400 hPa is 3.5420596599578857m/s compared to the threshold 5.06m/s. Combined, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "3.9577062129974365", + "actualvalue_1": "3.5420596599578857", + "auxvariables_0": "2.29", + "auxvariables_1": "5.06", + "checks": [ + { + "name": "cond0", + "actual": "3.9577062129974365", + "op": ">", + "th": "2.29", + "ok": true + }, + { + "name": "cond1", + "actual": "3.5420596599578857", + "op": "<", + "th": "5.06", + "ok": true + } + ], + "justification": "In {{regions_0}}, the maximum {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to the threshold {{auxvariables_1}}{{units_1}}. Combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} and does the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {{auxvariables_1}}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 500, + 400 + ], + "regions": [ + "Novo Selo, North Macedonia" + ], + "units": [ + "m/s", + "m/s" + ], + "duration": 114, + "auxvariables": [ + "2.29", + "5.06" + ], + "auxvariables_0": "2.29", + "auxvariables_1": "5.06" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "38598:38698:1" + }, + "rng_seed": null, + "justification": { + "text": "In Novo Selo, North Macedonia, the maximum u_component_of_wind at 500 hPa is 3.9577062129974365m/s compared to the threshold 2.29m/s, and the maximum u_component_of_wind at 400 hPa is 3.5420596599578857m/s compared to the threshold 5.06m/s. Combined, this makes the statement True." + }, + "question_id": "5WVWUb", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f04c702c55905cdf" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38578:38598:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38922:38940:1'} The data starts from August 22 12:00 and ends on August 26 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 90 hours after the end of the given time window, does the minimum value of temperature at 50 hPa within Saint Andrew, Jamaica remain above 209.72999572753906K throughout Saint Andrew, Jamaica?", + "response": "In Saint Andrew, Jamaica, the minimum value of temperature at 50 hPa over the 90 hours window is 210.70623779296875K, which is compared to the threshold 209.72999572753906K; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "210.70623779296875", + "auxvariables_0": "209.72999572753906", + "checks": [ + { + "name": "min_above_threshold", + "actual": "210.70623779296875", + "op": ">=", + "th": "209.72999572753906", + "ok": true + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Saint Andrew, Jamaica" + ], + "units": [ + "K" + ], + "duration": 90, + "auxvariables_0_provenance": [ + "var=temperature, tail=P10 over window [0,90]" + ], + "auxvariables": [ + "209.72999572753906" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "38940:39040:1" + }, + "rng_seed": null, + "justification": { + "text": "In Saint Andrew, Jamaica, the minimum value of temperature at 50 hPa over the 90 hours window is 210.70623779296875K, which is compared to the threshold 209.72999572753906K; this makes the statement True." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "bb89fd0154f3e6fd" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38922:38940:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48981:48995:1'} The data starts from July 11 06:00 and ends on July 14 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 78 hours after the end of the given time window, does the maximum value of v_component_of_wind at 500 hPa within Jalapa, Guatemala exceed 11.55m/s, while the maximum value of v_component_of_wind at 500 hPa within Bolama, Guinea-Bissau remains below 0.0m/s?", + "response": "In 78 hours after the end of the given time window, the maximum value of v_component_of_wind at 500 hPa within Jalapa, Guatemala is 10.31071949005127m/s, relative to the threshold 11.55m/s; while within Bolama, Guinea-Bissau the maximum is 0.3357938826084137m/s, relative to the threshold 0.0m/s. Combined with the gating condition, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "10.31071949005127", + "actualvalue_1": "0.3357938826084137", + "auxvariables_0": "11.55", + "auxvariables_1": "0.0", + "checks": [ + { + "name": "cond0", + "actual": "10.31071949005127", + "op": ">", + "th": "11.55", + "ok": false + }, + { + "name": "cond1", + "actual": "0.3357938826084137", + "op": "<", + "th": "0.0", + "ok": false + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Jalapa, Guatemala", + "Bolama, Guinea-Bissau" + ], + "units": [ + "m/s" + ], + "duration": 78, + "auxvariables": [ + "11.55", + "0.0" + ], + "auxvariables_0": "11.55", + "auxvariables_1": "0.0" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "48995:49095:1" + }, + "rng_seed": null, + "justification": { + "text": "In 78 hours after the end of the given time window, the maximum value of v_component_of_wind at 500 hPa within Jalapa, Guatemala is 10.31071949005127m/s, relative to the threshold 11.55m/s; while within Bolama, Guinea-Bissau the maximum is 0.3357938826084137m/s, relative to the threshold 0.0m/s. Combined with the gating condition, this makes the statement False." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2837b54a495461a8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48981:48995:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91829:91836:1'} The data starts from November 08 06:00 and ends on November 09 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 48 hours after the end of the given time window, does the maximum value of temperature at 500 hPa within Napo, Ecuador exceed 264.48K, while the maximum value of temperature at 500 hPa within Cop\u00e1n, Honduras remains below 271.43K?", + "response": "In 48 hours after the end of the given time window, the maximum value of temperature at 500 hPa within Napo, Ecuador is 269.8729553222656K, relative to the threshold 264.48K; while within Cop\u00e1n, Honduras the maximum is 266.1072082519531K, relative to the threshold 271.43K. Combined with the gating condition, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "269.8729553222656", + "actualvalue_1": "266.1072082519531", + "auxvariables_0": "264.48", + "auxvariables_1": "271.43", + "checks": [ + { + "name": "cond0", + "actual": "269.8729553222656", + "op": ">", + "th": "264.48", + "ok": true + }, + { + "name": "cond1", + "actual": "266.1072082519531", + "op": "<", + "th": "271.43", + "ok": true + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Napo, Ecuador", + "Cop\u00e1n, Honduras" + ], + "units": [ + "K" + ], + "duration": 48, + "auxvariables": [ + "264.48", + "271.43" + ], + "auxvariables_0": "264.48", + "auxvariables_1": "271.43" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "91836:91936:1" + }, + "rng_seed": null, + "justification": { + "text": "In 48 hours after the end of the given time window, the maximum value of temperature at 500 hPa within Napo, Ecuador is 269.8729553222656K, relative to the threshold 264.48K; while within Cop\u00e1n, Honduras the maximum is 266.1072082519531K, relative to the threshold 271.43K. Combined with the gating condition, this makes the statement True." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f754b9bcdaf5ed24" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91829:91836:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57389:57392:1'} The data starts from April 13 06:00 and ends on April 13 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 198 hours after the end of the given time window, does the minimum value of geopotential at 150 hPa within Gulf of Mexico remain above 132889.61m\u00b2/s\u00b2 throughout Gulf of Mexico?", + "response": "In Gulf of Mexico, the minimum value of geopotential at 150 hPa over the 198 hours window is 135601.640625m\u00b2/s\u00b2, which is compared to the threshold 132889.61m\u00b2/s\u00b2; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "135601.640625", + "auxvariables_0": "132889.61", + "checks": [ + { + "name": "min_above_threshold", + "actual": "135601.640625", + "op": ">=", + "th": "132889.61", + "ok": true + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Gulf of Mexico" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 198, + "auxvariables_0_provenance": [ + "var=geopotential, tail=P10 over window [0,198]" + ], + "auxvariables": [ + "132889.61" + ], + "auxvariables_0": "132889.61" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "57392:57492:1" + }, + "rng_seed": null, + "justification": { + "text": "In Gulf of Mexico, the minimum value of geopotential at 150 hPa over the 198 hours window is 135601.640625m\u00b2/s\u00b2, which is compared to the threshold 132889.61m\u00b2/s\u00b2; this makes the statement True." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6967f338231be657" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57389:57392:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88763:88773:1'} The data starts from October 03 18:00 and ends on October 06 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 36 hours after the end of the given time window, does the maximum value of v_component_of_wind at 1000 hPa within New Zealand occur at a latitude that is at least 0.01 degrees farther north than its maximum within US Naval Base Guantanamo Bay?", + "response": "In 36 hours after the end of the given time window, the northward latitude difference between the maximum v_component_of_wind at 1000 hPa in New Zealand (-51.0) and US Naval Base Guantanamo Bay (19.499999999999996) is at least 0.01 degrees if and only if the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-51.0", + "actualvalue_1": "19.499999999999996", + "auxvariables_0": "0.01", + "checks": [ + { + "name": "north_lat_diff", + "actual": "-70.5", + "op": ">=", + "th": "0.01", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "New Zealand", + "US Naval Base Guantanamo Bay" + ], + "duration": 36, + "auxvariables": [ + "0.01" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "88773:88873:1" + }, + "rng_seed": null, + "justification": { + "text": "In 36 hours after the end of the given time window, the northward latitude difference between the maximum v_component_of_wind at 1000 hPa in New Zealand (-51.0) and US Naval Base Guantanamo Bay (19.499999999999996) is at least 0.01 degrees if and only if the statement is False." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "267a5c51fd73a232" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88763:88773:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52497:52513:1'} The data starts from December 07 06:00 and ends on December 11 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 174 hours after the end of the given time window, does the maximum value of u_component_of_wind at 1000 hPa within Iraq exceed 0.94m/s?", + "response": "In 174 hours after the end of the given time window, the maximum value of u_component_of_wind at 1000 hPa within Iraq is 2.6585354804992676m/s, compared to the threshold 0.94m/s; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "2.6585354804992676", + "auxvariables_0": "0.94", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "2.6585354804992676", + "op": ">", + "th": "0.94", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Iraq" + ], + "units": [ + "m/s" + ], + "duration": 174, + "auxvariables": [ + "0.94" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "52513:52613:1" + }, + "rng_seed": null, + "justification": { + "text": "In 174 hours after the end of the given time window, the maximum value of u_component_of_wind at 1000 hPa within Iraq is 2.6585354804992676m/s, compared to the threshold 0.94m/s; this makes the statement True." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f912175caf4ef828" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52497:52513:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43301:43323:1'} The data starts from August 21 06:00 and ends on August 26 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, does the area within Cook Islands where temperature at 1000 hPa exceeds 298.7699890136719K cover more than 66.951 percent of Cook Islands?", + "response": "In 30 hours after the end of the given time window, the area within Cook Islands where temperature at 1000 hPa exceeds 298.7699890136719K covers 0.0 percent of Cook Islands, compared to the threshold 66.951 percent; the statement is thus False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0", + "actualvalue_1": "66.951", + "auxvariables_0": "298.7699890136719", + "auxvariables_1": "66.951", + "checks": [ + { + "name": "gating_exceeds", + "actual": "0.0", + "op": ">", + "th": "0.0", + "ok": false + }, + { + "name": "primary_percent", + "actual": "0.0", + "op": ">", + "th": "66.951", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area within {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} covers {{actualvalue_0}} percent of {{regions_0}}, compared to the threshold {{auxvariables_1}} percent; the statement is thus {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_023.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_023.py", + "template_id": "tmpl_023", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds {auxvariables_0}{units_0} cover more than {auxvariables_1} percent of {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Cook Islands" + ], + "units": [ + "K" + ], + "duration": 30, + "auxvariables": [ + "298.7699890136719", + "66.951" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "43323:43423:1" + }, + "rng_seed": null, + "justification": { + "text": "In 30 hours after the end of the given time window, the area within Cook Islands where temperature at 1000 hPa exceeds 298.7699890136719K covers 0.0 percent of Cook Islands, compared to the threshold 66.951 percent; the statement is thus False." + }, + "question_id": "B7IciW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "962c9226e1efecd9" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43301:43323:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92934:92962:1'} The data starts from August 11 12:00 and ends on August 18 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 132 hours after the end of the given time window, does the maximum value of geopotential at 700 hPa within Lesotho exceed the maximum value of geopotential at 700 hPa within American Samoa by more than 31533.61m\u00b2/s\u00b2?", + "response": "In 132 hours after the end of the given time window, the maximum value of geopotential at 700 hPa within Lesotho exceeds the maximum value within American Samoa by 31391.298828125 - 30915.302734375, compared to the threshold 31533.61m\u00b2/s\u00b2; therefore, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "30915.302734375", + "actualvalue_1": "31391.298828125", + "auxvariables_0": "31533.61", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "475.99609375", + "op": ">", + "th": "31533.61", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "American Samoa", + "Lesotho" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 132, + "auxvariables": [ + "31533.61" + ], + "auxvariables_0": "31533.61" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "92962:93062:1" + }, + "rng_seed": null, + "justification": { + "text": "In 132 hours after the end of the given time window, the maximum value of geopotential at 700 hPa within Lesotho exceeds the maximum value within American Samoa by 31391.298828125 - 30915.302734375, compared to the threshold 31533.61m\u00b2/s\u00b2; therefore, the statement is False." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0355c03b1192bdad" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92934:92962:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54867:54882:1'} The data starts from July 21 18:00 and ends on July 25 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 24 hours after the end of the given time window, does the area-averaged temperature at 300 hPa within Isle of Man exceed 226.75K and the area-averaged temperature at 925 hPa within Isle of Man remain below 290.17K?", + "response": "In 24 hours after the end of the given time window, the area-averaged temperature at 300 hPa within Isle of Man is 231.37326855014794K relative to the threshold 226.75K, and the area-averaged temperature at 925 hPa within Isle of Man is 284.47810132113784K relative to the threshold 290.17K; combined, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "231.37326855014794", + "actualvalue_1": "284.47810132113784", + "auxvariables_0": "226.75", + "auxvariables_1": "290.17", + "checks": [ + { + "name": "cond0", + "actual": "231.37326855014794", + "op": ">", + "th": "226.75", + "ok": true + }, + { + "name": "gate0", + "actual": "284.47810132113784", + "op": "<", + "th": "290.17", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area-averaged {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}} relative to the threshold {{auxvariables_0}}{{units_0}}, and the area-averaged {{wb2varnames_1}}{{levelsuffixes_1}} within {{regions_0}} is {{actualvalue_1}}{{units_1}} relative to the threshold {{auxvariables_1}}{{units_1}}; combined, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area-averaged {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0} and the area-averaged {wb2varnames_1}{levelsuffixes_1} within {regions_0} remain below {auxvariables_1}{units_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 1 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 300, + 925 + ], + "regions": [ + "Isle of Man" + ], + "units": [ + "K", + "K" + ], + "duration": 24, + "auxvariables": [ + "226.75", + "290.17" + ], + "auxvariables_0": "226.75", + "auxvariables_1": "290.17" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "54882:54982:1" + }, + "rng_seed": null, + "justification": { + "text": "In 24 hours after the end of the given time window, the area-averaged temperature at 300 hPa within Isle of Man is 231.37326855014794K relative to the threshold 226.75K, and the area-averaged temperature at 925 hPa within Isle of Man is 284.47810132113784K relative to the threshold 290.17K; combined, this makes the statement True." + }, + "question_id": "Rdqpmf", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5bda7a3d750ede77" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54867:54882:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89285:89310:1'} The data starts from February 11 06:00 and ends on February 17 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 186 hours after the end of the given time window, does the maximum value of geopotential at 600 hPa within US Naval Base Guantanamo Bay exceed 43608.0m\u00b2/s\u00b2?", + "response": "In 186 hours after the end of the given time window, the maximum value of geopotential at 600 hPa within US Naval Base Guantanamo Bay is 43346.01171875m\u00b2/s\u00b2, compared to the threshold 43608.0m\u00b2/s\u00b2; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "43346.01171875", + "auxvariables_0": "43608.0", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "43346.01171875", + "op": ">", + "th": "43608.0", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "US Naval Base Guantanamo Bay" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 186, + "auxvariables": [ + "43608.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "89310:89410:1" + }, + "rng_seed": null, + "justification": { + "text": "In 186 hours after the end of the given time window, the maximum value of geopotential at 600 hPa within US Naval Base Guantanamo Bay is 43346.01171875m\u00b2/s\u00b2, compared to the threshold 43608.0m\u00b2/s\u00b2; this makes the statement False." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e8dabd0cb040493d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89285:89310:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88684:88694:1'} The data starts from September 14 00:00 and ends on September 16 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 96 hours after the end of the given time window, does the maximum value of specific_humidity at 300 hPa within Gulf of Aqaba exceed 0.000299000006634742kg/kg, while the maximum value of specific_humidity at 300 hPa within Queen Charlotte Sound remains below 0.0002739999908953905kg/kg?", + "response": "In 96 hours after the end of the given time window, the maximum value of specific_humidity at 300 hPa within Gulf of Aqaba is 0.00022551724396180362kg/kg, relative to the threshold 0.000299000006634742kg/kg; while within Queen Charlotte Sound the maximum is 0.0001971575984498486kg/kg, relative to the threshold 0.0002739999908953905kg/kg. Combined with the gating condition, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.00022551724396180362", + "actualvalue_1": "0.0001971575984498486", + "auxvariables_0": "0.000299000006634742", + "auxvariables_1": "0.0002739999908953905", + "checks": [ + { + "name": "cond0", + "actual": "0.00022551724396180362", + "op": ">", + "th": "0.000299000006634742", + "ok": false + }, + { + "name": "cond1", + "actual": "0.0001971575984498486", + "op": "<", + "th": "0.0002739999908953905", + "ok": true + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Gulf of Aqaba", + "Queen Charlotte Sound" + ], + "units": [ + "kg/kg" + ], + "duration": 96, + "auxvariables": [ + "0.000299000006634742", + "0.0002739999908953905" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "88694:88794:1" + }, + "rng_seed": null, + "justification": { + "text": "In 96 hours after the end of the given time window, the maximum value of specific_humidity at 300 hPa within Gulf of Aqaba is 0.00022551724396180362kg/kg, relative to the threshold 0.000299000006634742kg/kg; while within Queen Charlotte Sound the maximum is 0.0001971575984498486kg/kg, relative to the threshold 0.0002739999908953905kg/kg. Combined with the gating condition, this makes the statement False." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5a4a7ef5901f27fc" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88684:88694:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69051:69067:1'} The data starts from April 06 18:00 and ends on April 10 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 90 hours after the end of the given time window, does the maximum value of v_component_of_wind at 1000 hPa within Qatar occur at a latitude that is at least 7.5 degrees farther north than its maximum within Antigua and Barbuda?", + "response": "In 90 hours after the end of the given time window, the northward latitude difference between the maximum v_component_of_wind at 1000 hPa in Qatar (25.49999999999999) and Antigua and Barbuda (18.0) is at least 7.5 degrees if and only if the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "25.49999999999999", + "actualvalue_1": "18.0", + "auxvariables_0": "7.5", + "checks": [ + { + "name": "north_lat_diff", + "actual": "7.499999999999989", + "op": ">=", + "th": "7.5", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Qatar", + "Antigua and Barbuda" + ], + "duration": 90, + "auxvariables": [ + "7.5" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "69067:69167:1" + }, + "rng_seed": null, + "justification": { + "text": "In 90 hours after the end of the given time window, the northward latitude difference between the maximum v_component_of_wind at 1000 hPa in Qatar (25.49999999999999) and Antigua and Barbuda (18.0) is at least 7.5 degrees if and only if the statement is False." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "fa070e986568d686" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69051:69067:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73658:73675:1'} The data starts from June 01 12:00 and ends on June 05 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 90 hours after the end of the given time window, does the area within Vincennes Bay where temperature at 925 hPa exceeds 260.9200134277344K cover more than 66.951 percent of Vincennes Bay?", + "response": "In 90 hours after the end of the given time window, the area within Vincennes Bay where temperature at 925 hPa exceeds 260.9200134277344K covers 0.0 percent of Vincennes Bay, compared to the threshold 66.951 percent; the statement is thus False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0", + "actualvalue_1": "66.951", + "auxvariables_0": "260.9200134277344", + "auxvariables_1": "66.951", + "checks": [ + { + "name": "gating_exceeds", + "actual": "0.0", + "op": ">", + "th": "0.0", + "ok": false + }, + { + "name": "primary_percent", + "actual": "0.0", + "op": ">", + "th": "66.951", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area within {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} covers {{actualvalue_0}} percent of {{regions_0}}, compared to the threshold {{auxvariables_1}} percent; the statement is thus {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_023.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_023.py", + "template_id": "tmpl_023", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds {auxvariables_0}{units_0} cover more than {auxvariables_1} percent of {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Vincennes Bay" + ], + "units": [ + "K" + ], + "duration": 90, + "auxvariables": [ + "260.9200134277344", + "66.951" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "73675:73775:1" + }, + "rng_seed": null, + "justification": { + "text": "In 90 hours after the end of the given time window, the area within Vincennes Bay where temperature at 925 hPa exceeds 260.9200134277344K covers 0.0 percent of Vincennes Bay, compared to the threshold 66.951 percent; the statement is thus False." + }, + "question_id": "B7IciW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "87db272baff7627f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73658:73675:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 168 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92127:92155:1'} The data starts from January 21 18:00 and ends on January 28 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 138 hours after the end of the given time window, does the maximum value of specific_humidity at 850 hPa within Cook Inlet occur at a latitude that is at least 21.0 degrees farther north than its maximum within Bo Hai?", + "response": "In 138 hours after the end of the given time window, the northward latitude difference between the maximum specific_humidity at 850 hPa in Cook Inlet (58.5) and Bo Hai (37.499999999999986) is at least 21.0 degrees if and only if the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "58.5", + "actualvalue_1": "37.499999999999986", + "auxvariables_0": "21.0", + "checks": [ + { + "name": "north_lat_diff", + "actual": "21.000000000000014", + "op": ">=", + "th": "21.0", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Cook Inlet", + "Bo Hai" + ], + "duration": 138, + "auxvariables": [ + "21.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "92155:92255:1" + }, + "rng_seed": null, + "justification": { + "text": "In 138 hours after the end of the given time window, the northward latitude difference between the maximum specific_humidity at 850 hPa in Cook Inlet (58.5) and Bo Hai (37.499999999999986) is at least 21.0 degrees if and only if the statement is True." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2bfeeb6592b07a08" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92127:92155:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74329:74340:1'} The data starts from November 16 06:00 and ends on November 18 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 102 hours after the end of the given time window, does the minimum value of v_component_of_wind at 250 hPa within Bight of Biafra remain above 15.12m/s throughout Bight of Biafra?", + "response": "In Bight of Biafra, the minimum value of v_component_of_wind at 250 hPa over the 102 hours window is 13.040143966674805m/s, which is compared to the threshold 15.12m/s; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "13.040143966674805", + "auxvariables_0": "15.12", + "checks": [ + { + "name": "min_above_threshold", + "actual": "13.040143966674805", + "op": ">=", + "th": "15.12", + "ok": false + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Bight of Biafra" + ], + "units": [ + "m/s" + ], + "duration": 102, + "auxvariables_0_provenance": [ + "var=v_component_of_wind, tail=P10 over window [0,102]" + ], + "auxvariables": [ + "15.12" + ], + "auxvariables_0": "15.12" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "74340:74440:1" + }, + "rng_seed": null, + "justification": { + "text": "In Bight of Biafra, the minimum value of v_component_of_wind at 250 hPa over the 102 hours window is 13.040143966674805m/s, which is compared to the threshold 15.12m/s; this makes the statement False." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d48884b58b80fceb" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74329:74340:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79426:79452:1'} The data starts from May 13 12:00 and ends on May 19 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 36 hours after the end of the given time window, does the maximum value of geopotential at 400 hPa within Solomon Islands occur at a latitude that is at least -18.2 degrees farther north than its maximum within Mauritius?", + "response": "In 36 hours after the end of the given time window, the northward latitude difference between the maximum geopotential at 400 hPa in Solomon Islands (-12.000000000000004) and Mauritius (-19.500000000000007) is at least -18.2 degrees if and only if the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "-12.000000000000004", + "actualvalue_1": "-19.500000000000007", + "auxvariables_0": "-18.2", + "checks": [ + { + "name": "north_lat_diff", + "actual": "7.5000000000000036", + "op": ">=", + "th": "-18.2", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "Solomon Islands", + "Mauritius" + ], + "duration": 36, + "auxvariables": [ + "-18.2" + ], + "auxvariables_0": "-18.2" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "79452:79552:1" + }, + "rng_seed": null, + "justification": { + "text": "In 36 hours after the end of the given time window, the northward latitude difference between the maximum geopotential at 400 hPa in Solomon Islands (-12.000000000000004) and Mauritius (-19.500000000000007) is at least -18.2 degrees if and only if the statement is True." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "25590cd36cff8a99" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79426:79452:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67458:67471:1'} The data starts from March 04 12:00 and ends on March 07 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, does the maximum value of temperature at 500 hPa within Peacock Sound exceed 252.0K, while the maximum value of temperature at 500 hPa within Darnley Bay remains below 237.0K?", + "response": "In 30 hours after the end of the given time window, the maximum value of temperature at 500 hPa within Peacock Sound is 251.7074432373047K, relative to the threshold 252.0K; while within Darnley Bay the maximum is 238.55801391601562K, relative to the threshold 237.0K. Combined with the gating condition, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "251.7074432373047", + "actualvalue_1": "238.55801391601562", + "auxvariables_0": "252.0", + "auxvariables_1": "237.0", + "checks": [ + { + "name": "cond0", + "actual": "251.7074432373047", + "op": ">", + "th": "252.0", + "ok": false + }, + { + "name": "cond1", + "actual": "238.55801391601562", + "op": "<", + "th": "237.0", + "ok": false + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Peacock Sound", + "Darnley Bay" + ], + "units": [ + "K" + ], + "duration": 30, + "auxvariables": [ + "252.0", + "237.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "67471:67571:1" + }, + "rng_seed": null, + "justification": { + "text": "In 30 hours after the end of the given time window, the maximum value of temperature at 500 hPa within Peacock Sound is 251.7074432373047K, relative to the threshold 252.0K; while within Darnley Bay the maximum is 238.55801391601562K, relative to the threshold 237.0K. Combined with the gating condition, this makes the statement False." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2f70761456cc1b70" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67458:67471:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65986:66006:1'} The data starts from March 01 12:00 and ends on March 06 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 48 hours after the end of the given time window, does the maximum value of u_component_of_wind at 150 hPa within Bahrain exceed the maximum value of u_component_of_wind at 150 hPa within United Republic of Tanzania by more than 0.789m/s?", + "response": "In 48 hours after the end of the given time window, the maximum value of u_component_of_wind at 150 hPa within Bahrain exceeds the maximum value within United Republic of Tanzania by 7.311707019805908 - -12.841047286987305, compared to the threshold 0.789m/s; therefore, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "-12.841047286987305", + "actualvalue_1": "7.311707019805908", + "auxvariables_0": "0.789", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "20.152754306793213", + "op": ">", + "th": "0.789", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "United Republic of Tanzania", + "Bahrain" + ], + "units": [ + "m/s" + ], + "duration": 48, + "auxvariables": [ + "0.789" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "66006:66106:1" + }, + "rng_seed": null, + "justification": { + "text": "In 48 hours after the end of the given time window, the maximum value of u_component_of_wind at 150 hPa within Bahrain exceeds the maximum value within United Republic of Tanzania by 7.311707019805908 - -12.841047286987305, compared to the threshold 0.789m/s; therefore, the statement is True." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2109d4a7b3557c87" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65986:66006:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37613:37640:1'} The data starts from September 29 06:00 and ends on October 05 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 36 hours after the end of the given time window, does the minimum value of temperature at 150 hPa within Ronne Entrance remain above 192.49200439453125K throughout Ronne Entrance?", + "response": "In Ronne Entrance, the minimum value of temperature at 150 hPa over the 36 hours window is 197.39553833007812K, which is compared to the threshold 192.49200439453125K; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "197.39553833007812", + "auxvariables_0": "192.49200439453125", + "checks": [ + { + "name": "min_above_threshold", + "actual": "197.39553833007812", + "op": ">=", + "th": "192.49200439453125", + "ok": true + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Ronne Entrance" + ], + "units": [ + "K" + ], + "duration": 36, + "auxvariables_0_provenance": [ + "var=temperature, tail=P10 over window [0,36]" + ], + "auxvariables": [ + "192.49200439453125" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "37640:37740:1" + }, + "rng_seed": null, + "justification": { + "text": "In Ronne Entrance, the minimum value of temperature at 150 hPa over the 36 hours window is 197.39553833007812K, which is compared to the threshold 192.49200439453125K; this makes the statement True." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e01db4603602adfb" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37613:37640:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88080:88105:1'} The data starts from April 16 00:00 and ends on April 22 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 156 hours after the end of the given time window, does the maximum value of v_component_of_wind at 250 hPa within Baa, Maldives occur at a latitude that is at least 0.01 degrees farther north than its maximum within Luxembourg, Belgium?", + "response": "In 156 hours after the end of the given time window, the northward latitude difference between the maximum v_component_of_wind at 250 hPa in Baa, Maldives (4.5) and Luxembourg, Belgium (50.999999999999986) is at least 0.01 degrees if and only if the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "4.5", + "actualvalue_1": "50.999999999999986", + "auxvariables_0": "0.01", + "checks": [ + { + "name": "north_lat_diff", + "actual": "-46.499999999999986", + "op": ">=", + "th": "0.01", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Baa, Maldives", + "Luxembourg, Belgium" + ], + "duration": 156, + "auxvariables": [ + "0.01" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "88105:88205:1" + }, + "rng_seed": null, + "justification": { + "text": "In 156 hours after the end of the given time window, the northward latitude difference between the maximum v_component_of_wind at 250 hPa in Baa, Maldives (4.5) and Luxembourg, Belgium (50.999999999999986) is at least 0.01 degrees if and only if the statement is False." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "628043e5949ae168" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88080:88105:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30820:30825:1'} The data starts from February 05 00:00 and ends on February 06 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 138 hours after the end of the given time window, does the maximum value of specific_humidity at 100 hPa within South America exceed 3.000000106112566e-06kg/kg, while the maximum value of specific_humidity at 100 hPa within Europe remains below 3.000000106112566e-06kg/kg?", + "response": "In 138 hours after the end of the given time window, the maximum value of specific_humidity at 100 hPa within South America is 3.571631850718404e-06kg/kg, relative to the threshold 3.000000106112566e-06kg/kg; while within Europe the maximum is 2.851384351743036e-06kg/kg, relative to the threshold 3.000000106112566e-06kg/kg. Combined with the gating condition, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "3.571631850718404e-06", + "actualvalue_1": "2.851384351743036e-06", + "auxvariables_0": "3.000000106112566e-06", + "auxvariables_1": "3.000000106112566e-06", + "checks": [ + { + "name": "cond0", + "actual": "3.571631850718404e-06", + "op": ">", + "th": "3.000000106112566e-06", + "ok": true + }, + { + "name": "cond1", + "actual": "2.851384351743036e-06", + "op": "<", + "th": "3.000000106112566e-06", + "ok": true + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "South America", + "Europe" + ], + "units": [ + "kg/kg" + ], + "duration": 138, + "auxvariables": [ + "3.000000106112566e-06", + "3.000000106112566e-06" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "30825:30925:1" + }, + "rng_seed": null, + "justification": { + "text": "In 138 hours after the end of the given time window, the maximum value of specific_humidity at 100 hPa within South America is 3.571631850718404e-06kg/kg, relative to the threshold 3.000000106112566e-06kg/kg; while within Europe the maximum is 2.851384351743036e-06kg/kg, relative to the threshold 3.000000106112566e-06kg/kg. Combined with the gating condition, this makes the statement True." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0e0f10694eaef58e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30820:30825:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90978:91005:1'} The data starts from April 09 12:00 and ends on April 16 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 198 hours after the end of the given time window, does the maximum value of geopotential at 400 hPa within Gulf of Kutch exceed the maximum value of geopotential at 400 hPa within Husky Lakes by more than 309.8999938964844m\u00b2/s\u00b2?", + "response": "In 198 hours after the end of the given time window, the maximum value of geopotential at 400 hPa within Gulf of Kutch exceeds the maximum value within Husky Lakes by 73943.6640625 - 69030.7890625, compared to the threshold 309.8999938964844m\u00b2/s\u00b2; therefore, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "69030.7890625", + "actualvalue_1": "73943.6640625", + "auxvariables_0": "309.8999938964844", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "4912.875", + "op": ">", + "th": "309.8999938964844", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "Husky Lakes", + "Gulf of Kutch" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 198, + "auxvariables": [ + "309.8999938964844" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "91005:91105:1" + }, + "rng_seed": null, + "justification": { + "text": "In 198 hours after the end of the given time window, the maximum value of geopotential at 400 hPa within Gulf of Kutch exceeds the maximum value within Husky Lakes by 73943.6640625 - 69030.7890625, compared to the threshold 309.8999938964844m\u00b2/s\u00b2; therefore, the statement is True." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6b1971e4734e8535" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90978:91005:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92541:92564:1'} The data starts from May 05 06:00 and ends on May 10 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 138 hours after the end of the given time window, does the maximum value of geopotential at 1000 hPa within Australia exceed 2290.0m\u00b2/s\u00b2, while the maximum value of geopotential at 1000 hPa within South Georgia and the Islands remains below 1050.0m\u00b2/s\u00b2?", + "response": "In 138 hours after the end of the given time window, the maximum value of geopotential at 1000 hPa within Australia is 1871.3978271484375m\u00b2/s\u00b2, relative to the threshold 2290.0m\u00b2/s\u00b2; while within South Georgia and the Islands the maximum is 1855.25m\u00b2/s\u00b2, relative to the threshold 1050.0m\u00b2/s\u00b2. Combined with the gating condition, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "1871.3978271484375", + "actualvalue_1": "1855.25", + "auxvariables_0": "2290.0", + "auxvariables_1": "1050.0", + "checks": [ + { + "name": "cond0", + "actual": "1871.3978271484375", + "op": ">", + "th": "2290.0", + "ok": false + }, + { + "name": "cond1", + "actual": "1855.25", + "op": "<", + "th": "1050.0", + "ok": false + } + ], + "justification": "In {duration} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, relative to the threshold {{auxvariables_0}}{{units_0}}; while within {{regions_1}} the maximum is {{actualvalue_1}}{{units_0}}, relative to the threshold {{auxvariables_1}}{{units_0}}. Combined with the gating condition, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_004.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_004.py", + "template_id": "tmpl_004", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} remains below {auxvariables_1}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Australia", + "South Georgia and the Islands" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 138, + "auxvariables": [ + "2290.0", + "1050.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "92564:92664:1" + }, + "rng_seed": null, + "justification": { + "text": "In 138 hours after the end of the given time window, the maximum value of geopotential at 1000 hPa within Australia is 1871.3978271484375m\u00b2/s\u00b2, relative to the threshold 2290.0m\u00b2/s\u00b2; while within South Georgia and the Islands the maximum is 1855.25m\u00b2/s\u00b2, relative to the threshold 1050.0m\u00b2/s\u00b2. Combined with the gating condition, this makes the statement False." + }, + "question_id": "cSJHXI", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2b13825fbd267f1c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92541:92564:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48043:48067:1'} The data starts from November 19 18:00 and ends on November 25 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 138 hours after the end of the given time window, does the maximum value of specific_humidity at 1000 hPa within Africa exceed the maximum value of specific_humidity at 1000 hPa within South America by more than 0.00019999999494757503kg/kg?", + "response": "In 138 hours after the end of the given time window, the maximum value of specific_humidity at 1000 hPa within Africa exceeds the maximum value within South America by 0.017765868455171585 - 0.019769344478845596, compared to the threshold 0.00019999999494757503kg/kg; therefore, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.019769344478845596", + "actualvalue_1": "0.017765868455171585", + "auxvariables_0": "0.00019999999494757503", + "checks": [ + { + "name": "region_diff_exceeds_threshold", + "actual": "-0.0020034760236740112", + "op": ">", + "th": "0.00019999999494757503", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_1}} exceeds the maximum value within {{regions_0}} by {{actualvalue_1}} - {{actualvalue_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; therefore, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} exceed the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} by more than {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "South America", + "Africa" + ], + "units": [ + "kg/kg" + ], + "duration": 138, + "auxvariables": [ + "0.00019999999494757503" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "48067:48167:1" + }, + "rng_seed": null, + "justification": { + "text": "In 138 hours after the end of the given time window, the maximum value of specific_humidity at 1000 hPa within Africa exceeds the maximum value within South America by 0.017765868455171585 - 0.019769344478845596, compared to the threshold 0.00019999999494757503kg/kg; therefore, the statement is False." + }, + "question_id": "c0Asz3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "eb6fb47c59ff9e5e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48043:48067:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45778:45786:1'} The data starts from May 02 12:00 and ends on May 04 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 204 hours after the end of the given time window, does the area within Southern Patagonian Ice Field where geopotential at 1000 hPa exceeds 1258.7099609375m\u00b2/s\u00b2 cover more than 66.951 percent of Southern Patagonian Ice Field?", + "response": "In 204 hours after the end of the given time window, the area within Southern Patagonian Ice Field where geopotential at 1000 hPa exceeds 1258.7099609375m\u00b2/s\u00b2 covers 0.0 percent of Southern Patagonian Ice Field, compared to the threshold 66.951 percent; the statement is thus False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0", + "actualvalue_1": "66.951", + "auxvariables_0": "1258.7099609375", + "auxvariables_1": "66.951", + "checks": [ + { + "name": "gating_exceeds", + "actual": "0.0", + "op": ">", + "th": "0.0", + "ok": false + }, + { + "name": "primary_percent", + "actual": "0.0", + "op": ">", + "th": "66.951", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the area within {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} covers {{actualvalue_0}} percent of {{regions_0}}, compared to the threshold {{auxvariables_1}} percent; the statement is thus {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_023.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_023.py", + "template_id": "tmpl_023", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the area within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds {auxvariables_0}{units_0} cover more than {auxvariables_1} percent of {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0, + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Southern Patagonian Ice Field" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "duration": 204, + "auxvariables": [ + "1258.7099609375", + "66.951" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "45786:45886:1" + }, + "rng_seed": null, + "justification": { + "text": "In 204 hours after the end of the given time window, the area within Southern Patagonian Ice Field where geopotential at 1000 hPa exceeds 1258.7099609375m\u00b2/s\u00b2 covers 0.0 percent of Southern Patagonian Ice Field, compared to the threshold 66.951 percent; the statement is thus False." + }, + "question_id": "B7IciW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6124b24fdfc8acb4" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45778:45786:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76922:76924:1'} The data starts from August 26 12:00 and ends on August 26 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 138 hours after the end of the given time window, does the maximum value of specific_humidity at 700 hPa within Amazon River occur at a latitude that is at least 67.48 degrees farther north than its maximum within Marguerite Bay?", + "response": "In 138 hours after the end of the given time window, the northward latitude difference between the maximum specific_humidity at 700 hPa in Amazon River (-1.5000000000000084) and Marguerite Bay (-67.5) is at least 67.48 degrees if and only if the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-1.5000000000000084", + "actualvalue_1": "-67.5", + "auxvariables_0": "67.48", + "checks": [ + { + "name": "north_lat_diff", + "actual": "65.99999999999999", + "op": ">=", + "th": "67.48", + "ok": false + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the northward latitude difference between the maximum {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} ({{actualvalue_0}}) and {{regions_1}} ({{actualvalue_1}}) is at least {{auxvariables_0}} degrees if and only if the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_025.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_025.py", + "template_id": "tmpl_025", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude that is at least {{auxvariables_0}} degrees farther north than its maximum within {regions_1}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Amazon River", + "Marguerite Bay" + ], + "duration": 138, + "auxvariables": [ + "67.48" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "76924:77024:1" + }, + "rng_seed": null, + "justification": { + "text": "In 138 hours after the end of the given time window, the northward latitude difference between the maximum specific_humidity at 700 hPa in Amazon River (-1.5000000000000084) and Marguerite Bay (-67.5) is at least 67.48 degrees if and only if the statement is False." + }, + "question_id": "ZdH3mW", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "575b044b8db5e06b" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76922:76924:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29677:29691:1'} The data starts from April 25 06:00 and ends on April 28 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, does the maximum value of v_component_of_wind at 850 hPa within North America exceed 2.59m/s?", + "response": "In 30 hours after the end of the given time window, the maximum value of v_component_of_wind at 850 hPa within North America is 26.34554672241211m/s, compared to the threshold 2.59m/s; this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "26.34554672241211", + "auxvariables_0": "2.59", + "checks": [ + { + "name": "region_max_exceeds_threshold", + "actual": "26.34554672241211", + "op": ">", + "th": "2.59", + "ok": true + } + ], + "justification": "In {{duration}} hours after the end of the given time window, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "North America" + ], + "units": [ + "m/s" + ], + "duration": 30, + "auxvariables": [ + "2.59" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "29691:29791:1" + }, + "rng_seed": null, + "justification": { + "text": "In 30 hours after the end of the given time window, the maximum value of v_component_of_wind at 850 hPa within North America is 26.34554672241211m/s, compared to the threshold 2.59m/s; this makes the statement True." + }, + "question_id": "Q0avwV", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1eefe03a0012e499" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29677:29691:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73094:73097:1'} The data starts from January 11 12:00 and ends on January 12 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 120 hours after the end of the given time window, does the minimum value of specific_humidity at 600 hPa within Persian Gulf remain above 0.5kg/kg throughout Persian Gulf?", + "response": "In Persian Gulf, the minimum value of specific_humidity at 600 hPa over the 120 hours window is 0.0001375584542984143kg/kg, which is compared to the threshold 0.5kg/kg; this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0001375584542984143", + "auxvariables_0": "0.5", + "checks": [ + { + "name": "min_above_threshold", + "actual": "0.0001375584542984143", + "op": ">=", + "th": "0.5", + "ok": false + } + ], + "justification": "In {{regions_0}}, the minimum value of {{wb2varnames_0}}{{levelsuffixes_0}} over the {{duration}} hour window is {{actualvalue_0}}{{units_0}}, which is compared to the threshold {{auxvariables_0}}{{units_0}}; this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/non_snapshot/tmpl_022.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/non_snapshot/sampling_tmpl_022.py", + "template_id": "tmpl_022", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, does the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain above {{auxvariables_0}}{units_0} throughout {regions_0}?", + "template_type": "timeseries", + "mode": "boolean", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Persian Gulf" + ], + "units": [ + "kg/kg" + ], + "duration": 120, + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P10 over window [0,120]" + ], + "auxvariables": [ + "0.5" + ], + "auxvariables_0": "0.5" + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "73097:73197:1" + }, + "rng_seed": null, + "justification": { + "text": "In Persian Gulf, the minimum value of specific_humidity at 600 hPa over the 120 hours window is 0.0001375584542984143kg/kg, which is compared to the threshold 0.5kg/kg; this makes the statement False." + }, + "question_id": "WucO3b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "dfc6a3d393b0ce9b" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73094:73097:1" + } + } +] \ No newline at end of file diff --git a/level2b_boolean_part1.json b/level2b_boolean_part1.json new file mode 100644 index 0000000000000000000000000000000000000000..7b69ea669c4dc26575e762a7a4d5ce8bef3492dd --- /dev/null +++ b/level2b_boolean_part1.json @@ -0,0 +1,7147 @@ +[ + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33339:33340:1'} The data corresponds to corresponds to a snapshot on October 26 18:00. Based on the above data, answer the following question:", + "question": "At 114 hours into the future, does the maximum temperature at 200 hPa within Queen Charlotte Strait occur at a latitude greater than the maximum temperature at 200 hPa within Orense, Spain?", + "response": "At 114 hours, the maximum of temperature at 200 hPa in Queen Charlotte Strait occurs at latitude 50.999999999999986, which is True greater than the latitude 41.999999999999986 of the maximum in Orense, Spain.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "50.999999999999986", + "actualvalue_1": "41.999999999999986", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "50.999999999999986", + "op": ">", + "th": "41.999999999999986", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Queen Charlotte Strait", + "Orense, Spain" + ], + "times": [ + 114 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "33340:33440:1" + }, + "rng_seed": null, + "justification": { + "text": "At 114 hours, the maximum of temperature at 200 hPa in Queen Charlotte Strait occurs at latitude 50.999999999999986, which is True greater than the latitude 41.999999999999986 of the maximum in Orense, Spain." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "11da5fac947897e3" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33339:33340:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62222:62223:1'} The data corresponds to corresponds to a snapshot on August 03 12:00. Based on the above data, answer the following question:", + "question": "At 216 hours into the future, does the mean value of geopotential at 850 hPa within Asia exceed the mean value within Ankara, Turkey by at least 116.08m\u00b2/s\u00b2?", + "response": "At 216 hours into the future, the mean value of geopotential at 850 hPa within Asia (14485.109375) compared to Ankara, Turkey (14288.521484375) differs by 116.08m\u00b2/s\u00b2 or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "14485.109375", + "actualvalue_1": "14288.521484375", + "auxvariables_0": "116.08", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "196.587890625", + "op": ">=", + "th": "116.08", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Asia", + "Ankara, Turkey" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "times": [ + 216 + ], + "auxvariables": [ + "116.08" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "62223:62323:1" + }, + "rng_seed": null, + "justification": { + "text": "At 216 hours into the future, the mean value of geopotential at 850 hPa within Asia (14485.109375) compared to Ankara, Turkey (14288.521484375) differs by 116.08m\u00b2/s\u00b2 or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ab0114521ff349b8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62222:62223:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67360:67361:1'} The data corresponds to corresponds to a snapshot on February 08 00:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 500 hPa within South America exceed 15.094m/s?", + "response": "In South America, the mean of u_component_of_wind at 500 hPa at 66 hours is 2.075031042098999m/s, compared to the threshold 15.094m/s; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "2.075031042098999", + "auxvariables_0": "15.094", + "checks": [ + { + "name": "mean-exceed", + "actual": "2.075031042098999", + "op": ">", + "th": "15.094", + "ok": false + } + ], + "justification": "In {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} at {{times_0}} hours is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {{auxvariables_0}}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "South America" + ], + "units": [ + "m/s" + ], + "times": [ + 66 + ], + "auxvariables": [ + "15.094" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "67361:67461:1" + }, + "rng_seed": null, + "justification": { + "text": "In South America, the mean of u_component_of_wind at 500 hPa at 66 hours is 2.075031042098999m/s, compared to the threshold 15.094m/s; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "UMSBBr", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b1654bb2634bd098" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67360:67361:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60880:60881:1'} The data corresponds to corresponds to a snapshot on September 02 00:00. Based on the above data, answer the following question:", + "question": "At 78 hours into the future, does geopotential at 1000 hPa exceed 1555.3599853515625 within any part of North America?", + "response": "In North America, geopotential at 1000 hPa is 2589.705322265625 relative to the threshold 1555.3599853515625; combined with any other stated conditions, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "2589.705322265625", + "auxvariables_0": "1555.3599853515625", + "checks": [ + { + "name": "cond0", + "actual": "2589.705322265625", + "op": ">", + "th": "1555.3599853515625", + "ok": true + } + ], + "justification": "In {{regions_0}}, {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}} relative to the threshold {{auxvariables_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0} within any part of {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "North America" + ], + "times": [ + 78 + ], + "auxvariables": [ + "1555.3599853515625" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "60881:60981:1" + }, + "rng_seed": null, + "justification": { + "text": "In North America, geopotential at 1000 hPa is 2589.705322265625 relative to the threshold 1555.3599853515625; combined with any other stated conditions, this makes the statement True." + }, + "question_id": "AxHIS3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1ba748f33ed771fe" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60880:60881:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29704:29705:1'} The data corresponds to corresponds to a snapshot on May 02 00:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of u_component_of_wind at 500 hPa within Europe occur within the same grid point as, or adjacent to, the maximum value of u_component_of_wind at 150 hPa within Europe?", + "response": "At 6 hours lead, in Europe, the maximum value of u_component_of_wind at 500 hPa (39.46725082397461) occurs at the same or adjacent grid point as the maximum value of u_component_of_wind at 150 hPa (36.60725402832031); thus, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "39.46725082397461", + "actualvalue_1": "36.60725402832031", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": false + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 500, + 150 + ], + "regions": [ + "Europe" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "29705:29805:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Europe, the maximum value of u_component_of_wind at 500 hPa (39.46725082397461) occurs at the same or adjacent grid point as the maximum value of u_component_of_wind at 150 hPa (36.60725402832031); thus, the statement is False." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3d864cbfae422c90" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29704:29705:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81076:81077:1'} The data corresponds to corresponds to a snapshot on June 30 00:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of geopotential at 600 hPa within Muta, Slovenia occur within the same grid point as, or adjacent to, the maximum value of geopotential at 925 hPa within Muta, Slovenia?", + "response": "At 6 hours lead, in Muta, Slovenia, the maximum value of geopotential at 600 hPa (41514.66015625) occurs at the same or adjacent grid point as the maximum value of geopotential at 925 hPa (7277.25830078125); thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "41514.66015625", + "actualvalue_1": "7277.25830078125", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": true + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 600, + 925 + ], + "regions": [ + "Muta, Slovenia" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "81077:81177:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Muta, Slovenia, the maximum value of geopotential at 600 hPa (41514.66015625) occurs at the same or adjacent grid point as the maximum value of geopotential at 925 hPa (7277.25830078125); thus, the statement is True." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f7086227e3d7542a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81076:81077:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39775:39776:1'} The data corresponds to corresponds to a snapshot on March 23 18:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 850 hPa within Mali exceed -1.18123197555542m/s, while the maximum of u_component_of_wind at 700 hPa within Mali remains below -0.5216574668884277m/s?", + "response": "At 66 hours in Mali, the mean of u_component_of_wind at 850 hPa is -3.636728286743164m/s compared to the threshold -1.18123197555542m/s, and the maximum of u_component_of_wind at 700 hPa is -0.08520487695932388m/s compared to -0.5216574668884277m/s; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-3.636728286743164", + "actualvalue_1": "-0.08520487695932388", + "auxvariables_0": "-1.18123197555542", + "auxvariables_1": "-0.5216574668884277", + "checks": [ + { + "name": "cond0", + "actual": "-3.636728286743164", + "op": ">", + "th": "-1.18123197555542", + "ok": false + }, + { + "name": "cond1", + "actual": "-0.08520487695932388", + "op": "<", + "th": "-0.5216574668884277", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 850, + 700 + ], + "regions": [ + "Mali" + ], + "units": [ + "m/s", + "m/s" + ], + "auxvariables_0_provenance": [ + "var=u_component_of_wind, fallback median (N=2)" + ], + "auxvariables_1_provenance": [ + "var=u_component_of_wind, fallback median (N=2)" + ], + "times": [ + 66 + ], + "auxvariables": [ + "-1.18123197555542", + "-0.5216574668884277" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "39776:39876:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Mali, the mean of u_component_of_wind at 850 hPa is -3.636728286743164m/s compared to the threshold -1.18123197555542m/s, and the maximum of u_component_of_wind at 700 hPa is -0.08520487695932388m/s compared to -0.5216574668884277m/s; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c2e51637e8dddc3e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39775:39776:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32709:32710:1'} The data corresponds to corresponds to a snapshot on May 22 06:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of geopotential at 300 hPa within Sweden exceed 90528.68m\u00b2/s\u00b2, while the maximum of temperature at 50 hPa within Sweden remains below 220.073K?", + "response": "At 66 hours in Sweden, the mean of geopotential at 300 hPa is 89968.703125m\u00b2/s\u00b2 compared to the threshold 90528.68m\u00b2/s\u00b2, and the maximum of temperature at 50 hPa is 223.06044006347656K compared to 220.073K; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "89968.703125", + "actualvalue_1": "223.06044006347656", + "auxvariables_0": "90528.68", + "auxvariables_1": "220.073", + "checks": [ + { + "name": "cond0", + "actual": "89968.703125", + "op": ">", + "th": "90528.68", + "ok": false + }, + { + "name": "cond1", + "actual": "223.06044006347656", + "op": "<", + "th": "220.073", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "temperature" + ], + "levelsuffixes": [ + 300, + 50 + ], + "regions": [ + "Sweden" + ], + "units": [ + "m\u00b2/s\u00b2", + "K" + ], + "auxvariables_0_provenance": [ + "var=geopotential, tail=P94 over region (58 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=temperature, tail=P15 over region (58 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "90528.68", + "220.073" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "temperature" + ], + "time_range": "32710:32810:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Sweden, the mean of geopotential at 300 hPa is 89968.703125m\u00b2/s\u00b2 compared to the threshold 90528.68m\u00b2/s\u00b2, and the maximum of temperature at 50 hPa is 223.06044006347656K compared to 220.073K; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "64d98649452060ff" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32709:32710:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36669:36670:1'} The data corresponds to corresponds to a snapshot on February 06 06:00. Based on the above data, answer the following question:", + "question": "At 78 hours into the future, does the mean value of specific_humidity at 1000 hPa within Cyprus No Mans Area exceed the mean value within Cabo Verde by at least 0.000807kg/kg?", + "response": "At 78 hours into the future, the mean value of specific_humidity at 1000 hPa within Cyprus No Mans Area (0.007661775220185518) compared to Cabo Verde (0.010281870141625404) differs by 0.000807kg/kg or more: this is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.007661775220185518", + "actualvalue_1": "0.010281870141625404", + "auxvariables_0": "0.000807", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "-0.002620094921439886", + "op": ">=", + "th": "0.000807", + "ok": false + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Cyprus No Mans Area", + "Cabo Verde" + ], + "units": [ + "kg/kg" + ], + "times": [ + 78 + ], + "auxvariables": [ + "0.000807" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "36670:36770:1" + }, + "rng_seed": null, + "justification": { + "text": "At 78 hours into the future, the mean value of specific_humidity at 1000 hPa within Cyprus No Mans Area (0.007661775220185518) compared to Cabo Verde (0.010281870141625404) differs by 0.000807kg/kg or more: this is False." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "93b6dc10c0b4f6d9" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36669:36670:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54907:54908:1'} The data corresponds to corresponds to a snapshot on July 31 18:00. Based on the above data, answer the following question:", + "question": "At 126 hours into the future, does the maximum v_component_of_wind at 400 hPa within Indian Ocean Territories occur at a latitude greater than the maximum v_component_of_wind at 400 hPa within Myanmar?", + "response": "At 126 hours, the maximum of v_component_of_wind at 400 hPa in Indian Ocean Territories occurs at latitude -10.500000000000009, which is False greater than the latitude 24.0 of the maximum in Myanmar.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-10.500000000000009", + "actualvalue_1": "24.0", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "-10.500000000000009", + "op": ">", + "th": "24.0", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "Indian Ocean Territories", + "Myanmar" + ], + "times": [ + 126 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "54908:55008:1" + }, + "rng_seed": null, + "justification": { + "text": "At 126 hours, the maximum of v_component_of_wind at 400 hPa in Indian Ocean Territories occurs at latitude -10.500000000000009, which is False greater than the latitude 24.0 of the maximum in Myanmar." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f6e334287b75d989" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54907:54908:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42595:42596:1'} The data corresponds to corresponds to a snapshot on February 26 18:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of v_component_of_wind at 1000 hPa within James Bay exceed -2.489m/s?", + "response": "In James Bay, the mean of v_component_of_wind at 1000 hPa at 66 hours is -7.916131019592285m/s, compared to the threshold -2.489m/s; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-7.916131019592285", + "auxvariables_0": "-2.489", + "checks": [ + { + "name": "mean-exceed", + "actual": "-7.916131019592285", + "op": ">", + "th": "-2.489", + "ok": false + } + ], + "justification": "In {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} at {{times_0}} hours is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {{auxvariables_0}}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "James Bay" + ], + "units": [ + "m/s" + ], + "times": [ + 66 + ], + "auxvariables": [ + "-2.489" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "42596:42696:1" + }, + "rng_seed": null, + "justification": { + "text": "In James Bay, the mean of v_component_of_wind at 1000 hPa at 66 hours is -7.916131019592285m/s, compared to the threshold -2.489m/s; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "UMSBBr", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "61a45cabba1a51c1" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42595:42596:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63713:63714:1'} The data corresponds to corresponds to a snapshot on August 11 06:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of v_component_of_wind at 100 hPa within Anseba, Eritrea exceed -1.003657579421997m/s, while the maximum of u_component_of_wind at 200 hPa within Anseba, Eritrea remains below -21.12456703186035m/s?", + "response": "At 66 hours in Anseba, Eritrea, the mean of v_component_of_wind at 100 hPa is 3.26560115814209m/s compared to the threshold -1.003657579421997m/s, and the maximum of u_component_of_wind at 200 hPa is -22.338605880737305m/s compared to -21.12456703186035m/s; together, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "3.26560115814209", + "actualvalue_1": "-22.338605880737305", + "auxvariables_0": "-1.003657579421997", + "auxvariables_1": "-21.12456703186035", + "checks": [ + { + "name": "cond0", + "actual": "3.26560115814209", + "op": ">", + "th": "-1.003657579421997", + "ok": true + }, + { + "name": "cond1", + "actual": "-22.338605880737305", + "op": "<", + "th": "-21.12456703186035", + "ok": true + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 100, + 200 + ], + "regions": [ + "Anseba, Eritrea" + ], + "units": [ + "m/s", + "m/s" + ], + "auxvariables_0_provenance": [ + "var=v_component_of_wind, fallback median (N=2)" + ], + "auxvariables_1_provenance": [ + "var=u_component_of_wind, fallback median (N=2)" + ], + "times": [ + 66 + ], + "auxvariables": [ + "-1.003657579421997", + "-21.12456703186035" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "u_component_of_wind" + ], + "time_range": "63714:63814:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Anseba, Eritrea, the mean of v_component_of_wind at 100 hPa is 3.26560115814209m/s compared to the threshold -1.003657579421997m/s, and the maximum of u_component_of_wind at 200 hPa is -22.338605880737305m/s compared to -21.12456703186035m/s; together, this makes the statement True." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5b463f072fe54d7f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63713:63714:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70948:70949:1'} The data corresponds to corresponds to a snapshot on July 25 00:00. Based on the above data, answer the following question:", + "question": "At 42 hours into the future, does the maximum value of geopotential at 300 hPa within Indonesia remain lower than the maximum value of geopotential at 300 hPa within Indonesia at 234 hours into the future?", + "response": "At 42 hours, the maximum of geopotential at 300 hPa within Indonesia (94825.1953125) is compared to the maximum at 234 hours (94993.390625); the statement is True if the former is lower than the latter.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "94825.1953125", + "actualvalue_1": "94993.390625", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_t0_vs_max_t1", + "actual": "94825.1953125", + "op": "<", + "th": "94993.390625", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) is compared to the maximum at {{times_1}} hours ({{actualvalue_1}}); the statement is {{label}} if the former is lower than the latter." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_012.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_012.py", + "template_id": "tmpl_012", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain lower than the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} at {times_1} hours into the future?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Indonesia" + ], + "times": [ + 42, + 234 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "70949:71049:1" + }, + "rng_seed": null, + "justification": { + "text": "At 42 hours, the maximum of geopotential at 300 hPa within Indonesia (94825.1953125) is compared to the maximum at 234 hours (94993.390625); the statement is True if the former is lower than the latter." + }, + "question_id": "CslUZC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "538f917d3b769948" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70948:70949:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54123:54124:1'} The data corresponds to corresponds to a snapshot on January 17 18:00. Based on the above data, answer the following question:", + "question": "At 210 hours into the future, does geopotential at 500 hPa exceed 57410.3984375m\u00b2/s\u00b2 within more grid points in Oceania than in Gabon?", + "response": "At 210 hours, the number of grid points in Oceania where geopotential at 500 hPa exceeds 57410.3984375m\u00b2/s\u00b2 is 29.0, compared to 0.0 in Gabon; thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "29.0", + "actualvalue_1": "0.0", + "auxvariables_0": "57410.3984375", + "checks": [ + { + "name": "count_exceed_region0", + "actual": "29.0", + "op": ">", + "th": "0.0", + "ok": true + }, + { + "name": "threshold_region0", + "actual": "29.0", + "op": ">", + "th": "57410.3984375", + "ok": true + }, + { + "name": "threshold_region1", + "actual": "0.0", + "op": ">", + "th": "57410.3984375", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the number of grid points in {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} is {{actualvalue_0}}, compared to {{actualvalue_1}} in {{regions_1}}; thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0}{units_0} within more grid points in {regions_0} than in {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Oceania", + "Gabon" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "times": [ + 210 + ], + "auxvariables": [ + "57410.3984375" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "54124:54224:1" + }, + "rng_seed": null, + "justification": { + "text": "At 210 hours, the number of grid points in Oceania where geopotential at 500 hPa exceeds 57410.3984375m\u00b2/s\u00b2 is 29.0, compared to 0.0 in Gabon; thus, the statement is True." + }, + "question_id": "kI3y2R", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d337dea13711c9d8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54123:54124:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68811:68812:1'} The data corresponds to corresponds to a snapshot on February 05 18:00. Based on the above data, answer the following question:", + "question": "At 228 hours into the future, does the mean value of u_component_of_wind at 300 hPa within Noumbiel, Burkina Faso exceed the mean value within South America by at least 2.91m/s?", + "response": "At 228 hours into the future, the mean value of u_component_of_wind at 300 hPa within Noumbiel, Burkina Faso (8.082210540771484) compared to South America (6.622900009155273) differs by 2.91m/s or more: this is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "8.082210540771484", + "actualvalue_1": "6.622900009155273", + "auxvariables_0": "2.91", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "1.459310531616211", + "op": ">=", + "th": "2.91", + "ok": false + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Noumbiel, Burkina Faso", + "South America" + ], + "units": [ + "m/s" + ], + "times": [ + 228 + ], + "auxvariables": [ + "2.91" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "68812:68912:1" + }, + "rng_seed": null, + "justification": { + "text": "At 228 hours into the future, the mean value of u_component_of_wind at 300 hPa within Noumbiel, Burkina Faso (8.082210540771484) compared to South America (6.622900009155273) differs by 2.91m/s or more: this is False." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "824b880badd4625a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68811:68812:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61351:61352:1'} The data corresponds to corresponds to a snapshot on December 28 18:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 300 hPa within Ungava Bay exceed 16.638m/s, while the maximum of geopotential at 850 hPa within Ungava Bay remains below 13845.062m\u00b2/s\u00b2?", + "response": "At 66 hours in Ungava Bay, the mean of u_component_of_wind at 300 hPa is 10.480791091918945m/s compared to the threshold 16.638m/s, and the maximum of geopotential at 850 hPa is 14068.9052734375m\u00b2/s\u00b2 compared to 13845.062m\u00b2/s\u00b2; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "10.480791091918945", + "actualvalue_1": "14068.9052734375", + "auxvariables_0": "16.638", + "auxvariables_1": "13845.062", + "checks": [ + { + "name": "cond0", + "actual": "10.480791091918945", + "op": ">", + "th": "16.638", + "ok": false + }, + { + "name": "cond1", + "actual": "14068.9052734375", + "op": "<", + "th": "13845.062", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "geopotential" + ], + "levelsuffixes": [ + 300, + 850 + ], + "regions": [ + "Ungava Bay" + ], + "units": [ + "m/s", + "m\u00b2/s\u00b2" + ], + "auxvariables_0_provenance": [ + "var=u_component_of_wind, tail=P94 over region (10 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=geopotential, tail=P15 over region (10 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "16.638", + "13845.062" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "geopotential" + ], + "time_range": "61352:61452:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Ungava Bay, the mean of u_component_of_wind at 300 hPa is 10.480791091918945m/s compared to the threshold 16.638m/s, and the maximum of geopotential at 850 hPa is 14068.9052734375m\u00b2/s\u00b2 compared to 13845.062m\u00b2/s\u00b2; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f41def272e95d787" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61351:61352:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89304:89305:1'} The data corresponds to corresponds to a snapshot on February 16 00:00. Based on the above data, answer the following question:", + "question": "At 18 hours into the future, does the mean value of v_component_of_wind at 500 hPa within Jawa Barat, Indonesia exceed the mean value within Northern Cyprus by at least 0.58m/s?", + "response": "At 18 hours into the future, the mean value of v_component_of_wind at 500 hPa within Jawa Barat, Indonesia (2.0607573986053467) compared to Northern Cyprus (-4.437036037445068) differs by 0.58m/s or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "2.0607573986053467", + "actualvalue_1": "-4.437036037445068", + "auxvariables_0": "0.58", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "6.497793436050415", + "op": ">=", + "th": "0.58", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Jawa Barat, Indonesia", + "Northern Cyprus" + ], + "units": [ + "m/s" + ], + "times": [ + 18 + ], + "auxvariables": [ + "0.58" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "89305:89405:1" + }, + "rng_seed": null, + "justification": { + "text": "At 18 hours into the future, the mean value of v_component_of_wind at 500 hPa within Jawa Barat, Indonesia (2.0607573986053467) compared to Northern Cyprus (-4.437036037445068) differs by 0.58m/s or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ee15c2e55a24145d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89304:89305:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40976:40977:1'} The data corresponds to corresponds to a snapshot on January 18 00:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of specific_humidity at 600 hPa within Andorra la Vella, Andorra occur within the same grid point as, or adjacent to, the maximum value of u_component_of_wind at 150 hPa within Andorra la Vella, Andorra?", + "response": "At 6 hours lead, in Andorra la Vella, Andorra, the maximum value of specific_humidity at 600 hPa (0.00033925205934792757) occurs at the same or adjacent grid point as the maximum value of u_component_of_wind at 150 hPa (7.5309014320373535); thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.00033925205934792757", + "actualvalue_1": "7.5309014320373535", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": true + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "u_component_of_wind" + ], + "levelsuffixes": [ + 600, + 150 + ], + "regions": [ + "Andorra la Vella, Andorra" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "u_component_of_wind" + ], + "time_range": "40977:41077:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Andorra la Vella, Andorra, the maximum value of specific_humidity at 600 hPa (0.00033925205934792757) occurs at the same or adjacent grid point as the maximum value of u_component_of_wind at 150 hPa (7.5309014320373535); thus, the statement is True." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "68a43f44e91d3e95" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40976:40977:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86125:86126:1'} The data corresponds to corresponds to a snapshot on December 13 06:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of specific_humidity at 400 hPa within Europe exceed 0.000294kg/kg, while the maximum of u_component_of_wind at 300 hPa within Europe remains below -4.628m/s?", + "response": "At 66 hours in Europe, the mean of specific_humidity at 400 hPa is 0.000117859584861435kg/kg compared to the threshold 0.000294kg/kg, and the maximum of u_component_of_wind at 300 hPa is 59.270118713378906m/s compared to -4.628m/s; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.000117859584861435", + "actualvalue_1": "59.270118713378906", + "auxvariables_0": "0.000294", + "auxvariables_1": "-4.628", + "checks": [ + { + "name": "cond0", + "actual": "0.000117859584861435", + "op": ">", + "th": "0.000294", + "ok": false + }, + { + "name": "cond1", + "actual": "59.270118713378906", + "op": "<", + "th": "-4.628", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "u_component_of_wind" + ], + "levelsuffixes": [ + 400, + 300 + ], + "regions": [ + "Europe" + ], + "units": [ + "kg/kg", + "m/s" + ], + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P94 over region (2235 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=u_component_of_wind, tail=P15 over region (2235 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "0.000294", + "-4.628" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "u_component_of_wind" + ], + "time_range": "86126:86226:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Europe, the mean of specific_humidity at 400 hPa is 0.000117859584861435kg/kg compared to the threshold 0.000294kg/kg, and the maximum of u_component_of_wind at 300 hPa is 59.270118713378906m/s compared to -4.628m/s; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d421d3460d7aa1d4" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86125:86126:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60895:60896:1'} The data corresponds to corresponds to a snapshot on September 05 18:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of temperature at 300 hPa within Meneng, Nauru occur within the same grid point as, or adjacent to, the maximum value of u_component_of_wind at 250 hPa within Meneng, Nauru?", + "response": "At 6 hours lead, in Meneng, Nauru, the maximum value of temperature at 300 hPa (242.84873962402344) occurs at the same or adjacent grid point as the maximum value of u_component_of_wind at 250 hPa (-7.771847724914551); thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "242.84873962402344", + "actualvalue_1": "-7.771847724914551", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": true + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "u_component_of_wind" + ], + "levelsuffixes": [ + 300, + 250 + ], + "regions": [ + "Meneng, Nauru" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "u_component_of_wind" + ], + "time_range": "60896:60996:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Meneng, Nauru, the maximum value of temperature at 300 hPa (242.84873962402344) occurs at the same or adjacent grid point as the maximum value of u_component_of_wind at 250 hPa (-7.771847724914551); thus, the statement is True." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6a8f203ae8ffd5ce" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60895:60896:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50072:50073:1'} The data corresponds to corresponds to a snapshot on April 10 00:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 500 hPa within Asia exceed 23.119m/s, while the maximum of u_component_of_wind at 400 hPa within Asia remains below -2.278m/s?", + "response": "At 66 hours in Asia, the mean of u_component_of_wind at 500 hPa is 8.100480079650879m/s compared to the threshold 23.119m/s, and the maximum of u_component_of_wind at 400 hPa is 56.840858459472656m/s compared to -2.278m/s; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "8.100480079650879", + "actualvalue_1": "56.840858459472656", + "auxvariables_0": "23.119", + "auxvariables_1": "-2.278", + "checks": [ + { + "name": "cond0", + "actual": "8.100480079650879", + "op": ">", + "th": "23.119", + "ok": false + }, + { + "name": "cond1", + "actual": "56.840858459472656", + "op": "<", + "th": "-2.278", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 500, + 400 + ], + "regions": [ + "Asia" + ], + "units": [ + "m/s", + "m/s" + ], + "auxvariables_0_provenance": [ + "var=u_component_of_wind, tail=P94 over region (1831 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=u_component_of_wind, tail=P15 over region (1831 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "23.119", + "-2.278" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "50073:50173:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Asia, the mean of u_component_of_wind at 500 hPa is 8.100480079650879m/s compared to the threshold 23.119m/s, and the maximum of u_component_of_wind at 400 hPa is 56.840858459472656m/s compared to -2.278m/s; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ba773ad9a02048c4" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50072:50073:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80887:80888:1'} The data corresponds to corresponds to a snapshot on May 13 18:00. Based on the above data, answer the following question:", + "question": "At 54 hours into the future, does geopotential at 400 hPa exceed 74448.359375 within any part of South America?", + "response": "In South America, geopotential at 400 hPa is 74695.0234375 relative to the threshold 74448.359375; combined with any other stated conditions, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "74695.0234375", + "auxvariables_0": "74448.359375", + "checks": [ + { + "name": "cond0", + "actual": "74695.0234375", + "op": ">", + "th": "74448.359375", + "ok": true + } + ], + "justification": "In {{regions_0}}, {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}} relative to the threshold {{auxvariables_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0} within any part of {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "South America" + ], + "times": [ + 54 + ], + "auxvariables": [ + "74448.359375" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "80888:80988:1" + }, + "rng_seed": null, + "justification": { + "text": "In South America, geopotential at 400 hPa is 74695.0234375 relative to the threshold 74448.359375; combined with any other stated conditions, this makes the statement True." + }, + "question_id": "AxHIS3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3ce7d9be5e256343" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80887:80888:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71717:71718:1'} The data corresponds to corresponds to a snapshot on February 02 06:00. Based on the above data, answer the following question:", + "question": "At 126 hours into the future, does the mean value of v_component_of_wind at 700 hPa within Oceania exceed the mean value within Africa by at least 1.3m/s?", + "response": "At 126 hours into the future, the mean value of v_component_of_wind at 700 hPa within Oceania (0.182878777384758) compared to Africa (-1.1984221935272217) differs by 1.3m/s or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.182878777384758", + "actualvalue_1": "-1.1984221935272217", + "auxvariables_0": "1.3", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "1.3813009709119797", + "op": ">=", + "th": "1.3", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Oceania", + "Africa" + ], + "units": [ + "m/s" + ], + "times": [ + 126 + ], + "auxvariables": [ + "1.3" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "71718:71818:1" + }, + "rng_seed": null, + "justification": { + "text": "At 126 hours into the future, the mean value of v_component_of_wind at 700 hPa within Oceania (0.182878777384758) compared to Africa (-1.1984221935272217) differs by 1.3m/s or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "19323fac13dcff97" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71717:71718:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80402:80403:1'} The data corresponds to corresponds to a snapshot on January 12 12:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of geopotential at 500 hPa within Rojas, Latvia occur within the same grid point as, or adjacent to, the maximum value of v_component_of_wind at 250 hPa within Rojas, Latvia?", + "response": "At 6 hours lead, in Rojas, Latvia, the maximum value of geopotential at 500 hPa (50988.6328125) occurs at the same or adjacent grid point as the maximum value of v_component_of_wind at 250 hPa (-12.45788288116455); thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "50988.6328125", + "actualvalue_1": "-12.45788288116455", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": true + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "v_component_of_wind" + ], + "levelsuffixes": [ + 500, + 250 + ], + "regions": [ + "Rojas, Latvia" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "v_component_of_wind" + ], + "time_range": "80403:80503:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Rojas, Latvia, the maximum value of geopotential at 500 hPa (50988.6328125) occurs at the same or adjacent grid point as the maximum value of v_component_of_wind at 250 hPa (-12.45788288116455); thus, the statement is True." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f4a98f5e9202c642" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80402:80403:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36639:36640:1'} The data corresponds to corresponds to a snapshot on January 29 18:00. Based on the above data, answer the following question:", + "question": "At 90 hours into the future, does geopotential at 700 hPa exceed 30793.630859375m\u00b2/s\u00b2 within more grid points in Cascade, Seychelles than in Pamlico Sound?", + "response": "At 90 hours, the number of grid points in Cascade, Seychelles where geopotential at 700 hPa exceeds 30793.630859375m\u00b2/s\u00b2 is 0.0, compared to 0.0 in Pamlico Sound; thus, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0", + "actualvalue_1": "0.0", + "auxvariables_0": "30793.630859375", + "checks": [ + { + "name": "count_exceed_region0", + "actual": "0.0", + "op": ">", + "th": "0.0", + "ok": false + }, + { + "name": "threshold_region0", + "actual": "0.0", + "op": ">", + "th": "30793.630859375", + "ok": false + }, + { + "name": "threshold_region1", + "actual": "0.0", + "op": ">", + "th": "30793.630859375", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the number of grid points in {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} is {{actualvalue_0}}, compared to {{actualvalue_1}} in {{regions_1}}; thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0}{units_0} within more grid points in {regions_0} than in {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Cascade, Seychelles", + "Pamlico Sound" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "times": [ + 90 + ], + "auxvariables": [ + "30793.630859375" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "36640:36740:1" + }, + "rng_seed": null, + "justification": { + "text": "At 90 hours, the number of grid points in Cascade, Seychelles where geopotential at 700 hPa exceeds 30793.630859375m\u00b2/s\u00b2 is 0.0, compared to 0.0 in Pamlico Sound; thus, the statement is False." + }, + "question_id": "kI3y2R", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3ef2a3bbd3d361ae" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36639:36640:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91632:91633:1'} The data corresponds to corresponds to a snapshot on September 20 00:00. Based on the above data, answer the following question:", + "question": "At 132 hours into the future, does the mean value of specific_humidity at 150 hPa within Senegal exceed the mean value within Gibraltar by at least 0.0kg/kg?", + "response": "At 132 hours into the future, the mean value of specific_humidity at 150 hPa within Senegal (9.194816811941564e-06) compared to Gibraltar (6.1818063841201365e-06) differs by 0.0kg/kg or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "9.194816811941564e-06", + "actualvalue_1": "6.1818063841201365e-06", + "auxvariables_0": "0.0", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "3.0130104278214276e-06", + "op": ">=", + "th": "0.0", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Senegal", + "Gibraltar" + ], + "units": [ + "kg/kg" + ], + "times": [ + 132 + ], + "auxvariables": [ + "0.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "91633:91733:1" + }, + "rng_seed": null, + "justification": { + "text": "At 132 hours into the future, the mean value of specific_humidity at 150 hPa within Senegal (9.194816811941564e-06) compared to Gibraltar (6.1818063841201365e-06) differs by 0.0kg/kg or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "94a8f7920c9c256a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91632:91633:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81236:81237:1'} The data corresponds to corresponds to a snapshot on August 09 00:00. Based on the above data, answer the following question:", + "question": "At 84 hours into the future, does the maximum temperature at 500 hPa within Peru occur at a latitude greater than the maximum temperature at 500 hPa within North America?", + "response": "At 84 hours, the maximum of temperature at 500 hPa in Peru occurs at latitude -3.0000000000000044, which is False greater than the latitude 28.499999999999982 of the maximum in North America.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-3.0000000000000044", + "actualvalue_1": "28.499999999999982", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "-3.0000000000000044", + "op": ">", + "th": "28.499999999999982", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Peru", + "North America" + ], + "times": [ + 84 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "81237:81337:1" + }, + "rng_seed": null, + "justification": { + "text": "At 84 hours, the maximum of temperature at 500 hPa in Peru occurs at latitude -3.0000000000000044, which is False greater than the latitude 28.499999999999982 of the maximum in North America." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4d78d8f933916548" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81236:81237:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79903:79904:1'} The data corresponds to corresponds to a snapshot on September 09 18:00. Based on the above data, answer the following question:", + "question": "At 102 hours into the future, does the maximum geopotential at 500 hPa within Italy occur at a latitude greater than the maximum geopotential at 500 hPa within Saint Pierre and Miquelon?", + "response": "At 102 hours, the maximum of geopotential at 500 hPa in Italy occurs at latitude 36.0, which is False greater than the latitude 46.499999999999986 of the maximum in Saint Pierre and Miquelon.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "36.0", + "actualvalue_1": "46.499999999999986", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "36.0", + "op": ">", + "th": "46.499999999999986", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Italy", + "Saint Pierre and Miquelon" + ], + "times": [ + 102 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "79904:80004:1" + }, + "rng_seed": null, + "justification": { + "text": "At 102 hours, the maximum of geopotential at 500 hPa in Italy occurs at latitude 36.0, which is False greater than the latitude 46.499999999999986 of the maximum in Saint Pierre and Miquelon." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a335cb403ed97ddc" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79903:79904:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71000:71001:1'} The data corresponds to corresponds to a snapshot on August 07 00:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of u_component_of_wind at 300 hPa within Laos occur within the same grid point as, or adjacent to, the maximum value of geopotential at 1000 hPa within Laos?", + "response": "At 6 hours lead, in Laos, the maximum value of u_component_of_wind at 300 hPa (-0.3573431670665741) occurs at the same or adjacent grid point as the maximum value of geopotential at 1000 hPa (403.57806396484375); thus, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-0.3573431670665741", + "actualvalue_1": "403.57806396484375", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": false + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "geopotential" + ], + "levelsuffixes": [ + 300, + 1000 + ], + "regions": [ + "Laos" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "geopotential" + ], + "time_range": "71001:71101:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Laos, the maximum value of u_component_of_wind at 300 hPa (-0.3573431670665741) occurs at the same or adjacent grid point as the maximum value of geopotential at 1000 hPa (403.57806396484375); thus, the statement is False." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e2466b4c6d229e00" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71000:71001:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52320:52321:1'} The data corresponds to corresponds to a snapshot on October 24 00:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of v_component_of_wind at 1000 hPa within Hawke's Bay, New Zealand occur within the same grid point as, or adjacent to, the maximum value of v_component_of_wind at 600 hPa within Hawke's Bay, New Zealand?", + "response": "At 6 hours lead, in Hawke's Bay, New Zealand, the maximum value of v_component_of_wind at 1000 hPa (0.39311859011650085) occurs at the same or adjacent grid point as the maximum value of v_component_of_wind at 600 hPa (1.5540988445281982); thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.39311859011650085", + "actualvalue_1": "1.5540988445281982", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": true + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "levelsuffixes": [ + 1000, + 600 + ], + "regions": [ + "Hawke's Bay, New Zealand" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "time_range": "52321:52421:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Hawke's Bay, New Zealand, the maximum value of v_component_of_wind at 1000 hPa (0.39311859011650085) occurs at the same or adjacent grid point as the maximum value of v_component_of_wind at 600 hPa (1.5540988445281982); thus, the statement is True." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5e7124241fb1c2ba" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52320:52321:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89069:89070:1'} The data corresponds to corresponds to a snapshot on December 19 06:00. Based on the above data, answer the following question:", + "question": "At 108 hours into the future, does geopotential at 600 hPa exceed 42410.37109375m\u00b2/s\u00b2 within more grid points in Nepal than in Gulf of Thailand?", + "response": "At 108 hours, the number of grid points in Nepal where geopotential at 600 hPa exceeds 42410.37109375m\u00b2/s\u00b2 is 10.0, compared to 20.0 in Gulf of Thailand; thus, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "10.0", + "actualvalue_1": "20.0", + "auxvariables_0": "42410.37109375", + "checks": [ + { + "name": "count_exceed_region0", + "actual": "10.0", + "op": ">", + "th": "20.0", + "ok": false + }, + { + "name": "threshold_region0", + "actual": "10.0", + "op": ">", + "th": "42410.37109375", + "ok": true + }, + { + "name": "threshold_region1", + "actual": "20.0", + "op": ">", + "th": "42410.37109375", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the number of grid points in {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} is {{actualvalue_0}}, compared to {{actualvalue_1}} in {{regions_1}}; thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0}{units_0} within more grid points in {regions_0} than in {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Nepal", + "Gulf of Thailand" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "times": [ + 108 + ], + "auxvariables": [ + "42410.37109375" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "89070:89170:1" + }, + "rng_seed": null, + "justification": { + "text": "At 108 hours, the number of grid points in Nepal where geopotential at 600 hPa exceeds 42410.37109375m\u00b2/s\u00b2 is 10.0, compared to 20.0 in Gulf of Thailand; thus, the statement is False." + }, + "question_id": "kI3y2R", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "42e85adc8b95e393" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89069:89070:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77187:77188:1'} The data corresponds to corresponds to a snapshot on October 31 18:00. Based on the above data, answer the following question:", + "question": "At 12 hours into the future, does the mean value of specific_humidity at 150 hPa within North America exceed the mean value within Canada by at least 0.0kg/kg?", + "response": "At 12 hours into the future, the mean value of specific_humidity at 150 hPa within North America (3.904755885741906e-06) compared to Canada (3.42852854373632e-06) differs by 0.0kg/kg or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "3.904755885741906e-06", + "actualvalue_1": "3.42852854373632e-06", + "auxvariables_0": "0.0", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "4.7622734200558625e-07", + "op": ">=", + "th": "0.0", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "North America", + "Canada" + ], + "units": [ + "kg/kg" + ], + "times": [ + 12 + ], + "auxvariables": [ + "0.0" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "77188:77288:1" + }, + "rng_seed": null, + "justification": { + "text": "At 12 hours into the future, the mean value of specific_humidity at 150 hPa within North America (3.904755885741906e-06) compared to Canada (3.42852854373632e-06) differs by 0.0kg/kg or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d4b0b4e933504d7d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77187:77188:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74075:74076:1'} The data corresponds to corresponds to a snapshot on September 13 18:00. Based on the above data, answer the following question:", + "question": "At 198 hours into the future, does v_component_of_wind at 600 hPa exceed 3.7799999713897705m/s within more grid points in La Altagracia, Dominican Republic than in Lagoa dos Patos?", + "response": "At 198 hours, the number of grid points in La Altagracia, Dominican Republic where v_component_of_wind at 600 hPa exceeds 3.7799999713897705m/s is 0.0, compared to 0.0 in Lagoa dos Patos; thus, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0", + "actualvalue_1": "0.0", + "auxvariables_0": "3.7799999713897705", + "checks": [ + { + "name": "count_exceed_region0", + "actual": "0.0", + "op": ">", + "th": "0.0", + "ok": false + }, + { + "name": "threshold_region0", + "actual": "0.0", + "op": ">", + "th": "3.7799999713897705", + "ok": false + }, + { + "name": "threshold_region1", + "actual": "0.0", + "op": ">", + "th": "3.7799999713897705", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the number of grid points in {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} is {{actualvalue_0}}, compared to {{actualvalue_1}} in {{regions_1}}; thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0}{units_0} within more grid points in {regions_0} than in {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "La Altagracia, Dominican Republic", + "Lagoa dos Patos" + ], + "units": [ + "m/s" + ], + "times": [ + 198 + ], + "auxvariables": [ + "3.7799999713897705" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "74076:74176:1" + }, + "rng_seed": null, + "justification": { + "text": "At 198 hours, the number of grid points in La Altagracia, Dominican Republic where v_component_of_wind at 600 hPa exceeds 3.7799999713897705m/s is 0.0, compared to 0.0 in Lagoa dos Patos; thus, the statement is False." + }, + "question_id": "kI3y2R", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "8d72f5435a097696" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74075:74076:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72789:72790:1'} The data corresponds to corresponds to a snapshot on October 27 06:00. Based on the above data, answer the following question:", + "question": "At 84 hours into the future, does temperature at 100 hPa exceed 216.82000732421875K within more grid points in Golfe du Lion than in Laos?", + "response": "At 84 hours, the number of grid points in Golfe du Lion where temperature at 100 hPa exceeds 216.82000732421875K is 1.0, compared to 0.0 in Laos; thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "1.0", + "actualvalue_1": "0.0", + "auxvariables_0": "216.82000732421875", + "checks": [ + { + "name": "count_exceed_region0", + "actual": "1.0", + "op": ">", + "th": "0.0", + "ok": true + }, + { + "name": "threshold_region0", + "actual": "1.0", + "op": ">", + "th": "216.82000732421875", + "ok": true + }, + { + "name": "threshold_region1", + "actual": "0.0", + "op": ">", + "th": "216.82000732421875", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the number of grid points in {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} is {{actualvalue_0}}, compared to {{actualvalue_1}} in {{regions_1}}; thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0}{units_0} within more grid points in {regions_0} than in {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Golfe du Lion", + "Laos" + ], + "units": [ + "K" + ], + "times": [ + 84 + ], + "auxvariables": [ + "216.82000732421875" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "72790:72890:1" + }, + "rng_seed": null, + "justification": { + "text": "At 84 hours, the number of grid points in Golfe du Lion where temperature at 100 hPa exceeds 216.82000732421875K is 1.0, compared to 0.0 in Laos; thus, the statement is True." + }, + "question_id": "kI3y2R", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f86100a455335dac" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72789:72790:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90295:90296:1'} The data corresponds to corresponds to a snapshot on October 20 18:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of temperature at 250 hPa within Europe exceed 224.485K, while the maximum of v_component_of_wind at 200 hPa within Europe remains below -11.146m/s?", + "response": "At 66 hours in Europe, the mean of temperature at 250 hPa is 218.11309814453125K compared to the threshold 224.485K, and the maximum of v_component_of_wind at 200 hPa is 46.69209289550781m/s compared to -11.146m/s; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "218.11309814453125", + "actualvalue_1": "46.69209289550781", + "auxvariables_0": "224.485", + "auxvariables_1": "-11.146", + "checks": [ + { + "name": "cond0", + "actual": "218.11309814453125", + "op": ">", + "th": "224.485", + "ok": false + }, + { + "name": "cond1", + "actual": "46.69209289550781", + "op": "<", + "th": "-11.146", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "v_component_of_wind" + ], + "levelsuffixes": [ + 250, + 200 + ], + "regions": [ + "Europe" + ], + "units": [ + "K", + "m/s" + ], + "auxvariables_0_provenance": [ + "var=temperature, tail=P94 over region (2235 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=v_component_of_wind, tail=P15 over region (2235 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "224.485", + "-11.146" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "v_component_of_wind" + ], + "time_range": "90296:90396:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Europe, the mean of temperature at 250 hPa is 218.11309814453125K compared to the threshold 224.485K, and the maximum of v_component_of_wind at 200 hPa is 46.69209289550781m/s compared to -11.146m/s; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "391f54af3c1595a3" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90295:90296:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66448:66449:1'} The data corresponds to corresponds to a snapshot on June 25 00:00. Based on the above data, answer the following question:", + "question": "At 150 hours into the future, does temperature at 700 hPa exceed 253.64999389648438 within any part of Antarctica?", + "response": "In Antarctica, temperature at 700 hPa is 262.363037109375 relative to the threshold 253.64999389648438; combined with any other stated conditions, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "262.363037109375", + "auxvariables_0": "253.64999389648438", + "checks": [ + { + "name": "cond0", + "actual": "262.363037109375", + "op": ">", + "th": "253.64999389648438", + "ok": true + } + ], + "justification": "In {{regions_0}}, {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}} relative to the threshold {{auxvariables_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0} within any part of {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Antarctica" + ], + "times": [ + 150 + ], + "auxvariables": [ + "253.64999389648438" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "66449:66549:1" + }, + "rng_seed": null, + "justification": { + "text": "In Antarctica, temperature at 700 hPa is 262.363037109375 relative to the threshold 253.64999389648438; combined with any other stated conditions, this makes the statement True." + }, + "question_id": "AxHIS3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d0562cbcb3fdae9d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66448:66449:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52090:52091:1'} The data corresponds to corresponds to a snapshot on August 27 12:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 700 hPa within Harghita, Romania exceed 19.335m/s?", + "response": "In Harghita, Romania, the mean of u_component_of_wind at 700 hPa at 66 hours is 6.02800178527832m/s, compared to the threshold 19.335m/s; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "6.02800178527832", + "auxvariables_0": "19.335", + "checks": [ + { + "name": "mean-exceed", + "actual": "6.02800178527832", + "op": ">", + "th": "19.335", + "ok": false + } + ], + "justification": "In {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} at {{times_0}} hours is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {{auxvariables_0}}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Harghita, Romania" + ], + "units": [ + "m/s" + ], + "times": [ + 66 + ], + "auxvariables": [ + "19.335" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "52091:52191:1" + }, + "rng_seed": null, + "justification": { + "text": "In Harghita, Romania, the mean of u_component_of_wind at 700 hPa at 66 hours is 6.02800178527832m/s, compared to the threshold 19.335m/s; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "UMSBBr", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0661fcbf115b2bbd" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52090:52091:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35878:35879:1'} The data corresponds to corresponds to a snapshot on July 23 12:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 300 hPa within Saint Helena exceed 16.34799575805664m/s, while the maximum of temperature at 500 hPa within Saint Helena remains below 264.4102478027344K?", + "response": "At 66 hours in Saint Helena, the mean of u_component_of_wind at 300 hPa is 16.253931045532227m/s compared to the threshold 16.34799575805664m/s, and the maximum of temperature at 500 hPa is 265.06109619140625K compared to 264.4102478027344K; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "16.253931045532227", + "actualvalue_1": "265.06109619140625", + "auxvariables_0": "16.34799575805664", + "auxvariables_1": "264.4102478027344", + "checks": [ + { + "name": "cond0", + "actual": "16.253931045532227", + "op": ">", + "th": "16.34799575805664", + "ok": false + }, + { + "name": "cond1", + "actual": "265.06109619140625", + "op": "<", + "th": "264.4102478027344", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "temperature" + ], + "levelsuffixes": [ + 300, + 500 + ], + "regions": [ + "Saint Helena" + ], + "units": [ + "m/s", + "K" + ], + "auxvariables_0_provenance": [ + "var=u_component_of_wind, fallback median (N=1)" + ], + "auxvariables_1_provenance": [ + "var=temperature, fallback median (N=1)" + ], + "times": [ + 66 + ], + "auxvariables": [ + "16.34799575805664", + "264.4102478027344" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "temperature" + ], + "time_range": "35879:35979:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Saint Helena, the mean of u_component_of_wind at 300 hPa is 16.253931045532227m/s compared to the threshold 16.34799575805664m/s, and the maximum of temperature at 500 hPa is 265.06109619140625K compared to 264.4102478027344K; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "dcb03e6cb41d83cd" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35878:35879:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61723:61724:1'} The data corresponds to corresponds to a snapshot on March 31 18:00. Based on the above data, answer the following question:", + "question": "At 36 hours into the future, does v_component_of_wind at 850 hPa exceed 19.149999618530273 within any part of Clackmannanshire, United Kingdom?", + "response": "In Clackmannanshire, United Kingdom, v_component_of_wind at 850 hPa is 18.665176391601562 relative to the threshold 19.149999618530273; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "18.665176391601562", + "auxvariables_0": "19.149999618530273", + "checks": [ + { + "name": "cond0", + "actual": "18.665176391601562", + "op": ">", + "th": "19.149999618530273", + "ok": false + } + ], + "justification": "In {{regions_0}}, {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}} relative to the threshold {{auxvariables_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0} within any part of {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Clackmannanshire, United Kingdom" + ], + "times": [ + 36 + ], + "auxvariables": [ + "19.149999618530273" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "61724:61824:1" + }, + "rng_seed": null, + "justification": { + "text": "In Clackmannanshire, United Kingdom, v_component_of_wind at 850 hPa is 18.665176391601562 relative to the threshold 19.149999618530273; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "AxHIS3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ae4149a3a815ef7f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61723:61724:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75686:75687:1'} The data corresponds to corresponds to a snapshot on October 21 12:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of specific_humidity at 50 hPa within Lendava, Slovenia occur within the same grid point as, or adjacent to, the maximum value of specific_humidity at 1000 hPa within Lendava, Slovenia?", + "response": "At 6 hours lead, in Lendava, Slovenia, the maximum value of specific_humidity at 50 hPa (2.7639982818072895e-06) occurs at the same or adjacent grid point as the maximum value of specific_humidity at 1000 hPa (0.003981453366577625); thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "2.7639982818072895e-06", + "actualvalue_1": "0.003981453366577625", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": true + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 50, + 1000 + ], + "regions": [ + "Lendava, Slovenia" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "75687:75787:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Lendava, Slovenia, the maximum value of specific_humidity at 50 hPa (2.7639982818072895e-06) occurs at the same or adjacent grid point as the maximum value of specific_humidity at 1000 hPa (0.003981453366577625); thus, the statement is True." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2e4fd030ab43d5ed" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75686:75687:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34596:34597:1'} The data corresponds to corresponds to a snapshot on September 06 00:00. Based on the above data, answer the following question:", + "question": "At 168 hours into the future, does the mean value of temperature at 925 hPa within Oceania exceed the mean value within China by at least 1.22K?", + "response": "At 168 hours into the future, the mean value of temperature at 925 hPa within Oceania (288.82012939453125) compared to China (291.3847961425781) differs by 1.22K or more: this is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "288.82012939453125", + "actualvalue_1": "291.3847961425781", + "auxvariables_0": "1.22", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "-2.564666748046875", + "op": ">=", + "th": "1.22", + "ok": false + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Oceania", + "China" + ], + "units": [ + "K" + ], + "times": [ + 168 + ], + "auxvariables": [ + "1.22" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "34597:34697:1" + }, + "rng_seed": null, + "justification": { + "text": "At 168 hours into the future, the mean value of temperature at 925 hPa within Oceania (288.82012939453125) compared to China (291.3847961425781) differs by 1.22K or more: this is False." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6c3714d8526e9e77" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34596:34597:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77995:77996:1'} The data corresponds to corresponds to a snapshot on May 20 18:00. Based on the above data, answer the following question:", + "question": "At 48 hours into the future, does the mean value of temperature at 1000 hPa within Africa exceed the mean value within Tunisia by at least 0.9K?", + "response": "At 48 hours into the future, the mean value of temperature at 1000 hPa within Africa (304.1202697753906) compared to Tunisia (296.67047119140625) differs by 0.9K or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "304.1202697753906", + "actualvalue_1": "296.67047119140625", + "auxvariables_0": "0.9", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "7.449798583984375", + "op": ">=", + "th": "0.9", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Africa", + "Tunisia" + ], + "units": [ + "K" + ], + "times": [ + 48 + ], + "auxvariables": [ + "0.9" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "77996:78096:1" + }, + "rng_seed": null, + "justification": { + "text": "At 48 hours into the future, the mean value of temperature at 1000 hPa within Africa (304.1202697753906) compared to Tunisia (296.67047119140625) differs by 0.9K or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9585027752ab20e5" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77995:77996:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76930:76931:1'} The data corresponds to corresponds to a snapshot on August 28 12:00. Based on the above data, answer the following question:", + "question": "At 222 hours into the future, does the mean value of u_component_of_wind at 1000 hPa within Europe exceed the mean value within North, Hong Kong S.A.R. by at least 0.81m/s?", + "response": "At 222 hours into the future, the mean value of u_component_of_wind at 1000 hPa within Europe (0.8136798143386841) compared to North, Hong Kong S.A.R. (-2.0729105472564697) differs by 0.81m/s or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.8136798143386841", + "actualvalue_1": "-2.0729105472564697", + "auxvariables_0": "0.81", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "2.886590361595154", + "op": ">=", + "th": "0.81", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Europe", + "North, Hong Kong S.A.R." + ], + "units": [ + "m/s" + ], + "times": [ + 222 + ], + "auxvariables": [ + "0.81" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "76931:77031:1" + }, + "rng_seed": null, + "justification": { + "text": "At 222 hours into the future, the mean value of u_component_of_wind at 1000 hPa within Europe (0.8136798143386841) compared to North, Hong Kong S.A.R. (-2.0729105472564697) differs by 0.81m/s or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6e0acb8dee7416ef" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76930:76931:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80285:80286:1'} The data corresponds to corresponds to a snapshot on December 14 06:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of temperature at 700 hPa within Inhambane, Mozambique occur within the same grid point as, or adjacent to, the maximum value of geopotential at 50 hPa within Inhambane, Mozambique?", + "response": "At 6 hours lead, in Inhambane, Mozambique, the maximum value of temperature at 700 hPa (283.2059020996094) occurs at the same or adjacent grid point as the maximum value of geopotential at 50 hPa (201802.3125); thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "283.2059020996094", + "actualvalue_1": "201802.3125", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": true + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "geopotential" + ], + "levelsuffixes": [ + 700, + 50 + ], + "regions": [ + "Inhambane, Mozambique" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "geopotential" + ], + "time_range": "80286:80386:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Inhambane, Mozambique, the maximum value of temperature at 700 hPa (283.2059020996094) occurs at the same or adjacent grid point as the maximum value of geopotential at 50 hPa (201802.3125); thus, the statement is True." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3e57eb89e2a59d1d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80285:80286:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53847:53848:1'} The data corresponds to corresponds to a snapshot on November 09 18:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the mean value of geopotential at 200 hPa within Palmyra Atoll, United States Minor Outlying Islands exceed the mean value within Sea of Marmara by at least 429.73m\u00b2/s\u00b2?", + "response": "At 6 hours into the future, the mean value of geopotential at 200 hPa within Palmyra Atoll, United States Minor Outlying Islands (121529.078125) compared to Sea of Marmara (113815.0) differs by 429.73m\u00b2/s\u00b2 or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "121529.078125", + "actualvalue_1": "113815.0", + "auxvariables_0": "429.73", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "7714.078125", + "op": ">=", + "th": "429.73", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Palmyra Atoll, United States Minor Outlying Islands", + "Sea of Marmara" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "times": [ + 6 + ], + "auxvariables": [ + "429.73" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "53848:53948:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours into the future, the mean value of geopotential at 200 hPa within Palmyra Atoll, United States Minor Outlying Islands (121529.078125) compared to Sea of Marmara (113815.0) differs by 429.73m\u00b2/s\u00b2 or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a4940a8d108f2fb3" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53847:53848:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65011:65012:1'} The data corresponds to corresponds to a snapshot on July 01 18:00. Based on the above data, answer the following question:", + "question": "At 108 hours into the future, does the maximum specific_humidity at 1000 hPa within Chile occur at a latitude greater than the maximum specific_humidity at 1000 hPa within Ba\u00eda de Maraj\u00f3?", + "response": "At 108 hours, the maximum of specific_humidity at 1000 hPa in Chile occurs at latitude -26.999999999999996, which is False greater than the latitude -3.0000000000000044 of the maximum in Ba\u00eda de Maraj\u00f3.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-26.999999999999996", + "actualvalue_1": "-3.0000000000000044", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "-26.999999999999996", + "op": ">", + "th": "-3.0000000000000044", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Chile", + "Ba\u00eda de Maraj\u00f3" + ], + "times": [ + 108 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "65012:65112:1" + }, + "rng_seed": null, + "justification": { + "text": "At 108 hours, the maximum of specific_humidity at 1000 hPa in Chile occurs at latitude -26.999999999999996, which is False greater than the latitude -3.0000000000000044 of the maximum in Ba\u00eda de Maraj\u00f3." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b305a3a9014c452a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65011:65012:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48042:48043:1'} The data corresponds to corresponds to a snapshot on November 19 12:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of specific_humidity at 250 hPa within Antarctica exceed 1.5999999959603883e-05kg/kg?", + "response": "In Antarctica, the mean of specific_humidity at 250 hPa at 66 hours is 8.843659088597633e-06kg/kg, compared to the threshold 1.5999999959603883e-05kg/kg; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "8.843659088597633e-06", + "auxvariables_0": "1.5999999959603883e-05", + "checks": [ + { + "name": "mean-exceed", + "actual": "8.843659088597633e-06", + "op": ">", + "th": "1.5999999959603883e-05", + "ok": false + } + ], + "justification": "In {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} at {{times_0}} hours is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {{auxvariables_0}}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Antarctica" + ], + "units": [ + "kg/kg" + ], + "times": [ + 66 + ], + "auxvariables": [ + "1.6e-05" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "48043:48143:1" + }, + "rng_seed": null, + "justification": { + "text": "In Antarctica, the mean of specific_humidity at 250 hPa at 66 hours is 8.843659088597633e-06kg/kg, compared to the threshold 1.5999999959603883e-05kg/kg; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "UMSBBr", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "021f3680b6687728" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48042:48043:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52964:52965:1'} The data corresponds to corresponds to a snapshot on April 03 00:00. Based on the above data, answer the following question:", + "question": "At 108 hours into the future, does the mean value of v_component_of_wind at 150 hPa within Richard Collinson Inlet exceed the mean value within South America by at least 2.02m/s?", + "response": "At 108 hours into the future, the mean value of v_component_of_wind at 150 hPa within Richard Collinson Inlet (2.251677989959717) compared to South America (-2.212707996368408) differs by 2.02m/s or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "2.251677989959717", + "actualvalue_1": "-2.212707996368408", + "auxvariables_0": "2.02", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "4.464385986328125", + "op": ">=", + "th": "2.02", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Richard Collinson Inlet", + "South America" + ], + "units": [ + "m/s" + ], + "times": [ + 108 + ], + "auxvariables": [ + "2.02" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "52965:53065:1" + }, + "rng_seed": null, + "justification": { + "text": "At 108 hours into the future, the mean value of v_component_of_wind at 150 hPa within Richard Collinson Inlet (2.251677989959717) compared to South America (-2.212707996368408) differs by 2.02m/s or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "36339e8d5dedf23e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52964:52965:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44041:44042:1'} The data corresponds to corresponds to a snapshot on February 22 06:00. Based on the above data, answer the following question:", + "question": "At 126 hours into the future, does geopotential at 1000 hPa exceed 1023.6500244140625 within any part of Gulf of Martaban?", + "response": "In Gulf of Martaban, geopotential at 1000 hPa is 891.95751953125 relative to the threshold 1023.6500244140625; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "891.95751953125", + "auxvariables_0": "1023.6500244140625", + "checks": [ + { + "name": "cond0", + "actual": "891.95751953125", + "op": ">", + "th": "1023.6500244140625", + "ok": false + } + ], + "justification": "In {{regions_0}}, {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}} relative to the threshold {{auxvariables_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0} within any part of {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Gulf of Martaban" + ], + "times": [ + 126 + ], + "auxvariables": [ + "1023.6500244140625" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "44042:44142:1" + }, + "rng_seed": null, + "justification": { + "text": "In Gulf of Martaban, geopotential at 1000 hPa is 891.95751953125 relative to the threshold 1023.6500244140625; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "AxHIS3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6b9b9c700ff94523" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44041:44042:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58093:58094:1'} The data corresponds to corresponds to a snapshot on October 06 06:00. Based on the above data, answer the following question:", + "question": "At 138 hours into the future, does the mean value of u_component_of_wind at 400 hPa within Antarctica exceed the mean value within Uruguay by at least 1.87m/s?", + "response": "At 138 hours into the future, the mean value of u_component_of_wind at 400 hPa within Antarctica (2.5709643363952637) compared to Uruguay (11.627923965454102) differs by 1.87m/s or more: this is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "2.5709643363952637", + "actualvalue_1": "11.627923965454102", + "auxvariables_0": "1.87", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "-9.056959629058838", + "op": ">=", + "th": "1.87", + "ok": false + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "Antarctica", + "Uruguay" + ], + "units": [ + "m/s" + ], + "times": [ + 138 + ], + "auxvariables": [ + "1.87" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "58094:58194:1" + }, + "rng_seed": null, + "justification": { + "text": "At 138 hours into the future, the mean value of u_component_of_wind at 400 hPa within Antarctica (2.5709643363952637) compared to Uruguay (11.627923965454102) differs by 1.87m/s or more: this is False." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6eae3f1d71d800ca" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58093:58094:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61524:61525:1'} The data corresponds to corresponds to a snapshot on February 10 00:00. Based on the above data, answer the following question:", + "question": "At 24 hours into the future, does v_component_of_wind at 50 hPa exceed 5.260000228881836m/s within more grid points in Asia than in Visayan Sea?", + "response": "At 24 hours, the number of grid points in Asia where v_component_of_wind at 50 hPa exceeds 5.260000228881836m/s is 122.0, compared to 0.0 in Visayan Sea; thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "122.0", + "actualvalue_1": "0.0", + "auxvariables_0": "5.260000228881836", + "checks": [ + { + "name": "count_exceed_region0", + "actual": "122.0", + "op": ">", + "th": "0.0", + "ok": true + }, + { + "name": "threshold_region0", + "actual": "122.0", + "op": ">", + "th": "5.260000228881836", + "ok": true + }, + { + "name": "threshold_region1", + "actual": "0.0", + "op": ">", + "th": "5.260000228881836", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the number of grid points in {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} is {{actualvalue_0}}, compared to {{actualvalue_1}} in {{regions_1}}; thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0}{units_0} within more grid points in {regions_0} than in {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Asia", + "Visayan Sea" + ], + "units": [ + "m/s" + ], + "times": [ + 24 + ], + "auxvariables": [ + "5.260000228881836" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "61525:61625:1" + }, + "rng_seed": null, + "justification": { + "text": "At 24 hours, the number of grid points in Asia where v_component_of_wind at 50 hPa exceeds 5.260000228881836m/s is 122.0, compared to 0.0 in Visayan Sea; thus, the statement is True." + }, + "question_id": "kI3y2R", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "cc604b5a3d27f998" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61524:61525:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65746:65747:1'} The data corresponds to corresponds to a snapshot on January 01 12:00. Based on the above data, answer the following question:", + "question": "At 60 hours into the future, does the mean value of geopotential at 150 hPa within Danilovgrad, Montenegro exceed the mean value within North America by at least 1149.96m\u00b2/s\u00b2?", + "response": "At 60 hours into the future, the mean value of geopotential at 150 hPa within Danilovgrad, Montenegro (129254.46875) compared to North America (131046.1640625) differs by 1149.96m\u00b2/s\u00b2 or more: this is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "129254.46875", + "actualvalue_1": "131046.1640625", + "auxvariables_0": "1149.96", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "-1791.6953125", + "op": ">=", + "th": "1149.96", + "ok": false + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Danilovgrad, Montenegro", + "North America" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "times": [ + 60 + ], + "auxvariables": [ + "1149.96" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "65747:65847:1" + }, + "rng_seed": null, + "justification": { + "text": "At 60 hours into the future, the mean value of geopotential at 150 hPa within Danilovgrad, Montenegro (129254.46875) compared to North America (131046.1640625) differs by 1149.96m\u00b2/s\u00b2 or more: this is False." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6f1f6f1a9113d63e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65746:65747:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54971:54972:1'} The data corresponds to corresponds to a snapshot on August 16 18:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of v_component_of_wind at 400 hPa within Ash Shati', Libya occur within the same grid point as, or adjacent to, the maximum value of v_component_of_wind at 250 hPa within Ash Shati', Libya?", + "response": "At 6 hours lead, in Ash Shati', Libya, the maximum value of v_component_of_wind at 400 hPa (4.202662944793701) occurs at the same or adjacent grid point as the maximum value of v_component_of_wind at 250 hPa (12.71604061126709); thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "4.202662944793701", + "actualvalue_1": "12.71604061126709", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": true + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "levelsuffixes": [ + 400, + 250 + ], + "regions": [ + "Ash Shati', Libya" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "time_range": "54972:55072:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in Ash Shati', Libya, the maximum value of v_component_of_wind at 400 hPa (4.202662944793701) occurs at the same or adjacent grid point as the maximum value of v_component_of_wind at 250 hPa (12.71604061126709); thus, the statement is True." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "378be482759c2f40" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54971:54972:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83929:83930:1'} The data corresponds to corresponds to a snapshot on June 12 06:00. Based on the above data, answer the following question:", + "question": "At 156 hours into the future, does specific_humidity at 925 hPa exceed 0.008163000456988811kg/kg within more grid points in Bashkortostan, Russia than in Oman?", + "response": "At 156 hours, the number of grid points in Bashkortostan, Russia where specific_humidity at 925 hPa exceeds 0.008163000456988811kg/kg is 0.0, compared to 19.0 in Oman; thus, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.0", + "actualvalue_1": "19.0", + "auxvariables_0": "0.008163000456988811", + "checks": [ + { + "name": "count_exceed_region0", + "actual": "0.0", + "op": ">", + "th": "19.0", + "ok": false + }, + { + "name": "threshold_region0", + "actual": "0.0", + "op": ">", + "th": "0.008163000456988811", + "ok": false + }, + { + "name": "threshold_region1", + "actual": "19.0", + "op": ">", + "th": "0.008163000456988811", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the number of grid points in {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} is {{actualvalue_0}}, compared to {{actualvalue_1}} in {{regions_1}}; thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0}{units_0} within more grid points in {regions_0} than in {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Bashkortostan, Russia", + "Oman" + ], + "units": [ + "kg/kg" + ], + "times": [ + 156 + ], + "auxvariables": [ + "0.008163000456988811" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "83930:84030:1" + }, + "rng_seed": null, + "justification": { + "text": "At 156 hours, the number of grid points in Bashkortostan, Russia where specific_humidity at 925 hPa exceeds 0.008163000456988811kg/kg is 0.0, compared to 19.0 in Oman; thus, the statement is False." + }, + "question_id": "kI3y2R", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f297e4624797f0ba" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83929:83930:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83188:83189:1'} The data corresponds to corresponds to a snapshot on December 10 00:00. Based on the above data, answer the following question:", + "question": "At 48 hours into the future, does the maximum specific_humidity at 850 hPa within Northern Bahr el Ghazal, South Sudan occur at a latitude greater than the maximum specific_humidity at 850 hPa within El Salvador?", + "response": "At 48 hours, the maximum of specific_humidity at 850 hPa in Northern Bahr el Ghazal, South Sudan occurs at latitude 9.0, which is False greater than the latitude 14.999999999999996 of the maximum in El Salvador.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "9.0", + "actualvalue_1": "14.999999999999996", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "9.0", + "op": ">", + "th": "14.999999999999996", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Northern Bahr el Ghazal, South Sudan", + "El Salvador" + ], + "times": [ + 48 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "83189:83289:1" + }, + "rng_seed": null, + "justification": { + "text": "At 48 hours, the maximum of specific_humidity at 850 hPa in Northern Bahr el Ghazal, South Sudan occurs at latitude 9.0, which is False greater than the latitude 14.999999999999996 of the maximum in El Salvador." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f91176b00a3e800f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83188:83189:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37244:37245:1'} The data corresponds to corresponds to a snapshot on June 29 00:00. Based on the above data, answer the following question:", + "question": "At 42 hours into the future, does the maximum value of geopotential at 200 hPa within Europe remain lower than the maximum value of geopotential at 200 hPa within Europe at 234 hours into the future?", + "response": "At 42 hours, the maximum of geopotential at 200 hPa within Europe (122601.609375) is compared to the maximum at 234 hours (123187.1484375); the statement is True if the former is lower than the latter.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "122601.609375", + "actualvalue_1": "123187.1484375", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_t0_vs_max_t1", + "actual": "122601.609375", + "op": "<", + "th": "123187.1484375", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) is compared to the maximum at {{times_1}} hours ({{actualvalue_1}}); the statement is {{label}} if the former is lower than the latter." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_012.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_012.py", + "template_id": "tmpl_012", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain lower than the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} at {times_1} hours into the future?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Europe" + ], + "times": [ + 42, + 234 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "37245:37345:1" + }, + "rng_seed": null, + "justification": { + "text": "At 42 hours, the maximum of geopotential at 200 hPa within Europe (122601.609375) is compared to the maximum at 234 hours (123187.1484375); the statement is True if the former is lower than the latter." + }, + "question_id": "CslUZC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ce5db87e71d900ef" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37244:37245:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62367:62368:1'} The data corresponds to corresponds to a snapshot on September 08 18:00. Based on the above data, answer the following question:", + "question": "At 6 hours into the future, does the maximum value of v_component_of_wind at 850 hPa within INDIAN OCEAN occur within the same grid point as, or adjacent to, the maximum value of u_component_of_wind at 500 hPa within INDIAN OCEAN?", + "response": "At 6 hours lead, in INDIAN OCEAN, the maximum value of v_component_of_wind at 850 hPa (21.175479888916016) occurs at the same or adjacent grid point as the maximum value of u_component_of_wind at 500 hPa (45.7846565246582); thus, the statement is False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "21.175479888916016", + "actualvalue_1": "45.7846565246582", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_location_adjacent", + "actual": null, + "op": "adjacent", + "th": null, + "ok": false + } + ], + "justification": "At {{times_0}} hours lead, in {{regions_0}}, the maximum value of {{wb2varnames_0}}{{levelsuffixes_0}} ({{actualvalue_0}}) occurs at the same or adjacent grid point as the maximum value of {{wb2varnames_1}}{{levelsuffixes_1}} ({{actualvalue_1}}); thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_002.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_002.py", + "template_id": "tmpl_002", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur within the same grid point as, or adjacent to, the maximum value of {wb2varnames_1}{levelsuffixes_1} within {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 850, + 500 + ], + "regions": [ + "INDIAN OCEAN" + ], + "times": [ + 6 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "u_component_of_wind" + ], + "time_range": "62368:62468:1" + }, + "rng_seed": null, + "justification": { + "text": "At 6 hours lead, in INDIAN OCEAN, the maximum value of v_component_of_wind at 850 hPa (21.175479888916016) occurs at the same or adjacent grid point as the maximum value of u_component_of_wind at 500 hPa (45.7846565246582); thus, the statement is False." + }, + "question_id": "AX21HC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e68c786806507493" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62367:62368:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81142:81143:1'} The data corresponds to corresponds to a snapshot on July 16 12:00. Based on the above data, answer the following question:", + "question": "At 240 hours into the future, does temperature at 850 hPa exceed 290.6199951171875 within any part of Bulgaria?", + "response": "In Bulgaria, temperature at 850 hPa is 291.2864685058594 relative to the threshold 290.6199951171875; combined with any other stated conditions, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "291.2864685058594", + "auxvariables_0": "290.6199951171875", + "checks": [ + { + "name": "cond0", + "actual": "291.2864685058594", + "op": ">", + "th": "290.6199951171875", + "ok": true + } + ], + "justification": "In {{regions_0}}, {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}} relative to the threshold {{auxvariables_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0} within any part of {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Bulgaria" + ], + "times": [ + 240 + ], + "auxvariables": [ + "290.6199951171875" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "81143:81243:1" + }, + "rng_seed": null, + "justification": { + "text": "In Bulgaria, temperature at 850 hPa is 291.2864685058594 relative to the threshold 290.6199951171875; combined with any other stated conditions, this makes the statement True." + }, + "question_id": "AxHIS3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a6da2fa763b4d9df" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81142:81143:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74958:74959:1'} The data corresponds to corresponds to a snapshot on April 22 12:00. Based on the above data, answer the following question:", + "question": "At 36 hours into the future, does the mean value of specific_humidity at 700 hPa within North America exceed the mean value within Egypt by at least 0.000345kg/kg?", + "response": "At 36 hours into the future, the mean value of specific_humidity at 700 hPa within North America (0.0016955279279500246) compared to Egypt (0.0007729030912742019) differs by 0.000345kg/kg or more: this is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.0016955279279500246", + "actualvalue_1": "0.0007729030912742019", + "auxvariables_0": "0.000345", + "checks": [ + { + "name": "diff_vs_threshold", + "actual": "0.0009226248366758227", + "op": ">=", + "th": "0.000345", + "ok": true + } + ], + "justification": "At {{times_0}} hours into the future, the mean value of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) compared to {{regions_1}} ({{actualvalue_1}}) differs by {{auxvariables_0}}{{units_0}} or more: this is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed the mean value within {regions_1} by at least {auxvariables_0}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "North America", + "Egypt" + ], + "units": [ + "kg/kg" + ], + "times": [ + 36 + ], + "auxvariables": [ + "0.000345" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "74959:75059:1" + }, + "rng_seed": null, + "justification": { + "text": "At 36 hours into the future, the mean value of specific_humidity at 700 hPa within North America (0.0016955279279500246) compared to Egypt (0.0007729030912742019) differs by 0.000345kg/kg or more: this is True." + }, + "question_id": "FFOyXJ", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3d6b99aedbf28fc4" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74958:74959:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42928:42929:1'} The data corresponds to corresponds to a snapshot on May 20 00:00. Based on the above data, answer the following question:", + "question": "At 42 hours into the future, does temperature at 850 hPa exceed 292.2300109863281 within any part of Muramvya, Burundi?", + "response": "In Muramvya, Burundi, temperature at 850 hPa is 293.5468444824219 relative to the threshold 292.2300109863281; combined with any other stated conditions, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "293.5468444824219", + "auxvariables_0": "292.2300109863281", + "checks": [ + { + "name": "cond0", + "actual": "293.5468444824219", + "op": ">", + "th": "292.2300109863281", + "ok": true + } + ], + "justification": "In {{regions_0}}, {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}} relative to the threshold {{auxvariables_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0} within any part of {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Muramvya, Burundi" + ], + "times": [ + 42 + ], + "auxvariables": [ + "292.2300109863281" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "42929:43029:1" + }, + "rng_seed": null, + "justification": { + "text": "In Muramvya, Burundi, temperature at 850 hPa is 293.5468444824219 relative to the threshold 292.2300109863281; combined with any other stated conditions, this makes the statement True." + }, + "question_id": "AxHIS3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "836ce1e6508f049f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42928:42929:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34746:34747:1'} The data corresponds to corresponds to a snapshot on October 13 12:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of temperature at 400 hPa within Asia exceed 257.965K, while the maximum of specific_humidity at 100 hPa within Asia remains below 3e-06kg/kg?", + "response": "At 66 hours in Asia, the mean of temperature at 400 hPa is 250.00747680664062K compared to the threshold 257.965K, and the maximum of specific_humidity at 100 hPa is 4.398872988531366e-06kg/kg compared to 3e-06kg/kg; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "250.00747680664062", + "actualvalue_1": "4.398872988531366e-06", + "auxvariables_0": "257.965", + "auxvariables_1": "3e-06", + "checks": [ + { + "name": "cond0", + "actual": "250.00747680664062", + "op": ">", + "th": "257.965", + "ok": false + }, + { + "name": "cond1", + "actual": "4.398872988531366e-06", + "op": "<", + "th": "3e-06", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "specific_humidity" + ], + "levelsuffixes": [ + 400, + 100 + ], + "regions": [ + "Asia" + ], + "units": [ + "K", + "kg/kg" + ], + "auxvariables_0_provenance": [ + "var=temperature, tail=P94 over region (1831 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=specific_humidity, tail=P15 over region (1831 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "257.965", + "3e-06" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "specific_humidity" + ], + "time_range": "34747:34847:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Asia, the mean of temperature at 400 hPa is 250.00747680664062K compared to the threshold 257.965K, and the maximum of specific_humidity at 100 hPa is 4.398872988531366e-06kg/kg compared to 3e-06kg/kg; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a5de10e4d43f3a61" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34746:34747:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68845:68846:1'} The data corresponds to corresponds to a snapshot on February 14 06:00. Based on the above data, answer the following question:", + "question": "At 42 hours into the future, does the maximum value of temperature at 100 hPa within Saint Martin remain lower than the maximum value of temperature at 100 hPa within Saint Martin at 234 hours into the future?", + "response": "At 42 hours, the maximum of temperature at 100 hPa within Saint Martin (196.0061492919922) is compared to the maximum at 234 hours (194.1938018798828); the statement is False if the former is lower than the latter.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "196.0061492919922", + "actualvalue_1": "194.1938018798828", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_t0_vs_max_t1", + "actual": "196.0061492919922", + "op": "<", + "th": "194.1938018798828", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) is compared to the maximum at {{times_1}} hours ({{actualvalue_1}}); the statement is {{label}} if the former is lower than the latter." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_012.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_012.py", + "template_id": "tmpl_012", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain lower than the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} at {times_1} hours into the future?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Saint Martin" + ], + "times": [ + 42, + 234 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "68846:68946:1" + }, + "rng_seed": null, + "justification": { + "text": "At 42 hours, the maximum of temperature at 100 hPa within Saint Martin (196.0061492919922) is compared to the maximum at 234 hours (194.1938018798828); the statement is False if the former is lower than the latter." + }, + "question_id": "CslUZC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "eedb691b561ace61" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68845:68846:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41965:41966:1'} The data corresponds to corresponds to a snapshot on September 22 06:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of geopotential at 200 hPa within Dixon Entrance exceed 122135.797m\u00b2/s\u00b2?", + "response": "In Dixon Entrance, the mean of geopotential at 200 hPa at 66 hours is 116945.734375m\u00b2/s\u00b2, compared to the threshold 122135.797m\u00b2/s\u00b2; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "116945.734375", + "auxvariables_0": "122135.797", + "checks": [ + { + "name": "mean-exceed", + "actual": "116945.734375", + "op": ">", + "th": "122135.797", + "ok": false + } + ], + "justification": "In {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} at {{times_0}} hours is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {{auxvariables_0}}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Dixon Entrance" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "times": [ + 66 + ], + "auxvariables": [ + "122135.797" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "41966:42066:1" + }, + "rng_seed": null, + "justification": { + "text": "In Dixon Entrance, the mean of geopotential at 200 hPa at 66 hours is 116945.734375m\u00b2/s\u00b2, compared to the threshold 122135.797m\u00b2/s\u00b2; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "UMSBBr", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1a769fa0ee9d0fd8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41965:41966:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54237:54238:1'} The data corresponds to corresponds to a snapshot on February 15 06:00. Based on the above data, answer the following question:", + "question": "At 42 hours into the future, does the maximum value of specific_humidity at 500 hPa within Africa remain lower than the maximum value of specific_humidity at 500 hPa within Africa at 234 hours into the future?", + "response": "At 42 hours, the maximum of specific_humidity at 500 hPa within Africa (0.005222949665039778) is compared to the maximum at 234 hours (0.005120160058140755); the statement is False if the former is lower than the latter.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.005222949665039778", + "actualvalue_1": "0.005120160058140755", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_t0_vs_max_t1", + "actual": "0.005222949665039778", + "op": "<", + "th": "0.005120160058140755", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) is compared to the maximum at {{times_1}} hours ({{actualvalue_1}}); the statement is {{label}} if the former is lower than the latter." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_012.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_012.py", + "template_id": "tmpl_012", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain lower than the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} at {times_1} hours into the future?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Africa" + ], + "times": [ + 42, + 234 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "54238:54338:1" + }, + "rng_seed": null, + "justification": { + "text": "At 42 hours, the maximum of specific_humidity at 500 hPa within Africa (0.005222949665039778) is compared to the maximum at 234 hours (0.005120160058140755); the statement is False if the former is lower than the latter." + }, + "question_id": "CslUZC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "85fc08b8d3d9043c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54237:54238:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47493:47494:1'} The data corresponds to corresponds to a snapshot on July 05 06:00. Based on the above data, answer the following question:", + "question": "At 114 hours into the future, does specific_humidity at 925 hPa exceed 0.010085999965667725 within any part of Europe?", + "response": "In Europe, specific_humidity at 925 hPa is 0.014834988862276077 relative to the threshold 0.010085999965667725; combined with any other stated conditions, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "0.014834988862276077", + "auxvariables_0": "0.010085999965667725", + "checks": [ + { + "name": "cond0", + "actual": "0.014834988862276077", + "op": ">", + "th": "0.010085999965667725", + "ok": true + } + ], + "justification": "In {{regions_0}}, {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}} relative to the threshold {{auxvariables_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_006.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_006.py", + "template_id": "tmpl_006", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0} within any part of {regions_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Europe" + ], + "times": [ + 114 + ], + "auxvariables": [ + "0.010085999965667725" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "47494:47594:1" + }, + "rng_seed": null, + "justification": { + "text": "In Europe, specific_humidity at 925 hPa is 0.014834988862276077 relative to the threshold 0.010085999965667725; combined with any other stated conditions, this makes the statement True." + }, + "question_id": "AxHIS3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "459b39935657fc4a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47493:47494:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29314:29315:1'} The data corresponds to corresponds to a snapshot on January 24 12:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of temperature at 100 hPa within Crotene, Italy exceed 232.466K?", + "response": "In Crotene, Italy, the mean of temperature at 100 hPa at 66 hours is 208.55950927734375K, compared to the threshold 232.466K; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "208.55950927734375", + "auxvariables_0": "232.466", + "checks": [ + { + "name": "mean-exceed", + "actual": "208.55950927734375", + "op": ">", + "th": "232.466", + "ok": false + } + ], + "justification": "In {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} at {{times_0}} hours is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {{auxvariables_0}}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Crotene, Italy" + ], + "units": [ + "K" + ], + "times": [ + 66 + ], + "auxvariables": [ + "232.466" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "29315:29415:1" + }, + "rng_seed": null, + "justification": { + "text": "In Crotene, Italy, the mean of temperature at 100 hPa at 66 hours is 208.55950927734375K, compared to the threshold 232.466K; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "UMSBBr", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "986f8082f40c5965" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29314:29315:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77213:77214:1'} The data corresponds to corresponds to a snapshot on November 07 06:00. Based on the above data, answer the following question:", + "question": "At 198 hours into the future, does the maximum v_component_of_wind at 200 hPa within Strait of Belle Isle occur at a latitude greater than the maximum v_component_of_wind at 200 hPa within Asia?", + "response": "At 198 hours, the maximum of v_component_of_wind at 200 hPa in Strait of Belle Isle occurs at latitude 52.49999999999999, which is True greater than the latitude 28.499999999999982 of the maximum in Asia.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "52.49999999999999", + "actualvalue_1": "28.499999999999982", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "52.49999999999999", + "op": ">", + "th": "28.499999999999982", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Strait of Belle Isle", + "Asia" + ], + "times": [ + 198 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "77214:77314:1" + }, + "rng_seed": null, + "justification": { + "text": "At 198 hours, the maximum of v_component_of_wind at 200 hPa in Strait of Belle Isle occurs at latitude 52.49999999999999, which is True greater than the latitude 28.499999999999982 of the maximum in Asia." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "bdf014405d04d911" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77213:77214:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36495:36496:1'} The data corresponds to corresponds to a snapshot on December 24 18:00. Based on the above data, answer the following question:", + "question": "At 42 hours into the future, does the maximum value of temperature at 100 hPa within Africa remain lower than the maximum value of temperature at 100 hPa within Africa at 234 hours into the future?", + "response": "At 42 hours, the maximum of temperature at 100 hPa within Africa (219.14599609375) is compared to the maximum at 234 hours (212.49903869628906); the statement is False if the former is lower than the latter.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "219.14599609375", + "actualvalue_1": "212.49903869628906", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_t0_vs_max_t1", + "actual": "219.14599609375", + "op": "<", + "th": "212.49903869628906", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) is compared to the maximum at {{times_1}} hours ({{actualvalue_1}}); the statement is {{label}} if the former is lower than the latter." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_012.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_012.py", + "template_id": "tmpl_012", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain lower than the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} at {times_1} hours into the future?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Africa" + ], + "times": [ + 42, + 234 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "36496:36596:1" + }, + "rng_seed": null, + "justification": { + "text": "At 42 hours, the maximum of temperature at 100 hPa within Africa (219.14599609375) is compared to the maximum at 234 hours (212.49903869628906); the statement is False if the former is lower than the latter." + }, + "question_id": "CslUZC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "195e56920228a711" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36495:36496:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60386:60387:1'} The data corresponds to corresponds to a snapshot on May 01 12:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 400 hPa within Africa exceed 27.727m/s, while the maximum of temperature at 925 hPa within Africa remains below 293.3K?", + "response": "At 66 hours in Africa, the mean of u_component_of_wind at 400 hPa is 7.286901950836182m/s compared to the threshold 27.727m/s, and the maximum of temperature at 925 hPa is 308.210205078125K compared to 293.3K; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "7.286901950836182", + "actualvalue_1": "308.210205078125", + "auxvariables_0": "27.727", + "auxvariables_1": "293.3", + "checks": [ + { + "name": "cond0", + "actual": "7.286901950836182", + "op": ">", + "th": "27.727", + "ok": false + }, + { + "name": "cond1", + "actual": "308.210205078125", + "op": "<", + "th": "293.3", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "temperature" + ], + "levelsuffixes": [ + 400, + 925 + ], + "regions": [ + "Africa" + ], + "units": [ + "m/s", + "K" + ], + "auxvariables_0_provenance": [ + "var=u_component_of_wind, tail=P94 over region (1281 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=temperature, tail=P15 over region (1281 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "27.727", + "293.3" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "temperature" + ], + "time_range": "60387:60487:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Africa, the mean of u_component_of_wind at 400 hPa is 7.286901950836182m/s compared to the threshold 27.727m/s, and the maximum of temperature at 925 hPa is 308.210205078125K compared to 293.3K; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f7184ba1e8060cc6" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60386:60387:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72400:72401:1'} The data corresponds to corresponds to a snapshot on July 22 00:00. Based on the above data, answer the following question:", + "question": "At 42 hours into the future, does the maximum value of temperature at 50 hPa within Gulf of Maine remain lower than the maximum value of temperature at 50 hPa within Gulf of Maine at 234 hours into the future?", + "response": "At 42 hours, the maximum of temperature at 50 hPa within Gulf of Maine (218.21661376953125) is compared to the maximum at 234 hours (220.42552185058594); the statement is True if the former is lower than the latter.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "218.21661376953125", + "actualvalue_1": "220.42552185058594", + "auxvariables_0": null, + "auxvariables_1": null, + "checks": [ + { + "name": "max_t0_vs_max_t1", + "actual": "218.21661376953125", + "op": "<", + "th": "220.42552185058594", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} within {{regions_0}} ({{actualvalue_0}}) is compared to the maximum at {{times_1}} hours ({{actualvalue_1}}); the statement is {{label}} if the former is lower than the latter." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_012.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_012.py", + "template_id": "tmpl_012", + "template_str": "At {times_0} hours into the future, does the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} remain lower than the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} at {times_1} hours into the future?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Gulf of Maine" + ], + "times": [ + 42, + 234 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "72401:72501:1" + }, + "rng_seed": null, + "justification": { + "text": "At 42 hours, the maximum of temperature at 50 hPa within Gulf of Maine (218.21661376953125) is compared to the maximum at 234 hours (220.42552185058594); the statement is True if the former is lower than the latter." + }, + "question_id": "CslUZC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3c5627eed0567005" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72400:72401:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53639:53640:1'} The data corresponds to corresponds to a snapshot on September 18 18:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of v_component_of_wind at 150 hPa within Antarctica exceed 14.035m/s, while the maximum of geopotential at 600 hPa within Antarctica remains below 33804.645m\u00b2/s\u00b2?", + "response": "At 66 hours in Antarctica, the mean of v_component_of_wind at 150 hPa is -1.467332124710083m/s compared to the threshold 14.035m/s, and the maximum of geopotential at 600 hPa is 38809.109375m\u00b2/s\u00b2 compared to 33804.645m\u00b2/s\u00b2; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "-1.467332124710083", + "actualvalue_1": "38809.109375", + "auxvariables_0": "14.035", + "auxvariables_1": "33804.645", + "checks": [ + { + "name": "cond0", + "actual": "-1.467332124710083", + "op": ">", + "th": "14.035", + "ok": false + }, + { + "name": "cond1", + "actual": "38809.109375", + "op": "<", + "th": "33804.645", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "geopotential" + ], + "levelsuffixes": [ + 150, + 600 + ], + "regions": [ + "Antarctica" + ], + "units": [ + "m/s", + "m\u00b2/s\u00b2" + ], + "auxvariables_0_provenance": [ + "var=v_component_of_wind, tail=P94 over region (2890 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=geopotential, tail=P15 over region (2890 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "14.035", + "33804.645" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "geopotential" + ], + "time_range": "53640:53740:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Antarctica, the mean of v_component_of_wind at 150 hPa is -1.467332124710083m/s compared to the threshold 14.035m/s, and the maximum of geopotential at 600 hPa is 38809.109375m\u00b2/s\u00b2 compared to 33804.645m\u00b2/s\u00b2; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1fe234e527c3511d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53639:53640:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73625:73626:1'} The data corresponds to corresponds to a snapshot on May 24 06:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of specific_humidity at 500 hPa within South America exceed 0.004142kg/kg, while the maximum of v_component_of_wind at 300 hPa within South America remains below -6.836m/s?", + "response": "At 66 hours in South America, the mean of specific_humidity at 500 hPa is 0.001678437925875187kg/kg compared to the threshold 0.004142kg/kg, and the maximum of v_component_of_wind at 300 hPa is 49.2542610168457m/s compared to -6.836m/s; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "0.001678437925875187", + "actualvalue_1": "49.2542610168457", + "auxvariables_0": "0.004142", + "auxvariables_1": "-6.836", + "checks": [ + { + "name": "cond0", + "actual": "0.001678437925875187", + "op": ">", + "th": "0.004142", + "ok": false + }, + { + "name": "cond1", + "actual": "49.2542610168457", + "op": "<", + "th": "-6.836", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "v_component_of_wind" + ], + "levelsuffixes": [ + 500, + 300 + ], + "regions": [ + "South America" + ], + "units": [ + "kg/kg", + "m/s" + ], + "auxvariables_0_provenance": [ + "var=specific_humidity, tail=P94 over region (817 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=v_component_of_wind, tail=P15 over region (817 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "0.004142", + "-6.836" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "v_component_of_wind" + ], + "time_range": "73626:73726:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in South America, the mean of specific_humidity at 500 hPa is 0.001678437925875187kg/kg compared to the threshold 0.004142kg/kg, and the maximum of v_component_of_wind at 300 hPa is 49.2542610168457m/s compared to -6.836m/s; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "57d755d4fd1503c4" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73625:73626:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58878:58879:1'} The data corresponds to corresponds to a snapshot on April 20 12:00. Based on the above data, answer the following question:", + "question": "At 204 hours into the future, does geopotential at 600 hPa exceed 41979.0390625m\u00b2/s\u00b2 within more grid points in Hatay, Turkey than in Maine-et-Loire, France?", + "response": "At 204 hours, the number of grid points in Hatay, Turkey where geopotential at 600 hPa exceeds 41979.0390625m\u00b2/s\u00b2 is 2.0, compared to 0.0 in Maine-et-Loire, France; thus, the statement is True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "2.0", + "actualvalue_1": "0.0", + "auxvariables_0": "41979.0390625", + "checks": [ + { + "name": "count_exceed_region0", + "actual": "2.0", + "op": ">", + "th": "0.0", + "ok": true + }, + { + "name": "threshold_region0", + "actual": "2.0", + "op": ">", + "th": "41979.0390625", + "ok": true + }, + { + "name": "threshold_region1", + "actual": "0.0", + "op": ">", + "th": "41979.0390625", + "ok": false + } + ], + "justification": "At {{times_0}} hours, the number of grid points in {{regions_0}} where {{wb2varnames_0}}{{levelsuffixes_0}} exceeds {{auxvariables_0}}{{units_0}} is {{actualvalue_0}}, compared to {{actualvalue_1}} in {{regions_1}}; thus, the statement is {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "At {times_0} hours into the future, does {wb2varnames_0}{levelsuffixes_0} exceed {auxvariables_0}{units_0} within more grid points in {regions_0} than in {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Hatay, Turkey", + "Maine-et-Loire, France" + ], + "units": [ + "m\u00b2/s\u00b2" + ], + "times": [ + 204 + ], + "auxvariables": [ + "41979.0390625" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "58879:58979:1" + }, + "rng_seed": null, + "justification": { + "text": "At 204 hours, the number of grid points in Hatay, Turkey where geopotential at 600 hPa exceeds 41979.0390625m\u00b2/s\u00b2 is 2.0, compared to 0.0 in Maine-et-Loire, France; thus, the statement is True." + }, + "question_id": "kI3y2R", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "41d45e290f743125" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58878:58879:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92223:92224:1'} The data corresponds to corresponds to a snapshot on February 14 18:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of temperature at 50 hPa within South America exceed 216.646K?", + "response": "In South America, the mean of temperature at 50 hPa at 66 hours is 210.0284423828125K, compared to the threshold 216.646K; combined with any other stated conditions, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "210.0284423828125", + "auxvariables_0": "216.646", + "checks": [ + { + "name": "mean-exceed", + "actual": "210.0284423828125", + "op": ">", + "th": "216.646", + "ok": false + } + ], + "justification": "In {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} at {{times_0}} hours is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {{auxvariables_0}}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "South America" + ], + "units": [ + "K" + ], + "times": [ + 66 + ], + "auxvariables": [ + "216.646" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "92224:92324:1" + }, + "rng_seed": null, + "justification": { + "text": "In South America, the mean of temperature at 50 hPa at 66 hours is 210.0284423828125K, compared to the threshold 216.646K; combined with any other stated conditions, this makes the statement False." + }, + "question_id": "UMSBBr", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b52283e376aa5027" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92223:92224:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35010:35011:1'} The data corresponds to corresponds to a snapshot on December 18 12:00. Based on the above data, answer the following question:", + "question": "At 222 hours into the future, does the maximum temperature at 250 hPa within Nepal occur at a latitude greater than the maximum temperature at 250 hPa within Southern Patagonian Ice Field?", + "response": "At 222 hours, the maximum of temperature at 250 hPa in Nepal occurs at latitude 26.999999999999996, which is True greater than the latitude -49.5 of the maximum in Southern Patagonian Ice Field.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "26.999999999999996", + "actualvalue_1": "-49.5", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "26.999999999999996", + "op": ">", + "th": "-49.5", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Nepal", + "Southern Patagonian Ice Field" + ], + "times": [ + 222 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "35011:35111:1" + }, + "rng_seed": null, + "justification": { + "text": "At 222 hours, the maximum of temperature at 250 hPa in Nepal occurs at latitude 26.999999999999996, which is True greater than the latitude -49.5 of the maximum in Southern Patagonian Ice Field." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "18de7ce7109e282d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35010:35011:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68269:68270:1'} The data corresponds to corresponds to a snapshot on September 23 06:00. Based on the above data, answer the following question:", + "question": "At 138 hours into the future, does the maximum geopotential at 850 hPa within North America occur at a latitude greater than the maximum geopotential at 850 hPa within Antarctica?", + "response": "At 138 hours, the maximum of geopotential at 850 hPa in North America occurs at latitude 38.99999999999999, which is True greater than the latitude -72.0 of the maximum in Antarctica.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "38.99999999999999", + "actualvalue_1": "-72.0", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "38.99999999999999", + "op": ">", + "th": "-72.0", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "North America", + "Antarctica" + ], + "times": [ + 138 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "68270:68370:1" + }, + "rng_seed": null, + "justification": { + "text": "At 138 hours, the maximum of geopotential at 850 hPa in North America occurs at latitude 38.99999999999999, which is True greater than the latitude -72.0 of the maximum in Antarctica." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2d36398e02ecebaa" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68269:68270:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71480:71481:1'} The data corresponds to corresponds to a snapshot on December 05 00:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 200 hPa within Lagoa dos Patos exceed 44.533m/s?", + "response": "In Lagoa dos Patos, the mean of u_component_of_wind at 200 hPa at 66 hours is 55.04271697998047m/s, compared to the threshold 44.533m/s; combined with any other stated conditions, this makes the statement True.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "55.04271697998047", + "auxvariables_0": "44.533", + "checks": [ + { + "name": "mean-exceed", + "actual": "55.04271697998047", + "op": ">", + "th": "44.533", + "ok": true + } + ], + "justification": "In {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} at {{times_0}} hours is {{actualvalue_0}}{{units_0}}, compared to the threshold {{auxvariables_0}}{{units_0}}; combined with any other stated conditions, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {{auxvariables_0}}{units_0}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Lagoa dos Patos" + ], + "units": [ + "m/s" + ], + "times": [ + 66 + ], + "auxvariables": [ + "44.533" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "71481:71581:1" + }, + "rng_seed": null, + "justification": { + "text": "In Lagoa dos Patos, the mean of u_component_of_wind at 200 hPa at 66 hours is 55.04271697998047m/s, compared to the threshold 44.533m/s; combined with any other stated conditions, this makes the statement True." + }, + "question_id": "UMSBBr", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "af6d8633331d8924" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71480:71481:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48964:48965:1'} The data corresponds to corresponds to a snapshot on July 07 00:00. Based on the above data, answer the following question:", + "question": "At 126 hours into the future, does the maximum v_component_of_wind at 300 hPa within Disko Bay occur at a latitude greater than the maximum v_component_of_wind at 300 hPa within Peacock Sound?", + "response": "At 126 hours, the maximum of v_component_of_wind at 300 hPa in Disko Bay occurs at latitude 70.49999999999999, which is True greater than the latitude -72.0 of the maximum in Peacock Sound.", + "metadata": { + "true_value": true, + "actual_value": { + "actualvalue_0": "70.49999999999999", + "actualvalue_1": "-72.0", + "auxvariables_0": null, + "checks": [ + { + "name": "lat_compare", + "actual": "70.49999999999999", + "op": ">", + "th": "-72.0", + "ok": true + } + ], + "justification": "At {{times_0}} hours, the maximum of {{wb2varnames_0}}{{levelsuffixes_0}} in {{regions_0}} occurs at latitude {{actualvalue_0}}, which is {{label}} greater than the latitude {{actualvalue_1}} of the maximum in {{regions_1}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At {times_0} hours into the future, does the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} occur at a latitude greater than the maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Disko Bay", + "Peacock Sound" + ], + "times": [ + 126 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "48965:49065:1" + }, + "rng_seed": null, + "justification": { + "text": "At 126 hours, the maximum of v_component_of_wind at 300 hPa in Disko Bay occurs at latitude 70.49999999999999, which is True greater than the latitude -72.0 of the maximum in Peacock Sound." + }, + "question_id": "DvGYY3", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "8193ad120ac5373a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48964:48965:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89253:89254:1'} The data corresponds to corresponds to a snapshot on February 03 06:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of temperature at 150 hPa within Bo Hai exceed 219.211K, while the maximum of specific_humidity at 300 hPa within Bo Hai remains below 1.2e-05kg/kg?", + "response": "At 66 hours in Bo Hai, the mean of temperature at 150 hPa is 217.844482421875K compared to the threshold 219.211K, and the maximum of specific_humidity at 300 hPa is 6.57063937978819e-05kg/kg compared to 1.2e-05kg/kg; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "217.844482421875", + "actualvalue_1": "6.57063937978819e-05", + "auxvariables_0": "219.211", + "auxvariables_1": "1.2e-05", + "checks": [ + { + "name": "cond0", + "actual": "217.844482421875", + "op": ">", + "th": "219.211", + "ok": false + }, + { + "name": "cond1", + "actual": "6.57063937978819e-05", + "op": "<", + "th": "1.2e-05", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "specific_humidity" + ], + "levelsuffixes": [ + 150, + 300 + ], + "regions": [ + "Bo Hai" + ], + "units": [ + "K", + "kg/kg" + ], + "auxvariables_0_provenance": [ + "var=temperature, tail=P94 over region (10 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=specific_humidity, tail=P15 over region (10 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "219.211", + "1.2e-05" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "specific_humidity" + ], + "time_range": "89254:89354:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Bo Hai, the mean of temperature at 150 hPa is 217.844482421875K compared to the threshold 219.211K, and the maximum of specific_humidity at 300 hPa is 6.57063937978819e-05kg/kg compared to 1.2e-05kg/kg; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1b788f8d83f7317e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89253:89254:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42620:42621:1'} The data corresponds to corresponds to a snapshot on March 04 00:00. Based on the above data, answer the following question:", + "question": "At 66 hours into the future, does the mean value of u_component_of_wind at 500 hPa within Oceania exceed 13.714m/s, while the maximum of u_component_of_wind at 400 hPa within Oceania remains below -3.754m/s?", + "response": "At 66 hours in Oceania, the mean of u_component_of_wind at 500 hPa is 3.103139638900757m/s compared to the threshold 13.714m/s, and the maximum of u_component_of_wind at 400 hPa is 40.901851654052734m/s compared to -3.754m/s; together, this makes the statement False.", + "metadata": { + "true_value": false, + "actual_value": { + "actualvalue_0": "3.103139638900757", + "actualvalue_1": "40.901851654052734", + "auxvariables_0": "13.714", + "auxvariables_1": "-3.754", + "checks": [ + { + "name": "cond0", + "actual": "3.103139638900757", + "op": ">", + "th": "13.714", + "ok": false + }, + { + "name": "cond1", + "actual": "40.901851654052734", + "op": "<", + "th": "-3.754", + "ok": false + } + ], + "justification": "At {{times_0}} hours in {{regions_0}}, the mean of {{wb2varnames_0}}{{levelsuffixes_0}} is {{actualvalue_0}}{{units_0}} compared to the threshold {{auxvariables_0}}{{units_0}}, and the maximum of {{wb2varnames_1}}{{levelsuffixes_1}} is {{actualvalue_1}}{{units_1}} compared to {{auxvariables_1}}{{units_1}}; together, this makes the statement {{label}}." + }, + "code_path": "templates/synthetic_task_code/boolean/single_snapshot/tmpl_005.py", + "sampler_code_path": "templates/synthetic_task_code/boolean/single_snapshot/sampling_tmpl_005.py", + "template_id": "tmpl_005", + "template_str": "At {times_0} hours into the future, does the mean value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} exceed {auxvariables_0}{units_0}, while the maximum of {wb2varnames_1}{levelsuffixes_1} within {regions_0} remains below {auxvariables_1}{units_1}?", + "template_type": "snapshot", + "mode": "boolean", + "constraints": { + "version": 1 + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 500, + 400 + ], + "regions": [ + "Oceania" + ], + "units": [ + "m/s", + "m/s" + ], + "auxvariables_0_provenance": [ + "var=u_component_of_wind, tail=P94 over region (678 pts), op=mean" + ], + "auxvariables_1_provenance": [ + "var=u_component_of_wind, tail=P15 over region (678 pts), op=max" + ], + "times": [ + 66 + ], + "auxvariables": [ + "13.714", + "-3.754" + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "42621:42721:1" + }, + "rng_seed": null, + "justification": { + "text": "At 66 hours in Oceania, the mean of u_component_of_wind at 500 hPa is 3.103139638900757m/s compared to the threshold 13.714m/s, and the maximum of u_component_of_wind at 400 hPa is 40.901851654052734m/s compared to -3.754m/s; together, this makes the statement False." + }, + "question_id": "lCmiT7", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_boolean", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "47dca572406d17ca" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42620:42621:1" + } + } +] \ No newline at end of file diff --git a/level2b_numeric_part0.json b/level2b_numeric_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..e13ac161b658f147861d3cfe1beabe345363e321 --- /dev/null +++ b/level2b_numeric_part0.json @@ -0,0 +1,5611 @@ +[ + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85659:85678:1'} The data starts from August 18 18:00 and ends on August 23 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 54 hours after the end of the given time window, what is the maximum difference in v_component_of_wind at 200 hPa (m/s) between North America and South America?", + "response": "83.87308502197266\n\nJustification: The maximum difference in v_component_of_wind at 200 hPa (m/s) between North America and South America in the 54 hours after the end of the given time window is 83.87308502197266.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "83.87308502197266", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in v_component_of_wind at 200 hPa (m/s) between North America and South America in the 54 hours after the end of the given time window is 83.87308502197266." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "North America", + "South America" + ], + "units": [ + "m/s" + ], + "duration": 54 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "85678:85778:1" + }, + "rng_seed": null, + "justification": { + "text": "83.87308502197266\n\nJustification: The maximum difference in v_component_of_wind at 200 hPa (m/s) between North America and South America in the 54 hours after the end of the given time window is 83.87308502197266." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_200", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dbd11a366e17d194" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85659:85678:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37645:37670:1'} The data starts from October 07 06:00 and ends on October 13 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 72 hours after the end of the given time window, what is the displacement in kilometers of the location of minimum geopotential at 250 hPa within Durbes, Latvia compared to its location at the end of the given time window in the provided data?", + "response": "0.0\n\nJustification: The displacement in kilometers of the location of minimum geopotential at 250 hPa within Durbes, Latvia from the end of the given time window in the provided data to 72 hours after in the forecast snapshot is 0.0.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0", + "justification": "The displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} from the end of the given time window in the provided data to {duration} hours after in the forecast snapshot is {actualvalue_0}.", + "justification_text": "The displacement in kilometers of the location of minimum geopotential at 250 hPa within Durbes, Latvia from the end of the given time window in the provided data to 72 hours after in the forecast snapshot is 0.0." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_024.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_024.py", + "template_id": "tmpl_024", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} compared to its location at the end of the given time window in the provided data?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Durbes, Latvia" + ], + "duration": 72 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "37670:37770:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0\n\nJustification: The displacement in kilometers of the location of minimum geopotential at 250 hPa within Durbes, Latvia from the end of the given time window in the provided data to 72 hours after in the forecast snapshot is 0.0." + }, + "question_id": "0jwQYD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "5f2e0ea370974596" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37645:37670:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31905:31910:1'} The data starts from November 02 06:00 and ends on November 03 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 150 hours after the end of the given time window, what is the difference in the area-weighted mean value of v_component_of_wind at 850 hPa between Oceania and North America?", + "response": "1.6384340348627142\n\nJustification: The difference in the area-weighted mean value of v_component_of_wind at 850 hPa between Oceania and North America over the specified window is 1.6384340348627142.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "1.6384340348627142", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of v_component_of_wind at 850 hPa between Oceania and North America over the specified window is 1.6384340348627142." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Oceania", + "North America" + ], + "duration": 150 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "31910:32010:1" + }, + "rng_seed": null, + "justification": { + "text": "1.6384340348627142\n\nJustification: The difference in the area-weighted mean value of v_component_of_wind at 850 hPa between Oceania and North America over the specified window is 1.6384340348627142." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ec05b1581623827b" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31905:31910:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81775:81787:1'} The data starts from December 21 18:00 and ends on December 24 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 36 hours after the end of the given time window, what is the difference between the maximum value of temperature at 200 hPa within Canada and the maximum value of temperature at 200 hPa within Guam?", + "response": "14.694854736328125\n\nJustification: In 36 hours after the end of the given time window, the difference between the maximum value of temperature at 200 hPa within Canada and the maximum value of temperature at 200 hPa within Guam is 14.694854736328125.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "14.694854736328125", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 36 hours after the end of the given time window, the difference between the maximum value of temperature at 200 hPa within Canada and the maximum value of temperature at 200 hPa within Guam is 14.694854736328125." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Canada", + "Guam" + ], + "duration": 36 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "81787:81887:1" + }, + "rng_seed": null, + "justification": { + "text": "14.694854736328125\n\nJustification: In 36 hours after the end of the given time window, the difference between the maximum value of temperature at 200 hPa within Canada and the maximum value of temperature at 200 hPa within Guam is 14.694854736328125." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_200", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dba66f03439044a0" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81775:81787:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29872:29896:1'} The data starts from June 13 00:00 and ends on June 18 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 132 hours after the end of the given time window, what is the difference between the maximum value of specific_humidity at 300 hPa within Szeged, Hungary and the maximum value of specific_humidity at 300 hPa within Butuan, Philippines?", + "response": "-0.0005634648041450419\n\nJustification: In 132 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 300 hPa within Szeged, Hungary and the maximum value of specific_humidity at 300 hPa within Butuan, Philippines is -0.0005634648041450419.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-0.0005634648041450419", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 132 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 300 hPa within Szeged, Hungary and the maximum value of specific_humidity at 300 hPa within Butuan, Philippines is -0.0005634648041450419." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Szeged, Hungary", + "Butuan, Philippines" + ], + "duration": 132 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "29896:29996:1" + }, + "rng_seed": null, + "justification": { + "text": "-0.0005634648041450419\n\nJustification: In 132 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 300 hPa within Szeged, Hungary and the maximum value of specific_humidity at 300 hPa within Butuan, Philippines is -0.0005634648041450419." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_300", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "244346a6bfb168e2" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29872:29896:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42832:42853:1'} The data starts from April 26 00:00 and ends on May 01 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 120 hours after the end of the given time window, what is the difference in the area-weighted mean value of v_component_of_wind at 850 hPa between Mozambique Channel and Bight of Benin?", + "response": "6.452188611743172\n\nJustification: The difference in the area-weighted mean value of v_component_of_wind at 850 hPa between Mozambique Channel and Bight of Benin over the specified window is 6.452188611743172.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "6.452188611743172", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of v_component_of_wind at 850 hPa between Mozambique Channel and Bight of Benin over the specified window is 6.452188611743172." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Mozambique Channel", + "Bight of Benin" + ], + "duration": 120 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "42853:42953:1" + }, + "rng_seed": null, + "justification": { + "text": "6.452188611743172\n\nJustification: The difference in the area-weighted mean value of v_component_of_wind at 850 hPa between Mozambique Channel and Bight of Benin over the specified window is 6.452188611743172." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a40a5e450d13a844" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42832:42853:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39231:39247:1'} The data starts from November 07 18:00 and ends on November 11 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 102 hours after the end of the given time window, what is the value of u_component_of_wind at 925 hPa at the location within Prince William Sound where v_component_of_wind at 850 hPa reaches its maximum within that region?", + "response": "0.38874223828315735\n\nJustification: The value of u_component_of_wind at the location within Prince William Sound where v_component_of_wind reaches its maximum within that region, 102 hours after the end of the given time window, is 0.38874223828315735.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.38874223828315735", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of u_component_of_wind at the location within Prince William Sound where v_component_of_wind reaches its maximum within that region, 102 hours after the end of the given time window, is 0.38874223828315735." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "v_component_of_wind" + ], + "levelsuffixes": [ + 925, + 850 + ], + "regions": [ + "Prince William Sound" + ], + "duration": 102 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "v_component_of_wind" + ], + "time_range": "39247:39347:1" + }, + "rng_seed": null, + "justification": { + "text": "0.38874223828315735\n\nJustification: The value of u_component_of_wind at the location within Prince William Sound where v_component_of_wind reaches its maximum within that region, 102 hours after the end of the given time window, is 0.38874223828315735." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_925", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7e3c5c79e1d6b88b" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39231:39247:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83666:83684:1'} The data starts from April 07 12:00 and ends on April 11 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 126 hours after the end of the given time window, what is the difference in the area-weighted mean value of temperature at 250 hPa between Podujevo, Kosovo and Catanduanes, Philippines?", + "response": "-14.318225966563006\n\nJustification: The difference in the area-weighted mean value of temperature at 250 hPa between Podujevo, Kosovo and Catanduanes, Philippines over the specified window is -14.318225966563006.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-14.318225966563006", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of temperature at 250 hPa between Podujevo, Kosovo and Catanduanes, Philippines over the specified window is -14.318225966563006." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Podujevo, Kosovo", + "Catanduanes, Philippines" + ], + "duration": 126 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "83684:83784:1" + }, + "rng_seed": null, + "justification": { + "text": "-14.318225966563006\n\nJustification: The difference in the area-weighted mean value of temperature at 250 hPa between Podujevo, Kosovo and Catanduanes, Philippines over the specified window is -14.318225966563006." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_250", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8fb8de8b837c1d35" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83666:83684:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68649:68673:1'} The data starts from December 27 06:00 and ends on January 02 00:00 (1 year later). Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 72 hours after the end of the given time window, what is the difference between the maximum value of specific_humidity at 850 hPa within Strait of Singapore and the maximum value of specific_humidity at 850 hPa within Davao Gulf?", + "response": "-0.0011298451572656631\n\nJustification: In 72 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 850 hPa within Strait of Singapore and the maximum value of specific_humidity at 850 hPa within Davao Gulf is -0.0011298451572656631.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-0.0011298451572656631", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 72 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 850 hPa within Strait of Singapore and the maximum value of specific_humidity at 850 hPa within Davao Gulf is -0.0011298451572656631." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Strait of Singapore", + "Davao Gulf" + ], + "duration": 72 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "68673:68773:1" + }, + "rng_seed": null, + "justification": { + "text": "-0.0011298451572656631\n\nJustification: In 72 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 850 hPa within Strait of Singapore and the maximum value of specific_humidity at 850 hPa within Davao Gulf is -0.0011298451572656631." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b1ab7e82ba6c14b6" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68649:68673:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62454:62458:1'} The data starts from September 30 12:00 and ends on October 01 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 198 hours after the end of the given time window, what is the difference in the area-weighted mean value of geopotential at 700 hPa between Cumberland Sound and James Bay?", + "response": "-2692.7862870410063\n\nJustification: The difference in the area-weighted mean value of geopotential at 700 hPa between Cumberland Sound and James Bay over the specified window is -2692.7862870410063.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-2692.7862870410063", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of geopotential at 700 hPa between Cumberland Sound and James Bay over the specified window is -2692.7862870410063." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Cumberland Sound", + "James Bay" + ], + "duration": 198 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "62458:62558:1" + }, + "rng_seed": null, + "justification": { + "text": "-2692.7862870410063\n\nJustification: The difference in the area-weighted mean value of geopotential at 700 hPa between Cumberland Sound and James Bay over the specified window is -2692.7862870410063." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_700", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ef37c813d828272f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62454:62458:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34226:34239:1'} The data starts from June 05 12:00 and ends on June 08 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 120 hours after the end of the given time window, what is the maximum difference in temperature at 1000 hPa (K) between Denmark and Bosnia and Herzegovina?", + "response": "16.55731201171875\n\nJustification: The maximum difference in temperature at 1000 hPa (K) between Denmark and Bosnia and Herzegovina in the 120 hours after the end of the given time window is 16.55731201171875.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "16.55731201171875", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in temperature at 1000 hPa (K) between Denmark and Bosnia and Herzegovina in the 120 hours after the end of the given time window is 16.55731201171875." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Denmark", + "Bosnia and Herzegovina" + ], + "units": [ + "K" + ], + "duration": 120 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "34239:34339:1" + }, + "rng_seed": null, + "justification": { + "text": "16.55731201171875\n\nJustification: The maximum difference in temperature at 1000 hPa (K) between Denmark and Bosnia and Herzegovina in the 120 hours after the end of the given time window is 16.55731201171875." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_1000", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1db6e87c770cc272" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34226:34239:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56847:56864:1'} The data starts from November 28 18:00 and ends on December 02 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, what is the difference in the area-weighted mean value of specific_humidity at 500 hPa between Guatemala and Gambia?", + "response": "2.8908887354274493e-06\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 500 hPa between Guatemala and Gambia over the specified window is 2.8908887354274493e-06.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2.8908887354274493e-06", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of specific_humidity at 500 hPa between Guatemala and Gambia over the specified window is 2.8908887354274493e-06." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Guatemala", + "Gambia" + ], + "duration": 30 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "56864:56964:1" + }, + "rng_seed": null, + "justification": { + "text": "2.8908887354274493e-06\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 500 hPa between Guatemala and Gambia over the specified window is 2.8908887354274493e-06." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_500", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "70b3397d1d59ad6e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56847:56864:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36300:36319:1'} The data starts from November 06 00:00 and ends on November 10 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 180 hours after the end of the given time window, what is the maximum difference in v_component_of_wind at 250 hPa (m/s) between Ryanggang, North Korea and Ordino, Andorra?", + "response": "32.68436884880066\n\nJustification: The maximum difference in v_component_of_wind at 250 hPa (m/s) between Ryanggang, North Korea and Ordino, Andorra in the 180 hours after the end of the given time window is 32.68436884880066.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "32.68436884880066", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in v_component_of_wind at 250 hPa (m/s) between Ryanggang, North Korea and Ordino, Andorra in the 180 hours after the end of the given time window is 32.68436884880066." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Ryanggang, North Korea", + "Ordino, Andorra" + ], + "units": [ + "m/s" + ], + "duration": 180 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "36319:36419:1" + }, + "rng_seed": null, + "justification": { + "text": "32.68436884880066\n\nJustification: The maximum difference in v_component_of_wind at 250 hPa (m/s) between Ryanggang, North Korea and Ordino, Andorra in the 180 hours after the end of the given time window is 32.68436884880066." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_250", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "075eb3cff4a20cfb" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36300:36319:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34970:34971:1'} The data corresponds to corresponds to a snapshot on December 08 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 66 hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 200 hPa exceeding the mean of the median values for Europe computed from the all-time climatology baseline and the area-weighted centroid of such an area within South America, where the median values are computed from the same baseline for that region?", + "response": "52.72649108030896\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 200 hPa exceeding the mean of the median values for Europe computed from the all-time climatology baseline and the area-weighted centroid of such an area within South America, where the median values are computed from the same baseline for that region, after 66 hours is 52.72649108030896.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "52.72649108030896", + "justification": "The absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region, after {duration} hours is {{actualvalue_0}}.", + "justification_text": "The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 200 hPa exceeding the mean of the median values for Europe computed from the all-time climatology baseline and the area-weighted centroid of such an area within South America, where the median values are computed from the same baseline for that region, after 66 hours is 52.72649108030896." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_003.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_003.py", + "template_id": "tmpl_003", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Europe", + "South America" + ], + "duration": 66 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "34971:35071:1" + }, + "rng_seed": null, + "justification": { + "text": "52.72649108030896\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 200 hPa exceeding the mean of the median values for Europe computed from the all-time climatology baseline and the area-weighted centroid of such an area within South America, where the median values are computed from the same baseline for that region, after 66 hours is 52.72649108030896." + }, + "question_id": "0fC6F5", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "coordinate", + "forced_extreme_window": false, + "task_id": "0918ccb942ea82ec" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34970:34971:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 120 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71699:71719:1'} The data starts from January 28 18:00 and ends on February 02 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 174 hours after the end of the given time window, what is the difference between the maximum value of specific_humidity at 300 hPa within Keelung City, Taiwan and the maximum value of specific_humidity at 300 hPa within Mariehamn, Aland?", + "response": "1.6481775674037635e-05\n\nJustification: In 174 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 300 hPa within Keelung City, Taiwan and the maximum value of specific_humidity at 300 hPa within Mariehamn, Aland is 1.6481775674037635e-05.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "1.6481775674037635e-05", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 174 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 300 hPa within Keelung City, Taiwan and the maximum value of specific_humidity at 300 hPa within Mariehamn, Aland is 1.6481775674037635e-05." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Keelung City, Taiwan", + "Mariehamn, Aland" + ], + "duration": 174 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "71719:71819:1" + }, + "rng_seed": null, + "justification": { + "text": "1.6481775674037635e-05\n\nJustification: In 174 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 300 hPa within Keelung City, Taiwan and the maximum value of specific_humidity at 300 hPa within Mariehamn, Aland is 1.6481775674037635e-05." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_300", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9ae77f4fe9f8b27a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71699:71719:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30646:30650:1'} The data starts from December 23 12:00 and ends on December 24 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 126 hours after the end of the given time window, what is the difference in the area-weighted mean value of specific_humidity at 850 hPa between Antarctica and South America?", + "response": "-0.01062697980653579\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 850 hPa between Antarctica and South America over the specified window is -0.01062697980653579.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-0.01062697980653579", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of specific_humidity at 850 hPa between Antarctica and South America over the specified window is -0.01062697980653579." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Antarctica", + "South America" + ], + "duration": 126 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "30650:30750:1" + }, + "rng_seed": null, + "justification": { + "text": "-0.01062697980653579\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 850 hPa between Antarctica and South America over the specified window is -0.01062697980653579." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cdc66ba6f756773e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30646:30650:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72019:72021:1'} The data starts from April 17 18:00 and ends on April 18 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, what is the maximum difference in specific_humidity at 50 hPa (kg/kg) between Rond\u00f4nia, Brazil and Debrecen, Hungary?", + "response": "7.644357538083568e-07\n\nJustification: The maximum difference in specific_humidity at 50 hPa (kg/kg) between Rond\u00f4nia, Brazil and Debrecen, Hungary in the 30 hours after the end of the given time window is 7.644357538083568e-07.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "7.644357538083568e-07", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in specific_humidity at 50 hPa (kg/kg) between Rond\u00f4nia, Brazil and Debrecen, Hungary in the 30 hours after the end of the given time window is 7.644357538083568e-07." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Rond\u00f4nia, Brazil", + "Debrecen, Hungary" + ], + "units": [ + "kg/kg" + ], + "duration": 30 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "72021:72121:1" + }, + "rng_seed": null, + "justification": { + "text": "7.644357538083568e-07\n\nJustification: The maximum difference in specific_humidity at 50 hPa (kg/kg) between Rond\u00f4nia, Brazil and Debrecen, Hungary in the 30 hours after the end of the given time window is 7.644357538083568e-07." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_50", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "afddb6e11d278bba" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72019:72021:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74339:74361:1'} The data starts from November 18 18:00 and ends on November 24 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 90 hours after the end of the given time window, what is the value of u_component_of_wind at 500 hPa at the location within Souk Ahras, Algeria where u_component_of_wind at 600 hPa reaches its maximum within that region?", + "response": "17.024845123291016\n\nJustification: The value of u_component_of_wind at the location within Souk Ahras, Algeria where u_component_of_wind reaches its maximum within that region, 90 hours after the end of the given time window, is 17.024845123291016.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "17.024845123291016", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of u_component_of_wind at the location within Souk Ahras, Algeria where u_component_of_wind reaches its maximum within that region, 90 hours after the end of the given time window, is 17.024845123291016." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 500, + 600 + ], + "regions": [ + "Souk Ahras, Algeria" + ], + "duration": 90 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "74361:74461:1" + }, + "rng_seed": null, + "justification": { + "text": "17.024845123291016\n\nJustification: The value of u_component_of_wind at the location within Souk Ahras, Algeria where u_component_of_wind reaches its maximum within that region, 90 hours after the end of the given time window, is 17.024845123291016." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_500", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "168f1d942522be3a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74339:74361:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 138 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80126:80149:1'} The data starts from November 04 12:00 and ends on November 10 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 96 hours after the end of the given time window, what is the value of temperature at 200 hPa at the location within Ocotepeque, Honduras where temperature at 600 hPa reaches its maximum within that region?", + "response": "219.6651611328125\n\nJustification: The value of temperature at the location within Ocotepeque, Honduras where temperature reaches its maximum within that region, 96 hours after the end of the given time window, is 219.6651611328125.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "219.6651611328125", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of temperature at the location within Ocotepeque, Honduras where temperature reaches its maximum within that region, 96 hours after the end of the given time window, is 219.6651611328125." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 200, + 600 + ], + "regions": [ + "Ocotepeque, Honduras" + ], + "duration": 96 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "80149:80249:1" + }, + "rng_seed": null, + "justification": { + "text": "219.6651611328125\n\nJustification: The value of temperature at the location within Ocotepeque, Honduras where temperature reaches its maximum within that region, 96 hours after the end of the given time window, is 219.6651611328125." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_200", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "43725cbfdf7d61d5" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80126:80149:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74092:74111:1'} The data starts from September 18 00:00 and ends on September 22 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 114 hours after the end of the given time window, what is the mean value of u_component_of_wind at 925 hPa across all grid points in Zimbabwe where u_component_of_wind at 50 hPa exceeds its median value for Zimbabwe computed from the all-time climatology baseline?", + "response": "-2.7819981575012207\n\nJustification: The mean value of u_component_of_wind at 925 hPa across all grid points in Zimbabwe where u_component_of_wind at 50 hPa exceeds its median value for Zimbabwe computed from the all-time climatology baseline during the 114 hours-hour window after the given time is -2.7819981575012207.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-2.7819981575012207", + "justification": "The mean value of {{wb2varnames_0}}{{levelsuffixes_0}} across all grid points in {{regions_0}} where {{wb2varnames_1}}{{levelsuffixes_1}} exceeds its median value for {{regions_0}} computed from the all-time climatology baseline during the {{duration}}-hour window after the given time is {{actualvalue_0}}.", + "justification_text": "The mean value of u_component_of_wind at 925 hPa across all grid points in Zimbabwe where u_component_of_wind at 50 hPa exceeds its median value for Zimbabwe computed from the all-time climatology baseline during the 114 hours-hour window after the given time is -2.7819981575012207." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_026.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_026.py", + "template_id": "tmpl_026", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the mean value of {wb2varnames_0}{levelsuffixes_0} across all grid points in {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 925, + 50 + ], + "regions": [ + "Zimbabwe" + ], + "duration": 114 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "74111:74211:1" + }, + "rng_seed": null, + "justification": { + "text": "-2.7819981575012207\n\nJustification: The mean value of u_component_of_wind at 925 hPa across all grid points in Zimbabwe where u_component_of_wind at 50 hPa exceeds its median value for Zimbabwe computed from the all-time climatology baseline during the 114 hours-hour window after the given time is -2.7819981575012207." + }, + "question_id": "4eNWLw", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_925", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "33f4f0e6aa096399" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74092:74111:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61480:61489:1'} The data starts from January 30 00:00 and ends on February 01 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 78 hours after the end of the given time window, what is the maximum difference in u_component_of_wind at 400 hPa (m/s) between Ivano-Frankivs'k, Ukraine and Para\u00edba, Brazil?", + "response": "47.966407775878906\n\nJustification: The maximum difference in u_component_of_wind at 400 hPa (m/s) between Ivano-Frankivs'k, Ukraine and Para\u00edba, Brazil in the 78 hours after the end of the given time window is 47.966407775878906.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "47.966407775878906", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in u_component_of_wind at 400 hPa (m/s) between Ivano-Frankivs'k, Ukraine and Para\u00edba, Brazil in the 78 hours after the end of the given time window is 47.966407775878906." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "Ivano-Frankivs'k, Ukraine", + "Para\u00edba, Brazil" + ], + "units": [ + "m/s" + ], + "duration": 78 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "61489:61589:1" + }, + "rng_seed": null, + "justification": { + "text": "47.966407775878906\n\nJustification: The maximum difference in u_component_of_wind at 400 hPa (m/s) between Ivano-Frankivs'k, Ukraine and Para\u00edba, Brazil in the 78 hours after the end of the given time window is 47.966407775878906." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_400", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fae2001eaa952dab" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61480:61489:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31794:31819:1'} The data starts from October 05 12:00 and ends on October 11 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, what is the value of u_component_of_wind at 400 hPa at the location within East Siberian Sea where u_component_of_wind at 50 hPa reaches its maximum within that region?", + "response": "-15.302626609802246\n\nJustification: The value of u_component_of_wind at the location within East Siberian Sea where u_component_of_wind reaches its maximum within that region, 30 hours after the end of the given time window, is -15.302626609802246.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-15.302626609802246", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of u_component_of_wind at the location within East Siberian Sea where u_component_of_wind reaches its maximum within that region, 30 hours after the end of the given time window, is -15.302626609802246." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 400, + 50 + ], + "regions": [ + "East Siberian Sea" + ], + "duration": 30 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "31819:31919:1" + }, + "rng_seed": null, + "justification": { + "text": "-15.302626609802246\n\nJustification: The value of u_component_of_wind at the location within East Siberian Sea where u_component_of_wind reaches its maximum within that region, 30 hours after the end of the given time window, is -15.302626609802246." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_400", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c63dcc5898c7e28c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31794:31819:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 48 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31873:31881:1'} The data starts from October 25 06:00 and ends on October 27 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 132 hours after the end of the given time window, what is the value of geopotential at 100 hPa at the location within Africa where geopotential at 600 hPa reaches its maximum within that region?", + "response": "162282.40625\n\nJustification: The value of geopotential at the location within Africa where geopotential reaches its maximum within that region, 132 hours after the end of the given time window, is 162282.40625.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "162282.40625", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of geopotential at the location within Africa where geopotential reaches its maximum within that region, 132 hours after the end of the given time window, is 162282.40625." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 100, + 600 + ], + "regions": [ + "Africa" + ], + "duration": 132 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "31881:31981:1" + }, + "rng_seed": null, + "justification": { + "text": "162282.40625\n\nJustification: The value of geopotential at the location within Africa where geopotential reaches its maximum within that region, 132 hours after the end of the given time window, is 162282.40625." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_100", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a2287d536028a329" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31873:31881:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68073:68084:1'} The data starts from August 05 06:00 and ends on August 07 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 36 hours after the end of the given time window, what is the mean value of temperature at 600 hPa across all grid points in Antarctica where temperature at 200 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline?", + "response": "252.14108276367188\n\nJustification: The mean value of temperature at 600 hPa across all grid points in Antarctica where temperature at 200 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 36 hours-hour window after the given time is 252.14108276367188.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "252.14108276367188", + "justification": "The mean value of {{wb2varnames_0}}{{levelsuffixes_0}} across all grid points in {{regions_0}} where {{wb2varnames_1}}{{levelsuffixes_1}} exceeds its median value for {{regions_0}} computed from the all-time climatology baseline during the {{duration}}-hour window after the given time is {{actualvalue_0}}.", + "justification_text": "The mean value of temperature at 600 hPa across all grid points in Antarctica where temperature at 200 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 36 hours-hour window after the given time is 252.14108276367188." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_026.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_026.py", + "template_id": "tmpl_026", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the mean value of {wb2varnames_0}{levelsuffixes_0} across all grid points in {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 600, + 200 + ], + "regions": [ + "Antarctica" + ], + "duration": 36 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "68084:68184:1" + }, + "rng_seed": null, + "justification": { + "text": "252.14108276367188\n\nJustification: The mean value of temperature at 600 hPa across all grid points in Antarctica where temperature at 200 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 36 hours-hour window after the given time is 252.14108276367188." + }, + "question_id": "4eNWLw", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_600", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4c1c643db7fd695d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68073:68084:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45263:45272:1'} The data starts from December 24 18:00 and ends on December 26 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 186 hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 925 hPa exceeding the mean of the median values for Africa computed from the all-time climatology baseline and the area-weighted centroid of such an area within North America, where the median values are computed from the same baseline for that region?", + "response": "21.23710819405298\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 925 hPa exceeding the mean of the median values for Africa computed from the all-time climatology baseline and the area-weighted centroid of such an area within North America, where the median values are computed from the same baseline for that region, after 186 hours is 21.23710819405298.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "21.23710819405298", + "justification": "The absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region, after {duration} hours is {{actualvalue_0}}.", + "justification_text": "The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 925 hPa exceeding the mean of the median values for Africa computed from the all-time climatology baseline and the area-weighted centroid of such an area within North America, where the median values are computed from the same baseline for that region, after 186 hours is 21.23710819405298." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_003.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_003.py", + "template_id": "tmpl_003", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Africa", + "North America" + ], + "duration": 186 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "45272:45372:1" + }, + "rng_seed": null, + "justification": { + "text": "21.23710819405298\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 925 hPa exceeding the mean of the median values for Africa computed from the all-time climatology baseline and the area-weighted centroid of such an area within North America, where the median values are computed from the same baseline for that region, after 186 hours is 21.23710819405298." + }, + "question_id": "0fC6F5", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "coordinate", + "forced_extreme_window": false, + "task_id": "49d428cd55550273" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45263:45272:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87259:87278:1'} The data starts from September 22 18:00 and ends on September 27 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 102 hours after the end of the given time window, what is the displacement in kilometers of the location of minimum v_component_of_wind at 1000 hPa within St. Helena Bay compared to its location at the end of the given time window in the provided data?", + "response": "0.0\n\nJustification: The displacement in kilometers of the location of minimum v_component_of_wind at 1000 hPa within St. Helena Bay from the end of the given time window in the provided data to 102 hours after in the forecast snapshot is 0.0.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0", + "justification": "The displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} from the end of the given time window in the provided data to {duration} hours after in the forecast snapshot is {actualvalue_0}.", + "justification_text": "The displacement in kilometers of the location of minimum v_component_of_wind at 1000 hPa within St. Helena Bay from the end of the given time window in the provided data to 102 hours after in the forecast snapshot is 0.0." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_024.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_024.py", + "template_id": "tmpl_024", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} compared to its location at the end of the given time window in the provided data?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "St. Helena Bay" + ], + "duration": 102 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "87278:87378:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0\n\nJustification: The displacement in kilometers of the location of minimum v_component_of_wind at 1000 hPa within St. Helena Bay from the end of the given time window in the provided data to 102 hours after in the forecast snapshot is 0.0." + }, + "question_id": "0jwQYD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "a6d89be5c8c4e61c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87259:87278:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 12 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58172:58174:1'} The data starts from October 26 00:00 and ends on October 26 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 192 hours after the end of the given time window, what is the value of specific_humidity at 50 hPa at the location within North Macedonia where specific_humidity at 1000 hPa reaches its maximum within that region?", + "response": "2.7734895411413163e-06\n\nJustification: The value of specific_humidity at the location within North Macedonia where specific_humidity reaches its maximum within that region, 192 hours after the end of the given time window, is 2.7734895411413163e-06.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2.7734895411413163e-06", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of specific_humidity at the location within North Macedonia where specific_humidity reaches its maximum within that region, 192 hours after the end of the given time window, is 2.7734895411413163e-06." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 50, + 1000 + ], + "regions": [ + "North Macedonia" + ], + "duration": 192 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "58174:58274:1" + }, + "rng_seed": null, + "justification": { + "text": "2.7734895411413163e-06\n\nJustification: The value of specific_humidity at the location within North Macedonia where specific_humidity reaches its maximum within that region, 192 hours after the end of the given time window, is 2.7734895411413163e-06." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_50", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c6d0c58488f016c4" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58172:58174:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57989:58015:1'} The data starts from September 10 06:00 and ends on September 16 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 168 hours after the end of the given time window, what is the maximum difference in temperature at 850 hPa (K) between Arafura Sea and McMurdo Sound?", + "response": "44.51814270019531\n\nJustification: The maximum difference in temperature at 850 hPa (K) between Arafura Sea and McMurdo Sound in the 168 hours after the end of the given time window is 44.51814270019531.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "44.51814270019531", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in temperature at 850 hPa (K) between Arafura Sea and McMurdo Sound in the 168 hours after the end of the given time window is 44.51814270019531." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Arafura Sea", + "McMurdo Sound" + ], + "units": [ + "K" + ], + "duration": 168 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "58015:58115:1" + }, + "rng_seed": null, + "justification": { + "text": "44.51814270019531\n\nJustification: The maximum difference in temperature at 850 hPa (K) between Arafura Sea and McMurdo Sound in the 168 hours after the end of the given time window is 44.51814270019531." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fa457ca91bc14156" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57989:58015:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43822:43844:1'} The data starts from December 29 12:00 and ends on January 03 18:00 (1 year later). Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 90 hours after the end of the given time window, what is the value of geopotential at 50 hPa at the location within Irish Sea where geopotential at 150 hPa reaches its maximum within that region?", + "response": "202018.59375\n\nJustification: The value of geopotential at the location within Irish Sea where geopotential reaches its maximum within that region, 90 hours after the end of the given time window, is 202018.59375.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "202018.59375", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of geopotential at the location within Irish Sea where geopotential reaches its maximum within that region, 90 hours after the end of the given time window, is 202018.59375." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 50, + 150 + ], + "regions": [ + "Irish Sea" + ], + "duration": 90 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "43844:43944:1" + }, + "rng_seed": null, + "justification": { + "text": "202018.59375\n\nJustification: The value of geopotential at the location within Irish Sea where geopotential reaches its maximum within that region, 90 hours after the end of the given time window, is 202018.59375." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_50", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d4660194842b60c6" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43822:43844:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58215:58222:1'} The data starts from November 05 18:00 and ends on November 07 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 48 hours after the end of the given time window, what is the value of v_component_of_wind at 150 hPa at the location within Asia where u_component_of_wind at 400 hPa reaches its maximum within that region?", + "response": "16.362550735473633\n\nJustification: The value of v_component_of_wind at the location within Asia where u_component_of_wind reaches its maximum within that region, 48 hours after the end of the given time window, is 16.362550735473633.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "16.362550735473633", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of v_component_of_wind at the location within Asia where u_component_of_wind reaches its maximum within that region, 48 hours after the end of the given time window, is 16.362550735473633." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 150, + 400 + ], + "regions": [ + "Asia" + ], + "duration": 48 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "u_component_of_wind" + ], + "time_range": "58222:58322:1" + }, + "rng_seed": null, + "justification": { + "text": "16.362550735473633\n\nJustification: The value of v_component_of_wind at the location within Asia where u_component_of_wind reaches its maximum within that region, 48 hours after the end of the given time window, is 16.362550735473633." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_150", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cf8b612a65a346e1" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58215:58222:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89985:90002:1'} The data starts from August 04 06:00 and ends on August 08 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 174 hours after the end of the given time window, what is the displacement in kilometers of the location of minimum specific_humidity at 50 hPa within South America compared to its location at the end of the given time window in the provided data?", + "response": "4714.8822121831545\n\nJustification: The displacement in kilometers of the location of minimum specific_humidity at 50 hPa within South America from the end of the given time window in the provided data to 174 hours after in the forecast snapshot is 4714.8822121831545.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "4714.8822121831545", + "justification": "The displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} from the end of the given time window in the provided data to {duration} hours after in the forecast snapshot is {actualvalue_0}.", + "justification_text": "The displacement in kilometers of the location of minimum specific_humidity at 50 hPa within South America from the end of the given time window in the provided data to 174 hours after in the forecast snapshot is 4714.8822121831545." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_024.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_024.py", + "template_id": "tmpl_024", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} compared to its location at the end of the given time window in the provided data?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "South America" + ], + "duration": 174 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "90002:90102:1" + }, + "rng_seed": null, + "justification": { + "text": "4714.8822121831545\n\nJustification: The displacement in kilometers of the location of minimum specific_humidity at 50 hPa within South America from the end of the given time window in the provided data to 174 hours after in the forecast snapshot is 4714.8822121831545." + }, + "question_id": "0jwQYD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "0c4eaecd12ca689c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89985:90002:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77462:77468:1'} The data starts from January 08 12:00 and ends on January 09 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 126 hours after the end of the given time window, what is the mean value of specific_humidity at 300 hPa across all grid points in Antarctica where specific_humidity at 700 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline?", + "response": "3.864382961182855e-05\n\nJustification: The mean value of specific_humidity at 300 hPa across all grid points in Antarctica where specific_humidity at 700 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 126 hours-hour window after the given time is 3.864382961182855e-05.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "3.864382961182855e-05", + "justification": "The mean value of {{wb2varnames_0}}{{levelsuffixes_0}} across all grid points in {{regions_0}} where {{wb2varnames_1}}{{levelsuffixes_1}} exceeds its median value for {{regions_0}} computed from the all-time climatology baseline during the {{duration}}-hour window after the given time is {{actualvalue_0}}.", + "justification_text": "The mean value of specific_humidity at 300 hPa across all grid points in Antarctica where specific_humidity at 700 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 126 hours-hour window after the given time is 3.864382961182855e-05." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_026.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_026.py", + "template_id": "tmpl_026", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the mean value of {wb2varnames_0}{levelsuffixes_0} across all grid points in {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 300, + 700 + ], + "regions": [ + "Antarctica" + ], + "duration": 126 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "77468:77568:1" + }, + "rng_seed": null, + "justification": { + "text": "3.864382961182855e-05\n\nJustification: The mean value of specific_humidity at 300 hPa across all grid points in Antarctica where specific_humidity at 700 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 126 hours-hour window after the given time is 3.864382961182855e-05." + }, + "question_id": "4eNWLw", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_300", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "95086aa71988f3fd" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77462:77468:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89184:89210:1'} The data starts from January 17 00:00 and ends on January 23 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 66 hours after the end of the given time window, what is the difference in the area-weighted mean value of u_component_of_wind at 1000 hPa between \u00d8resund and Weddell Sea?", + "response": "10.394313070469604\n\nJustification: The difference in the area-weighted mean value of u_component_of_wind at 1000 hPa between \u00d8resund and Weddell Sea over the specified window is 10.394313070469604.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "10.394313070469604", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of u_component_of_wind at 1000 hPa between \u00d8resund and Weddell Sea over the specified window is 10.394313070469604." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "\u00d8resund", + "Weddell Sea" + ], + "duration": 66 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "89210:89310:1" + }, + "rng_seed": null, + "justification": { + "text": "10.394313070469604\n\nJustification: The difference in the area-weighted mean value of u_component_of_wind at 1000 hPa between \u00d8resund and Weddell Sea over the specified window is 10.394313070469604." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_1000", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ac14fb281a2db6d5" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89184:89210:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57858:57880:1'} The data starts from August 08 12:00 and ends on August 13 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 24 hours after the end of the given time window, what is the difference between the maximum value of geopotential at 200 hPa within Inner Sea and the maximum value of geopotential at 200 hPa within Disko Bay?", + "response": "8986.1953125\n\nJustification: In 24 hours after the end of the given time window, the difference between the maximum value of geopotential at 200 hPa within Inner Sea and the maximum value of geopotential at 200 hPa within Disko Bay is 8986.1953125.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "8986.1953125", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 24 hours after the end of the given time window, the difference between the maximum value of geopotential at 200 hPa within Inner Sea and the maximum value of geopotential at 200 hPa within Disko Bay is 8986.1953125." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Inner Sea", + "Disko Bay" + ], + "duration": 24 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "57880:57980:1" + }, + "rng_seed": null, + "justification": { + "text": "8986.1953125\n\nJustification: In 24 hours after the end of the given time window, the difference between the maximum value of geopotential at 200 hPa within Inner Sea and the maximum value of geopotential at 200 hPa within Disko Bay is 8986.1953125." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_200", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e53a7180a339e7be" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57858:57880:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 162 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35680:35707:1'} The data starts from June 04 00:00 and ends on June 10 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 96 hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 200 hPa exceeding the mean of the median values for Africa computed from the all-time climatology baseline and the area-weighted centroid of such an area within Asia, where the median values are computed from the same baseline for that region?", + "response": "18.653603758849354\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 200 hPa exceeding the mean of the median values for Africa computed from the all-time climatology baseline and the area-weighted centroid of such an area within Asia, where the median values are computed from the same baseline for that region, after 96 hours is 18.653603758849354.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "18.653603758849354", + "justification": "The absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region, after {duration} hours is {{actualvalue_0}}.", + "justification_text": "The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 200 hPa exceeding the mean of the median values for Africa computed from the all-time climatology baseline and the area-weighted centroid of such an area within Asia, where the median values are computed from the same baseline for that region, after 96 hours is 18.653603758849354." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_003.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_003.py", + "template_id": "tmpl_003", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Africa", + "Asia" + ], + "duration": 96 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "35707:35807:1" + }, + "rng_seed": null, + "justification": { + "text": "18.653603758849354\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with geopotential at 200 hPa exceeding the mean of the median values for Africa computed from the all-time climatology baseline and the area-weighted centroid of such an area within Asia, where the median values are computed from the same baseline for that region, after 96 hours is 18.653603758849354." + }, + "question_id": "0fC6F5", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "coordinate", + "forced_extreme_window": false, + "task_id": "8947bf6c12773063" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35680:35707:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50125:50134:1'} The data starts from April 23 06:00 and ends on April 25 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 180 hours after the end of the given time window, what is the value of specific_humidity at 850 hPa at the location within Malta where specific_humidity at 150 hPa reaches its maximum within that region?", + "response": "0.0044524106197059155\n\nJustification: The value of specific_humidity at the location within Malta where specific_humidity reaches its maximum within that region, 180 hours after the end of the given time window, is 0.0044524106197059155.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0044524106197059155", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of specific_humidity at the location within Malta where specific_humidity reaches its maximum within that region, 180 hours after the end of the given time window, is 0.0044524106197059155." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 850, + 150 + ], + "regions": [ + "Malta" + ], + "duration": 180 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "50134:50234:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0044524106197059155\n\nJustification: The value of specific_humidity at the location within Malta where specific_humidity reaches its maximum within that region, 180 hours after the end of the given time window, is 0.0044524106197059155." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1729eb07fe138575" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50125:50134:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31218:31232:1'} The data starts from May 14 12:00 and ends on May 17 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 204 hours after the end of the given time window, what is the value of geopotential at 700 hPa at the location within Longford, Ireland where geopotential at 150 hPa reaches its maximum within that region?", + "response": "29215.12890625\n\nJustification: The value of geopotential at the location within Longford, Ireland where geopotential reaches its maximum within that region, 204 hours after the end of the given time window, is 29215.12890625.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "29215.12890625", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of geopotential at the location within Longford, Ireland where geopotential reaches its maximum within that region, 204 hours after the end of the given time window, is 29215.12890625." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 700, + 150 + ], + "regions": [ + "Longford, Ireland" + ], + "duration": 204 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "31232:31332:1" + }, + "rng_seed": null, + "justification": { + "text": "29215.12890625\n\nJustification: The value of geopotential at the location within Longford, Ireland where geopotential reaches its maximum within that region, 204 hours after the end of the given time window, is 29215.12890625." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_700", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7c2891c92c2721d3" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31218:31232:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 84 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54605:54619:1'} The data starts from May 17 06:00 and ends on May 20 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 78 hours after the end of the given time window, what is the mean value of temperature at 100 hPa across all grid points in Antarctica where temperature at 400 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline?", + "response": "206.6741180419922\n\nJustification: The mean value of temperature at 100 hPa across all grid points in Antarctica where temperature at 400 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 78 hours-hour window after the given time is 206.6741180419922.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "206.6741180419922", + "justification": "The mean value of {{wb2varnames_0}}{{levelsuffixes_0}} across all grid points in {{regions_0}} where {{wb2varnames_1}}{{levelsuffixes_1}} exceeds its median value for {{regions_0}} computed from the all-time climatology baseline during the {{duration}}-hour window after the given time is {{actualvalue_0}}.", + "justification_text": "The mean value of temperature at 100 hPa across all grid points in Antarctica where temperature at 400 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 78 hours-hour window after the given time is 206.6741180419922." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_026.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_026.py", + "template_id": "tmpl_026", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the mean value of {wb2varnames_0}{levelsuffixes_0} across all grid points in {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 100, + 400 + ], + "regions": [ + "Antarctica" + ], + "duration": 78 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "54619:54719:1" + }, + "rng_seed": null, + "justification": { + "text": "206.6741180419922\n\nJustification: The mean value of temperature at 100 hPa across all grid points in Antarctica where temperature at 400 hPa exceeds its median value for Antarctica computed from the all-time climatology baseline during the 78 hours-hour window after the given time is 206.6741180419922." + }, + "question_id": "4eNWLw", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_100", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c4cc14354b851e41" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54605:54619:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79468:79478:1'} The data starts from May 24 00:00 and ends on May 26 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 162 hours after the end of the given time window, what is the mean value of v_component_of_wind at 600 hPa across all grid points in Lo\u0161ki Potok, Slovenia where v_component_of_wind at 925 hPa exceeds its median value for Lo\u0161ki Potok, Slovenia computed from the all-time climatology baseline?", + "response": "-19.588308334350586\n\nJustification: The mean value of v_component_of_wind at 600 hPa across all grid points in Lo\u0161ki Potok, Slovenia where v_component_of_wind at 925 hPa exceeds its median value for Lo\u0161ki Potok, Slovenia computed from the all-time climatology baseline during the 162 hours-hour window after the given time is -19.588308334350586.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-19.588308334350586", + "justification": "The mean value of {{wb2varnames_0}}{{levelsuffixes_0}} across all grid points in {{regions_0}} where {{wb2varnames_1}}{{levelsuffixes_1}} exceeds its median value for {{regions_0}} computed from the all-time climatology baseline during the {{duration}}-hour window after the given time is {{actualvalue_0}}.", + "justification_text": "The mean value of v_component_of_wind at 600 hPa across all grid points in Lo\u0161ki Potok, Slovenia where v_component_of_wind at 925 hPa exceeds its median value for Lo\u0161ki Potok, Slovenia computed from the all-time climatology baseline during the 162 hours-hour window after the given time is -19.588308334350586." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_026.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_026.py", + "template_id": "tmpl_026", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the mean value of {wb2varnames_0}{levelsuffixes_0} across all grid points in {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "levelsuffixes": [ + 600, + 925 + ], + "regions": [ + "Lo\u0161ki Potok, Slovenia" + ], + "duration": 162 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "time_range": "79478:79578:1" + }, + "rng_seed": null, + "justification": { + "text": "-19.588308334350586\n\nJustification: The mean value of v_component_of_wind at 600 hPa across all grid points in Lo\u0161ki Potok, Slovenia where v_component_of_wind at 925 hPa exceeds its median value for Lo\u0161ki Potok, Slovenia computed from the all-time climatology baseline during the 162 hours-hour window after the given time is -19.588308334350586." + }, + "question_id": "4eNWLw", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_600", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e5edc60722e124eb" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79468:79478:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85785:85800:1'} The data starts from September 19 06:00 and ends on September 22 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 144 hours after the end of the given time window, what is the difference between the maximum value of temperature at 1000 hPa within Cuba and the maximum value of temperature at 1000 hPa within Greece?", + "response": "6.305511474609375\n\nJustification: In 144 hours after the end of the given time window, the difference between the maximum value of temperature at 1000 hPa within Cuba and the maximum value of temperature at 1000 hPa within Greece is 6.305511474609375.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "6.305511474609375", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 144 hours after the end of the given time window, the difference between the maximum value of temperature at 1000 hPa within Cuba and the maximum value of temperature at 1000 hPa within Greece is 6.305511474609375." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Cuba", + "Greece" + ], + "duration": 144 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "85800:85900:1" + }, + "rng_seed": null, + "justification": { + "text": "6.305511474609375\n\nJustification: In 144 hours after the end of the given time window, the difference between the maximum value of temperature at 1000 hPa within Cuba and the maximum value of temperature at 1000 hPa within Greece is 6.305511474609375." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_1000", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "181d08bd56dc1039" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85785:85800:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 150 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75884:75909:1'} The data starts from December 10 00:00 and ends on December 16 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 48 hours after the end of the given time window, what is the value of specific_humidity at 600 hPa at the location within Antarctica where specific_humidity at 200 hPa reaches its maximum within that region?", + "response": "0.0003250815498176962\n\nJustification: The value of specific_humidity at the location within Antarctica where specific_humidity reaches its maximum within that region, 48 hours after the end of the given time window, is 0.0003250815498176962.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0003250815498176962", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of specific_humidity at the location within Antarctica where specific_humidity reaches its maximum within that region, 48 hours after the end of the given time window, is 0.0003250815498176962." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 600, + 200 + ], + "regions": [ + "Antarctica" + ], + "duration": 48 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "75909:76009:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0003250815498176962\n\nJustification: The value of specific_humidity at the location within Antarctica where specific_humidity reaches its maximum within that region, 48 hours after the end of the given time window, is 0.0003250815498176962." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_600", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8e694393f8a45fda" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75884:75909:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 102 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57259:57276:1'} The data starts from March 11 18:00 and ends on March 15 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 126 hours after the end of the given time window, what is the difference in the area-weighted mean value of specific_humidity at 100 hPa between Saint Lawrence River and Strait of Belle Isle?", + "response": "-8.895186822981382e-08\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 100 hPa between Saint Lawrence River and Strait of Belle Isle over the specified window is -8.895186822981382e-08.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-8.895186822981382e-08", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of specific_humidity at 100 hPa between Saint Lawrence River and Strait of Belle Isle over the specified window is -8.895186822981382e-08." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Saint Lawrence River", + "Strait of Belle Isle" + ], + "duration": 126 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "57276:57376:1" + }, + "rng_seed": null, + "justification": { + "text": "-8.895186822981382e-08\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 100 hPa between Saint Lawrence River and Strait of Belle Isle over the specified window is -8.895186822981382e-08." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_100", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "225bd0739770f60e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57259:57276:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 114 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81158:81177:1'} The data starts from July 20 12:00 and ends on July 25 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 150 hours after the end of the given time window, what is the difference in the area-weighted mean value of temperature at 500 hPa between North America and Oceania?", + "response": "-2.775264684403055\n\nJustification: The difference in the area-weighted mean value of temperature at 500 hPa between North America and Oceania over the specified window is -2.775264684403055.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-2.775264684403055", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of temperature at 500 hPa between North America and Oceania over the specified window is -2.775264684403055." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "North America", + "Oceania" + ], + "duration": 150 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "81177:81277:1" + }, + "rng_seed": null, + "justification": { + "text": "-2.775264684403055\n\nJustification: The difference in the area-weighted mean value of temperature at 500 hPa between North America and Oceania over the specified window is -2.775264684403055." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_500", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "82700a0277e1cc5a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81158:81177:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68235:68261:1'} The data starts from September 14 18:00 and ends on September 21 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 180 hours after the end of the given time window, what is the value of v_component_of_wind at 400 hPa at the location within Dhekelia Sovereign Base Area where v_component_of_wind at 925 hPa reaches its maximum within that region?", + "response": "-3.8388164043426514\n\nJustification: The value of v_component_of_wind at the location within Dhekelia Sovereign Base Area where v_component_of_wind reaches its maximum within that region, 180 hours after the end of the given time window, is -3.8388164043426514.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-3.8388164043426514", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of v_component_of_wind at the location within Dhekelia Sovereign Base Area where v_component_of_wind reaches its maximum within that region, 180 hours after the end of the given time window, is -3.8388164043426514." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "levelsuffixes": [ + 400, + 925 + ], + "regions": [ + "Dhekelia Sovereign Base Area" + ], + "duration": 180 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind", + "v_component_of_wind" + ], + "time_range": "68261:68361:1" + }, + "rng_seed": null, + "justification": { + "text": "-3.8388164043426514\n\nJustification: The value of v_component_of_wind at the location within Dhekelia Sovereign Base Area where v_component_of_wind reaches its maximum within that region, 180 hours after the end of the given time window, is -3.8388164043426514." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_400", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e04d645100f5a5f6" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68235:68261:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53910:53914:1'} The data starts from November 25 12:00 and ends on November 26 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 84 hours after the end of the given time window, what is the displacement in kilometers of the location of minimum v_component_of_wind at 200 hPa within Turkmenistan compared to its location at the end of the given time window in the provided data?", + "response": "1341.5953279922242\n\nJustification: The displacement in kilometers of the location of minimum v_component_of_wind at 200 hPa within Turkmenistan from the end of the given time window in the provided data to 84 hours after in the forecast snapshot is 1341.5953279922242.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "1341.5953279922242", + "justification": "The displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} from the end of the given time window in the provided data to {duration} hours after in the forecast snapshot is {actualvalue_0}.", + "justification_text": "The displacement in kilometers of the location of minimum v_component_of_wind at 200 hPa within Turkmenistan from the end of the given time window in the provided data to 84 hours after in the forecast snapshot is 1341.5953279922242." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_024.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_024.py", + "template_id": "tmpl_024", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} compared to its location at the end of the given time window in the provided data?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Turkmenistan" + ], + "duration": 84 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "53914:54014:1" + }, + "rng_seed": null, + "justification": { + "text": "1341.5953279922242\n\nJustification: The displacement in kilometers of the location of minimum v_component_of_wind at 200 hPa within Turkmenistan from the end of the given time window in the provided data to 84 hours after in the forecast snapshot is 1341.5953279922242." + }, + "question_id": "0jwQYD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "6c89a524afeb98ee" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53910:53914:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51099:51103:1'} The data starts from December 22 18:00 and ends on December 23 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 156 hours after the end of the given time window, what is the displacement in kilometers of the location of minimum u_component_of_wind at 500 hPa within Bight of Benin compared to its location at the end of the given time window in the provided data?", + "response": "0.0\n\nJustification: The displacement in kilometers of the location of minimum u_component_of_wind at 500 hPa within Bight of Benin from the end of the given time window in the provided data to 156 hours after in the forecast snapshot is 0.0.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0", + "justification": "The displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} from the end of the given time window in the provided data to {duration} hours after in the forecast snapshot is {actualvalue_0}.", + "justification_text": "The displacement in kilometers of the location of minimum u_component_of_wind at 500 hPa within Bight of Benin from the end of the given time window in the provided data to 156 hours after in the forecast snapshot is 0.0." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_024.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_024.py", + "template_id": "tmpl_024", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} compared to its location at the end of the given time window in the provided data?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Bight of Benin" + ], + "duration": 156 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "51103:51203:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0\n\nJustification: The displacement in kilometers of the location of minimum u_component_of_wind at 500 hPa within Bight of Benin from the end of the given time window in the provided data to 156 hours after in the forecast snapshot is 0.0." + }, + "question_id": "0jwQYD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "a9b46ba102f2f1fc" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51099:51103:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71258:71279:1'} The data starts from October 10 12:00 and ends on October 15 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 186 hours after the end of the given time window, what is the difference in the area-weighted mean value of specific_humidity at 50 hPa between Birgu, Malta and Litija, Slovenia?", + "response": "-8.601645361998912e-08\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 50 hPa between Birgu, Malta and Litija, Slovenia over the specified window is -8.601645361998912e-08.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-8.601645361998912e-08", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of specific_humidity at 50 hPa between Birgu, Malta and Litija, Slovenia over the specified window is -8.601645361998912e-08." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Birgu, Malta", + "Litija, Slovenia" + ], + "duration": 186 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "71279:71379:1" + }, + "rng_seed": null, + "justification": { + "text": "-8.601645361998912e-08\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 50 hPa between Birgu, Malta and Litija, Slovenia over the specified window is -8.601645361998912e-08." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_50", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "20162c3877c68b7b" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71258:71279:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64235:64250:1'} The data starts from December 19 18:00 and ends on December 23 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 90 hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with temperature at 600 hPa exceeding the mean of the median values for Oceania computed from the all-time climatology baseline and the area-weighted centroid of such an area within North America, where the median values are computed from the same baseline for that region?", + "response": "55.4278630002283\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with temperature at 600 hPa exceeding the mean of the median values for Oceania computed from the all-time climatology baseline and the area-weighted centroid of such an area within North America, where the median values are computed from the same baseline for that region, after 90 hours is 55.4278630002283.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "55.4278630002283", + "justification": "The absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region, after {duration} hours is {{actualvalue_0}}.", + "justification_text": "The absolute latitude difference in degrees between the area-weighted centroid of the area with temperature at 600 hPa exceeding the mean of the median values for Oceania computed from the all-time climatology baseline and the area-weighted centroid of such an area within North America, where the median values are computed from the same baseline for that region, after 90 hours is 55.4278630002283." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_003.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_003.py", + "template_id": "tmpl_003", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Oceania", + "North America" + ], + "duration": 90 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "64250:64350:1" + }, + "rng_seed": null, + "justification": { + "text": "55.4278630002283\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with temperature at 600 hPa exceeding the mean of the median values for Oceania computed from the all-time climatology baseline and the area-weighted centroid of such an area within North America, where the median values are computed from the same baseline for that region, after 90 hours is 55.4278630002283." + }, + "question_id": "0fC6F5", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "coordinate", + "forced_extreme_window": false, + "task_id": "4c732f3d76f1aef8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64235:64250:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 156 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79495:79521:1'} The data starts from May 30 18:00 and ends on June 06 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 204 hours after the end of the given time window, what is the displacement in kilometers of the location of minimum temperature at 925 hPa within Europe compared to its location at the end of the given time window in the provided data?", + "response": "2640.0716052891285\n\nJustification: The displacement in kilometers of the location of minimum temperature at 925 hPa within Europe from the end of the given time window in the provided data to 204 hours after in the forecast snapshot is 2640.0716052891285.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2640.0716052891285", + "justification": "The displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} from the end of the given time window in the provided data to {duration} hours after in the forecast snapshot is {actualvalue_0}.", + "justification_text": "The displacement in kilometers of the location of minimum temperature at 925 hPa within Europe from the end of the given time window in the provided data to 204 hours after in the forecast snapshot is 2640.0716052891285." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_024.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_024.py", + "template_id": "tmpl_024", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} compared to its location at the end of the given time window in the provided data?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Europe" + ], + "duration": 204 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "79521:79621:1" + }, + "rng_seed": null, + "justification": { + "text": "2640.0716052891285\n\nJustification: The displacement in kilometers of the location of minimum temperature at 925 hPa within Europe from the end of the given time window in the provided data to 204 hours after in the forecast snapshot is 2640.0716052891285." + }, + "question_id": "0jwQYD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "f508b7c9981ff2f9" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79495:79521:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 96 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71119:71135:1'} The data starts from September 05 18:00 and ends on September 09 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 108 hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with v_component_of_wind at 925 hPa exceeding the mean of the median values for Caribbean Sea computed from the all-time climatology baseline and the area-weighted centroid of such an area within Massachusetts Bay, where the median values are computed from the same baseline for that region?", + "response": "25.47134061215432\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with v_component_of_wind at 925 hPa exceeding the mean of the median values for Caribbean Sea computed from the all-time climatology baseline and the area-weighted centroid of such an area within Massachusetts Bay, where the median values are computed from the same baseline for that region, after 108 hours is 25.47134061215432.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "25.47134061215432", + "justification": "The absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region, after {duration} hours is {{actualvalue_0}}.", + "justification_text": "The absolute latitude difference in degrees between the area-weighted centroid of the area with v_component_of_wind at 925 hPa exceeding the mean of the median values for Caribbean Sea computed from the all-time climatology baseline and the area-weighted centroid of such an area within Massachusetts Bay, where the median values are computed from the same baseline for that region, after 108 hours is 25.47134061215432." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_003.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_003.py", + "template_id": "tmpl_003", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Caribbean Sea", + "Massachusetts Bay" + ], + "duration": 108 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "71135:71235:1" + }, + "rng_seed": null, + "justification": { + "text": "25.47134061215432\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with v_component_of_wind at 925 hPa exceeding the mean of the median values for Caribbean Sea computed from the all-time climatology baseline and the area-weighted centroid of such an area within Massachusetts Bay, where the median values are computed from the same baseline for that region, after 108 hours is 25.47134061215432." + }, + "question_id": "0fC6F5", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "coordinate", + "forced_extreme_window": false, + "task_id": "ca9aa20e489b41fb" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71119:71135:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40494:40509:1'} The data starts from September 19 12:00 and ends on September 23 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 126 hours after the end of the given time window, what is the maximum difference in u_component_of_wind at 1000 hPa (m/s) between eSwatini and The Bahamas?", + "response": "7.522212445735931\n\nJustification: The maximum difference in u_component_of_wind at 1000 hPa (m/s) between eSwatini and The Bahamas in the 126 hours after the end of the given time window is 7.522212445735931.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "7.522212445735931", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in u_component_of_wind at 1000 hPa (m/s) between eSwatini and The Bahamas in the 126 hours after the end of the given time window is 7.522212445735931." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "eSwatini", + "The Bahamas" + ], + "units": [ + "m/s" + ], + "duration": 126 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "40509:40609:1" + }, + "rng_seed": null, + "justification": { + "text": "7.522212445735931\n\nJustification: The maximum difference in u_component_of_wind at 1000 hPa (m/s) between eSwatini and The Bahamas in the 126 hours after the end of the given time window is 7.522212445735931." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_1000", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d8a362818d30f072" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40494:40509:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 108 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79677:79695:1'} The data starts from July 15 06:00 and ends on July 19 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 42 hours after the end of the given time window, what is the difference between the maximum value of geopotential at 200 hPa within Europe and the maximum value of geopotential at 200 hPa within South America?", + "response": "625.171875\n\nJustification: In 42 hours after the end of the given time window, the difference between the maximum value of geopotential at 200 hPa within Europe and the maximum value of geopotential at 200 hPa within South America is 625.171875.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "625.171875", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 42 hours after the end of the given time window, the difference between the maximum value of geopotential at 200 hPa within Europe and the maximum value of geopotential at 200 hPa within South America is 625.171875." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Europe", + "South America" + ], + "duration": 42 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "79695:79795:1" + }, + "rng_seed": null, + "justification": { + "text": "625.171875\n\nJustification: In 42 hours after the end of the given time window, the difference between the maximum value of geopotential at 200 hPa within Europe and the maximum value of geopotential at 200 hPa within South America is 625.171875." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_200", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f8e75ce25f8474fc" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79677:79695:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 132 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82047:82069:1'} The data starts from February 27 18:00 and ends on March 05 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 30 hours after the end of the given time window, what is the displacement in kilometers of the location of minimum temperature at 850 hPa within Spain compared to its location at the end of the given time window in the provided data?", + "response": "515.8259032138914\n\nJustification: The displacement in kilometers of the location of minimum temperature at 850 hPa within Spain from the end of the given time window in the provided data to 30 hours after in the forecast snapshot is 515.8259032138914.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "515.8259032138914", + "justification": "The displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} from the end of the given time window in the provided data to {duration} hours after in the forecast snapshot is {actualvalue_0}.", + "justification_text": "The displacement in kilometers of the location of minimum temperature at 850 hPa within Spain from the end of the given time window in the provided data to 30 hours after in the forecast snapshot is 515.8259032138914." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_024.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_024.py", + "template_id": "tmpl_024", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} compared to its location at the end of the given time window in the provided data?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Spain" + ], + "duration": 30 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "82069:82169:1" + }, + "rng_seed": null, + "justification": { + "text": "515.8259032138914\n\nJustification: The displacement in kilometers of the location of minimum temperature at 850 hPa within Spain from the end of the given time window in the provided data to 30 hours after in the forecast snapshot is 515.8259032138914." + }, + "question_id": "0jwQYD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "a89597406f97ef0e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82047:82069:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54272:54278:1'} The data starts from February 24 00:00 and ends on February 25 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 150 hours after the end of the given time window, what is the value of specific_humidity at 700 hPa at the location within Hungary where specific_humidity at 200 hPa reaches its maximum within that region?", + "response": "0.0005408343276940286\n\nJustification: The value of specific_humidity at the location within Hungary where specific_humidity reaches its maximum within that region, 150 hours after the end of the given time window, is 0.0005408343276940286.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0005408343276940286", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of specific_humidity at the location within Hungary where specific_humidity reaches its maximum within that region, 150 hours after the end of the given time window, is 0.0005408343276940286." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 700, + 200 + ], + "regions": [ + "Hungary" + ], + "duration": 150 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "54278:54378:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0005408343276940286\n\nJustification: The value of specific_humidity at the location within Hungary where specific_humidity reaches its maximum within that region, 150 hours after the end of the given time window, is 0.0005408343276940286." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_700", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "69641eb688cef74b" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54272:54278:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 60 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62481:62491:1'} The data starts from October 07 06:00 and ends on October 09 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 204 hours after the end of the given time window, what is the difference in the area-weighted mean value of specific_humidity at 925 hPa between South America and Asia?", + "response": "0.00526521788591623\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 925 hPa between South America and Asia over the specified window is 0.00526521788591623.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.00526521788591623", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of specific_humidity at 925 hPa between South America and Asia over the specified window is 0.00526521788591623." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "South America", + "Asia" + ], + "duration": 204 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "62491:62591:1" + }, + "rng_seed": null, + "justification": { + "text": "0.00526521788591623\n\nJustification: The difference in the area-weighted mean value of specific_humidity at 925 hPa between South America and Asia over the specified window is 0.00526521788591623." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_925", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "74de82a1ac249bce" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62481:62491:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 144 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58873:58897:1'} The data starts from April 19 06:00 and ends on April 25 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 162 hours after the end of the given time window, what is the displacement in kilometers of the location of minimum geopotential at 200 hPa within Floriana, Malta compared to its location at the end of the given time window in the provided data?", + "response": "0.0\n\nJustification: The displacement in kilometers of the location of minimum geopotential at 200 hPa within Floriana, Malta from the end of the given time window in the provided data to 162 hours after in the forecast snapshot is 0.0.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0", + "justification": "The displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} from the end of the given time window in the provided data to {duration} hours after in the forecast snapshot is {actualvalue_0}.", + "justification_text": "The displacement in kilometers of the location of minimum geopotential at 200 hPa within Floriana, Malta from the end of the given time window in the provided data to 162 hours after in the forecast snapshot is 0.0." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_024.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_024.py", + "template_id": "tmpl_024", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the displacement in kilometers of the location of minimum {wb2varnames_0}{levelsuffixes_0} within {regions_0} compared to its location at the end of the given time window in the provided data?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Floriana, Malta" + ], + "duration": 162 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "58897:58997:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0\n\nJustification: The displacement in kilometers of the location of minimum geopotential at 200 hPa within Floriana, Malta from the end of the given time window in the provided data to 162 hours after in the forecast snapshot is 0.0." + }, + "question_id": "0jwQYD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "d8f8a444d3b3abbc" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58873:58897:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 54 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47508:47517:1'} The data starts from July 09 00:00 and ends on July 11 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 120 hours after the end of the given time window, what is the mean value of specific_humidity at 200 hPa across all grid points in Al Jawf, Saudi Arabia where specific_humidity at 250 hPa exceeds its median value for Al Jawf, Saudi Arabia computed from the all-time climatology baseline?", + "response": "2.2266269297688268e-05\n\nJustification: The mean value of specific_humidity at 200 hPa across all grid points in Al Jawf, Saudi Arabia where specific_humidity at 250 hPa exceeds its median value for Al Jawf, Saudi Arabia computed from the all-time climatology baseline during the 120 hours-hour window after the given time is 2.2266269297688268e-05.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2.2266269297688268e-05", + "justification": "The mean value of {{wb2varnames_0}}{{levelsuffixes_0}} across all grid points in {{regions_0}} where {{wb2varnames_1}}{{levelsuffixes_1}} exceeds its median value for {{regions_0}} computed from the all-time climatology baseline during the {{duration}}-hour window after the given time is {{actualvalue_0}}.", + "justification_text": "The mean value of specific_humidity at 200 hPa across all grid points in Al Jawf, Saudi Arabia where specific_humidity at 250 hPa exceeds its median value for Al Jawf, Saudi Arabia computed from the all-time climatology baseline during the 120 hours-hour window after the given time is 2.2266269297688268e-05." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_026.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_026.py", + "template_id": "tmpl_026", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the mean value of {wb2varnames_0}{levelsuffixes_0} across all grid points in {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 200, + 250 + ], + "regions": [ + "Al Jawf, Saudi Arabia" + ], + "duration": 120 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "47517:47617:1" + }, + "rng_seed": null, + "justification": { + "text": "2.2266269297688268e-05\n\nJustification: The mean value of specific_humidity at 200 hPa across all grid points in Al Jawf, Saudi Arabia where specific_humidity at 250 hPa exceeds its median value for Al Jawf, Saudi Arabia computed from the all-time climatology baseline during the 120 hours-hour window after the given time is 2.2266269297688268e-05." + }, + "question_id": "4eNWLw", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_200", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a7d25bf5ee99cf07" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47508:47517:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42814:42815:1'} The data corresponds to corresponds to a snapshot on April 21 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 72 hours after the end of the given time window, what is the mean value of geopotential at 1000 hPa across all grid points in Paraguay where geopotential at 925 hPa exceeds its median value for Paraguay computed from the all-time climatology baseline?", + "response": "1004.305908203125\n\nJustification: The mean value of geopotential at 1000 hPa across all grid points in Paraguay where geopotential at 925 hPa exceeds its median value for Paraguay computed from the all-time climatology baseline during the 72 hours-hour window after the given time is 1004.305908203125.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "1004.305908203125", + "justification": "The mean value of {{wb2varnames_0}}{{levelsuffixes_0}} across all grid points in {{regions_0}} where {{wb2varnames_1}}{{levelsuffixes_1}} exceeds its median value for {{regions_0}} computed from the all-time climatology baseline during the {{duration}}-hour window after the given time is {{actualvalue_0}}.", + "justification_text": "The mean value of geopotential at 1000 hPa across all grid points in Paraguay where geopotential at 925 hPa exceeds its median value for Paraguay computed from the all-time climatology baseline during the 72 hours-hour window after the given time is 1004.305908203125." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_026.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_026.py", + "template_id": "tmpl_026", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the mean value of {wb2varnames_0}{levelsuffixes_0} across all grid points in {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 1000, + 925 + ], + "regions": [ + "Paraguay" + ], + "duration": 72 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "42815:42915:1" + }, + "rng_seed": null, + "justification": { + "text": "1004.305908203125\n\nJustification: The mean value of geopotential at 1000 hPa across all grid points in Paraguay where geopotential at 925 hPa exceeds its median value for Paraguay computed from the all-time climatology baseline during the 72 hours-hour window after the given time is 1004.305908203125." + }, + "question_id": "4eNWLw", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_1000", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8fb39f5e863c7c9d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42814:42815:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 126 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46845:46866:1'} The data starts from January 24 06:00 and ends on January 29 06:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 108 hours after the end of the given time window, what is the difference between the maximum value of u_component_of_wind at 700 hPa within Antarctica and the maximum value of u_component_of_wind at 700 hPa within Oceania?", + "response": "-15.996185302734375\n\nJustification: In 108 hours after the end of the given time window, the difference between the maximum value of u_component_of_wind at 700 hPa within Antarctica and the maximum value of u_component_of_wind at 700 hPa within Oceania is -15.996185302734375.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-15.996185302734375", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 108 hours after the end of the given time window, the difference between the maximum value of u_component_of_wind at 700 hPa within Antarctica and the maximum value of u_component_of_wind at 700 hPa within Oceania is -15.996185302734375." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Antarctica", + "Oceania" + ], + "duration": 108 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "46866:46966:1" + }, + "rng_seed": null, + "justification": { + "text": "-15.996185302734375\n\nJustification: In 108 hours after the end of the given time window, the difference between the maximum value of u_component_of_wind at 700 hPa within Antarctica and the maximum value of u_component_of_wind at 700 hPa within Oceania is -15.996185302734375." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_700", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e5ca4df9abbd2b6f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46845:46866:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61666:61672:1'} The data starts from March 17 12:00 and ends on March 18 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 156 hours after the end of the given time window, what is the value of u_component_of_wind at 400 hPa at the location within Guam where v_component_of_wind at 500 hPa reaches its maximum within that region?", + "response": "9.370022773742676\n\nJustification: The value of u_component_of_wind at the location within Guam where v_component_of_wind reaches its maximum within that region, 156 hours after the end of the given time window, is 9.370022773742676.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "9.370022773742676", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of u_component_of_wind at the location within Guam where v_component_of_wind reaches its maximum within that region, 156 hours after the end of the given time window, is 9.370022773742676." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "v_component_of_wind" + ], + "levelsuffixes": [ + 400, + 500 + ], + "regions": [ + "Guam" + ], + "duration": 156 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "v_component_of_wind" + ], + "time_range": "61672:61772:1" + }, + "rng_seed": null, + "justification": { + "text": "9.370022773742676\n\nJustification: The value of u_component_of_wind at the location within Guam where v_component_of_wind reaches its maximum within that region, 156 hours after the end of the given time window, is 9.370022773742676." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_400", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "305557eac6729a48" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61666:61672:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50919:50931:1'} The data starts from November 07 18:00 and ends on November 10 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 204 hours after the end of the given time window, what is the difference between the maximum value of temperature at 500 hPa within East Siberian Sea and the maximum value of temperature at 500 hPa within Shark Bay?", + "response": "-24.898941040039062\n\nJustification: In 204 hours after the end of the given time window, the difference between the maximum value of temperature at 500 hPa within East Siberian Sea and the maximum value of temperature at 500 hPa within Shark Bay is -24.898941040039062.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "-24.898941040039062", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 204 hours after the end of the given time window, the difference between the maximum value of temperature at 500 hPa within East Siberian Sea and the maximum value of temperature at 500 hPa within Shark Bay is -24.898941040039062." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "East Siberian Sea", + "Shark Bay" + ], + "duration": 204 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "50931:51031:1" + }, + "rng_seed": null, + "justification": { + "text": "-24.898941040039062\n\nJustification: In 204 hours after the end of the given time window, the difference between the maximum value of temperature at 500 hPa within East Siberian Sea and the maximum value of temperature at 500 hPa within Shark Bay is -24.898941040039062." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_500", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a611de017c5e5e1d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50919:50931:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 66 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41121:41132:1'} The data starts from February 23 06:00 and ends on February 25 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 36 hours after the end of the given time window, what is the value of geopotential at 100 hPa at the location within Karaginskiy Gulf where geopotential at 925 hPa reaches its maximum within that region?", + "response": "153693.90625\n\nJustification: The value of geopotential at the location within Karaginskiy Gulf where geopotential reaches its maximum within that region, 36 hours after the end of the given time window, is 153693.90625.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "153693.90625", + "justification": "The value of {{wb2varnames_0}} at the location within {{regions_0}} where {{wb2varnames_1}} reaches its maximum within that region, {{duration}} hours after the end of the given time window, is {{actualvalue_0}}.", + "justification_text": "The value of geopotential at the location within Karaginskiy Gulf where geopotential reaches its maximum within that region, 36 hours after the end of the given time window, is 153693.90625." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the value of {wb2varnames_0}{levelsuffixes_0} at the location within {regions_0} where {wb2varnames_1}{levelsuffixes_1} reaches its maximum within that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 100, + 925 + ], + "regions": [ + "Karaginskiy Gulf" + ], + "duration": 36 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "41132:41232:1" + }, + "rng_seed": null, + "justification": { + "text": "153693.90625\n\nJustification: The value of geopotential at the location within Karaginskiy Gulf where geopotential reaches its maximum within that region, 36 hours after the end of the given time window, is 153693.90625." + }, + "question_id": "0XV85i", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_100", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "de2b878150952f74" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41121:41132:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 72 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30424:30436:1'} The data starts from October 29 00:00 and ends on October 31 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 162 hours after the end of the given time window, what is the mean value of temperature at 150 hPa across all grid points in Asia where temperature at 300 hPa exceeds its median value for Asia computed from the all-time climatology baseline?", + "response": "205.67776489257812\n\nJustification: The mean value of temperature at 150 hPa across all grid points in Asia where temperature at 300 hPa exceeds its median value for Asia computed from the all-time climatology baseline during the 162 hours-hour window after the given time is 205.67776489257812.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "205.67776489257812", + "justification": "The mean value of {{wb2varnames_0}}{{levelsuffixes_0}} across all grid points in {{regions_0}} where {{wb2varnames_1}}{{levelsuffixes_1}} exceeds its median value for {{regions_0}} computed from the all-time climatology baseline during the {{duration}}-hour window after the given time is {{actualvalue_0}}.", + "justification_text": "The mean value of temperature at 150 hPa across all grid points in Asia where temperature at 300 hPa exceeds its median value for Asia computed from the all-time climatology baseline during the 162 hours-hour window after the given time is 205.67776489257812." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_026.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_026.py", + "template_id": "tmpl_026", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the mean value of {wb2varnames_0}{levelsuffixes_0} across all grid points in {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 150, + 300 + ], + "regions": [ + "Asia" + ], + "duration": 162 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "30436:30536:1" + }, + "rng_seed": null, + "justification": { + "text": "205.67776489257812\n\nJustification: The mean value of temperature at 150 hPa across all grid points in Asia where temperature at 300 hPa exceeds its median value for Asia computed from the all-time climatology baseline during the 162 hours-hour window after the given time is 205.67776489257812." + }, + "question_id": "4eNWLw", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_150", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "48cc92db4d81c3da" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30424:30436:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 18 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45768:45771:1'} The data starts from April 30 00:00 and ends on April 30 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 120 hours after the end of the given time window, what is the maximum difference in temperature at 300 hPa (K) between Chesapeake Bay and Delaware Bay?", + "response": "4.6329498291015625\n\nJustification: The maximum difference in temperature at 300 hPa (K) between Chesapeake Bay and Delaware Bay in the 120 hours after the end of the given time window is 4.6329498291015625.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "4.6329498291015625", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in temperature at 300 hPa (K) between Chesapeake Bay and Delaware Bay in the 120 hours after the end of the given time window is 4.6329498291015625." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Chesapeake Bay", + "Delaware Bay" + ], + "units": [ + "K" + ], + "duration": 120 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "45771:45871:1" + }, + "rng_seed": null, + "justification": { + "text": "4.6329498291015625\n\nJustification: The maximum difference in temperature at 300 hPa (K) between Chesapeake Bay and Delaware Bay in the 120 hours after the end of the given time window is 4.6329498291015625." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_300", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a7d90dd5293cd0b3" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45768:45771:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 78 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34278:34291:1'} The data starts from June 18 12:00 and ends on June 21 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 192 hours after the end of the given time window, what is the maximum difference in u_component_of_wind at 100 hPa (m/s) between Livorno, Italy and Bistrita-Nasaud, Romania?", + "response": "7.3539581298828125\n\nJustification: The maximum difference in u_component_of_wind at 100 hPa (m/s) between Livorno, Italy and Bistrita-Nasaud, Romania in the 192 hours after the end of the given time window is 7.3539581298828125.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "7.3539581298828125", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in u_component_of_wind at 100 hPa (m/s) between Livorno, Italy and Bistrita-Nasaud, Romania in the 192 hours after the end of the given time window is 7.3539581298828125." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Livorno, Italy", + "Bistrita-Nasaud, Romania" + ], + "units": [ + "m/s" + ], + "duration": 192 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "34291:34391:1" + }, + "rng_seed": null, + "justification": { + "text": "7.3539581298828125\n\nJustification: The maximum difference in u_component_of_wind at 100 hPa (m/s) between Livorno, Italy and Bistrita-Nasaud, Romania in the 192 hours after the end of the given time window is 7.3539581298828125." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_100", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a9579a0995dca5dd" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34278:34291:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 90 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46824:46839:1'} The data starts from January 19 00:00 and ends on January 22 12:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 72 hours after the end of the given time window, what is the maximum difference in temperature at 200 hPa (K) between Durr\u00ebs, Albania and Al-Muthannia, Iraq?", + "response": "13.927947998046875\n\nJustification: The maximum difference in temperature at 200 hPa (K) between Durr\u00ebs, Albania and Al-Muthannia, Iraq in the 72 hours after the end of the given time window is 13.927947998046875.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "13.927947998046875", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in temperature at 200 hPa (K) between Durr\u00ebs, Albania and Al-Muthannia, Iraq in the 72 hours after the end of the given time window is 13.927947998046875." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Durr\u00ebs, Albania", + "Al-Muthannia, Iraq" + ], + "units": [ + "K" + ], + "duration": 72 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "46839:46939:1" + }, + "rng_seed": null, + "justification": { + "text": "13.927947998046875\n\nJustification: The maximum difference in temperature at 200 hPa (K) between Durr\u00ebs, Albania and Al-Muthannia, Iraq in the 72 hours after the end of the given time window is 13.927947998046875." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_200", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c76fcacb928135ec" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46824:46839:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 30 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65812:65817:1'} The data starts from January 18 00:00 and ends on January 19 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 132 hours after the end of the given time window, what is the difference between the maximum value of specific_humidity at 850 hPa within French Southern and Antarctic Lands and the maximum value of specific_humidity at 850 hPa within Yemen?", + "response": "0.0018424717709422112\n\nJustification: In 132 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 850 hPa within French Southern and Antarctic Lands and the maximum value of specific_humidity at 850 hPa within Yemen is 0.0018424717709422112.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0018424717709422112", + "justification": "In {duration} hours after the end of the given time window, the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1} is {{actualvalue_0}}.", + "justification_text": "In 132 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 850 hPa within French Southern and Antarctic Lands and the maximum value of specific_humidity at 850 hPa within Yemen is 0.0018424717709422112." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_019.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_019.py", + "template_id": "tmpl_019", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference between the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the maximum value of {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "French Southern and Antarctic Lands", + "Yemen" + ], + "duration": 132 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "65817:65917:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0018424717709422112\n\nJustification: In 132 hours after the end of the given time window, the difference between the maximum value of specific_humidity at 850 hPa within French Southern and Antarctic Lands and the maximum value of specific_humidity at 850 hPa within Yemen is 0.0018424717709422112." + }, + "question_id": "YYzvuD", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "07f91b61ef00a5fb" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65812:65817:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 36 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70767:70773:1'} The data starts from June 09 18:00 and ends on June 11 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 192 hours after the end of the given time window, what is the difference in the area-weighted mean value of temperature at 100 hPa between Prince of Wales Strait and Andaman Sea?", + "response": "38.20040282276091\n\nJustification: The difference in the area-weighted mean value of temperature at 100 hPa between Prince of Wales Strait and Andaman Sea over the specified window is 38.20040282276091.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "38.20040282276091", + "justification": "The difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1} over the specified window is {actualvalue_0}.", + "justification_text": "The difference in the area-weighted mean value of temperature at 100 hPa between Prince of Wales Strait and Andaman Sea over the specified window is 38.20040282276091." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_000.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_000.py", + "template_id": "tmpl_000", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the difference in the area-weighted mean value of {wb2varnames_0}{levelsuffixes_0} between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Prince of Wales Strait", + "Andaman Sea" + ], + "duration": 192 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "70773:70873:1" + }, + "rng_seed": null, + "justification": { + "text": "38.20040282276091\n\nJustification: The difference in the area-weighted mean value of temperature at 100 hPa between Prince of Wales Strait and Andaman Sea over the specified window is 38.20040282276091." + }, + "question_id": "RGsu57", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_100", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dcc63ceac74ed279" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70767:70773:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 42 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78273:78280:1'} The data starts from July 29 06:00 and ends on July 30 18:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 138 hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with temperature at 200 hPa exceeding the mean of the median values for Guinea-Bissau computed from the all-time climatology baseline and the area-weighted centroid of such an area within Oman, where the median values are computed from the same baseline for that region?", + "response": "8.657348346056617\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with temperature at 200 hPa exceeding the mean of the median values for Guinea-Bissau computed from the all-time climatology baseline and the area-weighted centroid of such an area within Oman, where the median values are computed from the same baseline for that region, after 138 hours is 8.657348346056617.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "8.657348346056617", + "justification": "The absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region, after {duration} hours is {{actualvalue_0}}.", + "justification_text": "The absolute latitude difference in degrees between the area-weighted centroid of the area with temperature at 200 hPa exceeding the mean of the median values for Guinea-Bissau computed from the all-time climatology baseline and the area-weighted centroid of such an area within Oman, where the median values are computed from the same baseline for that region, after 138 hours is 8.657348346056617." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_003.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_003.py", + "template_id": "tmpl_003", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the absolute latitude difference in degrees between the area-weighted centroid of the area with {wb2varnames_0}{levelsuffixes_0} exceeding the mean of the median values for {regions_0} computed from the all-time climatology baseline and the area-weighted centroid of such an area within {regions_1}, where the median values are computed from the same baseline for that region?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Guinea-Bissau", + "Oman" + ], + "duration": 138 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "78280:78380:1" + }, + "rng_seed": null, + "justification": { + "text": "8.657348346056617\n\nJustification: The absolute latitude difference in degrees between the area-weighted centroid of the area with temperature at 200 hPa exceeding the mean of the median values for Guinea-Bissau computed from the all-time climatology baseline and the area-weighted centroid of such an area within Oman, where the median values are computed from the same baseline for that region, after 138 hours is 8.657348346056617." + }, + "question_id": "0fC6F5", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "coordinate", + "forced_extreme_window": false, + "task_id": "60e5a91208256f7e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78273:78280:1" + } + }, + { + "prompt": "The following data shows the global weather fields over a period of 24 hours, sampled at an interval of 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55325:55329:1'} The data starts from November 13 06:00 and ends on November 14 00:00. Based on the above data, answer the following question:", + "question": "In the 6-hour snapshot 138 hours after the end of the given time window, what is the maximum difference in temperature at 850 hPa (K) between Garabogaz Bay and Sea of Azov?", + "response": "2.73699951171875\n\nJustification: The maximum difference in temperature at 850 hPa (K) between Garabogaz Bay and Sea of Azov in the 138 hours after the end of the given time window is 2.73699951171875.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2.73699951171875", + "justification": "The maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1} in the {duration} hours after the end of the given time window is {actualvalue_0}.", + "justification_text": "The maximum difference in temperature at 850 hPa (K) between Garabogaz Bay and Sea of Azov in the 138 hours after the end of the given time window is 2.73699951171875." + }, + "code_path": "templates/synthetic_task_code/numeric/non_snapshot/tmpl_011.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/non_snapshot/sampling_tmpl_011.py", + "template_id": "tmpl_011", + "template_str": "In the 6-hour snapshot {duration} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} ({units_0}) between {regions_0} and {regions_1}?", + "template_type": "timeseries", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [ + 0 + ] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Garabogaz Bay", + "Sea of Azov" + ], + "units": [ + "K" + ], + "duration": 138 + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "55329:55429:1" + }, + "rng_seed": null, + "justification": { + "text": "2.73699951171875\n\nJustification: The maximum difference in temperature at 850 hPa (K) between Garabogaz Bay and Sea of Azov in the 138 hours after the end of the given time window is 2.73699951171875." + }, + "question_id": "HsrO9b", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b63a5b194526dc74" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55325:55329:1" + } + } +] \ No newline at end of file diff --git a/level2b_numeric_part1.json b/level2b_numeric_part1.json new file mode 100644 index 0000000000000000000000000000000000000000..4e211fd4c538b379d390476e435711bdc0e2bbcd --- /dev/null +++ b/level2b_numeric_part1.json @@ -0,0 +1,4833 @@ +[ + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72117:72118:1'} The data corresponds to corresponds to a snapshot on May 12 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Myanmar where both geopotential at 50 hPa and geopotential at 150 hPa exceed their respective median values for Myanmar computed from the all-time climatology baseline?", + "response": "658621.6885275303\n\nJustification: At 24 hours into the future, the area in square kilometers within Myanmar where both geopotential at 50 hPa and geopotential at 150 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 658621.6885275303.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "658621.6885275303", + "justification": "At {times_0} hours into the future, the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for that region computed from the all-time climatology baseline is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the area in square kilometers within Myanmar where both geopotential at 50 hPa and geopotential at 150 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 658621.6885275303." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 50, + 150 + ], + "regions": [ + "Myanmar" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "72118:72218:1" + }, + "rng_seed": null, + "justification": { + "text": "658621.6885275303\n\nJustification: At 24 hours into the future, the area in square kilometers within Myanmar where both geopotential at 50 hPa and geopotential at 150 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 658621.6885275303." + }, + "question_id": "N3bgvg", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "5424ca537e21ce3d" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72117:72118:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48599:48600:1'} The data corresponds to corresponds to a snapshot on April 06 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum specific_humidity at 50 hPa within Azerbaijan and the area-weighted centroid of the area of maximum specific_humidity at 50 hPa within Northern Mariana Islands?", + "response": "9112.156377947811\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 50 hPa in Azerbaijan and the area-weighted centroid of maximum specific_humidity at 50 hPa in Northern Mariana Islands is 9112.156377947811 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "9112.156377947811", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 50 hPa in Azerbaijan and the area-weighted centroid of maximum specific_humidity at 50 hPa in Northern Mariana Islands is 9112.156377947811 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Azerbaijan", + "Northern Mariana Islands" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "48600:48700:1" + }, + "rng_seed": null, + "justification": { + "text": "9112.156377947811\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 50 hPa in Azerbaijan and the area-weighted centroid of maximum specific_humidity at 50 hPa in Northern Mariana Islands is 9112.156377947811 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "239ca8004b2741cb" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48599:48600:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56342:56343:1'} The data corresponds to corresponds to a snapshot on July 25 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum specific_humidity at 250 hPa within Kriva Palanka, North Macedonia and the area-weighted centroid of the area of maximum specific_humidity at 250 hPa within Bauchi, Nigeria?", + "response": "3639.074330847647\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 250 hPa in Kriva Palanka, North Macedonia and the area-weighted centroid of maximum specific_humidity at 250 hPa in Bauchi, Nigeria is 3639.074330847647 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "3639.074330847647", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 250 hPa in Kriva Palanka, North Macedonia and the area-weighted centroid of maximum specific_humidity at 250 hPa in Bauchi, Nigeria is 3639.074330847647 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Kriva Palanka, North Macedonia", + "Bauchi, Nigeria" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "56343:56443:1" + }, + "rng_seed": null, + "justification": { + "text": "3639.074330847647\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 250 hPa in Kriva Palanka, North Macedonia and the area-weighted centroid of maximum specific_humidity at 250 hPa in Bauchi, Nigeria is 3639.074330847647 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "3a131574c1349130" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56342:56343:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29222:29223:1'} The data corresponds to corresponds to a snapshot on January 01 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Akrotiri Sovereign Base Area where geopotential at 250 hPa exceeds its median value for Akrotiri Sovereign Base Area computed from the initial snapshot (time index 0) of the provided data window?", + "response": "0.0\n\nJustification: At 24 hours, the area in square kilometers within Akrotiri Sovereign Base Area where geopotential at 250 hPa exceeds its median value for Akrotiri Sovereign Base Area computed from the initial snapshot (time index 0) of the provided data window is 0.0.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0", + "justification": "At {times_0} hours, the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window is {{actualvalue_0}}.", + "justification_text": "At 24 hours, the area in square kilometers within Akrotiri Sovereign Base Area where geopotential at 250 hPa exceeds its median value for Akrotiri Sovereign Base Area computed from the initial snapshot (time index 0) of the provided data window is 0.0." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_008.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_008.py", + "template_id": "tmpl_008", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Akrotiri Sovereign Base Area" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "29223:29323:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0\n\nJustification: At 24 hours, the area in square kilometers within Akrotiri Sovereign Base Area where geopotential at 250 hPa exceeds its median value for Akrotiri Sovereign Base Area computed from the initial snapshot (time index 0) of the provided data window is 0.0." + }, + "question_id": "KcX1QC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "ac1484685cf308a9" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29222:29223:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79740:79741:1'} The data corresponds to corresponds to a snapshot on July 31 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Falkirk, United Kingdom where both specific_humidity at 300 hPa and specific_humidity at 925 hPa exceed their respective median values for Falkirk, United Kingdom computed from the all-time climatology baseline?", + "response": "274.5139193745213\n\nJustification: At 24 hours into the future, the area in square kilometers within Falkirk, United Kingdom where both specific_humidity at 300 hPa and specific_humidity at 925 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 274.5139193745213.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "274.5139193745213", + "justification": "At {times_0} hours into the future, the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for that region computed from the all-time climatology baseline is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the area in square kilometers within Falkirk, United Kingdom where both specific_humidity at 300 hPa and specific_humidity at 925 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 274.5139193745213." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 300, + 925 + ], + "regions": [ + "Falkirk, United Kingdom" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "79741:79841:1" + }, + "rng_seed": null, + "justification": { + "text": "274.5139193745213\n\nJustification: At 24 hours into the future, the area in square kilometers within Falkirk, United Kingdom where both specific_humidity at 300 hPa and specific_humidity at 925 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 274.5139193745213." + }, + "question_id": "N3bgvg", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "2595bf8de89f0fef" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79740:79741:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75700:75701:1'} The data corresponds to corresponds to a snapshot on October 25 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in u_component_of_wind at 250 hPa between any two grid points within Delaware Bay?", + "response": "5.488016128540039\n\nJustification: At 24 hours into the future, the maximum difference in u_component_of_wind at 250 hPa between any two grid points within Delaware Bay is 5.488016128540039.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "5.488016128540039", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in u_component_of_wind at 250 hPa between any two grid points within Delaware Bay is 5.488016128540039." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Delaware Bay" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "75701:75801:1" + }, + "rng_seed": null, + "justification": { + "text": "5.488016128540039\n\nJustification: At 24 hours into the future, the maximum difference in u_component_of_wind at 250 hPa between any two grid points within Delaware Bay is 5.488016128540039." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_250", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "690f0570d433cd6c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75700:75701:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37986:37987:1'} The data corresponds to corresponds to a snapshot on December 31 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with specific_humidity at 300 hPa values above the median within Tougu\u00e9, Guinea and the centroid of the area with specific_humidity at 300 hPa values above the median within Nusa Tenggara Timur, Indonesia?", + "response": "14925.777479854869\n\nJustification: The displacement in kilometers between the centroid of the area with specific_humidity at 300 hPa values above the median within Tougu\u00e9, Guinea and the centroid of the area with specific_humidity at 300 hPa values above the median within Nusa Tenggara Timur, Indonesia at 24 hours into the future is 14925.777479854869.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "14925.777479854869", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with specific_humidity at 300 hPa values above the median within Tougu\u00e9, Guinea and the centroid of the area with specific_humidity at 300 hPa values above the median within Nusa Tenggara Timur, Indonesia at 24 hours into the future is 14925.777479854869." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Tougu\u00e9, Guinea", + "Nusa Tenggara Timur, Indonesia" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "37987:38087:1" + }, + "rng_seed": null, + "justification": { + "text": "14925.777479854869\n\nJustification: The displacement in kilometers between the centroid of the area with specific_humidity at 300 hPa values above the median within Tougu\u00e9, Guinea and the centroid of the area with specific_humidity at 300 hPa values above the median within Nusa Tenggara Timur, Indonesia at 24 hours into the future is 14925.777479854869." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "6d672f6c31113814" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37986:37987:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80460:80461:1'} The data corresponds to corresponds to a snapshot on January 27 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum temperature at 100 hPa within Udon Thani, Thailand and the area-weighted centroid of the area of maximum temperature at 100 hPa within La Guajira, Colombia?", + "response": "16614.88916042941\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 100 hPa in Udon Thani, Thailand and the area-weighted centroid of maximum temperature at 100 hPa in La Guajira, Colombia is 16614.88916042941 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "16614.88916042941", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum temperature at 100 hPa in Udon Thani, Thailand and the area-weighted centroid of maximum temperature at 100 hPa in La Guajira, Colombia is 16614.88916042941 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Udon Thani, Thailand", + "La Guajira, Colombia" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "80461:80561:1" + }, + "rng_seed": null, + "justification": { + "text": "16614.88916042941\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 100 hPa in Udon Thani, Thailand and the area-weighted centroid of maximum temperature at 100 hPa in La Guajira, Colombia is 16614.88916042941 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "79ddd8376dea010c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80460:80461:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62035:62036:1'} The data corresponds to corresponds to a snapshot on June 17 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in v_component_of_wind at 50 hPa between any two grid points within Europe?", + "response": "25.245052337646484\n\nJustification: At 24 hours into the future, the maximum difference in v_component_of_wind at 50 hPa between any two grid points within Europe is 25.245052337646484.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "25.245052337646484", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in v_component_of_wind at 50 hPa between any two grid points within Europe is 25.245052337646484." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Europe" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "62036:62136:1" + }, + "rng_seed": null, + "justification": { + "text": "25.245052337646484\n\nJustification: At 24 hours into the future, the maximum difference in v_component_of_wind at 50 hPa between any two grid points within Europe is 25.245052337646484." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_50", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2fd686eb9392ae38" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62035:62036:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37538:37539:1'} The data corresponds to corresponds to a snapshot on September 10 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in geopotential at 150 hPa between any two grid points within White Sea?", + "response": "740.46875\n\nJustification: At 24 hours into the future, the maximum difference in geopotential at 150 hPa between any two grid points within White Sea is 740.46875.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "740.46875", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in geopotential at 150 hPa between any two grid points within White Sea is 740.46875." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "White Sea" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "37539:37639:1" + }, + "rng_seed": null, + "justification": { + "text": "740.46875\n\nJustification: At 24 hours into the future, the maximum difference in geopotential at 150 hPa between any two grid points within White Sea is 740.46875." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "geopotential_150", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9a7fd1717848b7a0" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37538:37539:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40025:40026:1'} The data corresponds to corresponds to a snapshot on May 25 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Oceania where both geopotential at 1000 hPa and geopotential at 200 hPa exceed their respective median values for Oceania computed from the all-time climatology baseline?", + "response": "3687249.500315938\n\nJustification: At 24 hours into the future, the area in square kilometers within Oceania where both geopotential at 1000 hPa and geopotential at 200 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 3687249.500315938.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "3687249.500315938", + "justification": "At {times_0} hours into the future, the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for that region computed from the all-time climatology baseline is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the area in square kilometers within Oceania where both geopotential at 1000 hPa and geopotential at 200 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 3687249.500315938." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 1000, + 200 + ], + "regions": [ + "Oceania" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "40026:40126:1" + }, + "rng_seed": null, + "justification": { + "text": "3687249.500315938\n\nJustification: At 24 hours into the future, the area in square kilometers within Oceania where both geopotential at 1000 hPa and geopotential at 200 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 3687249.500315938." + }, + "question_id": "N3bgvg", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "3134db147737997e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40025:40026:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71348:71349:1'} The data corresponds to corresponds to a snapshot on November 02 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in temperature at 1000 hPa between any two grid points within Equatorial Guinea?", + "response": "1.489532470703125\n\nJustification: At 24 hours into the future, the maximum difference in temperature at 1000 hPa between any two grid points within Equatorial Guinea is 1.489532470703125.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "1.489532470703125", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in temperature at 1000 hPa between any two grid points within Equatorial Guinea is 1.489532470703125." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Equatorial Guinea" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "71349:71449:1" + }, + "rng_seed": null, + "justification": { + "text": "1.489532470703125\n\nJustification: At 24 hours into the future, the maximum difference in temperature at 1000 hPa between any two grid points within Equatorial Guinea is 1.489532470703125." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_1000", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5d0e5a939e0c4559" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71348:71349:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83761:83762:1'} The data corresponds to corresponds to a snapshot on May 01 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum temperature at 850 hPa within Antarctica and the area-weighted centroid of the area of maximum temperature at 850 hPa within Africa?", + "response": "12419.051548693615\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 850 hPa in Antarctica and the area-weighted centroid of maximum temperature at 850 hPa in Africa is 12419.051548693615 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "12419.051548693615", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum temperature at 850 hPa in Antarctica and the area-weighted centroid of maximum temperature at 850 hPa in Africa is 12419.051548693615 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 850 + ], + "regions": [ + "Antarctica", + "Africa" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "83762:83862:1" + }, + "rng_seed": null, + "justification": { + "text": "12419.051548693615\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 850 hPa in Antarctica and the area-weighted centroid of maximum temperature at 850 hPa in Africa is 12419.051548693615 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "8a872a91be270714" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83761:83762:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37524:37525:1'} The data corresponds to corresponds to a snapshot on September 07 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in specific_humidity at 700 hPa between any two grid points within British Virgin Islands?", + "response": "0.0002184854820370674\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 700 hPa between any two grid points within British Virgin Islands is 0.0002184854820370674.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0002184854820370674", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in specific_humidity at 700 hPa between any two grid points within British Virgin Islands is 0.0002184854820370674." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "British Virgin Islands" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "37525:37625:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0002184854820370674\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 700 hPa between any two grid points within British Virgin Islands is 0.0002184854820370674." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_700", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "10a29b162f8d5a09" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37524:37525:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92602:92603:1'} The data corresponds to corresponds to a snapshot on May 20 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in u_component_of_wind at 250 hPa between any two grid points within Heard Island and McDonald Islands?", + "response": "0.02623748779296875\n\nJustification: At 24 hours into the future, the maximum difference in u_component_of_wind at 250 hPa between any two grid points within Heard Island and McDonald Islands is 0.02623748779296875.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.02623748779296875", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in u_component_of_wind at 250 hPa between any two grid points within Heard Island and McDonald Islands is 0.02623748779296875." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Heard Island and McDonald Islands" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "92603:92703:1" + }, + "rng_seed": null, + "justification": { + "text": "0.02623748779296875\n\nJustification: At 24 hours into the future, the maximum difference in u_component_of_wind at 250 hPa between any two grid points within Heard Island and McDonald Islands is 0.02623748779296875." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_250", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "39d5aeb836801285" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92602:92603:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33280:33281:1'} The data corresponds to corresponds to a snapshot on October 12 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum specific_humidity at 925 hPa within Golfo San Jorge and the area-weighted centroid of the area of maximum specific_humidity at 925 hPa within McMurdo Sound?", + "response": "5960.577101203557\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 925 hPa in Golfo San Jorge and the area-weighted centroid of maximum specific_humidity at 925 hPa in McMurdo Sound is 5960.577101203557 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "5960.577101203557", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 925 hPa in Golfo San Jorge and the area-weighted centroid of maximum specific_humidity at 925 hPa in McMurdo Sound is 5960.577101203557 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Golfo San Jorge", + "McMurdo Sound" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "33281:33381:1" + }, + "rng_seed": null, + "justification": { + "text": "5960.577101203557\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 925 hPa in Golfo San Jorge and the area-weighted centroid of maximum specific_humidity at 925 hPa in McMurdo Sound is 5960.577101203557 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "96b534ab281ca80f" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33280:33281:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45454:45455:1'} The data corresponds to corresponds to a snapshot on February 10 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within North America where both u_component_of_wind at 700 hPa and u_component_of_wind at 100 hPa exceed their respective median values for North America computed from the all-time climatology baseline?", + "response": "13874126.105577795\n\nJustification: At 24 hours into the future, the area in square kilometers within North America where both u_component_of_wind at 700 hPa and u_component_of_wind at 100 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 13874126.105577795.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "13874126.105577795", + "justification": "At {times_0} hours into the future, the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for that region computed from the all-time climatology baseline is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the area in square kilometers within North America where both u_component_of_wind at 700 hPa and u_component_of_wind at 100 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 13874126.105577795." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 700, + 100 + ], + "regions": [ + "North America" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "45455:45555:1" + }, + "rng_seed": null, + "justification": { + "text": "13874126.105577795\n\nJustification: At 24 hours into the future, the area in square kilometers within North America where both u_component_of_wind at 700 hPa and u_component_of_wind at 100 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 13874126.105577795." + }, + "question_id": "N3bgvg", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "b14f93e94925067e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45454:45455:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77568:77569:1'} The data corresponds to corresponds to a snapshot on February 04 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with geopotential at 500 hPa values above the median within Vecpiebalgas, Latvia and the centroid of the area with geopotential at 500 hPa values above the median within Kunar, Afghanistan?", + "response": "4179.635996123922\n\nJustification: The displacement in kilometers between the centroid of the area with geopotential at 500 hPa values above the median within Vecpiebalgas, Latvia and the centroid of the area with geopotential at 500 hPa values above the median within Kunar, Afghanistan at 24 hours into the future is 4179.635996123922.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "4179.635996123922", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with geopotential at 500 hPa values above the median within Vecpiebalgas, Latvia and the centroid of the area with geopotential at 500 hPa values above the median within Kunar, Afghanistan at 24 hours into the future is 4179.635996123922." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 500 + ], + "regions": [ + "Vecpiebalgas, Latvia", + "Kunar, Afghanistan" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "77569:77669:1" + }, + "rng_seed": null, + "justification": { + "text": "4179.635996123922\n\nJustification: The displacement in kilometers between the centroid of the area with geopotential at 500 hPa values above the median within Vecpiebalgas, Latvia and the centroid of the area with geopotential at 500 hPa values above the median within Kunar, Afghanistan at 24 hours into the future is 4179.635996123922." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "8aa30bfc516b5753" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77568:77569:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72843:72844:1'} The data corresponds to corresponds to a snapshot on November 09 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with geopotential at 1000 hPa values above the median within Oceania and the centroid of the area with geopotential at 1000 hPa values above the median within North America?", + "response": "15425.244322874665\n\nJustification: The displacement in kilometers between the centroid of the area with geopotential at 1000 hPa values above the median within Oceania and the centroid of the area with geopotential at 1000 hPa values above the median within North America at 24 hours into the future is 15425.244322874665.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "15425.244322874665", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with geopotential at 1000 hPa values above the median within Oceania and the centroid of the area with geopotential at 1000 hPa values above the median within North America at 24 hours into the future is 15425.244322874665." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Oceania", + "North America" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "72844:72944:1" + }, + "rng_seed": null, + "justification": { + "text": "15425.244322874665\n\nJustification: The displacement in kilometers between the centroid of the area with geopotential at 1000 hPa values above the median within Oceania and the centroid of the area with geopotential at 1000 hPa values above the median within North America at 24 hours into the future is 15425.244322874665." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "78f997bcaa702871" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72843:72844:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30094:30095:1'} The data corresponds to corresponds to a snapshot on August 07 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in specific_humidity at 50 hPa between any two grid points within S\u00e3o Tom\u00e9 and Principe?", + "response": "2.527940523577854e-09\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 50 hPa between any two grid points within S\u00e3o Tom\u00e9 and Principe is 2.527940523577854e-09.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2.527940523577854e-09", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in specific_humidity at 50 hPa between any two grid points within S\u00e3o Tom\u00e9 and Principe is 2.527940523577854e-09." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "S\u00e3o Tom\u00e9 and Principe" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "30095:30195:1" + }, + "rng_seed": null, + "justification": { + "text": "2.527940523577854e-09\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 50 hPa between any two grid points within S\u00e3o Tom\u00e9 and Principe is 2.527940523577854e-09." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_50", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f3fa142c1941d680" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30094:30095:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49009:49010:1'} The data corresponds to corresponds to a snapshot on July 18 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with u_component_of_wind at 250 hPa values above the median within Selat Dampier and the centroid of the area with u_component_of_wind at 250 hPa values above the median within Amazon River?", + "response": "19848.321821685593\n\nJustification: The displacement in kilometers between the centroid of the area with u_component_of_wind at 250 hPa values above the median within Selat Dampier and the centroid of the area with u_component_of_wind at 250 hPa values above the median within Amazon River at 24 hours into the future is 19848.321821685593.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "19848.321821685593", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with u_component_of_wind at 250 hPa values above the median within Selat Dampier and the centroid of the area with u_component_of_wind at 250 hPa values above the median within Amazon River at 24 hours into the future is 19848.321821685593." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Selat Dampier", + "Amazon River" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "49010:49110:1" + }, + "rng_seed": null, + "justification": { + "text": "19848.321821685593\n\nJustification: The displacement in kilometers between the centroid of the area with u_component_of_wind at 250 hPa values above the median within Selat Dampier and the centroid of the area with u_component_of_wind at 250 hPa values above the median within Amazon River at 24 hours into the future is 19848.321821685593." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "555881e1f0462b55" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49009:49010:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29869:29870:1'} The data corresponds to corresponds to a snapshot on June 12 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum temperature at 700 hPa within Gulf of Mexico and the area-weighted centroid of the area of maximum temperature at 700 hPa within Gulf of Thailand?", + "response": "16011.09709174685\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 700 hPa in Gulf of Mexico and the area-weighted centroid of maximum temperature at 700 hPa in Gulf of Thailand is 16011.09709174685 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "16011.09709174685", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum temperature at 700 hPa in Gulf of Mexico and the area-weighted centroid of maximum temperature at 700 hPa in Gulf of Thailand is 16011.09709174685 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Gulf of Mexico", + "Gulf of Thailand" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "29870:29970:1" + }, + "rng_seed": null, + "justification": { + "text": "16011.09709174685\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 700 hPa in Gulf of Mexico and the area-weighted centroid of maximum temperature at 700 hPa in Gulf of Thailand is 16011.09709174685 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "e5f5d1f08459f10c" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29869:29870:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46385:46386:1'} The data corresponds to corresponds to a snapshot on October 01 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with specific_humidity at 50 hPa values above the median within Halmahera Sea and the centroid of the area with specific_humidity at 50 hPa values above the median within Queen Charlotte Sound?", + "response": "10843.556595138347\n\nJustification: The displacement in kilometers between the centroid of the area with specific_humidity at 50 hPa values above the median within Halmahera Sea and the centroid of the area with specific_humidity at 50 hPa values above the median within Queen Charlotte Sound at 24 hours into the future is 10843.556595138347.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "10843.556595138347", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with specific_humidity at 50 hPa values above the median within Halmahera Sea and the centroid of the area with specific_humidity at 50 hPa values above the median within Queen Charlotte Sound at 24 hours into the future is 10843.556595138347." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Halmahera Sea", + "Queen Charlotte Sound" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "46386:46486:1" + }, + "rng_seed": null, + "justification": { + "text": "10843.556595138347\n\nJustification: The displacement in kilometers between the centroid of the area with specific_humidity at 50 hPa values above the median within Halmahera Sea and the centroid of the area with specific_humidity at 50 hPa values above the median within Queen Charlotte Sound at 24 hours into the future is 10843.556595138347." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "d7b3cecede69e789" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46385:46386:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63315:63316:1'} The data corresponds to corresponds to a snapshot on May 03 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum v_component_of_wind at 200 hPa within Africa and the area-weighted centroid of the area of maximum v_component_of_wind at 200 hPa within Europe?", + "response": "2537.1375700343374\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum v_component_of_wind at 200 hPa in Africa and the area-weighted centroid of maximum v_component_of_wind at 200 hPa in Europe is 2537.1375700343374 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2537.1375700343374", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum v_component_of_wind at 200 hPa in Africa and the area-weighted centroid of maximum v_component_of_wind at 200 hPa in Europe is 2537.1375700343374 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 200 + ], + "regions": [ + "Africa", + "Europe" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "63316:63416:1" + }, + "rng_seed": null, + "justification": { + "text": "2537.1375700343374\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum v_component_of_wind at 200 hPa in Africa and the area-weighted centroid of maximum v_component_of_wind at 200 hPa in Europe is 2537.1375700343374 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "06d58593450677ca" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63315:63316:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67549:67550:1'} The data corresponds to corresponds to a snapshot on March 27 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum temperature at 50 hPa within Barbados and the area-weighted centroid of the area of maximum temperature at 50 hPa within French Polynesia?", + "response": "10249.751875089083\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 50 hPa in Barbados and the area-weighted centroid of maximum temperature at 50 hPa in French Polynesia is 10249.751875089083 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "10249.751875089083", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum temperature at 50 hPa in Barbados and the area-weighted centroid of maximum temperature at 50 hPa in French Polynesia is 10249.751875089083 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Barbados", + "French Polynesia" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "67550:67650:1" + }, + "rng_seed": null, + "justification": { + "text": "10249.751875089083\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 50 hPa in Barbados and the area-weighted centroid of maximum temperature at 50 hPa in French Polynesia is 10249.751875089083 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "c0fec3f7cfa251b6" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67549:67550:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76807:76808:1'} The data corresponds to corresponds to a snapshot on July 28 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Buryat, Russia where both geopotential at 100 hPa and geopotential at 700 hPa exceed their respective median values for Buryat, Russia computed from the all-time climatology baseline?", + "response": "363525.31888382114\n\nJustification: At 24 hours into the future, the area in square kilometers within Buryat, Russia where both geopotential at 100 hPa and geopotential at 700 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 363525.31888382114.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "363525.31888382114", + "justification": "At {times_0} hours into the future, the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for that region computed from the all-time climatology baseline is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the area in square kilometers within Buryat, Russia where both geopotential at 100 hPa and geopotential at 700 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 363525.31888382114." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential", + "geopotential" + ], + "levelsuffixes": [ + 100, + 700 + ], + "regions": [ + "Buryat, Russia" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential", + "geopotential" + ], + "time_range": "76808:76908:1" + }, + "rng_seed": null, + "justification": { + "text": "363525.31888382114\n\nJustification: At 24 hours into the future, the area in square kilometers within Buryat, Russia where both geopotential at 100 hPa and geopotential at 700 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 363525.31888382114." + }, + "question_id": "N3bgvg", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "b8392bf0037bb8d0" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76807:76808:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43276:43277:1'} The data corresponds to corresponds to a snapshot on August 15 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Ceram Sea where u_component_of_wind at 300 hPa exceeds its median value for Ceram Sea computed from the initial snapshot (time index 0) of the provided data window?", + "response": "23790.989222836834\n\nJustification: At 24 hours, the area in square kilometers within Ceram Sea where u_component_of_wind at 300 hPa exceeds its median value for Ceram Sea computed from the initial snapshot (time index 0) of the provided data window is 23790.989222836834.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "23790.989222836834", + "justification": "At {times_0} hours, the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window is {{actualvalue_0}}.", + "justification_text": "At 24 hours, the area in square kilometers within Ceram Sea where u_component_of_wind at 300 hPa exceeds its median value for Ceram Sea computed from the initial snapshot (time index 0) of the provided data window is 23790.989222836834." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_008.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_008.py", + "template_id": "tmpl_008", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 300 + ], + "regions": [ + "Ceram Sea" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "43277:43377:1" + }, + "rng_seed": null, + "justification": { + "text": "23790.989222836834\n\nJustification: At 24 hours, the area in square kilometers within Ceram Sea where u_component_of_wind at 300 hPa exceeds its median value for Ceram Sea computed from the initial snapshot (time index 0) of the provided data window is 23790.989222836834." + }, + "question_id": "KcX1QC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "3a834722f5838b24" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43276:43277:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62084:62085:1'} The data corresponds to corresponds to a snapshot on June 30 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in temperature at 50 hPa between any two grid points within Gulf of Aqaba?", + "response": "0.1286468505859375\n\nJustification: At 24 hours into the future, the maximum difference in temperature at 50 hPa between any two grid points within Gulf of Aqaba is 0.1286468505859375.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.1286468505859375", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in temperature at 50 hPa between any two grid points within Gulf of Aqaba is 0.1286468505859375." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "Gulf of Aqaba" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "62085:62185:1" + }, + "rng_seed": null, + "justification": { + "text": "0.1286468505859375\n\nJustification: At 24 hours into the future, the maximum difference in temperature at 50 hPa between any two grid points within Gulf of Aqaba is 0.1286468505859375." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_50", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "75fa8cc03748d24e" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62084:62085:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72582:72583:1'} The data corresponds to corresponds to a snapshot on September 05 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Oceania where temperature at 700 hPa exceeds its median value for Oceania computed from the initial snapshot (time index 0) of the provided data window?", + "response": "2911696.5022840314\n\nJustification: At 24 hours, the area in square kilometers within Oceania where temperature at 700 hPa exceeds its median value for Oceania computed from the initial snapshot (time index 0) of the provided data window is 2911696.5022840314.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2911696.5022840314", + "justification": "At {times_0} hours, the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window is {{actualvalue_0}}.", + "justification_text": "At 24 hours, the area in square kilometers within Oceania where temperature at 700 hPa exceeds its median value for Oceania computed from the initial snapshot (time index 0) of the provided data window is 2911696.5022840314." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_008.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_008.py", + "template_id": "tmpl_008", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Oceania" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "72583:72683:1" + }, + "rng_seed": null, + "justification": { + "text": "2911696.5022840314\n\nJustification: At 24 hours, the area in square kilometers within Oceania where temperature at 700 hPa exceeds its median value for Oceania computed from the initial snapshot (time index 0) of the provided data window is 2911696.5022840314." + }, + "question_id": "KcX1QC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "30d49c0e5b23a8f8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72582:72583:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55831:55832:1'} The data corresponds to corresponds to a snapshot on March 19 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Mecklenburger Bucht where both specific_humidity at 600 hPa and specific_humidity at 700 hPa exceed their respective median values for Mecklenburger Bucht computed from the all-time climatology baseline?", + "response": "0.0\n\nJustification: At 24 hours into the future, the area in square kilometers within Mecklenburger Bucht where both specific_humidity at 600 hPa and specific_humidity at 700 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 0.0.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0", + "justification": "At {times_0} hours into the future, the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for that region computed from the all-time climatology baseline is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the area in square kilometers within Mecklenburger Bucht where both specific_humidity at 600 hPa and specific_humidity at 700 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 0.0." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 600, + 700 + ], + "regions": [ + "Mecklenburger Bucht" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "55832:55932:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0\n\nJustification: At 24 hours into the future, the area in square kilometers within Mecklenburger Bucht where both specific_humidity at 600 hPa and specific_humidity at 700 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 0.0." + }, + "question_id": "N3bgvg", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "8eee8c89cd80e028" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55831:55832:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53416:53417:1'} The data corresponds to corresponds to a snapshot on July 25 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in specific_humidity at 150 hPa between any two grid points within New Zealand?", + "response": "4.7913440539559815e-06\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 150 hPa between any two grid points within New Zealand is 4.7913440539559815e-06.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "4.7913440539559815e-06", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in specific_humidity at 150 hPa between any two grid points within New Zealand is 4.7913440539559815e-06." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "New Zealand" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "53417:53517:1" + }, + "rng_seed": null, + "justification": { + "text": "4.7913440539559815e-06\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 150 hPa between any two grid points within New Zealand is 4.7913440539559815e-06." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_150", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e616499f082e5a31" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53416:53417:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46157:46158:1'} The data corresponds to corresponds to a snapshot on August 05 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum geopotential at 925 hPa within South America and the area-weighted centroid of the area of maximum geopotential at 925 hPa within Europe?", + "response": "9478.467310921778\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum geopotential at 925 hPa in South America and the area-weighted centroid of maximum geopotential at 925 hPa in Europe is 9478.467310921778 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "9478.467310921778", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum geopotential at 925 hPa in South America and the area-weighted centroid of maximum geopotential at 925 hPa in Europe is 9478.467310921778 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "South America", + "Europe" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "46158:46258:1" + }, + "rng_seed": null, + "justification": { + "text": "9478.467310921778\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum geopotential at 925 hPa in South America and the area-weighted centroid of maximum geopotential at 925 hPa in Europe is 9478.467310921778 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "ae9b8b5a046626c5" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46157:46158:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32509:32510:1'} The data corresponds to corresponds to a snapshot on April 02 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with specific_humidity at 1000 hPa values above the median within British Virgin Islands and the centroid of the area with specific_humidity at 1000 hPa values above the median within United States Virgin Islands?", + "response": "158.62877584019913\n\nJustification: The displacement in kilometers between the centroid of the area with specific_humidity at 1000 hPa values above the median within British Virgin Islands and the centroid of the area with specific_humidity at 1000 hPa values above the median within United States Virgin Islands at 24 hours into the future is 158.62877584019913.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "158.62877584019913", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with specific_humidity at 1000 hPa values above the median within British Virgin Islands and the centroid of the area with specific_humidity at 1000 hPa values above the median within United States Virgin Islands at 24 hours into the future is 158.62877584019913." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "British Virgin Islands", + "United States Virgin Islands" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "32510:32610:1" + }, + "rng_seed": null, + "justification": { + "text": "158.62877584019913\n\nJustification: The displacement in kilometers between the centroid of the area with specific_humidity at 1000 hPa values above the median within British Virgin Islands and the centroid of the area with specific_humidity at 1000 hPa values above the median within United States Virgin Islands at 24 hours into the future is 158.62877584019913." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "d17fab320487b4a0" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32509:32510:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88443:88444:1'} The data corresponds to corresponds to a snapshot on July 15 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within West Pomeranian, Poland where temperature at 50 hPa exceeds its median value for West Pomeranian, Poland computed from the initial snapshot (time index 0) of the provided data window?", + "response": "3571.0739076704053\n\nJustification: At 24 hours, the area in square kilometers within West Pomeranian, Poland where temperature at 50 hPa exceeds its median value for West Pomeranian, Poland computed from the initial snapshot (time index 0) of the provided data window is 3571.0739076704053.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "3571.0739076704053", + "justification": "At {times_0} hours, the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window is {{actualvalue_0}}.", + "justification_text": "At 24 hours, the area in square kilometers within West Pomeranian, Poland where temperature at 50 hPa exceeds its median value for West Pomeranian, Poland computed from the initial snapshot (time index 0) of the provided data window is 3571.0739076704053." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_008.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_008.py", + "template_id": "tmpl_008", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 50 + ], + "regions": [ + "West Pomeranian, Poland" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "88444:88544:1" + }, + "rng_seed": null, + "justification": { + "text": "3571.0739076704053\n\nJustification: At 24 hours, the area in square kilometers within West Pomeranian, Poland where temperature at 50 hPa exceeds its median value for West Pomeranian, Poland computed from the initial snapshot (time index 0) of the provided data window is 3571.0739076704053." + }, + "question_id": "KcX1QC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "b62b87a9bcfacfe3" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88443:88444:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89038:89039:1'} The data corresponds to corresponds to a snapshot on December 11 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with v_component_of_wind at 925 hPa values above the median within Ukraine and the centroid of the area with v_component_of_wind at 925 hPa values above the median within Southern Patagonian Ice Field?", + "response": "14650.200624298366\n\nJustification: The displacement in kilometers between the centroid of the area with v_component_of_wind at 925 hPa values above the median within Ukraine and the centroid of the area with v_component_of_wind at 925 hPa values above the median within Southern Patagonian Ice Field at 24 hours into the future is 14650.200624298366.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "14650.200624298366", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with v_component_of_wind at 925 hPa values above the median within Ukraine and the centroid of the area with v_component_of_wind at 925 hPa values above the median within Southern Patagonian Ice Field at 24 hours into the future is 14650.200624298366." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Ukraine", + "Southern Patagonian Ice Field" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "89039:89139:1" + }, + "rng_seed": null, + "justification": { + "text": "14650.200624298366\n\nJustification: The displacement in kilometers between the centroid of the area with v_component_of_wind at 925 hPa values above the median within Ukraine and the centroid of the area with v_component_of_wind at 925 hPa values above the median within Southern Patagonian Ice Field at 24 hours into the future is 14650.200624298366." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "54fcaafa998a64c8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89038:89039:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57284:57285:1'} The data corresponds to corresponds to a snapshot on March 18 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with v_component_of_wind at 250 hPa values above the median within Queen Charlotte Sound and the centroid of the area with v_component_of_wind at 250 hPa values above the median within Java Sea?", + "response": "12298.070690634113\n\nJustification: The displacement in kilometers between the centroid of the area with v_component_of_wind at 250 hPa values above the median within Queen Charlotte Sound and the centroid of the area with v_component_of_wind at 250 hPa values above the median within Java Sea at 24 hours into the future is 12298.070690634113.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "12298.070690634113", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with v_component_of_wind at 250 hPa values above the median within Queen Charlotte Sound and the centroid of the area with v_component_of_wind at 250 hPa values above the median within Java Sea at 24 hours into the future is 12298.070690634113." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Queen Charlotte Sound", + "Java Sea" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "57285:57385:1" + }, + "rng_seed": null, + "justification": { + "text": "12298.070690634113\n\nJustification: The displacement in kilometers between the centroid of the area with v_component_of_wind at 250 hPa values above the median within Queen Charlotte Sound and the centroid of the area with v_component_of_wind at 250 hPa values above the median within Java Sea at 24 hours into the future is 12298.070690634113." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "e907592a9da06a92" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57284:57285:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51496:51497:1'} The data corresponds to corresponds to a snapshot on April 01 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with specific_humidity at 700 hPa values above the median within Antarctica and the centroid of the area with specific_humidity at 700 hPa values above the median within North America?", + "response": "14358.2283687412\n\nJustification: The displacement in kilometers between the centroid of the area with specific_humidity at 700 hPa values above the median within Antarctica and the centroid of the area with specific_humidity at 700 hPa values above the median within North America at 24 hours into the future is 14358.2283687412.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "14358.2283687412", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with specific_humidity at 700 hPa values above the median within Antarctica and the centroid of the area with specific_humidity at 700 hPa values above the median within North America at 24 hours into the future is 14358.2283687412." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Antarctica", + "North America" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "51497:51597:1" + }, + "rng_seed": null, + "justification": { + "text": "14358.2283687412\n\nJustification: The displacement in kilometers between the centroid of the area with specific_humidity at 700 hPa values above the median within Antarctica and the centroid of the area with specific_humidity at 700 hPa values above the median within North America at 24 hours into the future is 14358.2283687412." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "e7d1840bcd88499a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51496:51497:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69895:69896:1'} The data corresponds to corresponds to a snapshot on November 03 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Gulf of Alaska where geopotential at 1000 hPa exceeds its median value for Gulf of Alaska computed from the initial snapshot (time index 0) of the provided data window?", + "response": "76470.28706115947\n\nJustification: At 24 hours, the area in square kilometers within Gulf of Alaska where geopotential at 1000 hPa exceeds its median value for Gulf of Alaska computed from the initial snapshot (time index 0) of the provided data window is 76470.28706115947.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "76470.28706115947", + "justification": "At {times_0} hours, the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window is {{actualvalue_0}}.", + "justification_text": "At 24 hours, the area in square kilometers within Gulf of Alaska where geopotential at 1000 hPa exceeds its median value for Gulf of Alaska computed from the initial snapshot (time index 0) of the provided data window is 76470.28706115947." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_008.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_008.py", + "template_id": "tmpl_008", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Gulf of Alaska" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "69896:69996:1" + }, + "rng_seed": null, + "justification": { + "text": "76470.28706115947\n\nJustification: At 24 hours, the area in square kilometers within Gulf of Alaska where geopotential at 1000 hPa exceeds its median value for Gulf of Alaska computed from the initial snapshot (time index 0) of the provided data window is 76470.28706115947." + }, + "question_id": "KcX1QC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "c3aef9a833363517" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69895:69896:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78994:78995:1'} The data corresponds to corresponds to a snapshot on January 25 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with u_component_of_wind at 700 hPa values above the median within Bolivia and the centroid of the area with u_component_of_wind at 700 hPa values above the median within Puerto Rico?", + "response": "3669.4376477065866\n\nJustification: The displacement in kilometers between the centroid of the area with u_component_of_wind at 700 hPa values above the median within Bolivia and the centroid of the area with u_component_of_wind at 700 hPa values above the median within Puerto Rico at 24 hours into the future is 3669.4376477065866.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "3669.4376477065866", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with u_component_of_wind at 700 hPa values above the median within Bolivia and the centroid of the area with u_component_of_wind at 700 hPa values above the median within Puerto Rico at 24 hours into the future is 3669.4376477065866." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Bolivia", + "Puerto Rico" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "78995:79095:1" + }, + "rng_seed": null, + "justification": { + "text": "3669.4376477065866\n\nJustification: The displacement in kilometers between the centroid of the area with u_component_of_wind at 700 hPa values above the median within Bolivia and the centroid of the area with u_component_of_wind at 700 hPa values above the median within Puerto Rico at 24 hours into the future is 3669.4376477065866." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "9191fb1e1e136bb5" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78994:78995:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52922:52923:1'} The data corresponds to corresponds to a snapshot on March 23 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Gulf of Sakhalin where v_component_of_wind at 1000 hPa exceeds its median value for Gulf of Sakhalin computed from the initial snapshot (time index 0) of the provided data window?", + "response": "14535.91498885427\n\nJustification: At 24 hours, the area in square kilometers within Gulf of Sakhalin where v_component_of_wind at 1000 hPa exceeds its median value for Gulf of Sakhalin computed from the initial snapshot (time index 0) of the provided data window is 14535.91498885427.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "14535.91498885427", + "justification": "At {times_0} hours, the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window is {{actualvalue_0}}.", + "justification_text": "At 24 hours, the area in square kilometers within Gulf of Sakhalin where v_component_of_wind at 1000 hPa exceeds its median value for Gulf of Sakhalin computed from the initial snapshot (time index 0) of the provided data window is 14535.91498885427." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_008.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_008.py", + "template_id": "tmpl_008", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where {wb2varnames_0}{levelsuffixes_0} exceeds its median value for {regions_0} computed from the initial snapshot (time index 0) of the provided data window?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "Gulf of Sakhalin" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "52923:53023:1" + }, + "rng_seed": null, + "justification": { + "text": "14535.91498885427\n\nJustification: At 24 hours, the area in square kilometers within Gulf of Sakhalin where v_component_of_wind at 1000 hPa exceeds its median value for Gulf of Sakhalin computed from the initial snapshot (time index 0) of the provided data window is 14535.91498885427." + }, + "question_id": "KcX1QC", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "755ad0711d612f6a" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52922:52923:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40459:40460:1'} The data corresponds to corresponds to a snapshot on September 10 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with temperature at 400 hPa values above the median within South Georgia and the Islands and the centroid of the area with temperature at 400 hPa values above the median within Heard Island and McDonald Islands?", + "response": "6505.699205391534\n\nJustification: The displacement in kilometers between the centroid of the area with temperature at 400 hPa values above the median within South Georgia and the Islands and the centroid of the area with temperature at 400 hPa values above the median within Heard Island and McDonald Islands at 24 hours into the future is 6505.699205391534.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "6505.699205391534", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with temperature at 400 hPa values above the median within South Georgia and the Islands and the centroid of the area with temperature at 400 hPa values above the median within Heard Island and McDonald Islands at 24 hours into the future is 6505.699205391534." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 400 + ], + "regions": [ + "South Georgia and the Islands", + "Heard Island and McDonald Islands" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "40460:40560:1" + }, + "rng_seed": null, + "justification": { + "text": "6505.699205391534\n\nJustification: The displacement in kilometers between the centroid of the area with temperature at 400 hPa values above the median within South Georgia and the Islands and the centroid of the area with temperature at 400 hPa values above the median within Heard Island and McDonald Islands at 24 hours into the future is 6505.699205391534." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "2d6ad384c7b4bbcb" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40459:40460:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67894:67895:1'} The data corresponds to corresponds to a snapshot on June 21 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum specific_humidity at 250 hPa within Archipel des Crozet, French Southern and Antarctic Lands and the area-weighted centroid of the area of maximum specific_humidity at 250 hPa within Br\u00e4nd\u00f6, Aland?", + "response": "12151.003077922907\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 250 hPa in Archipel des Crozet, French Southern and Antarctic Lands and the area-weighted centroid of maximum specific_humidity at 250 hPa in Br\u00e4nd\u00f6, Aland is 12151.003077922907 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "12151.003077922907", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 250 hPa in Archipel des Crozet, French Southern and Antarctic Lands and the area-weighted centroid of maximum specific_humidity at 250 hPa in Br\u00e4nd\u00f6, Aland is 12151.003077922907 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Archipel des Crozet, French Southern and Antarctic Lands", + "Br\u00e4nd\u00f6, Aland" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "67895:67995:1" + }, + "rng_seed": null, + "justification": { + "text": "12151.003077922907\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum specific_humidity at 250 hPa in Archipel des Crozet, French Southern and Antarctic Lands and the area-weighted centroid of maximum specific_humidity at 250 hPa in Br\u00e4nd\u00f6, Aland is 12151.003077922907 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "175a5c9f5f678b72" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67894:67895:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73683:73684:1'} The data corresponds to corresponds to a snapshot on June 07 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in specific_humidity at 100 hPa between any two grid points within Karelia, Russia?", + "response": "1.160144620371284e-07\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 100 hPa between any two grid points within Karelia, Russia is 1.160144620371284e-07.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "1.160144620371284e-07", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in specific_humidity at 100 hPa between any two grid points within Karelia, Russia is 1.160144620371284e-07." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 100 + ], + "regions": [ + "Karelia, Russia" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "73684:73784:1" + }, + "rng_seed": null, + "justification": { + "text": "1.160144620371284e-07\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 100 hPa between any two grid points within Karelia, Russia is 1.160144620371284e-07." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_100", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2462e51a96ca0131" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73683:73684:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82920:82921:1'} The data corresponds to corresponds to a snapshot on October 04 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum u_component_of_wind at 1000 hPa within West End, Anguilla and the area-weighted centroid of the area of maximum u_component_of_wind at 1000 hPa within Bogota, Colombia?", + "response": "2022.7288653062028\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum u_component_of_wind at 1000 hPa in West End, Anguilla and the area-weighted centroid of maximum u_component_of_wind at 1000 hPa in Bogota, Colombia is 2022.7288653062028 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "2022.7288653062028", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum u_component_of_wind at 1000 hPa in West End, Anguilla and the area-weighted centroid of maximum u_component_of_wind at 1000 hPa in Bogota, Colombia is 2022.7288653062028 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 1000 + ], + "regions": [ + "West End, Anguilla", + "Bogota, Colombia" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "82921:83021:1" + }, + "rng_seed": null, + "justification": { + "text": "2022.7288653062028\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum u_component_of_wind at 1000 hPa in West End, Anguilla and the area-weighted centroid of maximum u_component_of_wind at 1000 hPa in Bogota, Colombia is 2022.7288653062028 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "b809be0cb75a2cdd" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82920:82921:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70131:70132:1'} The data corresponds to corresponds to a snapshot on January 01 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in specific_humidity at 600 hPa between any two grid points within Samar Sea?", + "response": "0.0025836897548288107\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 600 hPa between any two grid points within Samar Sea is 0.0025836897548288107.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0025836897548288107", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in specific_humidity at 600 hPa between any two grid points within Samar Sea is 0.0025836897548288107." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Samar Sea" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity" + ], + "time_range": "70132:70232:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0025836897548288107\n\nJustification: At 24 hours into the future, the maximum difference in specific_humidity at 600 hPa between any two grid points within Samar Sea is 0.0025836897548288107." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_600", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "475f03cbabc945c2" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70131:70132:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38615:38616:1'} The data corresponds to corresponds to a snapshot on June 06 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Celje, Slovenia where both specific_humidity at 400 hPa and specific_humidity at 250 hPa exceed their respective median values for Celje, Slovenia computed from the all-time climatology baseline?", + "response": "98.47017123049436\n\nJustification: At 24 hours into the future, the area in square kilometers within Celje, Slovenia where both specific_humidity at 400 hPa and specific_humidity at 250 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 98.47017123049436.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "98.47017123049436", + "justification": "At {times_0} hours into the future, the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for that region computed from the all-time climatology baseline is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the area in square kilometers within Celje, Slovenia where both specific_humidity at 400 hPa and specific_humidity at 250 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 98.47017123049436." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 400, + 250 + ], + "regions": [ + "Celje, Slovenia" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "38616:38716:1" + }, + "rng_seed": null, + "justification": { + "text": "98.47017123049436\n\nJustification: At 24 hours into the future, the area in square kilometers within Celje, Slovenia where both specific_humidity at 400 hPa and specific_humidity at 250 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 98.47017123049436." + }, + "question_id": "N3bgvg", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "502d5f6e65cf6538" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38615:38616:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52551:52552:1'} The data corresponds to corresponds to a snapshot on December 20 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the area in square kilometers within Guatemala where both specific_humidity at 300 hPa and specific_humidity at 400 hPa exceed their respective median values for Guatemala computed from the all-time climatology baseline?", + "response": "0.0\n\nJustification: At 24 hours into the future, the area in square kilometers within Guatemala where both specific_humidity at 300 hPa and specific_humidity at 400 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 0.0.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.0", + "justification": "At {times_0} hours into the future, the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for that region computed from the all-time climatology baseline is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the area in square kilometers within Guatemala where both specific_humidity at 300 hPa and specific_humidity at 400 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 0.0." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_007.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_007.py", + "template_id": "tmpl_007", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the area in square kilometers within {regions_0} where both {wb2varnames_0}{levelsuffixes_0} and {wb2varnames_1}{levelsuffixes_1} exceed their respective median values for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 300, + 400 + ], + "regions": [ + "Guatemala" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "52552:52652:1" + }, + "rng_seed": null, + "justification": { + "text": "0.0\n\nJustification: At 24 hours into the future, the area in square kilometers within Guatemala where both specific_humidity at 300 hPa and specific_humidity at 400 hPa exceed their respective median values for that region computed from the all-time climatology baseline is 0.0." + }, + "question_id": "N3bgvg", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "area", + "forced_extreme_window": false, + "task_id": "11199a04062a112b" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52551:52552:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39782:39783:1'} The data corresponds to corresponds to a snapshot on March 25 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum temperature at 150 hPa within Guinea-Bissau and the area-weighted centroid of the area of maximum temperature at 150 hPa within Suriname?", + "response": "4493.1747391409235\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 150 hPa in Guinea-Bissau and the area-weighted centroid of maximum temperature at 150 hPa in Suriname is 4493.1747391409235 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "4493.1747391409235", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum temperature at 150 hPa in Guinea-Bissau and the area-weighted centroid of maximum temperature at 150 hPa in Suriname is 4493.1747391409235 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature" + ], + "levelsuffixes": [ + 150 + ], + "regions": [ + "Guinea-Bissau", + "Suriname" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature" + ], + "time_range": "39783:39883:1" + }, + "rng_seed": null, + "justification": { + "text": "4493.1747391409235\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum temperature at 150 hPa in Guinea-Bissau and the area-weighted centroid of maximum temperature at 150 hPa in Suriname is 4493.1747391409235 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "82bd3ea34d178cf8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39782:39783:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50238:50239:1'} The data corresponds to corresponds to a snapshot on May 21 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum geopotential at 250 hPa within Africa and the area-weighted centroid of the area of maximum geopotential at 250 hPa within North America?", + "response": "18301.649077550544\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum geopotential at 250 hPa in Africa and the area-weighted centroid of maximum geopotential at 250 hPa in North America is 18301.649077550544 kilometers.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "18301.649077550544", + "justification": "At {times_0} hours, the distance between the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_0} and the area-weighted centroid of maximum {wb2varnames_0}{levelsuffixes_0} in {regions_1} is {actualvalue_0} kilometers.", + "justification_text": "At 24 hours, the distance between the area-weighted centroid of maximum geopotential at 250 hPa in Africa and the area-weighted centroid of maximum geopotential at 250 hPa in North America is 18301.649077550544 kilometers." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_001.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_001.py", + "template_id": "tmpl_001", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the distance in kilometers between the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_0} and the area-weighted centroid of the area of maximum {wb2varnames_0}{levelsuffixes_0} within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Africa", + "North America" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "50239:50339:1" + }, + "rng_seed": null, + "justification": { + "text": "18301.649077550544\n\nJustification: At 24 hours, the distance between the area-weighted centroid of maximum geopotential at 250 hPa in Africa and the area-weighted centroid of maximum geopotential at 250 hPa in North America is 18301.649077550544 kilometers." + }, + "question_id": "6iN0Sq", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "3e91d685ae5236c3" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50238:50239:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79007:79008:1'} The data corresponds to corresponds to a snapshot on January 28 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with geopotential at 700 hPa values above the median within Sargasso Sea and the centroid of the area with geopotential at 700 hPa values above the median within Bay of Bengal?", + "response": "14756.27796013059\n\nJustification: The displacement in kilometers between the centroid of the area with geopotential at 700 hPa values above the median within Sargasso Sea and the centroid of the area with geopotential at 700 hPa values above the median within Bay of Bengal at 24 hours into the future is 14756.27796013059.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "14756.27796013059", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with geopotential at 700 hPa values above the median within Sargasso Sea and the centroid of the area with geopotential at 700 hPa values above the median within Bay of Bengal at 24 hours into the future is 14756.27796013059." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 700 + ], + "regions": [ + "Sargasso Sea", + "Bay of Bengal" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "79008:79108:1" + }, + "rng_seed": null, + "justification": { + "text": "14756.27796013059\n\nJustification: The displacement in kilometers between the centroid of the area with geopotential at 700 hPa values above the median within Sargasso Sea and the centroid of the area with geopotential at 700 hPa values above the median within Bay of Bengal at 24 hours into the future is 14756.27796013059." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "61eee56e23c5e933" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79007:79008:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58848:58849:1'} The data corresponds to corresponds to a snapshot on April 13 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with geopotential at 250 hPa values above the median within Gulf of Bothnia and the centroid of the area with geopotential at 250 hPa values above the median within Karaginskiy Gulf?", + "response": "6285.706846632152\n\nJustification: The displacement in kilometers between the centroid of the area with geopotential at 250 hPa values above the median within Gulf of Bothnia and the centroid of the area with geopotential at 250 hPa values above the median within Karaginskiy Gulf at 24 hours into the future is 6285.706846632152.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "6285.706846632152", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with geopotential at 250 hPa values above the median within Gulf of Bothnia and the centroid of the area with geopotential at 250 hPa values above the median within Karaginskiy Gulf at 24 hours into the future is 6285.706846632152." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "geopotential" + ], + "levelsuffixes": [ + 250 + ], + "regions": [ + "Gulf of Bothnia", + "Karaginskiy Gulf" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "geopotential" + ], + "time_range": "58849:58949:1" + }, + "rng_seed": null, + "justification": { + "text": "6285.706846632152\n\nJustification: The displacement in kilometers between the centroid of the area with geopotential at 250 hPa values above the median within Gulf of Bothnia and the centroid of the area with geopotential at 250 hPa values above the median within Karaginskiy Gulf at 24 hours into the future is 6285.706846632152." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "3045fb457219e6c8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58848:58849:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46447:46448:1'} The data corresponds to corresponds to a snapshot on October 16 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with u_component_of_wind at 600 hPa values above the median within Asia and the centroid of the area with u_component_of_wind at 600 hPa values above the median within Oceania?", + "response": "9129.174345486206\n\nJustification: The displacement in kilometers between the centroid of the area with u_component_of_wind at 600 hPa values above the median within Asia and the centroid of the area with u_component_of_wind at 600 hPa values above the median within Oceania at 24 hours into the future is 9129.174345486206.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "9129.174345486206", + "justification": "The displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1} at {times_0} hours into the future is {actualvalue_0}.", + "justification_text": "The displacement in kilometers between the centroid of the area with u_component_of_wind at 600 hPa values above the median within Asia and the centroid of the area with u_component_of_wind at 600 hPa values above the median within Oceania at 24 hours into the future is 9129.174345486206." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_010.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_010.py", + "template_id": "tmpl_010", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the displacement in kilometers between the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_0} and the centroid of the area with {wb2varnames_0}{levelsuffixes_0} values above the median within {regions_1}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind" + ], + "levelsuffixes": [ + 600 + ], + "regions": [ + "Asia", + "Oceania" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind" + ], + "time_range": "46448:46548:1" + }, + "rng_seed": null, + "justification": { + "text": "9129.174345486206\n\nJustification: The displacement in kilometers between the centroid of the area with u_component_of_wind at 600 hPa values above the median within Asia and the centroid of the area with u_component_of_wind at 600 hPa values above the median within Oceania at 24 hours into the future is 9129.174345486206." + }, + "question_id": "hy0f6Q", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "level": "2b_numeric", + "eval_type": "distance", + "forced_extreme_window": false, + "task_id": "fbeddea5092e4346" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46447:46448:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37087:37088:1'} The data corresponds to corresponds to a snapshot on May 20 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the maximum difference in v_component_of_wind at 925 hPa between any two grid points within Gulf of Papua?", + "response": "4.0806732177734375\n\nJustification: At 24 hours into the future, the maximum difference in v_component_of_wind at 925 hPa between any two grid points within Gulf of Papua is 4.0806732177734375.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "4.0806732177734375", + "justification": "At {times_0} hours into the future, the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0} is {actualvalue_0}.", + "justification_text": "At 24 hours into the future, the maximum difference in v_component_of_wind at 925 hPa between any two grid points within Gulf of Papua is 4.0806732177734375." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_015.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_015.py", + "template_id": "tmpl_015", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the maximum difference in {wb2varnames_0}{levelsuffixes_0} between any two grid points within {regions_0}?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "v_component_of_wind" + ], + "levelsuffixes": [ + 925 + ], + "regions": [ + "Gulf of Papua" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "v_component_of_wind" + ], + "time_range": "37088:37188:1" + }, + "rng_seed": null, + "justification": { + "text": "4.0806732177734375\n\nJustification: At 24 hours into the future, the maximum difference in v_component_of_wind at 925 hPa between any two grid points within Gulf of Papua is 4.0806732177734375." + }, + "question_id": "JKf1tH", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "v_component_of_wind_925", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ef7c39c242cfb841" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37087:37088:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30953:30954:1'} The data corresponds to corresponds to a snapshot on March 09 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the minimum value of temperature at 600 hPa within Bangladesh where temperature at 1000 hPa exceeds its median value for Bangladesh computed from the all-time climatology baseline?", + "response": "267.9670104980469\n\nJustification: At 24 hours into the future, the minimum value of temperature at 600 hPa within Bangladesh where temperature at 1000 hPa exceeds its median value for Bangladesh computed from the all-time climatology baseline is 267.9670104980469.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "267.9670104980469", + "justification": "At {times_0} hours into the future, the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline is {{actualvalue_0}}.", + "justification_text": "At 24 hours into the future, the minimum value of temperature at 600 hPa within Bangladesh where temperature at 1000 hPa exceeds its median value for Bangladesh computed from the all-time climatology baseline is 267.9670104980469." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 600, + 1000 + ], + "regions": [ + "Bangladesh" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "30954:31054:1" + }, + "rng_seed": null, + "justification": { + "text": "267.9670104980469\n\nJustification: At 24 hours into the future, the minimum value of temperature at 600 hPa within Bangladesh where temperature at 1000 hPa exceeds its median value for Bangladesh computed from the all-time climatology baseline is 267.9670104980469." + }, + "question_id": "f4FkBO", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_600", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "936bf40c72378e62" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30953:30954:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30959:30960:1'} The data corresponds to corresponds to a snapshot on March 10 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the minimum value of temperature at 925 hPa within Lagoa dos Patos where temperature at 500 hPa exceeds its median value for Lagoa dos Patos computed from the all-time climatology baseline?", + "response": "292.7351989746094\n\nJustification: At 24 hours into the future, the minimum value of temperature at 925 hPa within Lagoa dos Patos where temperature at 500 hPa exceeds its median value for Lagoa dos Patos computed from the all-time climatology baseline is 292.7351989746094.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "292.7351989746094", + "justification": "At {times_0} hours into the future, the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline is {{actualvalue_0}}.", + "justification_text": "At 24 hours into the future, the minimum value of temperature at 925 hPa within Lagoa dos Patos where temperature at 500 hPa exceeds its median value for Lagoa dos Patos computed from the all-time climatology baseline is 292.7351989746094." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 925, + 500 + ], + "regions": [ + "Lagoa dos Patos" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "30960:31060:1" + }, + "rng_seed": null, + "justification": { + "text": "292.7351989746094\n\nJustification: At 24 hours into the future, the minimum value of temperature at 925 hPa within Lagoa dos Patos where temperature at 500 hPa exceeds its median value for Lagoa dos Patos computed from the all-time climatology baseline is 292.7351989746094." + }, + "question_id": "f4FkBO", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_925", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "96c846d81e0f7ed8" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30959:30960:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74571:74572:1'} The data corresponds to corresponds to a snapshot on January 15 18:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the minimum value of temperature at 1000 hPa within Africa where temperature at 100 hPa exceeds its median value for Africa computed from the all-time climatology baseline?", + "response": "280.3740234375\n\nJustification: At 24 hours into the future, the minimum value of temperature at 1000 hPa within Africa where temperature at 100 hPa exceeds its median value for Africa computed from the all-time climatology baseline is 280.3740234375.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "280.3740234375", + "justification": "At {times_0} hours into the future, the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline is {{actualvalue_0}}.", + "justification_text": "At 24 hours into the future, the minimum value of temperature at 1000 hPa within Africa where temperature at 100 hPa exceeds its median value for Africa computed from the all-time climatology baseline is 280.3740234375." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 1000, + 100 + ], + "regions": [ + "Africa" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "74572:74672:1" + }, + "rng_seed": null, + "justification": { + "text": "280.3740234375\n\nJustification: At 24 hours into the future, the minimum value of temperature at 1000 hPa within Africa where temperature at 100 hPa exceeds its median value for Africa computed from the all-time climatology baseline is 280.3740234375." + }, + "question_id": "f4FkBO", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_1000", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ff8460bd7047d377" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74571:74572:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33557:33558:1'} The data corresponds to corresponds to a snapshot on December 20 06:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the minimum value of temperature at 850 hPa within South America where temperature at 100 hPa exceeds its median value for South America computed from the all-time climatology baseline?", + "response": "266.6722412109375\n\nJustification: At 24 hours into the future, the minimum value of temperature at 850 hPa within South America where temperature at 100 hPa exceeds its median value for South America computed from the all-time climatology baseline is 266.6722412109375.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "266.6722412109375", + "justification": "At {times_0} hours into the future, the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline is {{actualvalue_0}}.", + "justification_text": "At 24 hours into the future, the minimum value of temperature at 850 hPa within South America where temperature at 100 hPa exceeds its median value for South America computed from the all-time climatology baseline is 266.6722412109375." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 850, + 100 + ], + "regions": [ + "South America" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "33558:33658:1" + }, + "rng_seed": null, + "justification": { + "text": "266.6722412109375\n\nJustification: At 24 hours into the future, the minimum value of temperature at 850 hPa within South America where temperature at 100 hPa exceeds its median value for South America computed from the all-time climatology baseline is 266.6722412109375." + }, + "question_id": "f4FkBO", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_850", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c55d063bec48a204" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33557:33558:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91020:91021:1'} The data corresponds to corresponds to a snapshot on April 20 00:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the minimum value of temperature at 925 hPa within Vanuatu where temperature at 150 hPa exceeds its median value for Vanuatu computed from the all-time climatology baseline?", + "response": "291.69940185546875\n\nJustification: At 24 hours into the future, the minimum value of temperature at 925 hPa within Vanuatu where temperature at 150 hPa exceeds its median value for Vanuatu computed from the all-time climatology baseline is 291.69940185546875.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "291.69940185546875", + "justification": "At {times_0} hours into the future, the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline is {{actualvalue_0}}.", + "justification_text": "At 24 hours into the future, the minimum value of temperature at 925 hPa within Vanuatu where temperature at 150 hPa exceeds its median value for Vanuatu computed from the all-time climatology baseline is 291.69940185546875." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "temperature", + "temperature" + ], + "levelsuffixes": [ + 925, + 150 + ], + "regions": [ + "Vanuatu" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "temperature", + "temperature" + ], + "time_range": "91021:91121:1" + }, + "rng_seed": null, + "justification": { + "text": "291.69940185546875\n\nJustification: At 24 hours into the future, the minimum value of temperature at 925 hPa within Vanuatu where temperature at 150 hPa exceeds its median value for Vanuatu computed from the all-time climatology baseline is 291.69940185546875." + }, + "question_id": "f4FkBO", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "temperature_925", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d018940871b65274" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91020:91021:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78238:78239:1'} The data corresponds to corresponds to a snapshot on July 20 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the minimum value of specific_humidity at 1000 hPa within Oceania where specific_humidity at 500 hPa exceeds its median value for Oceania computed from the all-time climatology baseline?", + "response": "0.002160828560590744\n\nJustification: At 24 hours into the future, the minimum value of specific_humidity at 1000 hPa within Oceania where specific_humidity at 500 hPa exceeds its median value for Oceania computed from the all-time climatology baseline is 0.002160828560590744.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "0.002160828560590744", + "justification": "At {times_0} hours into the future, the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline is {{actualvalue_0}}.", + "justification_text": "At 24 hours into the future, the minimum value of specific_humidity at 1000 hPa within Oceania where specific_humidity at 500 hPa exceeds its median value for Oceania computed from the all-time climatology baseline is 0.002160828560590744." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "specific_humidity", + "specific_humidity" + ], + "levelsuffixes": [ + 1000, + 500 + ], + "regions": [ + "Oceania" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "specific_humidity", + "specific_humidity" + ], + "time_range": "78239:78339:1" + }, + "rng_seed": null, + "justification": { + "text": "0.002160828560590744\n\nJustification: At 24 hours into the future, the minimum value of specific_humidity at 1000 hPa within Oceania where specific_humidity at 500 hPa exceeds its median value for Oceania computed from the all-time climatology baseline is 0.002160828560590744." + }, + "question_id": "f4FkBO", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "specific_humidity_1000", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0f7b91694ee28903" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78238:78239:1" + } + }, + { + "prompt": "The following data shows a snapshot of the global weather fields. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79302:79303:1'} The data corresponds to corresponds to a snapshot on April 12 12:00. Based on the above data, answer the following question:", + "question": "At the snapshot 24 hours after the end of the given time window, what is the minimum value of u_component_of_wind at 500 hPa within Salish Sea where u_component_of_wind at 150 hPa exceeds its median value for Salish Sea computed from the all-time climatology baseline?", + "response": "10.063989639282227\n\nJustification: At 24 hours into the future, the minimum value of u_component_of_wind at 500 hPa within Salish Sea where u_component_of_wind at 150 hPa exceeds its median value for Salish Sea computed from the all-time climatology baseline is 10.063989639282227.", + "metadata": { + "true_value": null, + "actual_value": { + "actualvalue_0": "10.063989639282227", + "justification": "At {times_0} hours into the future, the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline is {{actualvalue_0}}.", + "justification_text": "At 24 hours into the future, the minimum value of u_component_of_wind at 500 hPa within Salish Sea where u_component_of_wind at 150 hPa exceeds its median value for Salish Sea computed from the all-time climatology baseline is 10.063989639282227." + }, + "code_path": "templates/synthetic_task_code/numeric/single_snapshot/tmpl_013.py", + "sampler_code_path": "templates/synthetic_task_code/numeric/single_snapshot/sampling_tmpl_013.py", + "template_id": "tmpl_013", + "template_str": "At the snapshot {times_0} hours after the end of the given time window, what is the minimum value of {wb2varnames_0}{levelsuffixes_0} within {regions_0} where {wb2varnames_1}{levelsuffixes_1} exceeds its median value for {regions_0} computed from the all-time climatology baseline?", + "template_type": "snapshot", + "mode": "numeric", + "constraints": { + "version": 1, + "wb2varnames": { + "family": "same" + }, + "regions": { + "granularity": "same" + }, + "units": { + "bind_to_var": [] + } + }, + "filled_values": { + "wb2varnames": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "levelsuffixes": [ + 500, + 150 + ], + "regions": [ + "Salish Sea" + ], + "times": [ + 24 + ] + }, + "wb2_data": { + "type": "wb2_reference", + "variables": [ + "u_component_of_wind", + "u_component_of_wind" + ], + "time_range": "79303:79403:1" + }, + "rng_seed": null, + "justification": { + "text": "10.063989639282227\n\nJustification: At 24 hours into the future, the minimum value of u_component_of_wind at 500 hPa within Salish Sea where u_component_of_wind at 150 hPa exceeds its median value for Salish Sea computed from the all-time climatology baseline is 10.063989639282227." + }, + "question_id": "f4FkBO", + "prompt_id": "HIuSnl", + "difficulty": "medium", + "target_variable": "u_component_of_wind_500", + "level": "2b_numeric", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "adf9b4022ae260bc" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79302:79303:1" + } + } +] diff --git a/level2c_part0.json b/level2c_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..4f1dbc9c54e15b9da5072a476e24fbeded5efad8 --- /dev/null +++ b/level2c_part0.json @@ -0,0 +1,3202 @@ +[ + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60264:60383:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: April-May-June 2000\nPRECIPITATION\nSlightly enhanced probabilities of above-normal precipitation over Senegal, Gambia, Guinea, Guinea-Bissau, Sierra Leone, and south-western Mali.\nSlightly enhanced probabilities of above-normal precipitation over Liberia and Ghana.\nSlightly enhanced probabilities of below-normal precipitation over Syria and north-western Iraq.\nEnhanced probabilities of below-normal precipitation over Jordan, south-eastern Iraq, and northern Saudia Arabia.\nEnhanced probabilities of below-normal precipitation over southern Saudi Arabia, and Yemen.\nEnhanced probabilities of below-normal precipitation over Equatorial Guinea, Gabon, and the near-coastal areas of Congo, Democratic Republic of Congo, and Angola.\nSlightly enhanced probabilities of above-normal precipitation over much of the Democratic Republic of Congo, eastern Angola, Zambia, Malawi, and much of northern and central Mozambique.\nSlightly enhanced probabilities of normal precipitation over Tanzania, Rwanda, Burundi, Uganda, and western Kenya.\nEnhanced probabilities of below-normal precipitation over south-eastern Ethiopia, eastern Kenya, and Somalia.\nSlightly enhanced probabilities of below-normal precipitation over the Seychelles.\nSlightly enhanced probabilities of below-normal precipitation over Reunion and Mauritius.\nSlightly enhanced probabilities of above-normal precipitation over southern Namibia, the far south-eastern part of Botswana, and much of the western half of South Africa, excluding the south-west and southern coasts.\nSlightly enhanced probabilities of above-normal precipitation over Lesotho, Swaziland, and much of the eastern half of South Africa.\n\nTEMPERATURE\nGreatly enhanced probabilities of above-normal temperatures over Tunisia, Algeria, and the northern half of Morocco.\nSlightly enhanced probabilities of above-normal temperatures over a large area extending north-westwards from Liberia and Ghana to the south-eastern half of the Arabian peninsula, and extending south-eastwards to Malawi and northern Mozambique.\nSlightly enhanced probabilities of normal temperatures over eastern Central African Republic, and cnetral and northern Democratic Republic of Congo.\nEnhanced probabilities of above-normal temperatures over a large area extending from Equatorial Guinea and Gabon southwards and eastwards to central and southern Mozambique, and including southern and central Madagascar.\n\nJuly - September 2000:\nPRECIPITATION\nSlightly enhanced probabilities of below-normal precipitation over the far northern parts of Algeria and Tunisia.\nSlightly enhanced probabilities of below-normal precipitation over the Sahelian belt from Senegal and Guinea to the easternmost part of Sudan.\nSlightly enhanced probabilities of below-normal precipitation over part of east Africa, including Uganda, Kenya, and Tanzania.\nSlightly enhanced probabilities of above-normal precipitation over the far south-western part of South Africa.\nSlightly enhanced probabilities of below-normal precipitation over southern Mozambique, Swaziland, and south-eastern South Africa.\n\nTEMPERATURE\nSlightly enhanced probabilities of normal temperatures over Morocco, Algeria, Tunisia, and northern Mauritania.\nGreatly enhanced probabilities of above-normal temperatures over a large area of West Africa between about 5 and 15N, extending eastwards through central Sudan.\nSlightly enhanced probabilities of above-normal temperatures over much of Africa south of about 5N, extending into Eritrea, Ethiopia, and Djibouti.\nEnhanced probabilities of above-normal temperatures over the south-eastern part of Africa, and over Madagascar.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "0b83738f4356da2a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60264:60383:1", + "start_date": "2000-04-01", + "end_date": "2000-04-30" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61968:62087:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Slightly below normal precipitation is forecast for a small portion of northern Algeria, with a separate region of in eastern Algeria, eastern Tunisia, western Libya, northern and most of eastern Egypt, Jordan, Israel, and most of Saudi Arabia.\nSlightly below normal precipitation is forecast for northern Senegal, much of southern Mauritania, central and eastern Mali, most of Niger, northern Chad, and central and northern Sudan.\nAbove normal rainfall is forecast for part of the Guinea Coast of Africa, including southeastern Liberia, and the central and southern portions of Cote D'Ivoire, Ghana, and Togo. Above normal precipitation is also forecast for Guinea-Bissau, Guinea, the southernmost portion of Mali, most of Burkina Faso, the northern portions of Cote D'Ivoire, Ghana and Togo, Benin, extreme southern Niger, and northern and western Nigeria.\nSlightly below normal precipitation is forecast for the immediate coast of Cameroon, Equatorial Guinea, most of Gabon, and the immediate coast of the Republic of the Congo.\nSlightly near normal precipitation is forecast for southern Ethiopia, southern Somalia and northeastern Kenya.\nSlightly above normal precipitation is forecast for northeastern Zimbabwe, central and southern Mozambique, southern Malawi and parts of Madagascar, and the Mascarene Islands.\nSlightly near normal precipitation is forecast for portions of southeastern South Africa.\nSlightly below normal precipitation is forecast for a very small portion of the immediate coast of southwestern South Africa.\n\nNear normal temperature is forecast for parts of northern Africa, including the Canary Islands, parts of coastal Western Sahara, most of Morocco, mainly northern and eastern Algeria, most of Tunisia and Libya, small northern portions of Niger, Chad and Sudan, and western Egypt.\nAbove normal temperature is forecast for a significant region along the Guinea coast and central Africa, including southeastern Liberia, and the central and southern portions of Cote D'Ivoire, Ghana, Togo, Benin, Nigeria, much of Cameroon, Equatorial Guinea, northern Gabon, western Central African Republic and southern Chad. Above normal temperature is also forecast for southern Gabon, Republic of the Congo, northern Angola, most of Democratic Republic of the Congo, Rwanda, Burundi, extreme northwestern Tanzania, southern and central Sudan, northern Ethiopia, southern Saudi Arabia, part of Chad, part of Niger, southern Mali, Burkina Faso, most of Guinea, Sierra Leone, and the northern portions of Liberia, Cote D'Ivoire, Ghana, Togo and Benin.\nAbove normal temperature is forecast for southern Namibia, extreme southern Botswana and northwestern South Africa. Above normal temperature is also forecast for the remainder of South Africa, Lesotho, Swaziland, eastern Botswana, Zimbabwe, most of Mozambique, southeastern Malawi, southern Tanzania, and Madagascar.\nAbove normal temperature is forecast for the Mascarene Islands, east of Madagascar.\nNear normal temperature is forecast for a small portion of the immediate coast of southwestern South Africa.\n\nOctober - December 2001:\nPRECIPITATION\nSlightly above normal precipitation is forecast for parts of northern Saudi Arabia.\nBelow normal precipitation is forecast for most of Tanzania, Burundi, Rwanda, eastern Uganda, and western Kenya. Below normal precipitation is also forecast for Somalia, southern Ethiopia, southeastern Sudan, the remainder of Uganda, eastern Democratic Republic of the Congo, northeastern Zambia, northern Malawi and northern Mozambique.\nAbove normal precipitation is forecast for Lesotho in southern Africa. Above normal precipitation is also forecast for Zimbabwe, central and southern Mozambique, Swaziland, and the eastern three-quarters of South Africa.\nSlightly near normal precipitation is forecast for extreme northern Madagascar, extreme southern Madagascar, and for Agalega Island and Cargados Carajos Island of Mauritius.\nSlightly above normal precipitation is forecast for the Mascarene Islands, to the east of Madagascar.\n\nTEMPERATURE\nSlightly below normal temperature is forecast for the Canary Islands, coastal Western Sahara, and southern and central Morocco.\nAbove normal temperature is forecast for much of central Africa, including part of Mali, part of Niger, part of Libya, extreme western Egypt, most of Chad and Cameroon, Equatorial Guinea, part of Gabon, most of Republic of the Congo and Democratic Republic of the Congo, Rwanda, Burundi, western Tanzania, northern Zambia, western and northern Uganda, and mainly southern Sudan. A separate region includes eastern Egypt, extreme northeastern Sudan, Jordan, and small portions of western and northern Saudi Arabia. Above normal temperature is also forecast for part of Saudi Arabia, much of Egypt, much of Sudan, part of Libya, a small part of eastern Algeria, northeastern Chad, southern Niger, much of Nigeria, northern portions of Benin, Togo, Ghana, and Cote D'Ivoire, southwestern Mali, and much of Senegal.\nSlightly below normal temperatures are forecast for part of Namibia, extreme southern Angola, most of Botswana, most of Zimbabwe, the northern tip of South Africa, southern Mozambique, Madagascar and the Comoros Islands.\nSlightly above normal temperature is forecast for the Mascarene Islands.\nSlightly above normal temperature is forecast for western and southern South Africa and Lesotho.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "66eb76d6459cc5a5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61968:62087:1", + "start_date": "2001-06-01", + "end_date": "2001-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62088:62211:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Near-average to slightly warmer than average conditions are predicted in the eastern equatorial Pacific for the next 6 to 9 months. Currently the sea surface temperatures across much of the eastern and central equatorial are near their long-term average, although slightly lower than average SSTs have developed along the immediate western coast of South America and warmer than average SSTs persist in the extreme western part of the basin. Near neutral equatorial Pacific SST conditions are in effect for August-September-October 2001, September-October-November 2001, and October-November-December 2001, while during November-December 2001-January 2002, they are expected to be slightly above average. The warmer than average SSTs that continue to dominate much of the Indian Ocean are expected to decrease slowly through January 2002. The area of above-average temperature in the tropical south Atlantic Ocean is expected to persist through at least October 2001.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c14ac9cfae14493b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62088:62211:1", + "start_date": "2001-07-01", + "end_date": "2001-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62212:62335:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Near-average to slightly warmer than average conditions are predicted in the eastern equatorial Pacific for the next 6 to 9 months. Currently the sea surface temperatures (SSTs) across much of the eastern and central equatorial Pacific are near their long-term average, with locally warm anomalies near the dateline, although slightly lower than average SSTs exist in the far eastern equatorial Pacific and warmer than average SSTs persist in the extreme western part of the basin. Near neutral (<0.5C) equatorial Pacific SST conditions are in effect for September-November 2001 and October-December 2001, while during November-December 2001-January 2002 and December 2001-February 2002, they are expected to be slightly above average (approximately 0.5C). The warmer than average SSTs that continue to dominate much of the Indian Ocean are expected to decrease slowly through the forecast period. The area of above-average temperature in the tropical Atlantic Ocean has weakened over the last couple months and is predicted to weaken further through the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d53d19945e0dbbbc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62212:62335:1", + "start_date": "2001-08-01", + "end_date": "2001-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62336:62455:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Near-average to slightly warmer than average conditions are predicted in the eastern equatorial Pacific for the next 6 to 9 months. Currently the sea surface temperatures across much of the eastern and central equatorial are near their long-term average, although slightly lower than average SSTs have developed along the immediate western coast of South America and warmer than average SSTs persist in the extreme western part of the basin. Near neutral equatorial Pacific SST conditions are in effect for the first season of the forecast, October-December 2001, November-January 2002, December-February 2002, while during the second season, January-March 2002, they are expected to be slightly above average. The warmer than average SSTs that continue to dominate much of the Indian Ocean are expected to decrease slowly through the forecast period. The area of above-average temperature in the tropical south Atlantic Ocean is expected to persist through at least the first half of the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "ae1104d1f3c4974a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62336:62455:1", + "start_date": "2001-09-01", + "end_date": "2001-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62456:62579:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Warmer than average conditions are predicted to develop in the eastern equatorial Pacific during the next 3 to 6 months. Currently the sea surface temperatures (SSTs) in the eastern equatorial Pacific are slightly below their long-term average, but have been increasing during the month of February. Above average SSTs continue in the central Pacific near the international date line, extending also across the western part of the basin. During March-May 2002, April-June 2002, May-July 2002, and June-August 2002, the SST anomalies in the eastern tropical Pacific are expected to increase, becoming above normal by April-June and continuing to warm through June-August. A developing weak El Nino is indicated. The warmer than average SSTs that have been present over much of the Indian Ocean are expected to decrease slowly through the forecast period. The area of above-average temperature in the tropical north Atlantic Ocean is predicted to persist but slowly weaken through the period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "ca6a60c3a1cf822d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62456:62579:1", + "start_date": "2001-10-01", + "end_date": "2001-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62580:62699:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Near-average conditions are predicted in the central and eastern equatorial Pacific through early March 2002. Currently the sea surface temperatures (SSTs) across much of the eastern and central equatorial are near their long-term average, although slightly lower than average SSTs exist in the eastern portion of the tropical Pacific basin and warmer than average SSTs persist in the central and western parts of the basin. The Nino 4 region remains slightly above normal, Nino 3.4 is near normal, and Nino 3 and Nino 1+2 are slightly below normal. Near neutral equatorial Pacific SST conditions are expected for December-February 2002 and January-March 2002. During the seasons February-April 2002 and March-May 2002, they are expected to become near to slightly above average in the eastern tropical Pacific with slightly below average SST becoming more limited to slightly south of the equator. The warmer than average SSTs that continue in much of the Indian Ocean are expected to decrease slowly toward normal through the forecast period. The area of above-average temperature in the tropical north Atlantic Ocean, and more weakly in part of the tropical south Atlantic, is expected to continue but weaken through the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d98606aa7c83dd1e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62580:62699:1", + "start_date": "2001-11-01", + "end_date": "2001-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62824:62947:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Near-average to warmer than average conditions are predicted for the eastern equatorial Pacific for the next 3 to 6 months. Currently the sea surface temperatures (SSTs) across most of the eastern equatorial Pacific are slightly below their long-term average, but have been increasing during the month of January. Above average SSTs continue in the central Pacific near the international date line, extending also across the western part of the basin. During the course of the four forecast seasons February-April 2002, March-May 2002, April-June 2002, May-July 2002, the SST anomalies in the eastern tropical Pacific are expected to increase, becoming above normal by Apr-May-Jun and farther above normal by May-Jun-Jul. A developing weak El Nino is indicated. The somewhat warmer than average SSTs that have been present over much of the Indian Ocean are expected to decrease slowly through the forecast period. The area of above-average temperature in the tropical north Atlantic Ocean is predicted to persist but slowly weaken through the period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "25c47d68dbd6fde9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62824:62947:1", + "start_date": "2002-01-01", + "end_date": "2002-01-31" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62948:63059:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Warmer than average conditions are to develop in the eastern equatorial Pacific during the next 3 to 6 months. Currently the sea surface temperatures (SSTs) in the eastern equatorial Pacific are slightly below their long-term average, but have been increasing during the month of February. Above average SSTs continue in the central Pacific near the international date line, extending also across the western part of the basin. The SST anomalies in the eastern tropical Pacific are expected to increase, becoming above normal by Apr-May-Jun and continuing to warm through Jun-Jul-Aug. A developing weak El Nino is indicated. The somewhat warmer than average SSTs that have been present over much of the Indian Ocean are expected to decrease slowly through the forecast period. The area of above-average temperature in the tropical north Atlantic Ocean is predicted to persist but slowly weaken through the period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "351b80cf55da299c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62948:63059:1", + "start_date": "2002-02-01", + "end_date": "2002-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63060:63183:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Eastern equatorial Pacific sea surface temperatures (SSTs) are predicted to warm to slightly above normal at the beginning of the first forecast period. In the coming 6 months, the SSTs anomalies are expected to increase somewhat more. Currently the SSTs across much of the eastern and central equatorial are slightly above their long-term average.\n\nHigher than average SSTs have been in place in the far western Pacific and near the international dateline over the last several months. Higher than average SSTs have also more recently developed immediately off the west coast of South America. This same general pattern is predicted to be maintained in the first season of the forecast April-June 2002, while during the second, third and fourth seasons of the forecast the equatorial SST thoughout the entire eastern and central Pacific is predicted to become progressively warmer, attaining the level of a weak El Nino by the fourth period May-July 2002, June-August 2002, July-September 2002.\n\nIn the other tropical oceans, warmer than average SSTs continue to dominate much of the Indian Ocean. These are expected to decrease slowly through the forecast period, becoming slightly below normal near the coast of southern Asia and in parts of western Indonesia in the later forecast periods. The area of above-average temperature in the tropical north Atlantic Ocean is expected to persist but gradually weaken through the four forecast periods.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b7a7faf516e980c2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63060:63183:1", + "start_date": "2002-03-01", + "end_date": "2002-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63184:63303:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Near-average to slightly warmer than average conditions are predicted in the eastern equatorial Pacific for the next 6 to 9 months. Currently the sea surface temperatures across much of the eastern and central equatorial are near their long-term average, although slightly lower than average sea surface temperatures have developed along the immediate western coast of South America and warmer than average sea surface temperatures persist in the extreme western part of the basin. Near neutral equatorial Pacific sea surface temperature conditions are in effect for May-July 2002, June-August 2002, and July-September 2002, while during August-October 2002, they are expected to be slightly above average. The warmer than average sea surface temperatures that continue to dominate much of the Indian Ocean are expected to decrease slowly through the forecast period. The area of above-average temperature in the tropical south Atlantic Ocean is expected to persist through at least the first half of the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "e6d250eeaabd10bf", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63184:63303:1", + "start_date": "2002-04-01", + "end_date": "2002-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63304:63427:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Warmer than average conditions are predicted in the equatorial Pacific for the next 6 to 9 months. Currently the sea surface temperatures (SSTs) across much of the eastern and central equatorial are near their long-term average, although warmer than average SSTs are in place near the coast of Peru and Ecuador and warmer than average SSTs persist in the central and western part of the basin. Weakly warm equatorial Pacific SST conditions (approximately 0.5 C) are in effect for June-August 2002, July-September 2002. The eastern Pacific is predicted to become progressively warmer, attaining the level of a weak El Nino (SSTs between 0.5 and 1 degree C above normal) by September-November 2002.\nIn the other tropical oceans, warmer than average SSTs continue to dominate much of the Indian Ocean, and are not expected to decrease as rapidly. The area of above-average temperature in the tropical south Atlantic Ocean is expected to persist through at least the first half of the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "836f21aed6ae709d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63304:63427:1", + "start_date": "2002-05-01", + "end_date": "2002-05-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63428:63547:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Warmer than average conditions are predicted in the equatorial Pacific for the next 6 to 9 months. Currently the sea surface temperatures across much of the eastern and central equatorial are warmer than their long-term average, particularly in the central and western part of the basin, where warm SST anomalies have been present for nearly a year, but now also closer to the coast of Peru and Ecuador. Weakly warm equatorial Pacific SST conditions of approximately 0.5 C are in effect for July-September 2002 and August-October 2002. The central equatorial Pacific is currently observed to be warmer, with SSTs over 1 degree C above normal. A weak El Nino, with SSTs between 0.5 and 1 degree C above normal, is predicted by the end of the forecast period, October-December 2002.\n\nIn the other tropical oceans, warmer than average SSTs continue to dominate much of the Indian Ocean and are not expected to decrease rapidly. The area of above-average temperature in the tropical south Atlantic Ocean is expected to persist through at least the first half of the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "266fa0afcd88d505", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63428:63547:1", + "start_date": "2002-06-01", + "end_date": "2002-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63548:63671:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Warmer than average conditions are predicted in the equatorial Pacific for the next 6 to 9 months. Currently the sea surface temperatures (SSTs) across much of the eastern and central equatorial Pacific are more than 0.5 degrees C warmer than their long-term average, particularly in the central part of the basin. Weakly warm equatorial Pacific SST conditions (near or slightly higher than 0.5 C) are in effect for August-October 2002, and September-November 2002. The central equatorial Pacific, near the dateline, is currently observed to be warmer (SSTs over 1 degree C above normal). Weak El Nino conditions (SSTs between 0.5 and 1 degree C above normal) are expected throughout the forecast period, increasing slightly in the last season November-January 2003.\n\nIn the other tropical oceans, warmer than average SSTs continue to dominate much of the Indian Ocean. The area of above-average temperature in the tropical south Atlantic Ocean (near the coast of Africa) is expected to persist through at least the first half of the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "20d25075f38f9490", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63548:63671:1", + "start_date": "2002-07-01", + "end_date": "2002-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63672:63795:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Warmer than average conditions are predicted in the central and eastern equatorial Pacific for the next 6 to 9 months. Currently the sea surface temperatures (SSTs) across much of the central and eastern equatorial Pacific are more than 1 degree C above their long-term average, although slightly lower than average SSTs were observed in the far eastern Pacific, near South America, during July. Weak warm SST anomalies (approximately 0.5 degree C) are predicted over the central and eastern equatorial Pacific throughout the forecast period, September-November 2002, October-December 2002, November-January 2003, December-February 2003. Warmer than average SSTs currently observed over the eastern half of the Indian Ocean are expected to migrate towards the central part of the basin, with slightly stronger amplitude. The area of above-average temperature in the equatorial Atlantic Ocean is expected to persist through at least the first half of the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "e8c0e6980d52803e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63672:63795:1", + "start_date": "2002-08-01", + "end_date": "2002-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63796:63915:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Warmer than average conditions in the central and eastern equatorial Pacific are predicted for the next 6 to 9 months. The sea surface temperatures across much of the central equatorial Pacific are more than 1 degree C above their long-term average, and have been so for the last several months. Weak warm SST anomalies for the central equatorial Pacific are predicted throughout the forecast period October-December 2002, November-January 2003, December-February 2003, and January-March 2003. The western Indian Ocean has been becoming warmer than normal over the last month, and those warm anomalies are expected to persist or increase slightly through the forecast period. The area of above-average temperature in the equatorial Atlantic Ocean, observed in August 2002, has already returned to normal. No substantial SST anomalies are predicted for the tropical Atlantic for this forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c009f0a86c28b0ad", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63796:63915:1", + "start_date": "2002-09-01", + "end_date": "2002-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63916:64039:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Warmer than average conditions in the central and eastern equatorial Pacific are predicted for the next 5 to 7 months. The sea surface temperatures (SSTs) across much of the central equatorial Pacific are more than 1 degree C above their long-term average, and have been so for the last several months. Weak warm SST anomalies for the central equatorial Pacific are predicted throughout the forecast period November-January 2003, December-February 2003, January-March 2003, February-April 2003. The western Indian Ocean has become warmer than normal, and those warm anomalies are expected to persist or increase slightly through the forecast period. No substantial SST anomalies are predicted for the tropical Atlantic for this forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "6f8dd0be5f34e477", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63916:64039:1", + "start_date": "2002-10-01", + "end_date": "2002-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64040:64159:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Moderate El Nino conditions are predicted in the central and eastern equatorial Pacific through the end of 2002 and lasting at least into early 2003. Sea surface temperatures across much of the central equatorial Pacific are more than 1 degree C above their long-term average, and have been so for the last several months. In the last month, SST anomalies in the central Pacific have exceeded 2 degrees C. Warm SST anomalies for the central equatorial Pacific are predicted throughout the forecast period December-February 2003, January-March 2003, February-April 2003, March-May 2003. The SST anomalies observed in the next 2-3 months may be warmer than forecast.\nWarmer than average SSTs continue to dominate much of the central and western Indian Ocean. These are expected to decrease slowly through the forecast period. There are currently no substantial SST anomalies in the tropical Atlantic Ocean, but this may change near the end of the forecast period as the northern tropical Atlantic frequently develops warm SST anomalies in the February-May season in response to El Nino conditions in the Pacific.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "00387d568107af47", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64040:64159:1", + "start_date": "2002-11-01", + "end_date": "2002-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64284:64407:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: A moderate El Niño is currently observed in the central and eastern equatorial Pacific. Equatorial Pacific sea surface temperatures are predicted to weaken, but remain warmer than normal, through the first half of 2003. The sea surface temperatures across much of the central equatorial Pacific are more than 1 degree C above their long-term average, and have been so since mid-2002. Much weaker, but still warm SST anomalies for the central equatorial Pacific are predicted throughout the forecast period.\n\nWarmer than average SSTs continue to dominate much of the central and western Indian Ocean. These are expected to decrease slowly through the forecast period. Warm SST anomalies currently exist in the northern sub-tropical Atlantic Ocean, and are expected to spread throughout the northern tropical Atlantic over the next several months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7b4fd45f7611a61c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64284:64407:1", + "start_date": "2003-01-01", + "end_date": "2003-01-31" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64408:64519:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Moderate El Niño conditions that have been observed in the central and eastern equatorial Pacific since mid-2002 are declining. Equatorial Pacific sea surface temperatures (SSTs) have returned to near-normal in the eastern equatorial Pacific. The SSTs in the central equatorial Pacific are currently about 1.5C above average, but are predicted to weaken to near-normal by mid-2003 (March-May 2003, April-June 2003, May-July 2003). Warmer than average SSTs continue to dominate much of the tropical Indian Ocean. These are expected to decrease through the forecast period. Warmer than average SSTs currently exist in the northern and southern sub-tropical Atlantic Ocean, and are predicted to weaken substantially over the next several months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "67a3928f1b390e64", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64408:64519:1", + "start_date": "2003-02-01", + "end_date": "2003-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64520:64643:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: El Niño conditions have been in continued decline in the central and eastern equatorial Pacific since mid-2002. As of early March 2003 equatorial Pacific sea surface temperatures (SSTs) have returned to near-normal in the eastern equatorial Pacific. The SSTs in the central equatorial Pacific are currently near 1C above average, and will most likely weaken to near-normal by mid-2003.\nWarmer than average SSTs continue to dominate much of the tropical Indian Ocean. These are predicted to decrease through the forecast period. Warmer than average SSTs currently exist in the northern and southern sub-tropical Atlantic Ocean. The above-normal SSTs north of the equator are predicted to weaken more rapidly than those south of the equator.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "1f745bd93bf830d0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64520:64643:1", + "start_date": "2003-03-01", + "end_date": "2003-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64644:64763:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The El Niño event that had been observed in the central and eastern equatorial Pacific since mid-2002 has declined to near-neutral conditions. As of early April 2003 equatorial Pacific sea surface temperatures have returned to near-normal in the eastern/central equatorial Pacific, and are approximately 0.5 degrees C below normal in the eastern equatorial Pacific. The SSTs in the central equatorial Pacific are currently near 0.5C above average, and will most likely weaken to near-normal by mid-2003.\nWarmer than average SSTs continue to dominate much of the tropical Indian Ocean. These are predicted to decrease slowly through the forecast period. Warmer than average SSTs currently exist in the northern and southern sub-tropical Atlantic Ocean, and also in the eastern equatorial Atlantic. The above-normal SSTs north of the equator are predicted to weaken more rapidly than those south of the equator.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "60147965334ef68e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64644:64763:1", + "start_date": "2003-04-01", + "end_date": "2003-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64764:64887:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Near-neutral conditions have been present in the equatorial Pacific since April 2003, as the El Nino event of 2002-03 declined rapidly with SSTs at some locations dropping by 4 degrees C over the last month. There is potential for the development of a La Nina event later this year. As of early May 2003, equatorial Pacific sea surface temperatures (SSTs) had returned to near-normal and are rapidly cooling toward below normal in the eastern/central equatorial Pacific; SSTs have cooled to approximately 1 to 1.5 degrees C below normal in the eastern equatorial Pacific. The SSTs in the central equatorial Pacific are currently near 0.3C above average and will most likely weaken to near-normal or below normal by July or August 2003. Warmer than average SSTs continue to occupy much of the tropical Indian Ocean. These are predicted to decrease slowly through the forecast period. Warmer than average SSTs currently exist in the northern and southern sub-tropical Atlantic Ocean, and also in the eastern equatorial Atlantic. The above-normal SSTs north of the equator are predicted to weaken more rapidly than those south of the equator. Near-neutral conditions are predicted for June-August 2003, July-September 2003, August-October 2003, and September-November 2003.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "f9aab19cd23842d5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64764:64887:1", + "start_date": "2003-05-01", + "end_date": "2003-05-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64888:65007:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: There is potential for a developing La Nina event. In early March 2003 equatorial Pacific sea surface temperatures (SSTs) returned from above-normal to near-normal in the eastern equatorial Pacific, and they continued cooling through early June reaching values on the order of -2C in the far eastern equatorial Pacific. Weak La Nina conditions are indicated. Warmer than average SSTs continue to dominate much of the tropical Western Pacific and Indian Ocean. These are predicted to decrease through the forecast period of July-September 2003, August-October 2003, September-November 2003, and October-December 2003. Warmer than average SSTs currently exist in the northern and southern sub-tropical Atlantic Ocean. In May the equatorial Atlantic developed a small region of below-normal SSTs. These anomalies are forecast to slowly damp. More recent observations show continuing development of cold anomalies on the equator and southward along the west coast of Africa.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "bab7a06361c97964", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64888:65007:1", + "start_date": "2003-06-01", + "end_date": "2003-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65008:65131:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The probability for a developing La Nina event is now estimated to be just slightly higher than that of an average year. In May of 2003 equatorial Pacific sea surface temperatures (SSTs) became colder than normal in the eastern and east-central equatorial Pacific, but returned to normal during June and early July. Neutral to slightly cooler than normal ENSO conditions are forecasted. Warmer than average SSTs continue to dominate much of the tropical Western Pacific and Indian Ocean. These are predicted to decrease during the forecast period (August-October 2003, September-November 2003, October-December 2003, November-January 2004). Warmer than average SSTs currently exist in the northern and southern sub-tropical Atlantic Ocean. These Atlantic SSTs are predicted to continue but weaken through the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b6ba9ff390ca9e69", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65008:65131:1", + "start_date": "2003-07-01", + "end_date": "2003-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65132:65255:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions, which have been observed since mid-June, will continue through the forecast periods. Warmer than average SSTs continue to dominate much of the tropical Western Pacific and Indian Ocean. These are predicted to decrease during the forecast period (September-November 2003, October-December 2003, November-January 2004, December-February 2004). Warmer than average SSTs currently exist in the southern sub-tropical Atlantic Ocean, and the western and central portions of the north-tropical Atlantic. This pattern is predicted to continue but weaken through the forecast period.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7965fff6d6cd12c1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65132:65255:1", + "start_date": "2003-08-01", + "end_date": "2003-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65256:65375:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions, which have been observed since mid-June, will likely continue through the forecast periods. Warmer than average SSTs continue to dominate much of the tropical Western Pacific and Indian Ocean. These are predicted to continue, but gradually weaken, during the forecast period (October-December 2003, November-January 2004, December-February 2004, January-March 2004). Warmer than average SSTs currently exist in the tropical and sub-tropical Atlantic Ocean. This pattern is predicted to continue but weaken through the forecast periods.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "2d50f1160fa30a1f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65256:65375:1", + "start_date": "2003-09-01", + "end_date": "2003-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65376:65499:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions, which have been observed since mid-June, will continue through the forecast periods. Warmer than average SSTs continue to dominate much of the tropical Western Pacific and Indian Ocean. These are predicted to continue, but gradually weaken, during the forecast period (November-January 2004, December-February 2004, January-March 2004, February-April 2004). Warmer than average SSTs currently exist in the tropical and sub-tropical Atlantic Ocean. This pattern is predicted to continue but weaken through the forecast periods.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "faeefd05ef0f2006", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65376:65499:1", + "start_date": "2003-10-01", + "end_date": "2003-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65500:65619:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions will continue through December 2003 - May 2004. Warmer than average SSTs dominate much of the tropical western and central Pacific, and Indian Ocean. These will continue, but gradually weaken, during December-February 2004, January-March 2004, February-April 2004, and March-May 2004. Warmer than average SSTs exist in the tropical and sub-tropical Atlantic Ocean. This pattern will continue but weaken through the forecast periods.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "efbbfd6bedbe2526", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65500:65619:1", + "start_date": "2003-11-01", + "end_date": "2003-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65744:65867:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions, which have been observed since mid-June, will continue through the forecast periods. Warmer than average SSTs, but near-normal with respect to ENSO, continue to dominate much of the tropical Pacific, particularly near and just west of the dateline. The Indian Ocean and especially the north tropical Atlantic Ocean also continue to show predominantly above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period of February-April 2004, March-May 2004, April-June 2004, and May-July 2004.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "1cf761633f6368ff", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65744:65867:1", + "start_date": "2004-01-01", + "end_date": "2004-01-31" + } + }, + { + "prompt": "The following data shows global data for 29 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65868:65983:1'}. The data starts from February 01 00:00 and ends on February 29 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions, which have been observed since mid-2003, will continue through the forecast periods. Warmer than average SSTs continue to dominate much of the tropical Pacific near and just west of the dateline. The Indian Ocean and especially the north tropical Atlantic Ocean also continue to show predominantly above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period (March-May 2004, April-June 2004, May-July 2004, June-August 2004).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "acbded7151636b3e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65868:65983:1", + "start_date": "2004-02-01", + "end_date": "2004-02-29" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65984:66107:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions, which have been observed since mid-2003, will continue through April - September 2004. Warmer than average SSTs continue to dominate much of the tropical Pacific from the dateline westward to eastern Indonesia. The Indian Ocean and especially the north tropical Atlantic Ocean also continue to show predominantly above-average SSTs. These are predicted to continue, but gradually weaken, during April-June 2004, May-July 2004, June-August 2004, and July-September 2004.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "1232dd23a9aa0b14", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65984:66107:1", + "start_date": "2004-03-01", + "end_date": "2004-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66108:66227:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions will occur in the first forecast period, becoming neutral to slightly warmer than normal for the last three forecast periods. Warmer than average SSTs continue in parts of the western tropical Pacific from the dateline to north of the Solomon Islands. The Indian Ocean and the north tropical Atlantic Ocean also continue to show predominantly above-average SSTs. These conditions will continue, but gradually weaken, during May-July 2004, June-August 2004, July-September 2004, and August-October 2004.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5393e30352caf555", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66108:66227:1", + "start_date": "2004-04-01", + "end_date": "2004-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66228:66351:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral to slightly warmer than average ENSO conditions will occur during the four forecast periods. Warmer than average SSTs continue in parts of the western tropical Pacific from the dateline to north of the Solomon Islands. Cooler than average SSTs exist in the far eastern tropical Pacific. The Indian Ocean and the north tropical Atlantic Ocean also continue to show predominantly above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period (June-August 2004, July-September 2004, August-October 2004, September-November 2004).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "8a8767ae9ef04cb8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66228:66351:1", + "start_date": "2004-05-01", + "end_date": "2004-05-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66352:66471:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral to slightly warmer than average ENSO conditions will occur during the forecast periods. Warmer than average SSTs prevail in parts of the east-central, central and western tropical Pacific from the dateline to north of the Solomon Islands. Cooler than average SSTs exist in the far eastern tropical Pacific. The Indian Ocean and the north tropical Atlantic Ocean also continue to show predominantly above-average SSTs. These are predicted to continue, but gradually weaken, during July-September 2004, August-October 2004, September-November 2004, and October-December 2004.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "82241c11c4f9d1ce", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66352:66471:1", + "start_date": "2004-06-01", + "end_date": "2004-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66472:66595:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Slightly warmer than average ENSO conditions are likely to occur during the four forecast periods. Warmer than average SSTs prevail in parts of the east-central, central and western tropical Pacific from the dateline to north of the Solomon Islands. Cooler than average SSTs exist farther east in the tropical Pacific. The majority of the area of the Indian Ocean and the north tropical Atlantic Ocean also continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period (August-October 2004, September-November 2004, October-December 2004, November-January 2005).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "2bb2c17b63fbd23a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66472:66595:1", + "start_date": "2004-07-01", + "end_date": "2004-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66596:66719:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: There is an approximately 50% likelihood that weak El Nino conditions will occur during the forecast periods. Warmer than average SSTs prevail in parts of the east-central, central and western tropical Pacific from 170E to 120W longitude. Cooler than average SSTs exist in the eastern equatorial Pacific and near the Maritime Continent. In the Indian Ocean there are currently below-average temperatures in the west and above-average temperatures in the central and eastern portions. The north tropical Atlantic Ocean continues to have above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period (September-November 2004, October-December 2004, November-January 2005, December-February 2005).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "03fb2ad95d82c3e1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66596:66719:1", + "start_date": "2004-08-01", + "end_date": "2004-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66720:66839:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Weak El Nino conditions are likely to occur. Warmer than average SSTs now prevail in the east-central, central and west-central tropical Pacific, while cooler than average SSTs exist in the eastern quarter of the tropical Pacific and in the vicinity of the Maritime Continent. Much of the area of the Indian Ocean and the north tropical Atlantic Ocean continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during October-December 2004, November-January 2005, December-February 2005, and January-March 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "77f6d2fa05f70452", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66720:66839:1", + "start_date": "2004-09-01", + "end_date": "2004-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66840:66963:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Weak El Nino conditions will likely occur. Warmer than average SSTs now prevail in the east-central, central and west-central tropical Pacific, near normal SSTs exist in the eastern quarter of the tropical Pacific, and cooler than normal SSTs are observed in the vicinity of the Maritime Continent. Much of the area of the Indian Ocean and the north tropical Atlantic Ocean continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during November-January 2005, December-February 2005, January-March 2005, and February-April 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c49a284ffc9dab6a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66840:66963:1", + "start_date": "2004-10-01", + "end_date": "2004-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66964:67083:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Weak El Nino conditions will occur during the at least the first three of the four forecast periods. Warmer than average SSTs now prevail in the east-central, central and west-central tropical Pacific, near normal SSTs exist in the eastern quarter of the tropical Pacific, and average to slightly cooler than normal SSTs are observed in the vicinity of the Maritime Continent. Much of the area of the Indian Ocean and the north tropical Atlantic Ocean continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period of December-February 2005, January-March 2005, February-April 2005, and March-May 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "a59fcf710bbd7ac9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66964:67083:1", + "start_date": "2004-11-01", + "end_date": "2004-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67208:67331:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Weak El Nino conditions will exist during at least the first one, and possibly two, of the four forecast periods. Warmer than average SSTs are now observed in the east-central, central and west-central tropical Pacific; near to slightly above normal SSTs exist in the eastern quarter of the tropical Pacific and in the vicinity of the Maritime Continent. Much of the Indian Ocean and the north tropical Atlantic Ocean continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period of February-April 2005, March-May 2005, April-June 2005, and May-July 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b1f76ed86e74f00d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67208:67331:1", + "start_date": "2005-01-01", + "end_date": "2005-01-31" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67332:67443:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Weak El Nino conditions will continue during the first 3-month forecast period, and tend to dissipate thereafter. Warmer than average SSTs are now observed in the central and west-central tropical Pacific; near normal SSTs exist in the eastern quarter of the tropical Pacific, and slightly above normal SSTs are found in the vicinity of the Maritime Continent. Much of the Indian Ocean and the north tropical Atlantic Ocean continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period (March-May 2005, April-June 2005, May-July 2005, June-August 2005).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "32836c03b71d5a43", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67332:67443:1", + "start_date": "2005-02-01", + "end_date": "2005-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67444:67567:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions will remain neutral during the first 3-month forecast period. Warmer than average SSTs are now observed in the central and west-central tropical Pacific, near to slightly below normal SSTs exist in the eastern tropical Pacific, and slightly above normal SSTs are found in the vicinity of the Maritime Continent. Much of the Indian Ocean and the north tropical Atlantic Ocean continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period of April-June 2005, May-July 2005, June-August 2005, and July-September 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "306be15997a0e64e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67444:67567:1", + "start_date": "2005-03-01", + "end_date": "2005-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67568:67687:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Presently neutral ENSO conditions will remain neutral during the four 3-month forecast periods. Warmer than average SSTs are now observed in the central and west-central tropical Pacific, near normal SSTs exist in the eastern tropical Pacific, and slightly above normal SSTs are found in the vicinity of the Maritime Continent. Much of the Indian Ocean and the north tropical Atlantic Ocean continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period (May-July 2005, June-August 2005, July-September 2005, August-October 2005).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "da0f2675191e9258", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67568:67687:1", + "start_date": "2005-04-01", + "end_date": "2005-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67688:67811:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Presently neutral ENSO conditions will remain neutral during the four 3-month forecast periods. Slightly to somewhat warmer than average SSTs are now observed in the western, central, and east-central tropical Pacific. Much of the Indian Ocean, and particularly the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period of June-August 2005, July-September 2005, August-October 2005, and September-November 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "24a92764286fbc11", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67688:67811:1", + "start_date": "2005-05-01", + "end_date": "2005-05-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67812:67931:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The presently neutral ENSO conditions will remain neutral during the four 3-month forecast periods. Slightly to somewhat warmer than average SSTs are now observed in the western and central tropical Pacific. Much of the Indian Ocean, and particularly the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period July-September 2005, August-October 2005, September-November 2005, October-December 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "909b7c5c039a412c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67812:67931:1", + "start_date": "2005-06-01", + "end_date": "2005-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67932:68055:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The presently neutral ENSO conditions will remain neutral during the four 3-month forecast periods. Slightly to somewhat warmer than average SSTs are now observed in the western, central, and east-central tropical Pacific. The eastern and central Indian Ocean, and particularly the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period (August-October 2005, September-November 2005, October-December 2005, November-January 2006).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "dc4527774c9dcc99", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67932:68055:1", + "start_date": "2005-07-01", + "end_date": "2005-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68056:68179:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Presently neutral ENSO conditions will remain neutral during the four 3-month forecast periods. Slightly warmer than average SSTs are now observed in the western tropical Pacific. The eastern Indian Ocean, and particularly the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period (September-November 2005, October-December 2005, November-January 2006, December-February 2006).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "68af975128f76c1a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68056:68179:1", + "start_date": "2005-08-01", + "end_date": "2005-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68180:68299:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Presently neutral ENSO conditions will remain neutral during the four 3-month forecast periods. Slightly warmer than average SSTs are now observed in the western tropical Pacific. The eastern and central Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. These will continue, but gradually weaken, during the forecast period (October-December 2006, November-January 2006, December-February 2006, January-March 2006).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5013a9760709549f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68180:68299:1", + "start_date": "2005-09-01", + "end_date": "2005-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68300:68423:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Neutral ENSO conditions will remain neutral. Slightly warmer than average SSTs are now observed in the west-central tropical Pacific. The eastern and central Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during November-January 2006, December-February 2006, January-March 2006, and February-April 2006.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "6dd61fed81cefb9a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68300:68423:1", + "start_date": "2005-10-01", + "end_date": "2005-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68424:68543:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The presently neutral ENSO conditions will remain neutral during the four 3-month forecast periods. Slightly warmer than average SSTs are now observed in the west-central tropical Pacific, while slightly below average SSTs are present in the eastern one-third of the tropical Pacific. The eastern and central Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to continue, but gradually weaken, during the forecast period December-February 2006, January-March 2006, February-April 2006, March-May 2006.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "0ffa9598bff2282f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68424:68543:1", + "start_date": "2005-11-01", + "end_date": "2005-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68668:68791:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The present weak cold ENSO conditions will ease slightly, reverting to the cool but ENSO-neutral range during the first, and more certainly by the second of the four 3-month forecast periods. Somewhat warmer than average SSTs are now observed in the western tropical Pacific, while minimal La Nina level SSTs are present in the eastern half of the tropical Pacific, excluding the immediate vicinity of the dateline. The eastern portion of the Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to weaken over the course of the the forecast periods (February-April 2006, March-May 2006, April-June 2006, May-July 2006).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "96b11294d8100a87", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68668:68791:1", + "start_date": "2006-01-01", + "end_date": "2006-01-31" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68792:68903:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The present weak cold ENSO conditions will continue but slowly weaken, reverting to cool but ENSO-neutral levels during the second of the four 3-month forecast periods. Warmer than average SSTs are now observed in the western tropical Pacific, while weak La Nina SSTs are present in the eastern half of the tropical Pacific, extending to just west of the dateline. The eastern portion of the equatorial Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to slowly weaken over the course of the forecast periods March-May 2006, April-June 2006, May-July 2006, and June-August 2006.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "ef6b3af17a60db47", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68792:68903:1", + "start_date": "2006-02-01", + "end_date": "2006-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68904:69027:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Weak cold ENSO conditions will continue but slowly weaken, reverting to cool but ENSO-neutral levels during the second of the four 3-month forecast periods. Warmer than average SSTs are now observed in the western tropical Pacific, while weak La Nina SSTs are present in the central and east-central tropical Pacific. The equatorial SSTs near the west coast of South America have become somewhat above normal. The eastern portion of the equatorial Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. These will slowly weaken over the course of the forecast periods (April-June 2006, May-July 2006, June-August 2006, July-September 2006).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d7c695f7ab042867", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68904:69027:1", + "start_date": "2006-03-01", + "end_date": "2006-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69028:69147:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The present neutral but slightly cool tropical Pacific SSTs will continue to rise to average over the coming months, becoming neutral but very slightly warmer than average by the fourth forecast season. Somewhat warmer than average SSTs are now observed in the western tropical Pacific, while near average SSTs are present in the central and east-central tropical Pacific. The equatorial SSTs near the west coast of South America, however, are below normal. The equatorial Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to slowly change their pattern or weaken over the course of the forecast periods: May-July 2006, June-August 2006, July-September 2006, and August-October 2006.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "38eadd2b07b9493c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69028:69147:1", + "start_date": "2006-04-01", + "end_date": "2006-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69148:69271:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Present neutral tropical Pacific SSTs will continue, and rise to slightly above average over the coming months. Somewhat warmer than average SSTs are now observed in the western tropical Pacific, near average SSTs are present in the central and east-central tropical Pacific, and below normal SSTs are found near the South American coastline. The central equatorial Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. These are predicted to slowly change their pattern or weaken over the course of the forecast periods June-August 2006, July-September 2006, August-October 2006, and September-November 2006.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7c4616f4e1484a03", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69148:69271:1", + "start_date": "2006-05-01", + "end_date": "2006-05-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69272:69391:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: There is a moderately strong likelihood that the present neutral to slightly above average tropical Pacific SSTs will continue, and perhaps rise to slightly farther above average over the coming months. Somewhat warmer than average SSTs are now observed in the western and central tropical Pacific, with near average SSTs in the eastern tropical Pacific, and slightly below normal SSTs near the South American coastline. The central equatorial Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. The Indian Ocean SSTs are predicted to weaken over the course of the forecast periods, and the north tropical Atlantic SSTs are predicted to slowly change their pattern favoring positive departures from normal near the Gulf of Guinea by northern mid-summer. (July-September 2006, August-October 2006, September-November 2006, October-December 2006).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "2007a4a431e2aa5f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69272:69391:1", + "start_date": "2006-06-01", + "end_date": "2006-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69392:69515:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The present neutral to slightly above average tropical Pacific SSTs will continue. Somewhat slightly warmer than average SSTs are now observed in the western and central tropical Pacific, as well as in certain pockets farther east of the dateline. The central equatorial Indian Ocean, and the north tropical Atlantic Ocean, continue to show above-average SSTs. The Indian Ocean SSTs are predicted to slowly weaken over the course of the forecast periods, and the north tropical Atlantic SSTs are predicted to remain near their current anomaly strength but somewhat change their pattern for the periods August-October 2006, September-November 2006, October-December 2006, and November-January 2007.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "917c27087ee7b48a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69392:69515:1", + "start_date": "2006-07-01", + "end_date": "2006-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69516:69639:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Slightly to somewhat above average tropical Pacific SSTs will continue. Somewhat warmer than average SSTs are now observed in the western, central, and eastern tropical Pacific. The central equatorial Indian Ocean, and much of the tropical Atlantic Ocean, continue to show above-average SSTs. Both the Indian Ocean and tropical Atlantic SSTs are predicted to slowly weaken over the course of the forecast periods (September-November 2006, October-December 2006, November-January 2007, December-February 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "36ac3e86aa46aef2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69516:69639:1", + "start_date": "2006-08-01", + "end_date": "2006-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69640:69759:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The present somewhat above average tropical Pacific SSTs will continue. Weak El Nino tropical Pacific conditions are present. Somewhat warmer than average SSTs are now observed in the west-central, central, and eastern tropical Pacific, while slightly below normal SSTs are found in the far western tropical Pacific. Much of the equatorial Indian Ocean, and much of the tropical Atlantic Ocean (particularly north of the equator), show above-average SSTs. The tropical Atlantic SSTs are predicted to slowly weaken over the course of the forecast periods. (October-December 2006, November-January 2007, December-February 2007, January-March 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "66e90d6aaa85527c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69640:69759:1", + "start_date": "2006-09-01", + "end_date": "2006-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69760:69883:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The present somewhat above average tropical Pacific SSTs will continue. Weak El Nino tropical Pacific conditions are expected. Somewhat warmer than average SSTs are now observed in the central and eastern tropical Pacific, while below normal SSTs are found in the western tropical Pacific. The eastern equatorial Indian Ocean has below normal SSTs, while the central and eastern Indian Ocean has above normal SSTs. Much of the northern tropical Atlantic Ocean shows above-average SSTs. The Indian Ocean and tropical Atlantic SST anomalies will slowly weaken over the forecast periods of November-January 2007, December-February 2007, January-March 2007, and February-April 2007.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7fc3a452031c572b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69760:69883:1", + "start_date": "2006-10-01", + "end_date": "2006-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69884:70003:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Weak to moderate El Nino conditions in the tropical Pacific SSTs will continue. Warmer than average SSTs are now observed in the central and eastern tropical Pacific, while below normal SSTs are found in the western tropical Pacific. The eastern equatorial Indian Ocean has markedly below normal SSTs, while the central and eastern Indian Ocean has above normal SSTs. Much of the northern tropical Atlantic Ocean shows above-average SSTs. The Indian Ocean and tropical Atlantic SST anomalies are predicted to slowly weaken over the course of the forecast periods (December-February 2007, January-March 2007, February-April 2007, March-May 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "049ae45b038d4a57", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69884:70003:1", + "start_date": "2006-11-01", + "end_date": "2006-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70128:70251:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The present weak to moderate El Nino conditions in the tropical Pacific SSTs will continue at weak to moderate strength through the first forecast period and weaken toward neutral thereafter. Warmer than average SSTs are now observed in the central and eastern tropical Pacific, while normal SSTs are found in the western tropical Pacific. The eastern equatorial Indian Ocean has near to slightly below normal SSTs, and the central and eastern Indian Ocean has above normal SSTs. Much of the northern tropical Atlantic Ocean shows above-average SSTs. The SST anomalies in the Indian Ocean are predicted to slowly weaken over the course of the forecast periods, and the SST anomalies in the tropical Atlantic are expected to continue, and the gradient of the anomalies from southern to northern tropical Atlantic are expected to strengthen somewhat. (February-April 2007, March-May 2007, April-June 2007, May-July 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "0eea681876fe42bf", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70128:70251:1", + "start_date": "2007-01-01", + "end_date": "2007-01-31" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70252:70363:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The present El Nino conditions in the tropical Pacific SSTs, having weakened to only borderline strength, will continue at borderline intensity into only the first month or two of the first forecast period, and then become neutral thereafter. Warmer than average SSTs are now observed in most of the central and eastern tropical Pacific, while normal to slightly above normal SSTs are found in the western tropical Pacific. Much of the equatorial Indian Ocean has at least slightly above normal SSTs. The northern tropical Atlantic Ocean shows above-average SSTs, and southern tropical Atlantic has near to slightly above normal SSTs. The SST anomalies in the Indian Ocean and the tropical Atlantic Ocean are predicted to slowly weaken over the course of the forecast periods (March-May 2007, April-June 2007, May-July 2007, June-August 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "eb0b177b440d76e8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70252:70363:1", + "start_date": "2007-02-01", + "end_date": "2007-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70364:70487:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs, having transitioned from El Nino to neutral conditions between January and early March, will continue to cool during the coming weeks and months, possibly remaining ENSO-neutral but becoming cooler than average and approaching the borderline of La Nina, or possibly crossing into weak La Nina conditions by May or June. Warmer than average SSTs are now observed in part of the central and western tropical Pacific, while normal to slightly below normal SSTs are found in much of the eastern part, with below normal SSTs observed within 20 degrees of 125W. Much of the equatorial Indian Ocean has slightly above normal SSTs, particularly just south of the equator. The northern tropical Atlantic Ocean shows above-average SSTs, and southern tropical Atlantic has near to slightly above normal SSTs. The SST anomalies in the Indian Ocean are predicted to very slowly weaken, and those in the tropical Atlantic will also weaken north of the equator, while some mildly below normal SST is predicted to develop near and south of the equator in the eastern portion. (April-June 2007, May-July 2007, June-August 2007, July-September 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "08309d6199c4f478", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70364:70487:1", + "start_date": "2007-03-01", + "end_date": "2007-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70488:70607:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs, having transitioned from El Nino to neutral conditions between January and early March, will continue to cool during the coming weeks and months, becoming cooler than average and possibly crossing into weak La Nina conditions by May or June, particularly in the eastern portion of the basin. Slightly warmer than average SSTs are now observed in the west-central tropical Pacific, while below normal SSTs are found in much of the eastern one-third, from about 130W eastward. Much of the equatorial Indian Ocean has slightly above normal SSTs. The northern tropical Atlantic Ocean shows above-average SSTs, and southern tropical Atlantic has near to slightly above normal SSTs. The positive SST anomalies in the Indian Ocean are predicted to remain approximately constant or very slowly weaken over the course of the forecast periods, and those in the tropical Atlantic will very slowly weaken north of the equator, while immediately on and south of the equator the weak anomalies will remain approximately constant or very slowly weaken further. (May-July 2007, June-August 2007, July-September 2007, August-October 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "718b2c88bd246f1d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70488:70607:1", + "start_date": "2007-04-01", + "end_date": "2007-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70608:70731:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will become cooler during the coming weeks and months, becoming cooler than average and possibly crossing into weak La Nina conditions by June to August. Slightly warmer than average SSTs are now observed in the western and west-central tropical Pacific, while slightly below normal SSTs are found in much of the eastern one-third, from about 130W eastward. Much of the equatorial Indian Ocean has slightly above normal SSTs. The northern tropical Atlantic Ocean shows slightly above average SSTs, and southern tropical Atlantic SSTs are near average. The warmer than average SSTs in the Indian Ocean are predicted to slowly weaken, and those in the tropical Atlantic will very slowly weaken north of the equator, while south of the equator the average SSTs are expected to continue. (June-August 2007, July-September 2007, August-October 2007, September-November 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "69ce906dd9561f52", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70608:70731:1", + "start_date": "2007-05-01", + "end_date": "2007-05-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70732:70851:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will become slightly cooler during the coming few months, becoming cooler than average and possibly crossing the borderline into weak La Nina conditions by late July or August. Slightly warmer than average SSTs are now observed in the western and west-central tropical Pacific, while below normal SSTs are found in much of the eastern one-third, concentrated most strongly from about 125W eastward. The below average SSTs in the eastern portion are expected to become somewhat more evenly distributed between South America and the Dateline. Much of the equatorial Indian Ocean has slightly above normal SSTs. The northern tropical Atlantic Ocean shows slightly above average SSTs, while the southern tropical Atlantic SSTs are near average. The positive SST anomalies in the Indian Ocean are predicted to weaken over the course of the forecast periods, and those in the tropical Atlantic will very slowly weaken north of the equator, while south of the equator the average SSTs are expected to continue. (July-September 2007, August-October 2007, September-November 2007, October-December 2007).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "9bed51307caf3bfd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70732:70851:1", + "start_date": "2007-06-01", + "end_date": "2007-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70852:70975:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will assume weak La Nina conditions by late August or September. Slightly warmer than average SSTs are now observed in the western tropical Pacific, while somewhat below normal SSTs are found in much of the eastern one-third, concentrated most strongly from about 140W eastward. The below average SSTs in the eastern portion are expected to become more evenly distributed between South America and the dateline, more resembling a standard ENSO pattern. Much of the equatorial Indian Ocean has above normal SSTs, and the tendency toward a positive Indian Ocean dipole pattern has diminished. The tropical Atlantic Ocean shows a mixed anomaly pattern, with most substantially above average SSTs well north of the equator. The positive SST anomalies in the Indian Ocean are predicted to weaken over the course of the forecast periods, and those in the tropical Atlantic will slowly weaken north of the equator, while south of the equator mainly average SSTs are expected to continue. (August-October 2007, September-November 2007, October-December 2007, November-January 2008).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5643fdee4716722e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70852:70975:1", + "start_date": "2007-07-01", + "end_date": "2007-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70976:71099:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will likely assume weak La Nina conditions by late August or September. Warmer than average SSTs are now observed in parts of the western tropical Pacific, while below normal SSTs are found in much of the eastern one-third, concentrated most strongly from about 150W eastward. The below average SSTs in the eastern portion are expected to become more evenly distributed between South America and the dateline, more resembling a standard ENSO pattern. There is a pocket of below normal SST near and south of the equator in the region bordered by northern Australia, Papua New Guinea and eastern Indonesia that does not conform to the typical La Nina pattern. Much of the equatorial Indian Ocean continues to have above normal SSTs. The tropical Atlantic Ocean has most substantially above average SSTs north of the equator in the Caribbean and immediately along the equator in the Gulf of Guinea. The positive SST anomalies in the Indian Ocean are predicted to weaken over the course of the forecast periods, and those in the tropical Atlantic will slowly weaken. (September-November 2007, October-December 2007, November-January 2008, December-February 2008).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "284637f250c03c96", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70976:71099:1", + "start_date": "2007-08-01", + "end_date": "2007-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71100:71219:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will assume weak La Nina conditions through most of the four forecast periods. Warmer than average SSTs are now observed in parts of the western tropical Pacific, while below normal SSTs are found in most of the eastern half, concentrated most strongly from about 160W eastward. More strongly below average SSTs in the eastern portion are expected to become more evenly distributed between South America and the dateline. There is a pocket of below normal SST just south of the equator near and west of Java. Much of the equatorial Indian Ocean continues to have slightly above normal SSTs. The tropical Atlantic Ocean shows a mixed anomaly pattern, with a small area of above average SSTs near the Gulf of Guinea. The positive SST anomalies in the Indian Ocean are predicted to weaken over the course of the forecast periods, and those in the tropical Atlantic to remain fairly weak. (October-December 2007, November-January 2008, December-February 2008, January-March 2008).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5d65a1b8f1a619e8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71100:71219:1", + "start_date": "2007-09-01", + "end_date": "2007-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71220:71343:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will likely assume La Nina conditions through the four forecast periods. Warmer than average SSTs are now observed in parts of the western tropical Pacific, while below normal SSTs are found in most of the eastern two-thirds. Below normal SST is positioned just south of the equator near and west of Java. Much of the equatorial Indian Ocean continues to have slightly above normal SSTs. The tropical Atlantic Ocean shows a mixed anomaly pattern, with an area of above average SSTs in the Gulf of Guinea. The positive SST anomalies in the Indian Ocean are predicted to weaken over the course of the forecast periods, and those in the tropical Atlantic are expected also to become closer to average. (November-January 2008, December-February 2008, January-March 2008, February-April 2008).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "cc63f2a03d9d9635", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71220:71343:1", + "start_date": "2007-10-01", + "end_date": "2007-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71344:71463:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: There is a likelihood that the tropical Pacific SSTs will assume La Nina conditions through the forecast periods from December 2007 to May 2008, particularly the first two periods. Warmer than average SSTs are now observed in the western tropical Pacific, while below normal SSTs are found in most of the eastern two-thirds. Below normal SST is observed in the west-central and western portions of Indonesia. Much of the equatorial Indian Ocean continues to have slightly above normal SSTs. The tropical Atlantic Ocean has weakly above average SSTs north of the equator in the vicinity of the Gulf of Guinea, and weakly below average SSTs south of the equator in the western side. The SST anomalies in both the Indian and Atlantic oceans are predicted to weaken over the course of the forecast periods: December-February 2008, January-March 2008, February-April 2008, and March-May 2008.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "9162373f8e359125", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71344:71463:1", + "start_date": "2007-11-01", + "end_date": "2007-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71588:71711:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will assume La Nina conditions through the first three forecast periods, most in the first period and progressively less strongly for the two following periods. Warmer than average SSTs are observed in the western tropical Pacific, while below normal SSTs are found in the eastern two-thirds. Much of the equatorial Indian Ocean has slightly below normal SSTs. The tropical Atlantic Ocean shows weak negative anomalies. The negative SST anomalies in the Indian ocean will become somewhat negative, and the negative SST anomalies in the north tropical Atlantic will strengthen slightly during northern spring season. (February-April 2008, March-May 2008, April-June 2008, May-July 2008).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "1f46554ebfbd2913", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71588:71711:1", + "start_date": "2008-01-01", + "end_date": "2008-01-31" + } + }, + { + "prompt": "The following data shows global data for 29 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71712:71827:1'}. The data starts from February 01 00:00 and ends on February 29 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Tropical Pacific SSTs are likely to assume La Nina conditions through the first forecast period, and progressively more weakly in the three following periods. Warmer than average SSTs are observed in the western tropical Pacific, while below normal SSTs are found in the eastern two-thirds. Much of the equatorial Indian Ocean has near normal SSTs but with a developing tendency toward below normal SSTs in the western part and above normal in the eastern part. The tropical Atlantic Ocean shows mainly weak anomalies. The negative SST anomalies developing in the western Indian ocean are expected to continue, and negative SST anomalies in the north tropical Atlantic are predicted to slightly strengthen during the coming two 3-month periods.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "649a721941d4fc8d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71712:71827:1", + "start_date": "2008-02-01", + "end_date": "2008-02-29" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71828:71951:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will show La Nina conditions during April-June 2008, and progressively weaker La Nina conditions during May-July 2008, June-August 2008, and July-September 2008. Warmer than average SSTs are observed in the western tropical Pacific, while below normal SSTs are found in the eastern two-thirds except for the extreme east, close to the South American coast, where there are warmer than average SSTs. The western equatorial Indian Ocean has below normal SSTs while the eastern part has above normal SSTs. The tropical Atlantic Ocean shows weak anomalies, with some below normal SSTs north of the equator. The negative SST anomalies in the western Indian ocean are expected to slowly weaken, the positive anomalies in the eastern Indian Ocean weaken more slowly, and negative SST anomalies in the north tropical Atlantic are predicted to slowly weaken during June-August 2008 and July-September 2008.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7bce32358332fec7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71828:71951:1", + "start_date": "2008-03-01", + "end_date": "2008-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71952:72071:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will show weak to moderate La Nina conditions during the first forecast period (May-July 2008), and progressively weaker La Nina conditions in the three subsequent forecast periods (June-August 2008, July-September 2008, August-October 2008). Warmer than average SSTs are observed in the far western tropical Pacific, while below normal SSTs are found in the eastern two-thirds except for the extreme east, close to the South American coast, where there are warmer than average SSTs. Most of the equatorial Indian Ocean has below normal SSTs, with the exception of the far eastern portion north of Australia, and most of the tropical Atlantic Ocean shows somewhat above average SSTs, an exception being a pocket in the northwest portion near northern South America. The negative SST anomalies in most of the Indian ocean are expected to slowly weaken, the positive anomalies in the tropical Atlantic to weaken, and the negative SST anomalies in the northwest tropical Atlantic to persist and to assume a somewhat more zonal pattern.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "6c0a0ee043cd0c15", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71952:72071:1", + "start_date": "2008-04-01", + "end_date": "2008-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72072:72195:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will show weak La Nina conditions during the first forecast period, weakening to cool-neutral conditions during the second, third and fourth forecast periods. Warmer than average SSTs are observed in the far western tropical Pacific, while below normal SSTs are found in the eastern two-thirds except for the extreme east, where SSTs are near to slightly above average. Most of the equatorial Indian Ocean has below normal SSTs, with the exception of the far eastern portion north of Australia. Most of the tropical Atlantic Ocean shows somewhat above average SSTs, except for the region north of South America in the far eastern Caribbean. The negative SST anomalies in most of the Indian ocean are expected to slowly weaken, and both the positive and negative anomalies in the tropical Atlantic are also expected to weaken. (June-August 2008, July-September 2008, August-October 2008, September-November 2008).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "355ee23c505290a0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72072:72195:1", + "start_date": "2008-05-01", + "end_date": "2008-05-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72196:72315:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The tropical Pacific SSTs will continue to be weakly below average during the first two forecast periods in the central and west-central portions, but the eastern portion may be slightly above average. During the later forecast periods this same anomaly configuration is likely to continue but with weakening of the positive SST anomalies in the eastern portion. In the equatorial Indian Ocean, slightly below average SSTs are predicted for the eastern portion, with above average near the African coast. This pattern continues but weakens for the longer lead forecast periods. In the tropical Atlantic Ocean, SSTs are predicted to be above average in the eastern portion and near average elsewhere. This pattern continues but weakens for the longer lead forecast periods. (July-September 2008, August-October 2008, September-November 2008, October-December 2008).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "cad61df326c51a4c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72196:72315:1", + "start_date": "2008-06-01", + "end_date": "2008-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72316:72439:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Tropical Pacific SSTs will continue to be weakly below average during the first two forecast periods in a small pocket in the central/west-central portions, but the eastern quarter of the basin may continue to be slightly above average. During the later forecast periods this same anomaly configuration is likely, but with weakening positive SST anomalies in the east. A weak positive dipole is predicted in the equatorial Indian Ocean, with slightly below average SSTs near Indonesia and slightly above average SSTs near the African coast. This pattern continues but weakens for the longer lead forecast periods. SSTs are predicted to be slightly above average in most of the tropical Atlantic, weakening at longer lead times (August-October 2008, September-November 2008, October-December 2008, November-January 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "ae6f3f21791bdf2a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72316:72439:1", + "start_date": "2008-07-01", + "end_date": "2008-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72440:72563:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central tropical Pacific SSTs will be weakly below average during the four forecast periods, but weakly above normal in the eastern quarter of the basin the first one to two period. During the later forecast periods the eastern quarter is expected to return to near-average, and the central portion to remain slightly below normal. A weak positive dipole is predicted in the equatorial Indian Ocean for the first one to two periods, weakening thereafter and even slightly reversing such that below average SSTs may appear near the African coast. SSTs are predicted to be somewhat above average in most of the tropical Atlantic for the first forecast period, but weakening at longer lead times (September-November 2008, October-December 2008, November-January 2009, December-February 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "16bc3dd6a4b729f2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72440:72563:1", + "start_date": "2008-08-01", + "end_date": "2008-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72564:72683:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central tropical Pacific SSTs will be weakly below average during the four forecast periods, while weakly above normal SSTs in the eastern quarter of the basin will return to average during the first period. A weak positive dipole is predicted in the equatorial Indian Ocean for the first one to two periods, but with the positive anomaly closer to the central than to the western Indian Ocean. This structure is predicted to weaken with increasing lead time. SSTs are predicted to be above average in the north tropical Atlantic, and below normal in part of the south tropical Atlantic, during the first forecast periods, weakening toward average thereafter. (October-December 2008, November-January 2009, December-February 2009, January-March 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "dd55190838de9ad2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72564:72683:1", + "start_date": "2008-09-01", + "end_date": "2008-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72684:72807:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Central and east-central tropical Pacific SSTs will be weakly below average during the four forecast periods, while weakly above normal SSTs in the eastern quarter of the basin will return to average during the first period and then become weakly below average for longer leads. SSTs will be very weakly above average in the equatorial Indian Ocean for all four forecast periods. SSTs will be above average in the north tropical Atlantic, and near to slightly below average in part of the south tropical Atlantic, during the first forecast periods, continuing into the longer lead forecast periods but weakening in the 3rd and 4th periods. (November-January 2009, December-February 2009, January-March 2009, February-April 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "10c0f36bb20e8b6f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72684:72807:1", + "start_date": "2008-10-01", + "end_date": "2008-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72808:72927:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be weakly below average during the four forecast periods, at borderline La Nina levels. Very weakly above average SST is predicted in the equatorial Indian Ocean except near the African coast for the first two forecast periods, moving slowly toward slightly below normal across all of the basin toward the final (fourth) period. SSTs are predicted to be very slightly above average in much of the northern and equatorial tropical Atlantic, and near to slightly below average in part of the south tropical Atlantic, during the four forecast periods. (December-February 2009, January-March 2009, February-April 2009, March-May 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "fe33e3105f29713b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72808:72927:1", + "start_date": "2008-11-01", + "end_date": "2008-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73052:73175:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be slightly below average during the four forecast periods, at a weak La Nina level for the first forecast season, borderline La Nina level for the second forecast season, and low-neutral ENSO conditions for the last two seasons. SSTs in the equatorial Indian Ocean will be very weakly below average, except for the easternmost portion, for all four forecast periods. SSTs will be close to average in most of the tropical Atlantic during the four forecast periods. (February-April 2009, March-May 2009, April-June 2009, May-July 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "682b3d06ab755f4d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73052:73175:1", + "start_date": "2009-01-01", + "end_date": "2009-01-31" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73176:73287:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be slightly below average during the first few forecast periods, at a weak La Nina level for the first forecast season, low-neutral ENSO conditions for the second and third seasons, and close to average by the fourth season. A tendency for very weakly below average SST is predicted in the equatorial Indian Ocean, and weakly above average SST in the southern tropical Indian Ocean, for all four forecast periods. SSTs are predicted to be close to average in most of the tropical Atlantic during the four forecast periods. (March-May 2009, April-June 2009, May-July 2009, June-August 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "85477e9da303b41f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73176:73287:1", + "start_date": "2009-02-01", + "end_date": "2009-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73288:73411:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be slightly below average during the first one to two forecast periods, at a borderline La Nina level for the first forecast season, low-neutral ENSO conditions for the second and third seasons, and close to average by the fourth season. Near average SST is predicted in the equatorial Indian Ocean for all four forecast periods. SSTs are predicted to be just slightly below average in most of the tropical Atlantic during the four forecast periods. (April-June 2009, May-July 2009, June-August 2009, July-September 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "bd8f611a11006573", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73288:73411:1", + "start_date": "2009-03-01", + "end_date": "2009-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73412:73531:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be slightly below average during May-July 2009, and below average during June-August 2009, July-September 2009, and August-October 2009. During all periods, ENSO conditions are expected to be in the low-neutral range. Slightly above average SST is predicted in the equatorial Indian Ocean for May-July 2009, followed by near-average SST for June-August, July-September, and August-October 2009. SSTs are predicted to be slightly below average in much of the tropical Atlantic during the four forecast periods.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "efbe7bdbd910689d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73412:73531:1", + "start_date": "2009-04-01", + "end_date": "2009-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73532:73655:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be near-average during the first two forecast periods, becoming very slightly above average during the third and fourth forecast periods. During all periods, ENSO conditions are expected to be in the neutral range. SST will be very slightly above average in the equatorial Indian Ocean during the four forecast periods, except for the far eastern portion during the second through fourth periods. SSTs will be very slightly below average in the tropical Atlantic during the four forecast periods. The forecast periods are June-August 2009, July-September 2009, August-October 2009, and September-November 2009.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d64cc8826f0aa8bb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73532:73655:1", + "start_date": "2009-05-01", + "end_date": "2009-05-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73656:73775:1'}. The data starts from June 01 00:00 and ends on June 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will become somewhat above average during the first forecast period (indicative of borderline or weak El Nino conditions), increasing to more substantially above average during the second, third and fourth forecast periods (indicative of weak to moderate El Nino conditions). Slightly above average SST is predicted in the western equatorial Indian Ocean during the four forecast periods, with near-average SST in the eastern equatorial Indian Ocean. SSTs in the tropical Atlantic, currently below normal north of the equator and above normal south of the equator, are predicted to retain this pattern but greatly weaken during the first forecast period, followed by a more complete dissipation of any pattern, becoming near-average throughout, during the later three periods. (July-September 2009, August-October 2009, September-November 2009, October-December 2009).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "afc7854de4fd1239", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73656:73775:1", + "start_date": "2009-06-01", + "end_date": "2009-06-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73776:73899:1'}. The data starts from July 01 00:00 and ends on July 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be somewhat above average during the first forecast period, with weak or weak/moderate El Nino conditions, increasing to become farther above average during the second, third and fourth forecast periods, with weak/moderate or moderate El Nino conditions. SSTs will be slightly above average in the western equatorial Indian Ocean during the four forecast periods, with near-average SST in the eastern equatorial Indian Ocean. SSTs in the tropical Atlantic, currently slightly below normal north of the equator and above normal south of the equator, will become near-average to slightly above average both south and north of the equator by the second forecast period, and remain that way during the later three periods. (August-October 2009, September-November 2009, October-December 2009, November-January 2010).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "a0573ddd58b4394b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73776:73899:1", + "start_date": "2009-07-01", + "end_date": "2009-07-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73900:74023:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be above average during all four of the forecast periods, and most strongly above average during the second and third periods, indicative of moderate El Nino conditions. Above average SST is predicted in the western equatorial Indian Ocean during all four forecast periods, with near-average to slightly above average SST in the eastern equatorial Indian Ocean. SSTs in the tropical Atlantic, currently slightly below average along the immediate equator and slightly above average both north and south of the equator, are predicted to become somewhat farther above average north of the equator and remain slightly above average south of the equator during the four forecast periods. (September-November 2009, October-December 2009, November-January 2010, December-February 2010).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "deec02d7b9d6ce20", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73900:74023:1", + "start_date": "2009-08-01", + "end_date": "2009-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74024:74143:1'}. The data starts from September 01 00:00 and ends on September 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Central and east-central tropical Pacific sea surface temperatures will be above average during all four of the forecast periods, and most strongly above average during the second period, indicative of moderate El Nino conditions. Above average sea surface temperatures will occur in the western equatorial Indian Ocean during all four forecast periods, with slightly weaker but still above average sea surface temperatures in the eastern equatorial Indian Ocean. Sea surface temperatures in the tropical Atlantic, currently slightly below average along the immediate equator and slightly above average both north and south of the equator, will become somewhat farther above average north of the equator and remain slightly above average south of the equator during the four forecast periods. (October-December 2009, November-January 2010, December-February 2010, January-March 2010).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "faa61714eb6f7786", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74024:74143:1", + "start_date": "2009-09-01", + "end_date": "2009-09-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74144:74267:1'}. The data starts from October 01 00:00 and ends on October 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be above average during all four of the forecast periods, and most strongly above average during the first and second periods, indicative of weak to moderate El Nino conditions. Above average SST is predicted in the equatorial Indian Ocean during all four forecast periods, especially in the central part of the basin. SSTs in the tropical Atlantic, currently slightly below average along the immediate equator and slightly above average both north and south of the equator, are predicted to become somewhat farther above average north of the equator and to be near to slightly above average south of the equator during the four forecast periods. (November-January 2010, December-February 2010, January-March 2010, February-April 2010).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "0aa43a21726e1cbb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74144:74267:1", + "start_date": "2009-10-01", + "end_date": "2009-10-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74268:74387:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be above average during December-February 2010, January-March 2010, and February-April 2010, and most strongly above average during December-February 2010 and January-March 2010, indicative of weak to moderate El Nino conditions. Above average SST is predicted in the equatorial Indian Ocean during December-February 2010, January-March 2010, February-April 2010, and March-May 2010, especially in the central part of the basin. SSTs in the tropical Atlantic are predicted to be somewhat above average north of the equator and near-average along and south of the equator during December-February 2010, January-March 2010, February-April 2010, and March-May 2010.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5675d2966a53bed2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74268:74387:1", + "start_date": "2009-11-01", + "end_date": "2009-11-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74512:74635:1'}. The data starts from January 01 00:00 and ends on January 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be above average during the first three forecast periods, and most strongly above average during the first period, indicative of moderate El Nino conditions. Above average SST is predicted in the equatorial Indian Ocean during all four forecast periods, especially in the central and west-central part of the basin. SSTs in the tropical Atlantic are predicted to be somewhat above average north of the equator and near-average along and south of the equator during the four forecast periods. (February-April 2010, March-May 2010, April-June 2010, May-July 2010).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "421eded899dd8ffb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74512:74635:1", + "start_date": "2010-01-01", + "end_date": "2010-01-31" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74636:74747:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be above average during the first forecast period, indicative of moderate El Nino conditions, with a progressively weaker El Nino pattern through the later seasons. Above average SST is expected in much of the equatorial Indian Ocean during all four forecast periods, but weakening by the fourth period. SSTs in the tropical Atlantic are expected to be somewhat above average north of the equator and approximately near-average along and south of the equator during the four forecast periods. (March-May 2010, April-June 2010, May-July 2010, June-August 2010).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "92fa624c14a241e2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74636:74747:1", + "start_date": "2010-02-01", + "end_date": "2010-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74748:74871:1'}. The data starts from March 01 00:00 and ends on March 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs will be above average during the first forecast period, indicative of moderate El Nino conditions, with a progressively weaker El Nino pattern out to the fourth season. Above average SST is predicted in much of the equatorial Indian Ocean during all four forecast periods, but weakening by the fourth period. SSTs in the tropical Atlantic are predicted to be somewhat above average north of the equator and approximately near-average along and south of the equator during the four forecast periods. (March-May 2010, April-June 2010, May-July 2010, June-August 2010).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7e7d457de164d498", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74748:74871:1", + "start_date": "2010-03-01", + "end_date": "2010-03-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74872:74991:1'}. The data starts from April 01 00:00 and ends on April 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: Central and east-central tropical Pacific SSTs during May-July 2010 will be above average in the central portion (a remnant of dissipating El Nino conditions), but below average in the eastern portion. During June-August 2010 through August-October 2010, the below average SST will expand westward, indicating a borderline or weak La Nina condition for July-September 2010 and August-October 2010. Above average SST is predicted in much of the equatorial Indian Ocean during May-July 2010, slowly weakening with increasing lead time. SSTs in the tropical Atlantic are predicted to be somewhat above average both north and south of the equator (but more strongly north of the equator) during May-July 2010, weakening toward average with increasing lead time.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "355d2747452dded0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74872:74991:1", + "start_date": "2010-04-01", + "end_date": "2010-04-30" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74992:75115:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate a comprehensive global climate forecast report for temperature and precipitation for the next 3 months. Use the provided atmospheric data as context to inform your forecast. Provide region-specific predictions using probability language (e.g., 'enhanced probabilities of above-normal temperatures', 'slightly enhanced probabilities of below-normal precipitation'). Structure your response with separate sections for PRECIPITATION and TEMPERATURE. For each variable, give specific regional forecasts covering major continental areas and notable climate zones. If precipitation data is not directly available in the dataset, use related variables (pressure systems, temperature patterns, wind patterns) to infer precipitation likelihood.", + "response": "The global forecast report for temperature and precipitation over the next 3 months is: The central and east-central tropical Pacific SSTs during the first forecast period will be slightly above average in the central portion, but below average in the eastern portion. During the second through fourth forecast period the below average SST will strengthen and expand westward, indicating a borderline or weak La Nina condition by the third forecast period and a likely weak La Nina during the fourth period. Above average SST is predicted in much of the equatorial Indian Ocean during the first forecast period, slowly weakening with increasing lead time. SSTs in the tropical Atlantic are predicted to be above average both north and south of the equator, but more strongly so north of the equator, during the first forecast period, weakening toward average between the first and fourth periods. (June-August 2010, July-September 2010, August-October 2010, September-November 2010).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "U1gBEF", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c30977ba43825d6c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74992:75115:1", + "start_date": "2010-05-01", + "end_date": "2010-05-31" + } + } +] \ No newline at end of file diff --git a/level2c_part1.json b/level2c_part1.json new file mode 100644 index 0000000000000000000000000000000000000000..607cbac32b78e51407e959689fad01a78dbaeef2 --- /dev/null +++ b/level2c_part1.json @@ -0,0 +1,3102 @@ +[ + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42414:42418:1'} The data starts from January 12 12:00 and ends on January 13 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Strong Pacific weather front will move ashore over Washington, Oregon, and northern California in the last 12-hour period, bringing the greatest areal coverage of precipitation. Immediate coastal areas and coastal ranges of Washington, Oregon, the northern half of the Cascades, Vancouver Island, and coastal British Columbia will experience the brunt of this system due to the track of the strongest upper-level dynamics. High winds are likely in coastal sections due to a tight surface pressure gradient.\n\nUpper-level dynamics breaking through the ridge over central North America will enhance developing widespread warm air advection in the wake of building surface high pressure across the eastern United States.\n\nA strengthening cold front will move off the East Coast within 24 hours, maintaining winter conditions, especially over New York and New England, where the strongest cold air advection is expected. A cold blast will also extend farther southwest over the eastern United States.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "42b47c54cebcf21f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42414:42418:1", + "date": "1988-01-13 07:13:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42416:42420:1'} The data starts from January 13 00:00 and ends on January 13 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A dry pattern is in store for most of the eastern half of the nation as a large surface ridge dominates behind the front now approaching the East Coast. Only some lingering lake effect snows are expected beyond tonight. There is a possibility of a few showers along the Texas Gulf Coast at 48 hours, with moisture beginning to return and a shortwave in the southern stream approaching from the west.\n\nThe west will experience increased onshore flow throughout the period along the coast. The frontal system associated with the first wave will be approaching the northwest coast tonight and is forecast to dissipate as it crosses the mountains. The second wave and associated surface features appear strong enough to make it across the mountains intact. The west is expected to remain rather wet with the onshore flow and available dynamics.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "608a8f041f2b67e1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42416:42420:1", + "date": "1988-01-13 19:11:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42418:42422:1'} The data starts from January 13 12:00 and ends on January 14 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A deepening cyclone near 145W and an associated Pacific weather front just east of 135W are on schedule, with the front expected to come ashore by 18Z and a deep low headed for Queen Charlotte Island. A powerful upper-level jet core shifts eastward to about 150W in 48 hours, with associated dynamics at lower levels likely to suppress the front to lower California.\n\nThe strength of upper dynamics and onshore flow with the main system is likely to generate the greatest areal coverage of precipitation of recent events. A surge of mild maritime air pushes the rain-snow line well inland. Strongest upper dynamics are headed across southwestern Canada with subsequent falling 500 heights likely to induce a deepening surface low over central Alberta.\n\nA much subdued maritime front will have pushed to the northern Plains, Four Corners, and southern California positions. Elsewhere, some moderation over central US is expected with another cold blast across much of the eastern US.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "602349c5a334c288", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42418:42422:1", + "date": "1988-01-14 07:08:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42420:42424:1'} The data starts from January 14 00:00 and ends on January 14 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A frontal system is approaching the West Coast. Energy with the accompanying upper dynamics is forecast to split, with a portion heading into southwestern Canada while the southern wave tracks across northern California and the central Rockies. A broad upper trough will eventually cover the western and central U.S., while ridging and warm advection will move over the eastern U.S. The final trough is expected in the vicinity of 145W at 48 hours.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "eab976b8cef979b1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42420:42424:1", + "date": "1988-01-14 19:12:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42422:42426:1'} The data starts from January 14 12:00 and ends on January 15 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A train of short waves in the eastern Pacific is forecast to push eastward, knocking down the upper ridge over the Rockies and developing zonal flow across the western and central U.S. over the next 48 hours. The jet will sink southward to southern California and through the southern Rockies. An abundance of moisture is forecast to spread across the entire western U.S., and this combined with rapidly moving disturbances should spread broken to scattered precipitation from the Pacific Coast to the eastern slopes of the Rockies. Precipitation should be in the form of snow from the Cascades and Sierra Range eastward, while rain is expected along the Pacific Coast.\n\nDuring the second 24-hour period, moisture from the Gulf of Mexico should stream northward through the Mississippi Valley, permitting rain to spread along eastern Texas and the lower Mississippi Valley, then northward along a frontal boundary. Along the northern tier states, snow is expected to spread across the region as a short wave moves through the northern Rockies to the upper Mississippi Valley. Most precipitation is likely to remain north of the border in the vicinity of a surface low. The surface low forecast to move through southern Canada is expected to draw down a new surge of arctic air; however, this should not enter the northern U.S. until after 48 hours.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b1de5f41eb6fcbbc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42422:42426:1", + "date": "1988-01-15 07:06:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42424:42428:1'} The data starts from January 15 00:00 and ends on January 15 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Multiple shortwaves in fast zonal flow in the Pacific are forecast to continue, with the pattern evolving to a broad upper trough covering much of the western and central U.S. The main frontal system will continue tracking across the Rockies and northern Plains. Precipitation will increase along and ahead of this front as Gulf moisture interacts with dynamics from a wave in the southern stream near southern Baja this morning.\n\nA stronger wave will be approaching the northern California coast on day 2, with uncertainty in the strength of the associated surface low.\n\nThe week will remain quiet along the East Coast after tonight as the wave which brought a snowstorm to the Carolinas moves into the Atlantic. A persistent surface ridge will likely keep temperatures from warming quickly.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "a6ef1d8adbd4abdf", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42424:42428:1", + "date": "1988-01-15 19:13:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42426:42430:1'} The data starts from January 15 12:00 and ends on January 16 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Systems just off the Washington coast should push northeastward into the Pacific Northwest and British Columbia very early in the period, bringing the first round of broken precipitation. Another weak shortwave just west of 140 will spread the next round of broken precipitation into the West Coast later in day 1 as fast flow across the Pacific continues. The final and strongest shortwave of the group should reach the western United States on day 2, accompanied by a decent frontal system and the last batch of enhanced precipitation.\n\nIn the East, return flow around retreating surface high and southwesterly upper flow indicate that an expanding warm advection rainfall pattern will develop, starting in the southern Plains and lower Mississippi Valley on day 1 and reaching the eastern Great Lakes, Ohio Valley, and southeastern states on day 2. The front and rainfall will be slower in their eastward progress.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "9f853faef19a26ba", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42426:42430:1", + "date": "1988-01-16 07:15:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42428:42432:1'} The data starts from January 16 00:00 and ends on January 16 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A trough is well located near 150W and will become the next major storm for the southwestern U.S. An impressive jet is driving this system, increasing its strength. Heavy precipitation is already occurring near the Texas and Louisiana coastal region as a warm advection pattern is aided by a shortwave. Widespread broken precipitation is expected to spread along and ahead of the cold front in the Plains. This front should weaken and slow down as it encounters persistent surface ridging in the East. The surface pattern indicates neither excessive ridge movement nor excessive system filling.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "ea9d848ed669ae6e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42428:42432:1", + "date": "1988-01-16 19:11:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42430:42434:1'} The data starts from January 16 12:00 and ends on January 17 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong and developing shortwave is about to impact the central California coast and is forecast to continue moving rapidly east-southeastward, setting up a major trough over the southern Rockies by the end of the period. This will result in significant precipitation for central and southern California on day 1, with activity shifting into the southern Rockies and southern High Plains by the end of the period. The eastern U.S. will experience a stalled approaching cold front due to a southwesterly upper flow, allowing milder air to reach the East Coast and maintaining occasional wet conditions over much of the eastern third of the nation.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5c8a62042d70a25e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42430:42434:1", + "date": "1988-01-17 07:12:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42432:42436:1'} The data starts from January 17 00:00 and ends on January 17 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong upper level trough is moving toward the southwestern U.S. This storm is currently bringing heavy rainfall to southern California coastal sections, with heavy snow likely over the Sierras. As the storm continues southeastward, widespread snow will spread tonight to the higher terrain of Utah, Arizona, and New Mexico, as well as to the Colorado Rockies. Snow will increase over northern New Mexico and Colorado by day 2 as low level easterly flow develops.\n\nMeanwhile, much of the eastern U.S. is also experiencing active weather as warm advection continues along with the passage of a shortwave. This feature is forecast to move to the Ohio Valley on day 1 and on to New England on day 2.\n\nA portion of the southwestern trough is expected to lift out, with varying positions of the main surface low.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "15610e2d9041f9ca", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42432:42436:1", + "date": "1988-01-17 19:11:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42434:42438:1'} The data starts from January 17 12:00 and ends on January 18 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A vigorous upper low and associated short wave off the southern California coast is forecast to move east-northeast along the southern Rockies to the central Plains over the next 48 hours. A major storm should spread a swath of snow and rain from the southern California coast eastward through the Intermountain region and southern Rockies to the entire Mississippi Valley, Ohio Valley, and Mid-Atlantic states. A new cold front is forecast to push southward through eastern Canada to the southern Mid-Atlantic states as a backdoor cold front. Precipitation associated with this boundary is likely to be scattered over New England during day 1. Preceding this cold front is a short wave from Missouri, which is forecast to move through the southern Great Lakes and off the New England coast, spreading an area of broken rain along the associated surface trough. Back west, a series of short waves should be forced into western Canada as an upper ridge builds along the west coast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "077a11c95722525c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42434:42438:1", + "date": "1988-01-18 07:19:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42436:42440:1'} The data starts from January 18 00:00 and ends on January 18 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A major portion of the southwestern upper trough will lift northeast toward the southern Great Lakes region by 48 hours. The jet is forecast to move from Texas across the southeastern U.S. to the southern Appalachians. Widespread precipitation will continue ahead of the system with the aid of warm advection in southerly low-level flow. The pattern suggests continued precipitation, but amounts beyond 24 hours may be overestimated.\n\nIngredients are coming together for a significant severe weather episode as strong vorticity lobes are forecast to move through the main trough. The main threat will be over eastern Texas, Arkansas, and Louisiana on day 1 and move eastward to Mississippi and Alabama on day 2.\n\nAs the main frontal system continues eastward, high pressure will build southward and enhance precipitation in New Mexico and Colorado with low-level upslope flow. Low-level easterly winds should also help lingering snow showers in the area on day 2.\n\nIn the West, a building upper ridge will keep the weather quiet for a while, but a strong wave with associated precipitation will affect the northwest coast at 36 hours and beyond.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b4aea6e93904823a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42436:42440:1", + "date": "1988-01-18 19:11:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42438:42442:1'} The data starts from January 18 12:00 and ends on January 19 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A big storm is lifting out of the Southern Plains, with an amplifying system expected to hit the Northwest Coast during day 2. Reformation of a Southern Plains low is expected early in day 1 in response to a strong shortwave rounding the base of the trough. Widespread overrunning rainfall will precede the storm through day 2, with active convection likely along the trailing cold front in the Southern States on day 1, diminishing on day 2 in the Southeast as the best upper dynamics lift north-northeastward away from the front. A major snowstorm is likely to the left of the storm track, enhanced by a lifting out upper low. A shortwave initially near 160W should reach the West Coast early in day 2. Decent rainfall should reach the West Coast during day 2, with snow stretching into the Northern Rockies by the end of the period.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "142e64da4dc3593a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42438:42442:1", + "date": "1988-01-19 07:14:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42440:42444:1'} The data starts from January 19 00:00 and ends on January 19 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: An upper low lifting northeast from the Plains will allow a surface low to reform and move northeast through the Great Lakes. Widespread overnight rain and snow will continue associated with this system into Wednesday. A trailing band of snow showers will extend southwest under the upper trough through the Great Lakes into the Plains after the main low passes. Convection in the southeastern U.S. should ease Wednesday as Gulf inflow and low-level convergence weaken.\n\nThe next system of concern is a vigorous disturbance near 48N 155W, which will break through the mean West Coast ridge and plunge into Alberta or northern Montana within 48 hours. This should produce a good chance of snow for Idaho, Montana, and North Dakota Wednesday night and Thursday. Confidence is fairly high that this will be a strong storm.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d1676853c1a7cade", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42440:42444:1", + "date": "1988-01-19 18:58:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42442:42446:1'} The data starts from January 19 12:00 and ends on January 20 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A short wave associated with an upper trough is forecast to move through British Columbia and Alberta to southern Saskatchewan while dragging a cold front southward through the Intermountain region to southern Colorado and the central Plains. Most precipitation is expected to remain north of the border and near the vicinity of the vorticity maximum, with scattered snow showers accompanying the cold front. An intense storm in the central Plains is forecast to lift northeastward and weaken slightly as the upper low dissipates and moves as an open wave. A dry slot is moving through the Ohio Valley and is forecast to push eastward into New England, sparing that area from the brunt of the storm. Significant precipitation is likely to occur through the upper Mississippi Valley and Great Lakes near the weakening upper low, and over the southeastern U.S. in the vicinity of a slow moving cold front.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "860299584fb3a730", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42442:42446:1", + "date": "1988-01-20 07:26:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42444:42448:1'} The data starts from January 20 00:00 and ends on January 20 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The most recent winter storm is exiting northeastward from the Great Lakes. In this region, lingering troughiness and moisture should bring a slow end to the snow, including lake effect snow.\n\nFarther south, a turn to more anticyclonic flow over the Gulf suggests that one more short wave may bring a day 2 frontal wave in the Atlantic and a slower end to the southeastern coastal precipitation.\n\nIn the far west, the next short wave topping the western ridge over British Columbia is strong and is expected to move southeastward into the northern Plains within 48 hours. The track of this system should allow it to tap some Arctic air, but not as severe as in recent weeks.\n\nA strong high in the west should keep the West and Plains cold.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "9d50b9f258daf8ec", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42444:42448:1", + "date": "1988-01-20 19:09:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42446:42450:1'} The data starts from January 20 12:00 and ends on January 21 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: High amplitude flow will continue over the U.S. during the next 48 hours. Rapid building of a ridge in the Gulf of Alaska is expected in the next 24 hours, followed by flattening of the ridge as a powerful jet targets the British Columbia coast. An associated Pacific frontal system should cross the Pacific Northwest on day 2, with rain spreading over Washington and Oregon and snow inland over the extreme northern Rockies. A cold front in the northwest U.S. is expected to push rapidly into the Plains states behind northwest flow aloft, weakening as upper support splits with main energy diving toward the southern Rockies. The southern Rockies system should produce some light snow across portions of New Mexico on day 2 despite relatively low relative humidity. A secondary surge of arctic air should spill into the central and northern Plains eastward into the Mississippi Valley, accompanied by light snow in northern portions near best upper support. In the East, one more short wave is expected to move intact across the Southeast, inducing cyclogenesis off the South Carolina coast. A well-defined vortex center over Oklahoma continues to drop southeastward. Precipitation in the Southeast is expected to linger.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "4015603f81ea4744", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42446:42450:1", + "date": "1988-01-21 07:06:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42448:42452:1'} The data starts from January 21 00:00 and ends on January 21 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The blocking pattern should continue, but the intrusion of the strong Pacific jet stream will modify its amplitude. Breakdown of the southwestern Atlantic ridge will allow the slow-moving cold front in the East to move south through Florida within 24 hours, aided by a developing coastal low. A strong shearing flow aloft will keep this system weak, but there will be rain along the southeastern coast. The short wave moving from the Canadian Rockies will bring a return of Arctic air to northern areas. With the polar vortex far north, the system will track mostly eastward, with Arctic air only filtering southward. Much of the central and western US will remain chilly with cold air already in place. In the far West, relaxation of the Pacific ridge and a strong push of westerlies will bring frontal systems inland, with the next system arriving early on day 2. Sustained overrunning in the northwestern US is unlikely, so precipitation in that area will be reduced on day 2.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "38dcd6d713ab4ee6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42448:42452:1", + "date": "1988-01-21 19:09:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42450:42454:1'} The data starts from January 21 12:00 and ends on January 22 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong short wave is expected to impact the Northwest coast early in the period. An associated frontal system is expected to move southeastward across the northwest tier of states, bringing rain along coastal sections of Washington and Oregon and inland snow from the northern Intermountain region into the central and northern Plains. The upper ridge near 120W is forecast to reload rapidly near 135W while the short wave moves southeastward toward the southern Plains. This should begin to move at least a portion of the energy from a cutoff low currently dropping southward from New Mexico and allow overrunning precipitation to move northward along the western Gulf Coast within 48 hours. A stronger impulse is expected to drop southeastward into the southern Plains. A weak arctic front dropping southeastward from Canada should bring some light snow from the northern Plains into the Great Lakes and New England by the end of the period. High pressure building in behind the exiting cold front in the Southeast should allow for dry weather and seasonable temperatures from the Tennessee Valley eastward.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "74816c0656457366", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42450:42454:1", + "date": "1988-01-22 07:02:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42452:42456:1'} The data starts from January 22 00:00 and ends on January 22 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A weak to moderate storm is expected to move across the northern Plains and into the western Great Lakes by 48 hours. The frontal system moving eastward across the Great Lakes is weak and is expected to continue weakening as it drifts eastward. In southern Texas, overrunning is expected to cause most of the rain.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "1fe7bce9523cd6a6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42452:42456:1", + "date": "1988-01-22 19:07:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42454:42458:1'} The data starts from January 22 12:00 and ends on January 23 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A deeper low will develop over the northern Plains, weakening through the Great Lakes on day 2. There will be slower eastward movement with an associated front into the East and a weak surface wave over the Southeast states. Remnants of a cutoff low over northern Mexico are forecast to move east into the Gulf of Mexico, which will likely increase precipitation potential in the Southeast by 48 hours. In the West, with an upper ridge anchored along the coast, dry northwest flow and seasonably cool temperatures will prevail from the Rockies westward. Widespread snow is expected to spread across the northern Plains and northern Mississippi Valley eastward into the Great Lakes and western portions of New England by the end of the period.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "824b5bfba22cac15", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42454:42458:1", + "date": "1988-01-23 06:57:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42456:42460:1'} The data starts from January 23 00:00 and ends on January 23 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The strong short wave over the Dakotas is expected to dominate the weather pattern across the Plains and Great Lakes, leading to a deeper surface system and more extensive precipitation through the first day of the forecast. After this period, most of the short wave energy will move quickly southward toward the Gulf in fast northerly flow, shifting the weather focus to the Gulf region for possible development of a surface low during the second day of the forecast. Another short wave is expected to bring another shot of arctic air into the central US within 48 hours. In the West, weather will be dominated by a strong basin high.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "a09aa3ae7b93f261", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42456:42460:1", + "date": "1988-01-23 19:11:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42458:42462:1'} The data starts from January 23 12:00 and ends on January 24 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Deep surface low over the mid Mississippi Valley is now lifting and weakening as a short wave is pushed out by the western extension of the polar vortex dropping southward from western James Bay. Widespread snow should gradually decrease across the Great Lakes and upper Ohio Valley, but a second surge of light snow is expected on day 2 across the western Lakes and upper Mississippi Valley as the polar vortex and a new arctic surge drop rapidly southward. In the south, strong short wave energy is dropping southeastward through the central Rockies. With heights already quite low across Oklahoma and Texas and the system still developing, a much weaker system is expected to track across the Gulf Coast states over the next 24 hours. Strong wave development on the cold front through the southeast by the end of the period is expected. Strong baroclinicity and dynamics with this system, including possible phasing with the northern stream polar vortex after 48 hours, imply a significant storm and precipitation event across the southeast and mid-Atlantic states. In the west, a strong upper ridge should hold firm, so expect light, if any, precipitation with the frontal system as it moves through the Pacific Northwest on day 2.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "bf10ad2f168bdb43", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42458:42462:1", + "date": "1988-01-24 06:54:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42460:42464:1'} The data starts from January 24 00:00 and ends on January 24 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A major winter storm is expected to develop along the eastern seaboard during the next two days. There will be strong cold advection and near perfect positioning of the strong jet stream, resulting in a deep central pressure at 48 hours. Moisture will be abundant during all phases of development, leading to the potential for convection along the Gulf Coast and significant snow from the North Carolina mountains northeastward. The rain/snow line is expected to straddle all of the major northeastern cities. An upper low is expected to begin closing off at 48 hours, setting the stage for a major snowfall across inland areas of the northeastern US from 48 hours onward. Farther west, strong northerly flow from Canada is driving a powerful arctic front southward across the Plains, with the very cold air expected to reach all the way into the Gulf of Mexico by 48 hours. This is expected to be the last arctic blast in this cycle.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "fef7db4630b3ba92", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42460:42464:1", + "date": "1988-01-24 19:07:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42462:42466:1'} The data starts from January 24 12:00 and ends on January 25 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A significant winter storm is expected for the Middle Atlantic States into New England. Rapid deepening of the surface low is forecast as it moves up the East Coast. Moderate to heavy snow is expected from portions of western North Carolina northeastward along and west of the I-95 corridor. Lagging vorticity through the lower Mississippi Valley should help prolong lighter snowfall once the main precipitation passes.\n\nTo the south, a trailing cold front should sweep through Florida accompanied by showers and thunderstorms. A strong arctic surge currently through the central Plains will move rapidly through the Deep South early in the period.\n\nIn the West, a weak impulse will drag a weak cold front across the extreme northern tier of the U.S. over the next 48 hours with minimal precipitation.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "e4203e321da6656f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42462:42466:1", + "date": "1988-01-25 06:58:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42464:42468:1'} The data starts from January 25 00:00 and ends on January 25 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A slow progressive pattern aloft will dominate from coast to coast over the next 48 hours, with an upper ridge over the western states keeping the region cold and dry except for some cloudiness near the mean upper jet position. Any precipitation will remain well to the north over western Canada along a stationary frontal zone.\n\nA complex shortwave over the Canadian Rockies will move over the ridge and dig into the eastern trough, causing a strong shortwave to move over the lower Mississippi Valley this morning. This will generate cyclogenesis from the coastal Carolinas northward, with the lead frontal wave near Wilmington, NC to Cape Hatteras expected to become the primary surface low. A strong jet core aloft will lead to deepening cyclogenesis northeastward to just east of Boston in 24 hours. There will be a snowstorm of moderate size and intensity from the Mid-Atlantic region north-northeastward along the spine of the Appalachians eastward to the rain-snow line over the coastal plain. The forecast rain-snow line should spare major East Coast cities from significant snow, but interior sections of New York and New England may experience heavier snowfall as the storm deepens off the New England coast. The fast movement of the developing system will help limit impacts.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c87b5985685f5a6d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42464:42468:1", + "date": "1988-01-25 19:01:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42466:42470:1'} The data starts from January 25 12:00 and ends on January 26 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A persistent high amplitude ridge along the West Coast is being pushed eastward by a developing storm system near 39N/152W. Moisture ahead of this system, associated with a weak short wave trough near 35N/135W, will be carried in southwest flow, spreading scattered showers along the northern Pacific Coast by 36 hours. An approaching cold front associated with the developing Pacific low is expected to bring more significant precipitation to the northern Pacific Coast by 48 hours.\n\nA rapidly deepening winter storm off the Delmarva Coast will continue moving quickly up the coast on day 1 under a strong southerly jet, bringing moderate to heavy snow over interior portions of New York through New England and rain along the coast.\n\nOver the southern Great Lakes, a closed polar low over Illinois and Indiana is forecast to open up and begin lifting toward the east-northeast later in the period, but not before producing some locally heavy snows early on across northern sections of Ohio into northwest Pennsylvania.\n\nInstability associated with frigid cold air behind the arctic front should continue to produce scattered snow showers from the Ohio Valley eastward to the Mid-Atlantic Coast on day 1, with conditions improving on day 2 as cyclonic flow relaxes.\n\nHigh pressure over the remainder of the country should keep conditions cold but dry, except for the northern Plains where some light snow is possible on day 1 ahead of a warm front.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "862e376cca5fadfa", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42466:42470:1", + "date": "1988-01-26 07:07:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42468:42472:1'} The data starts from January 26 00:00 and ends on January 26 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A twin-centered upper trough along 145W will translate eastward slowly, mainly due to an upper low near 40N, 150W. The mean ridge over the far western states will deflect the upper low east-northeastward with some weakening. The northern portion of the upper trough will become nearly stationary over southeastern Alaska. A Pacific weather front will push over the northern Great Basin to central California within 48 hours but will be substantially weakened, with most precipitation confined to coastal and Cascade areas of Washington, Oregon, and northern California as upper support moves northeast along the coast. Once the upper ridge line reaches the Canadian-US divide, moderately strong surface baroclinicity will develop over southwestern Canada as another building Arctic air mass and Pacific maritime air collide over the region. Elsewhere, a dry, weak stationary boundary will extend southeastward over the Plains states under continued strong northwesterly flow aloft.\n\nEast of that boundary to the East Coast, cold Arctic-like air will become well entrenched under a deep upper trough. The thrust of the cold air mass will move across the lower Appalachians and Mid-Atlantic areas northward, with even northern Florida receiving a glancing blow over the next two days as polar air dips over southern Alabama, Georgia, and northern Florida. The only precipitation will be lingering lake-effect snows.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "59a3f2df9b77f7f9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42468:42472:1", + "date": "1988-01-26 19:00:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42470:42474:1'} The data starts from January 26 12:00 and ends on January 27 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: High surface pressure from coast to coast should keep most of the country cold and dry through the next two days. The exception is the far West, where much of the energy from the large eastern Pacific trough will drift onshore during day 2, squeezed eastward by a digging Gulf of Alaska vortex and fringe effects from the Pacific jet. Best precipitation should be limited from the Cascades westward before jumping to the northern Rockies at 48 hours. The Arctic airmass in Alaska should settle southward only as fast as the upper pattern digs, since it will be fighting a ridging pattern in the westerlies. East of the Rockies, this should allow downslope-induced warmer air to work eastward as a warm front, teaming up with some moderating air from the southwestern US. In the central and eastern US, it will remain cold as another shot of Arctic air filters southeastward.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "937c33fe26c00943", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42470:42474:1", + "date": "1988-01-27 07:06:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42472:42476:1'} The data starts from January 27 00:00 and ends on January 27 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A near zonal pattern is expected to develop by 48 hours as the upper ridge over the Pacific Coast states moves northward to the central US, somewhat suppressed by a new polar vortex dropping to Hudson Bay. A barotropic low near 40N, 140W continues to fill northeastward, causing some height falls southward over the West Coast. This allows the southeastern Alaska vortex to slide south-southeastward along the Alaska-British Columbia coast, maintaining low pressure over British Columbia and frontogenesis southwestward to off the Washington-Oregon coast. The path of upper dynamics should generate broken precipitation over coastal sections and the Cascade Range, diminishing to scattered inland as upper dynamics are dampened by the developing pattern aloft over Canada.\n\nThe Hudson Bay to Alaska-British Columbia upper trough connection will draw southeastward another cold, building high from the Arctic region of Alaska-Yukon, originating from far eastern Siberia. For now, the lower 48 states will be spared the Arctic onslaught due to the developing zonal pattern. Associated frontal precipitation should remain along and north of the front where low-level flow and thermal gradient are most favorable. Elsewhere, dry conditions are expected, with the East remaining unseasonably cold through 48 hours and the central US continuing to warm considerably as the recent strong upper trough exits off the East Coast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7671fe6250a60b86", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42472:42476:1", + "date": "1988-01-27 19:00:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42474:42478:1'} The data starts from January 27 12:00 and ends on January 28 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A return to zonal flow is expected as the mean pattern becomes more progressive and the Pacific westerlies move through. Farther north, two centers of the polar vortex are expected to slowly move southward, which should suppress wave action in the westerlies and bring the return of Arctic air closer to the northern states. However, interaction between these lows and a generally west-southwesterly flow over the US will slow the arrival of cold air. Meanwhile, large high pressure cells in the east should continue to drift eastward and offshore within 48 hours. In the region of rising heights and zonal flow, this indicates a warming trend and continued dry weather for most of the country east of the Rockies. In the west, the progressive pattern and intrusion of the westerlies should slowly erode the basin high and allow much of the interior west to warm up. These same westerlies will also push Pacific systems inland, with the first currently off the northwestern coast. However, the main Pacific moisture is expected to be directed into Canada, limiting significant precipitation to the northern mountains and areas west of the Cascades.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "fa55c8fc8316759c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42474:42478:1", + "date": "1988-01-28 07:14:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42476:42480:1'} The data starts from January 28 00:00 and ends on January 28 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The pattern is expected to become zonal aloft, sparing the lower 48 states from any Arctic outbreaks. A weakening Pacific weather system along the West Coast will continue to weaken as an upper ridge amplifies northward across the eastern Pacific and Alaska. This allows some dynamics to move south-southeast on the east side, while a shortwave moves from southeastern Alaska to southern British Columbia. The polar vortex over Hudson Bay dominates circulation and suppresses ridging over the western states, further weakening the Pacific system. Associated precipitation is likely to diminish further over inland areas.\n\nThe evolving pattern over Canada will allow Arctic high pressure over the Yukon to move slowly east-southeastward, with the Arctic boundary moving toward the vicinity of the US-Canadian border, intruding into the Dakotas, Minnesota, and western Great Lakes region. Any associated precipitation should remain along and north of the front where dynamics and low-level flow will be more favorable.\n\nElsewhere, especially over the eastern states, a warming trend will be evident as the upper trough exits rapidly off the East Coast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b820eb1193c24141", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42476:42480:1", + "date": "1988-01-28 19:05:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42478:42482:1'} The data starts from January 28 12:00 and ends on January 29 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A ridge building in the east central Pacific will continue to lower heights in the western US, resulting in a broad mean trough by 48 hours. This will allow a portion of the polar vortex to move southwest toward the northern Rockies by that time. Some phasing between this system and its stronger parent low over Hudson Bay should slow the advance of arctic air, especially west of the Divide. The shift to a trough in the west will break down the persistent Great Basin high.\n\nThe best of the Pacific moisture inflow is already inland, so further western precipitation should be scattered and light, except in mountain areas. Farther east, this pattern will induce a low-level trough in the lee of the Rockies, strong enough to become a weak cold front by 36 hours as the height falls spread eastward. Initially, temperature contrast will be minimal, but this boundary will eventually mark the leading edge of colder air as arctic air moves southward from 48 hours on. Minimal Gulf inflow suggests only scattered precipitation on day 2 in the central US ahead of this front.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "19a9c0022c662509", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42478:42482:1", + "date": "1988-01-29 07:13:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42480:42484:1'} The data starts from January 29 00:00 and ends on January 29 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: An upper ridge amplifies over the eastern Pacific northward across Alaska through 48 hours. Wave energy drives southward from the Arctic region while moving along the British Columbia upper trough. By 48 hours, a position near Saskatchewan-eastern Montana is expected. The upper ridge holds a shortwave off California on an easterly course ahead of the British Columbia trough. A broad, rather flat ridge over the central US is forced off the East Coast by 48 hours, with an extensive major trough covering most of the continent.\n\nThis scenario allows an Arctic boundary to sink southwest over Washington to the Continental Divide and deep into the central Plains. The Plains surface trough undergoes frontogenesis from falling heights aloft and subsequent thickness packing east of the Rockies.\n\nThe British Columbia trough should generate some back-range snows mainly post-frontal, with some scattered snow between the low-level trough-ridge axis to the lee of the Rockies due to upslope flow.\n\nLate in the period, as the frontal wave nears the western Great Lakes and Gulf moisture moves northward, an increase in precipitation along the cold front and north of the surface low over the cold air mass is expected, aided by increased surface baroclinicity and upper support over the region.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "8c4d9c3468c7b322", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42480:42484:1", + "date": "1988-01-29 19:04:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42482:42486:1'} The data starts from January 29 12:00 and ends on January 30 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A ridge building in the eastern Pacific will expand during the next two days, aiding in the development of a large trough over the western two-thirds of the country by 48 hours. As this pattern unfolds, a short wave over British Columbia will move southward and then eastward. This system will slow the southward push of arctic air east of the Divide, while allowing some cold air to spill into the northwestern US. At the same time, Pacific inflow will shut down, resulting in drying in the far West. East of the Rockies, lowering heights will use the remaining Pacific moisture, with the best chance of snow behind the arctic boundary in the Rockies and upslope areas to the east, including the environs of Alberta. Downstream, Gulf inflow will remain weak due to lingering surface ridging across the Gulf, and the lack of a dominating short wave will keep precipitation scattered across the central US. In the East, a warming trend is expected.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "830ef243092fa7da", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42482:42486:1", + "date": "1988-01-30 07:18:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42484:42488:1'} The data starts from January 30 00:00 and ends on January 30 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A high amplitude blocking ridge is present in the eastern Pacific, with a large broad trough settling over the western and central U.S., and remnants of a strong upper ridge in the East. An Alberta shortwave will maintain light snow across much of southern Canada as it moves east-southeast, while also pulling Arctic air into the Pacific Northwest early in the period. Arctic air will move southward down the High Plains and eastern Rockies, with at least scattered snow likely behind the front throughout the period, supported by easterly low-level upslope flow. The front remains well defined against the Rockies even at 48 hours. The main difference in the forecast is the speed of the cold front pushing through the south-central states, especially on day 2. Strong low-level south-southwesterly flow off the Gulf will support an expanding area of rainfall ahead of the front, beginning in the central U.S. on day 1 and reaching the Ohio Valley and Great Lakes on day 2. A long-awaited warming trend will dominate much of the eastern U.S. for the duration.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d93fc345908f3b27", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42484:42488:1", + "date": "1988-01-30 19:06:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42486:42490:1'} The data starts from January 30 12:00 and ends on January 31 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Arctic air is moving southward into the Plains as a short wave moves east of the Rockies. The arctic boundary will remain intact through most of Day 1, then will strengthen the slow-moving polar front in the southern Plains. Even with the arctic air behind it, the cold front will move eastward slowly because it is nearly parallel to the flow at most levels. Prefontal precipitation will be slow to move eastward. On Day 2, a short wave crossing the Plains will pass over the arctic air, making it difficult to bring along significant moisture. Strong southwesterly flow ahead of this system will prevent frontal moisture from moving westward. The slow-moving and wavy nature of the front and southwesterly flow aloft support some postfrontal overrunning in the Ohio Valley and eastern Great Lakes at that time.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "755b57a99cb42d33", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42486:42490:1", + "date": "1988-01-31 07:13:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42488:42492:1'} The data starts from January 31 00:00 and ends on January 31 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A slow-moving surface front is expected through the East and South, with increasing moisture and instability streaming north-northeast ahead of the front. An expanding area of broken precipitation will accompany the front, with scattered rainfall likely in the warm air well in advance of the front due to high moisture and instability. An overrunning event, mostly snow, is likely beginning early on Day 2 across the mid to upper Mississippi Valley and into the Great Lakes, in response to a system moving into the central Rockies by Monday morning and into the western Lakes region by Tuesday morning. Snow is expected to continue much of the period in the northern and central Rockies due to overrunning very cold Arctic air combined with low-level upslope flow.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5a62b5cbcf5ba6aa", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42488:42492:1", + "date": "1988-01-31 19:08:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42490:42494:1'} The data starts from January 31 12:00 and ends on February 01 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: One of the coldest Arctic outbreaks of the winter is sliding southward along both sides of the Divide. As this air drives southward through the Plains, it should moderate somewhat due to southwesterly flow aloft. The ridge in the Pacific is expected to hold strong and extend back into Siberia. The pattern should continue delivering Arctic air southward, with the next cold blast in this series approaching the northwest within 48 hours. As a consequence, Pacific westerlies are forced to undercut the ridge and direct Pacific systems inland at southern latitudes. The first of these systems is moving steadily eastward and should affect the southwestern US during Day 2. Farther east, the Arctic plunge should push the slow-moving cold front in the central US, but the mild air in the East should only retreat slowly due to the strong southwesterly flow aloft. Gulf inflow is improving, supporting overrunning precipitation. The best precipitation should occur with and ahead of the front and back to the marked airmass contrast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7566a32e445cb760", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42490:42494:1", + "date": "1988-02-01 07:16:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42492:42496:1'} The data starts from February 01 00:00 and ends on February 01 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: An arctic air mass is presently dropping southward through the Central Plains and northern Rockies and is forecast to gradually move southeastward through the Ohio Valley and New England to the Mid-Atlantic states over the next 48 hours. Each short wave rotating around the polar vortex is expected to lower heights over the eastern half of the U.S., allowing a cold front to sink southward, though slowly, through the Ohio-Tennessee Valleys. With the arctic high moving northeast to eastern Canada, a backdoor cold front should move southward more rapidly through New England to the Mid-Atlantic states, reaching North Carolina by 48 hours. The air mass should be shallow along the eastern slopes of the Appalachians since the mid-upper level flow is southwesterly and the real cold air is well behind the frontal boundary. A swath of overrunning precipitation is forecast to spread from the Mississippi Valley eastward through the Great Lakes and New England to the Mid-Atlantic states. Convection is likely along and ahead of the cold front as it crosses the southern tier states over the next 2 days. Further north, a new surge of arctic air is forecast to push through the Pacific Northwest and northern Rockies to the northern Plains as a short wave drops southward from the Northwest Territory to southern British Columbia. This system should spread snow across the Pacific Northwest coast to the northern Rockies, while snow is also likely to accompany a weak surface low moving through southern Canada. A short wave near 130W is forecast to move northeast to southern California over the next 48 hours, spreading moisture rapidly northeast through the southern Rockies to Colorado. Rain is expected along southern California and Arizona, while snow is likely through the southern plateau and Rockies.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5a56509a916a80e3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42492:42496:1", + "date": "1988-02-01 19:02:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42494:42498:1'} The data starts from February 01 12:00 and ends on February 02 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Upper ridge remains stationary off the west coast of North America through the period, somewhat distorted over Alaska by an intense Arctic ridge-trough combination. The upper pattern over western North America is characterized by split flow. The southern stream will transport an upper trough to over Arizona and New Mexico in 48 hours. Expect scattered to broken precipitation across southern California into the Four Corners. The northern/Arctic stream will increasingly dominate as the next polar vortex settles southward over Hudson Bay, resulting in a strong upper jet from Alaska and northwestern Canada southeastward over the northern Plains. A weak Arctic boundary will sweep east and south over the north-central US with a frontal wave along the British Columbia coast southeastward to Montana and Wyoming in 48 hours. Some light snow is expected in the cold sector along and north of the jet. Over the central US, conditions will be basically dry under upper stream convergence.\n\nElsewhere, a complex vortex pattern in the southern stream over the southwest develops into several minor shortwaves, making it difficult to focus on a single feature. A wavy cold front will persist despite the strong upper flow, causing continued overrunning along and north of the front from the upper pattern and a strong cold surface ridge passing to the north over the St. Lawrence Valley, which aids the cold front in undercutting the warmer upper pattern. The strength of warming aloft should keep the rain-snow line well to the north for now.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "488bf8d6a6a6164f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42494:42498:1", + "date": "1988-02-02 06:41:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42496:42500:1'} The data starts from February 02 00:00 and ends on February 02 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Arctic high over the northern Mississippi Valley is forecast to lift northeastward to the Maritimes while dragging a moderate cold front southward and east of the Appalachians to the Georgia-South Carolina border. The southern portion of the frontal boundary is forecast to stall over the lower Mississippi Valley in the next 24 hours as heights remain relatively unchanged. A short wave off the southern California coast is forecast to move eastward through the southern Rockies to the central Plains, where it will then be absorbed by a broad upper trough over the Mississippi Valley. This impulse will generate a new surface low along the stationary boundary in the Tennessee Valley. This surface low will then move eastward to the Mid-Atlantic region and drag a new cold front through the southern tier states. The short wave off the California coast is forecast to spread rain and snow from southern California northeastward through the southern Rockies to the central Plains, then it should reestablish overrunning precipitation across the Mississippi-Ohio Valleys to the Mid-Atlantic and New England regions. Convection is likely to remain scattered across southern tier states along and ahead of stationary and cold frontal boundaries. A series of short waves are forecast to rotate around the polar vortex through British Columbia to the northern Plains. These impulses are expected to spread broken to scattered snow across the Pacific Northwest and northern Rockies in the next 48 hours.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "8b72e5372c2bf9f2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42496:42500:1", + "date": "1988-02-02 18:57:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42498:42502:1'} The data starts from February 02 12:00 and ends on February 03 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Upper ridge shifts eastward to the west coast of North America by 48 hours in response to a deep upper trough digging in upstream. This allows the Arctic jet and subsequent dynamics over Alaska and western Canada to move southeastward while digging over the mid-continent. The strongest Arctic high of the season builds slowly southeastward, nosing southward over the Plains and eastward over the Great Lakes for this period. A band of snow forms along the Arctic boundary draped over the divide from surface baroclinicity and upper jet.\n\nMeanwhile, a shortwave in the southern stream moves eastward, influenced track-wise through the period by a strong Arctic jet. Resulting broken precipitation moves eastward over the central Plains as dynamics override steady high pressure at the surface to over Texas. The shortwave induces a wave along the frontal boundary over the Texas Gulf Coast by 24 hours while moving over the lower Mississippi Valley. Strong west-southwest flow aloft and persistent surface ridge over the Gulf preclude any mass infusion of moisture into the system, but good precipitable water values en route should make up the difference. Best precipitation will be over the cold sector where overrunning and dynamics are strongest. The frontal wave should cut across the lower mid-Atlantic area before moving northeastward along the east coast as upper dynamics sharpen over the eastern states. This system may produce a moderate size snowstorm across New York and New England by the end of the period.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "0fb1e8465e718d38", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42498:42502:1", + "date": "1988-02-03 07:12:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42500:42504:1'} The data starts from February 03 00:00 and ends on February 03 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A short wave is presently spreading snow across the central Plains and is forecast to weaken as it moves into the Mississippi Valley. It should then be replaced by a southern Plateau short wave, which is forecast to move rapidly northeast through the Ohio Valley and New England to the Maritimes within 48 hours. This second impulse will supply the necessary cold air to turn a stationary boundary across the western Gulf and Gulf Coastal States into a cold front. Meanwhile, a surface low associated with the short wave is forecast to move through the southern Appalachians and off the Mid-Atlantic coast, then northeast to near Sable Island. The surface low is expected to intensify once it moves over water, especially with a significant amount of cold air advecting into the system. This system should spread a swath of overrunning precipitation from the central Plains eastward to the Atlantic coast. Precipitation is expected to be in the form of snow through the Ohio Valley and New England, with rain changing to snow over the Appalachians and Mid-Atlantic as cold air moves into the regions.\n\nFurther north, pieces of an arctic high are forecast to slip southeast through the northern Rockies and northern Plains to the Mississippi Valley, as impulses rotate around the Hudson Bay cut-off. These impulses should spread scattered to broken snow from western Canada southeastward north of the stationary boundary and along the developing cold front. A mass of moisture off the Baja coast is expected to develop overrunning precipitation across southern New Mexico and southern to western Texas. With a surface ridge extending far south through central Texas, the problem of freezing rain is a serious consideration.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d217739869cca48c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42500:42504:1", + "date": "1988-02-03 19:06:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42502:42506:1'} The data starts from February 03 12:00 and ends on February 04 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Precipitous height falls are expected over the Ohio Valley eastward to the Mid-Atlantic region and northward within 48 hours due to a series of shortwaves in the polar jet sweeping around the polar vortex over Hudson Bay. This will result in a mass infusion of arctic air over these regions.\n\nIn the wake of a rapidly developing and exiting surface low near the central Appalachians, it will turn sharply colder, with upper dynamics generating snow shower activity across the Great Lakes and Appalachians. Before this, a deepening surface low will move northeast at high speed under a strong upper-level jet over the east-central US, giving New York and most of New England a quick moderate snowstorm.\n\nAnother intense arctic shortwave is approaching near Great Slave Lake and is expected to move in within 48 hours.\n\nOver western North America, the southern stream has tapped into subtropical moisture, with an upper trough forecast to reach lower New Mexico within 48 hours. Some lead vortices will eject in advance of the main feature, eventually triggering a low-level moisture field early on over the cold sector north of a cold front settled over northern Gulf waters. Precipitation should stay along the Gulf Coast with strong cold ridge and westerly flow to the north.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "cbbeceacaa8f5925", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42502:42506:1", + "date": "1988-02-04 07:09:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42504:42508:1'} The data starts from February 04 00:00 and ends on February 04 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A broad upper trough over the eastern half of the U.S. is forecast to remain intact over the next 48 hours. A series of short waves are forecast to move from the polar vortex, bringing new waves of arctic air southward through the northern Rockies to Texas and Gulf Coastal states and eastward through the Great Lakes to the East Coast. Precipitation associated with a surface low moving south of the New England coast is forecast to end in the next 12-18 hours, with snow and snow showers becoming the main weather feature after that period across the Great Lakes and western slopes of the Appalachians.\n\nA strong upper ridge over the eastern Pacific is forecast to shift eastward to eastern Washington and weaken. High pressures are expected to be maintained over the western half of the U.S., with higher pressures extending southward through Texas, bringing a colder air mass. This should allow for freezing precipitation to push farther south. Disturbances are expected to move eastward across Texas and along the Gulf Coastal states to the southeast coast over the next 48 hours. Northwesterly flow aloft is expected to suppress the precipitation shield southward and along the Gulf Coastline. Relative humidity fields suggest precipitation is likely in this area.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "9a3e0809ba31d52a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42504:42508:1", + "date": "1988-02-04 19:10:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42506:42510:1'} The data starts from February 04 12:00 and ends on February 05 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Split flow over western North America shows signs of abatement by 48 hours as an upper trough in the southern stream moves slowly east-southeastward to the upper Rio Grande while becoming somewhat flattened by the polar/arctic jet from across western Canada and northwest Canada. Developing precipitation area over most of Texas from this feature peaks and then diminishes in areal coverage to over the Rio Grande Valley by 48 hours as flow aloft becomes west-northwesterly, thereby weakening upper support. Massive cold surface high overspreads everywhere east of the Rockies, and west-northwesterly flow aloft also diminishes precipitation threat along the Gulf Coast and Florida by 48 hours, although low-level easterlies may keep Florida damp.\n\nA complex shortwave structure along 140W moves north-northeastward before moving across British Columbia, driven by a stronger upstream shortwave driving east-northeastward along 45-50N. The shortwave across British Columbia induces a leeside low over Alberta by 36 hours without phasing with the next arctic shortwave digging south from near Victoria Island of the Northwest Territories. There is a thermal distinction between the next arctic boundary and what is left of the weaker maritime front. Snow is expected near northeast Montana and the Dakotas from upper dynamics and the thermal ridge, mainly over the cold sector.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "596c89b69ba41ff0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42506:42510:1", + "date": "1988-02-05 07:12:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42508:42512:1'} The data starts from February 05 00:00 and ends on February 05 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A series of short waves in the Pacific is forecast to break down the high amplitude upper ridge over the West Coast in the next 48 hours. The first system in the series will move eastward to the Oregon-Washington Cascades and weaken, then slide southeastward to the west-central Plains. This system should spread mainly scattered rain along the Oregon-Washington coasts and snow at higher elevations.\n\nAnother short wave near 155W-160W is forecast to move northeastward to near Vancouver Island. A third and more intense system upstream is forecast to move south of the Aleutians. This latest system should help build an upper ridge near 140W-150W and amplify a downstream upper trough near 130W. The associated surface low with the short wave near Vancouver Island appears to be deep, so pressures have been scaled up.\n\nA complex of vorticity centers is forecast to rotate around the polar vortex, with a strong short wave expected to dig southward through central Canada to Lake Winnipeg. This should push a new surge of cold air southward through the northern Rockies-Plains to the upper Mississippi Valley. Most precipitation should remain north of the border, with some scattered overrunning possible ahead of a retreating warm front.\n\nDuring the next 24 hours, lake effect snows are likely to continue, then diminish during the second 24-hour period as flow shifts southwesterly on the backside of the surface ridge. Abundant moisture and weak impulses forecast to move through the base of a broad upper trough should spread rain and snow from west Texas eastward along the Gulf Coastal states to the southeast coast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "daddbce884a647ef", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42508:42512:1", + "date": "1988-02-05 19:09:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42510:42514:1'} The data starts from February 05 12:00 and ends on February 06 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong shortwave near 50N, 150W continues lifting out over the Gulf of Alaska, becoming blocked and weakened by a strong upper ridge northward over Alaska and the Arctic Basin. Meanwhile, a strong upper jet moves a closely following shortwave eastward through the mean ridge position over the west coast of North America to southern British Columbia in 48 hours. A surface low from the Pacific weather front is expected to propagate east-southeast under the maximum jet to southern British Columbia, then move across the divide and redevelop on the lee side over eastern Montana by 48 hours along the Arctic boundary. Most precipitation activity is expected to occur on the cold side of the stationary Arctic boundary.\n\nElsewhere, a shortwave over the Northwest Territories digs south then southeast to north of the Great Lakes in 48 hours, causing cyclogenesis from lower Alberta to southwestern Quebec, with a frontal boundary penetrating the states to just south of the Great Lakes and back west to the Montana lee side low. The best precipitation activity should be near the Great Lakes with this system, given upper dynamics and cold cyclonic advection in the low and mid levels across the remaining unfrozen portions of the lakes. Meanwhile, a complex large-scale vortex pattern within the eastern trough will progress rapidly eastward to well off the east coast, taking a portion of the cold surface high with it. The lower Rio Grande remains damp through the period as a weak upper trough over northern Mexico drifts across extreme southern Texas and the western Gulf States.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "aa84805b4e80600e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42510:42514:1", + "date": "1988-02-06 07:09:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42512:42516:1'} The data starts from February 06 00:00 and ends on February 06 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Most of the country will remain cold but dry over the next 48 hours. Upper ridge will rebuild along 130W and a broad upper trough will dominate the eastern two-thirds of the U.S. A frontal system will bring generally light precipitation into the Pacific Northwest on day 1. A surface wave will slide rapidly east-southeast through the northern Plains with attendant light overrunning snows from eastern Montana southeastward through western Iowa. Further east, a short wave dropping southeast from the Northwest Territories will drag another arctic front from the northern Plains eastward to the Mid-Atlantic coast by 48 hours, accompanied by scattered light snows with frontal passage, followed by some renewed lake effect snows over the eastern Great Lakes. A short wave trough in central Oregon appears strong enough to move a portion of the northern Mexico upper low eastward as it heads toward the southern Plains. Rain is expected along the Texas and central Gulf coasts, with inland mixed precipitation now sliding eastward on day 2, with precipitation being restricted to the central Gulf and northwest Florida coasts. Moist easterly flow and proximity to a weakening cold front off the coast should continue the risk of showers along the southeast Florida coast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "98f80f25d2eb5de8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42512:42516:1", + "date": "1988-02-06 19:03:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42514:42518:1'} The data starts from February 06 12:00 and ends on February 07 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: As the subtropical ridge remains strong and stationary west-southwest of California, dynamic features moving through the northern Pacific are directed east-northeast toward Alaska and the British Columbia coasts. The upper low over the Gulf of Alaska weakens to the north, while a lower vortex pattern to the southeast moves through the ridge position, followed by shallow digging into the upper Mississippi Valley in 48 hours. Another arctic shortwave from the Victoria Island area pushes southward without merging with incoming Pacific systems. The combined effect of these upper dynamic features keeps the arctic boundary in place along the lower Canadian Rockies once a frontal wave moves southeast to the upper Mississippi Valley. The combination of shearing vortex and low-level easterlies over western Canada produces most snow along and north of the front.\n\nUpper dynamics and modest warm advection associated with an open frontal wave over the upper Mississippi Valley should generate some snow activity over the cold sector north of the front.\n\nA shortwave originating near Lake Winnipeg moves north of the Great Lakes to over the Maritimes, helping to drive a cold front across the northeastern United States and reinforcing cold air already covering most of the eastern US. Temperatures may moderate slightly but only minimally. More lake-effect snows are expected with this frontal passage.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "0f747d7a39ee03c5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42514:42518:1", + "date": "1988-02-07 07:10:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42516:42520:1'} The data starts from February 07 00:00 and ends on February 07 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A complex weather situation is expected across the U.S. over the next two days as three separate flow regimes affect the country. In the Pacific, several short waves will develop out ahead of a deep low near 160W and move northeast, weakening into a mean ridge position along the Pacific Coast. A Pacific front will move into the West Coast by 48 hours, with a weaker system expected. \n\nA short wave moving through the Great Lakes by 00Z will drag a reinforcing arctic front through the northeastern U.S., bringing some light snow from the Great Lakes into New England. Meanwhile, a Pacific short wave digging southeast from British Columbia will bring an associated surface low southeast along the arctic boundary into the lower Great Lakes by the end of day 2. Light snow is likely in weak overrunning of the arctic front from eastern Montana southeast into the Ohio Valley. By 48 hours, possible phasing of this system with a polar short wave dropping south from Canada may increase snowfall potential over the Great Lakes and northern Ohio Valley.\n\nFurther south, an impulse moving across the central Gulf Coast states should continue to spread mixed precipitation across the central Gulf Coast states, with snowfall possible as far south as the Florida Panhandle. By day 2, this system should begin to produce cyclogenesis along a stationary front off the southeast Florida coast and help spread rain northward along the southeast coast, with snow and sleet possible in inland sections of North Carolina and South Carolina.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b3c0f9edc4562561", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42516:42520:1", + "date": "1988-02-07 19:17:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42518:42522:1'} The data starts from February 07 12:00 and ends on February 08 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A series of short waves are forecast to move southeastward around the Hudson Bay upper low. The initial short wave is expected to move southward to the northern Great Lakes in the next 48 hours. The surface low forecast to develop off the southeast coast in the next 24 hours should move nearer to the Atlantic coastline, primarily affecting the coast of North Carolina and southeast Virginia. The Great Lakes system should spread broken snow across that region and into New England, while areas between could have some scattered snow.\n\nA train of vorticity centers is forecast to move southeastward across the northern Rockies to the central Plains. Short waves should spread broken precipitation across the Pacific Northwest and northern Rockies in the next 48 hours, with precipitation expected to be in the form of snow north of the cold front and stationary boundary in the northern Rockies.\n\nScattered to broken precipitation is likely over eastern and southern Florida as an inverted trough develops and a weak cold front moves through. Southern Texas is forecast to have scattered rain due to return flow of the surface ridge and weak vorticity centers moving through the southern stream.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7578e4f70fbead40", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42518:42522:1", + "date": "1988-02-08 07:15:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42520:42524:1'} The data starts from February 08 00:00 and ends on February 08 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A weakening frontal system near 135W is expected to move into the Northwest coast on Day 1, spreading generally light rain across much of Washington and northern Oregon. Moist onshore flow will continue the risk of precipitation into Day 2. An upper short wave will move through the mean ridge in the next 24 hours, after which tracking short wave energy becomes more complex.\n\nA weak surface wave is expected to develop along the arctic front from the northern Rockies into the central Plains by 48 hours, with overrunning snow from northern Idaho southeastward into Missouri. By 48 hours, a second, stronger impulse digging into the central Rockies and increased easterly moisture will produce an expanding area of snow across favored upslope areas of Colorado and Wyoming as a surface low develops near the Oklahoma Panhandle.\n\nA weak wave along a reinforcing arctic front in South Dakota will lift rapidly through the Northeast, dragging an associated cold front southward into Oklahoma and eastward to the mid-Atlantic coast. Generally light snows are expected from the northern Ohio Valley and Great Lakes region northeastward, with some locally heavier lake effect snows over Michigan.\n\nA low off the Southeast coast should continue to slide off to the northeast, with only light precipitation expected along the Southeast coastal plain. Over the western Gulf, a weak return of moisture around a surface ridge, aided by weak vorticity in the southern stream, should be sufficient for light rains to spread slowly northward from the Texas Gulf Coast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "cdb1f845bbdf4c7d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42520:42524:1", + "date": "1988-02-08 19:03:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42522:42526:1'} The data starts from February 08 12:00 and ends on February 09 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A broad eastern Pacific ridge will remain in place during this period as a series of closely spaced shortwaves sweep inland over British Columbia and then shear southeastward through the central Plains. The last of several arctic highs building southeast from central Canada will reinforce a strong baroclinic zone already in place from the northern Rockies to the central Plains. This pattern favors a widespread area of light to moderate snow in the northern high Plains and northern Rockies, as upper shortwaves with moist Pacific air interact with low-level upslope flow along the arctic front.\n\nShortwave energy will move east-southeastward through the mid Mississippi Valley after 00Z/10, spreading light to moderate snow over portions of Missouri, Iowa, Illinois, and Indiana, but without significant surface development. By 48 hours, some of the energy dropping more southeastward toward the Gulf will likely result in scattered to broken rain along an inverted surface trough in the east Texas and Louisiana region.\n\nIn the East, moderate lake effect activity is likely in western New York as arctic air spills southward behind the shortwave now over Michigan. A broken area of snow is also expected to remain active ahead of this system as it tracks eastward across New England later today. Light precipitation is forecast for day 1 in the eastern Carolinas, where a slowly developing offshore wave and good easterly fetch will prevail.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7d5a2a6393c70cf6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42522:42526:1", + "date": "1988-02-09 07:25:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42524:42528:1'} The data starts from February 09 00:00 and ends on February 09 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Arctic front dropping southward across the central states will become the focal point for much of the precipitation over the U.S. during the next 48 hours. A weak short wave trough moving eastward from the extreme northern Plains will spread mainly light snow across the Great Lakes into New England over the next 24 hours, with the exception of usually heavier lake effect snows, while the associated arctic front sweeps off the Mid Atlantic coast. \n\nUpstream, a double-barreled vortex pattern over the northwest U.S. and British Columbia is expected. A weakening surface low will move across the central Plains into the mid Mississippi Valley with light to moderate snows in weak overrunning of the arctic front. By 48 hours, the primary surface wave associated with a sharpening upper trough in the central U.S. should move from the central Rockies into the central Gulf Coast states. Increasing warm advection and influx of Gulf moisture should produce significant snows across portions of the mid Mississippi, Tennessee, and Ohio Valleys, while rain and possible thunderstorms develop ahead of the cold front in the southern Gulf Coast region. Weak upslope snows are likely to persist along the frontal boundary over the central and northern Rockies as additional vortex energy drops southeastward from western Canada. The upper ridge will keep the southwest dry but weak onshore flow will continue the risk of precipitation along the northwest coast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "40d48c25a984a427", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42524:42528:1", + "date": "1988-02-09 18:57:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42526:42530:1'} The data starts from February 09 12:00 and ends on February 10 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A weaker surface low and flatter surface pattern are expected. During the second 24-hour period, the majority of energy should move to the Mid-Atlantic coast for secondary development along the frontal boundary, which was pushed southward by an arctic high forecast to build through the Great Lakes to New England and eastern Canada. This strong arctic high will make it difficult for any surface low to develop in the cold air, forcing development toward the Atlantic coast. A series of short waves should combine with developing surface lows to spread a swath of overrunning snow from the eastern Rockies eastward to the Mid-Atlantic states and New England over the next 48 hours. Rain is expected along and behind the cold front as it moves through the southern tier states. In the west, an upper ridge is forecast to amplify and move eastward to the West Coast, forcing any precipitation into western Canada, while scattered snow is possible near the stationary boundary through the northern Rockies.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c85ea8de4d594ee2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42526:42530:1", + "date": "1988-02-10 07:32:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42528:42532:1'} The data starts from February 10 00:00 and ends on February 10 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Developments along the eastern seaboard will require close scrutiny over the next 48 hours. Before this, western states eastward to the Divide will remain dry as Pacific weather fronts move over southwestern Canada. The extreme northern Rockies will be the exception with some scattered precipitation.\n\nA shortwave now digging in over the southern and central Plains will induce a weak frontal wave near central Tennessee in 24 hours. A strong system over British Columbia will aid in the eventual deepening of the upper trough over the eastern US by 48 hours. The deepening trough will sharpen the short ridge over New England and the Maritimes, with building and slowing of cold arctic surface high now bridging the St. Lawrence Valley and protruding southward along and east of the Appalachians. The stage will be set for a potential major snowstorm from the Mid-Atlantic region northward.\n\nA 250 mb jet core will remain across the southeastern states and then move northeast off the coast near Norfolk, based on upstream shortwaves. Coastal development is preferred. Strong easterly winds will enhance relative humidity deficiency.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "17dd52dd551a67a5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42528:42532:1", + "date": "1988-02-10 19:11:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42530:42534:1'} The data starts from February 10 12:00 and ends on February 11 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A significant surface low is expected to develop along the edge of the arctic air mass as a short wave moves through southeastern Colorado and the Texas Panhandle, rotating around a developing Great Lakes upper low. A secondary low should develop along the mid-Atlantic coast and is forecast to move along or slightly inland before consolidating with the surface low near Albany in 48 hours. This scenario leads to a major snowstorm for the Ohio Valley to New England states, especially with a negative axis at 500mb and strong low-level inflow from the Atlantic. Precipitation is expected to change to rain along the immediate New England coast as strong low-level inflow brings in warmer air. Preceding this short wave, an upper ridge is expected to build from the West Coast to the northern Rockies, forcing precipitation northward into western Canada, while some scattered snow is likely on the front side of the upper ridge and ahead of a warm front. Otherwise, the western half of the U.S. is expected to remain tranquil for the next two days.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "2fc10d9873ffb473", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42530:42534:1", + "date": "1988-02-11 07:08:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42532:42536:1'} The data starts from February 11 00:00 and ends on February 11 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The polar jet from the North Pacific across North America will remain active, driving a system eastward during the period. The associated Pacific weather front should be east of the Continental Divide, over the northern Plains, central Rockies, and weakening over central and southern California. The best precipitation is likely across Washington and northern Oregon, then eastward over the northern Rockies. The remainder of the U.S. will stay dry eastward to the Mississippi Valley under a progressive short ridge and northwesterly flow aloft, with bitterly cold conditions over the Plains early on.\n\nA major development is expected along the eastern seaboard. An intensifying secondary vortex near the lower Mississippi Valley will be driven northeastward by strong upstream wave energy originating over southwestern Canada. The jet stream should take the secondary vortex along a track with subsequent coastal development near the lower Delmarva Peninsula. Major metro areas from the Mid-Atlantic region northward will be spared any snow this time.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7e0f7b1b9a808ac2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42532:42536:1", + "date": "1988-02-11 19:12:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42534:42538:1'} The data starts from February 11 12:00 and ends on February 12 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A major snowstorm is starting to develop in the northeastern U.S. Pressure falls are being observed along the Delmarva area. A secondary surface low is forecast to remain just inland as it moves northeastward to southern New York, where it will consolidate with a surface low moving through the Ohio Valley to produce one intense surface low. The slower progression is expected, as the 500mb low is forecast to deepen and the associated upper trough is expected to take on a negative axis. This should cause the surface low and Atlantic moisture to wrap around the upper low while the associated frontal boundary moves rapidly eastward. Consequently, the snowstorm will be slow to exit the northeastern U.S. over the next two days. There is also the possibility of snow showers lingering over the Great Lakes and western slopes of the Appalachians from residual moisture and strong cyclonic circulation from the intense surface low.\n\nA short wave near 150W is forecast to move eastward and ride the western U.S. upper ridge to eastern Montana over the next 48 hours. This should drag a cold front rapidly southeastward through the Pacific Northwest to the Plateau and northern Rockies. The initial surge of moisture from this system is forecast to spread broken rain along the Pacific Northwest coast and Idaho, then diminish to scattered snow over the northern Rockies after frontal passage. A surface low associated with this short wave is forecast to help draw down a new surge of arctic air to northern Montana by 48 hours, aided by a polar vortex forecast to drop southward from near 75N to the vicinity of 60N while ejecting a weak vorticity lobe to Saskatchewan.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "5a3ac59f2b53b683", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42534:42538:1", + "date": "1988-02-12 07:08:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42536:42540:1'} The data starts from February 12 00:00 and ends on February 12 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The northeastern winter storm is consolidating into a fast-moving coastal low, and snow will continue in New England for some time. The surface low is expected to decelerate and track just inland across New England, deepening as it moves. Significant snow is expected for interior New England, with Atlantic inflow bringing backwash snow spreading westward into the Great Lakes during Day 1 and persisting in the northeastern mountains and St. Lawrence Valley through most of Day 2.\n\nIn the West, the next short wave topping the eastern Pacific ridge is strong and is expected to bring a sizeable amount of short wave energy inland, with some shearing eastward in the northern stream and another batch digging southeastward as the eastern Pacific ridge rebuilds. There is expected to be enough energy for a double-barrelled system, with the northern portion bringing wet conditions and the southern portion attempting to reestablish some mean troughing in the southwestern US.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "f90865594d6ac8be", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42536:42540:1", + "date": "1988-02-12 19:06:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42538:42542:1'} The data starts from February 12 12:00 and ends on February 13 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Current satellite trends show a coastal low off Cape Cod becoming the primary surface low. This low should continue slowly northward as the supporting negatively tilted trough becomes closed off over New England. Significant snowfalls are likely over portions of interior New England on day 1, while wrap-around and lake effect snows are likely over the eastern Great Lakes through the St. Lawrence Valley.\n\nIn the West, a strong shortwave trough will amplify into the western Plains by 48 hours, supporting the spread of generally light rain and/or snow across the northern Intermountain and Rocky Mountain regions on day 1. Weak warm advection snows will spread across the northern Plains and upper Mississippi Valley ahead of an approaching warm front. As the upper trough and associated cold front progress eastward, rain is expected to overspread much of the central U.S., with broken snow from the upper Mississippi Valley into the Great Lakes by the end of day 2. Light snows are also likely to accompany the trailing arctic front as it weakens into the central Plains on day 2. By 48 hours, scattered showers are likely to develop over the western Gulf Coast region as Gulf inflow becomes established, aided by a prefrontal trough.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "aacbd8c4368c28c4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42538:42542:1", + "date": "1988-02-13 06:51:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42540:42544:1'} The data starts from February 13 00:00 and ends on February 13 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: In the East, the winter storm is making a fast exit northward, though snows will linger in northern New England through most of day 1 until the upper low lifts out. Upstream, a powerful Pacific short wave has maintained its strength after moving inland. The eastern Pacific ridge is rebounding, positioning this system for digging on the lee of the Rockies. Some of this short wave energy should move eastward in the northern stream of the westerlies, so upper troffing as a whole should behave more like a full latitude trough rather than fragmented short waves. At low levels, this scenario should pull cold Canadian air quickly southward into the Plains once the upper flow turns northwest. Breakdown of surface ridging in the western Gulf by 24 hours should allow a rapid influx of Gulf moisture, suggesting day 2 rains will spread from eastern Texas into the lower and mid Mississippi Valley by 48 hours.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "f1568e1aa1a5c46a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42540:42544:1", + "date": "1988-02-13 18:45:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42542:42546:1'} The data starts from February 13 12:00 and ends on February 14 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Main weather focus for the next 24 hours will be with the progression of an impressive short wave trough digging southeastward through Colorado. A deeper surface low will track through the mid Mississippi Valley by 24 hours, then into the Great Lakes by the end of the period. Although initial moisture is rather limited in advance of the cold front, rapid erosion of the surface ridge over the Gulf Coast is underway, which should ensure swift moisture return. Rain with embedded convection over the Gulf Coast should spread northward from east Texas into southern New England by 48 hours. Further north, an arctic front dropping southward into the Plains appears to be the last true arctic airmass to affect the U.S. for the foreseeable future, as broad split upper flow develops, allowing only Pacific fronts to spread across the country. One such Pacific front will enter the northwest Pacific coast on day 1, bringing mainly coastal rains over Washington. On day 2, the front will race quickly across the northern tier of states with mainly light precipitation, while lee troughing develops over the central and southern Plains, signaling a warmup there. Lingering snows will be on the wane early over northern New England, but warm frontal type snows are likely to spread across the region from the Great Lakes on day 2 ahead of the central Plains system.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "8d92cd9e19430c23", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42542:42546:1", + "date": "1988-02-14 07:01:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42544:42548:1'} The data starts from February 14 00:00 and ends on February 14 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: In the east, a strong short wave over Kansas is expected to induce widespread frontal precipitation as it moves eastward into the moist Gulf inflow regime along the Mississippi Valley. The primary surface low and the best short wave energy will track northeast across the Great Lakes and St. Lawrence Valley. There is a possibility of a flat frontal wave moving northeast from the Carolinas during Day 2. Sustained inflow and good dynamics should bring frontal and prefrontal precipitation, primarily rain, to most areas of the eastern US, with the best chance for snow limited to overrunning areas of the northern regions.\n\nIn the west, the next onshore system moving over the west coast ridge is weaker than the previous one. However, advance moisture inflow and falling heights should produce snow in the Rockies and Canadian Plains.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "60603ce031b30929", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42544:42548:1", + "date": "1988-02-14 19:03:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42546:42550:1'} The data starts from February 14 12:00 and ends on February 15 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A Pacific frontal system is currently moving through Washington State and will move rapidly southeastward as an upper short wave trough along 125W moves toward the central U.S. Shearing of short wave energy will support a weakening band of mainly frontal precipitation, with stronger northern dynamics suggesting the best, though light, snow will spread across the northern Plains into the upper Mississippi Valley. A trailing vorticity maximum forecast to drop into the central Rockies is expected to induce weak wave action on the cold front in the southern Plains within 48 hours; however, precipitation should be scattered at best due to limited moisture.\n\nA Pacific coast short wave trough will act as a kicker to a strong Mississippi Valley trough, sweeping an associated frontal system off the East Coast on day 2. Secondary wave development is expected in the southeastern states on day 1 as the primary surface low lifts northeastward through the eastern Great Lakes, spreading a swath of snow from the lower Great Lakes northeastward. The strength of the southern vorticity maximum, along with good inflow, supports the idea of widespread prefrontal rains over the southeastern U.S. northward, with snow in the overrunning band over northern New England. Thunderstorms should continue to spread eastward along the Gulf Coast, aided by a strong upper jet.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "273d4353716f2129", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42546:42550:1", + "date": "1988-02-15 06:53:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42548:42552:1'} The data starts from February 15 00:00 and ends on February 15 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The eastward movement of Pacific short waves continues. The easternmost of these has tapped Gulf moisture and should continue to spread precipitation along the eastern US front. Surface and 850 data suggest the low heading towards the St. Lawrence Valley will persist longer than previously forecast, though satellite shows a rapid weakening of upper support. This allows for quick development of a Carolina low triggered by the strong Tennessee short wave. Strong southwesterly flow and little cold air should make this storm act more like a spring storm, with the best precipitation as rain and most energy supplied by latent heat release. In the West, the next short wave is driving a Pacific cold front across the Rockies. Rebound of the western ridge and strong northwesterly flow aloft should keep the front moving quickly until about 48 hours from now, when the gradient relaxes and the flow turns more westerly. Lack of moisture should keep this system mostly dry except for southern Canada and the northern Great Lakes.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "6e928cc8b0179bc5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42548:42552:1", + "date": "1988-02-15 18:59:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42550:42554:1'} The data starts from February 15 12:00 and ends on February 16 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Cold front currently moving southeast from the northern Plains through the central Rockies will move rapidly east during the period, forced along by a short wave dropping southeast from Washington State. Due to the rapid movement of the front and lack of significant moisture, only scattered showers are expected to accompany the front from the central Plains south and east. Exceptions are from the Great Lakes eastward, where light overrunning snows will be aided by moderate positive vorticity advection, and over the southern Plains and Gulf Coast states, where return Gulf inflow combined with weak impulses in the upper flow will increase chances of showers. Large Pacific high will build in behind the front with seasonable temperatures and generally dry weather over much of the West. Cold front in the East will be swept rapidly off the East Coast early on Day 1 as a strong vorticity maximum in the Southeast lifts quickly northeast. Good warm advection and moisture associated with secondary low development over the Mid Atlantic coast will spread some decent rains over the Northeast early Day 1 with some locally moderate snows over eastern Maine. Scattered showers and thunderstorms should end early Day 1 over Florida as the trailing cold front slides off to the southeast.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "63f2f5892fa5caa1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42550:42554:1", + "date": "1988-02-16 06:42:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42552:42556:1'} The data starts from February 16 00:00 and ends on February 16 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: An active southern stream is developing due to continued building and slight retrogression of the West Coast ridge. The first short wave in this stream is expected to temporarily settle over Arizona, briefly closing off before moving eastward. A short wave in western Texas at 48 hours and a strong southwesterly flow aloft suggest a substantial area of overrunning and frontal precipitation will spread from southeastern New Mexico to the southern Appalachians as Gulf inflow becomes well established. Before that time, any short waves in the renewed southern stream are expected to be weak and shearing, with precipitation moving eastward from the western Gulf before dissipating in the drier southeastern United States. There is uncertainty regarding the strength of the system originating in central Mexico.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b3ac3ecfdc38fbfb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42552:42556:1", + "date": "1988-02-16 19:09:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42554:42558:1'} The data starts from February 16 12:00 and ends on February 17 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Higher amplitude upper flow is expected across the U.S. during the next 48 hours as a ridge off the Pacific Coast allows shortwaves to move toward the Southwest U.S. A shortwave trough initially over the Southwest U.S. is forecast to briefly close off before being ejected eastward by a series of shortwaves. A strong vortex center is developing near 50N/140W. The associated cold front is expected to move through the Pacific Northwest into the Plains within 48 hours, with most precipitation occurring in the West. Some snow is likely to accompany the vortex maximum as it moves toward the Desert Southwest on day 2. Minor impulses in the subtropical flow will gradually increase the risk of showers along the western Gulf Coast states on day 1 as the cold front settles across the southern Plains and Mississippi Valley. By day 2, increased inflow of Gulf moisture combined with upper support from a shortwave lifting out from the Southwest should set up an overrunning pattern over much of the Gulf Coast into the Tennessee Valley. Scattered snow will spread across the Northeast, but upper support is lacking for significant amounts.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b0219b1997ac654c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42554:42558:1", + "date": "1988-02-17 06:58:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42556:42560:1'} The data starts from February 17 00:00 and ends on February 17 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong shortwave system is crossing the northwest coast and will replace the current system in the southwestern US. This northern feature is expected to remain strong as it moves southward. Good attendant moisture and strong dynamics suggest that the Rockies and Basin regions will receive snow. As this system moves into place, it will push the current southwestern low eastward. Widespread precipitation is expected to spread from Texas to the Middle Atlantic states, enhanced by good Gulf inflow and overrunning/warm advection. Both high and low level factors suggest a deepening low over the central Gulf states at 48 hours. Meanwhile, a southward shift in the polar vortex indicates a return of arctic air near the northern US border.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c03c5d70f1538ca2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42556:42560:1", + "date": "1988-02-17 19:00:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42558:42562:1'} The data starts from February 17 12:00 and ends on February 18 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A high amplitude split flow pattern is becoming established over the U.S. A short wave trough that amplified into the Southwest U.S. is being followed closely by an even stronger short wave currently dropping through the Pacific Northwest on the backside of a sharpening upper ridge. The Southwest U.S. upper trough should quickly lift to the east. Several weaker impulses preceding the main short wave trough have helped saturate the airmass along a slow-moving frontal boundary over the southern Plains and Gulf Coast states, setting up a good overrunning pattern. As the main upper trough moves out of the Southwest, expect a surface low to take shape along the Texas coast and track northeast along the frontal boundary. This should be a wet system for much of the South on Day 1, with rain spreading into the Mid Atlantic states and southern New England on Day 2, aided by good moisture inflow and warm advection. On Day 2, precipitation is expected over portions of the southern Plateau and southern Rockies as dynamics associated with the strong short wave, expected to close off in the Southwest, combine with increasing easterly flow and residual moisture for some broken snows.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "89b6e94d0d650d2d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42558:42562:1", + "date": "1988-02-18 07:02:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42560:42564:1'} The data starts from February 18 00:00 and ends on February 18 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Quasi-split flow within a broad-scale long wave trough over North America in the next 48 hours will be the highlight of the pattern aloft. A Great Basin shortwave digs in across the Four Corners before ejecting eastward by 48 hours in a more southerly stream, subsequently allowing a strong upper ridge position to shift eastward to along the West Coast and northward over extreme western Canada. This results in continued dry weather over the western states and southwest Canada. The Great Basin shortwave kicks out the Texas shortwave in the same stream, propagating a Texas coastal frontal wave across the lower Mississippi Valley. Meanwhile, a vortex straddling the Gulf Coast from Gulf warm frontogenesis becomes embedded in an amplifying short ridge across the southeastern states, which eventually induces a coastal trough and subsequent warm frontal wave/coastal secondary as better upper support nears the Carolinas. The primary surface low is expected to deepen and move north-northeastward to off Boston/Cape Cod by 48 hours. Much warming at low levels and aloft will keep most precipitation over most of New York and central/southern New England as rain. Elsewhere, a Yukon shortwave signals the beginning of the next series of arctic outbreaks, digging to south-central Canada and driving an arctic front to over central Great Lakes by 48 hours with typical snow squalls.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "bd42b32ce1ef9cb1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42560:42564:1", + "date": "1988-02-18 19:07:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42562:42566:1'} The data starts from February 18 12:00 and ends on February 19 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Eastern Pacific upper ridge is maintained while heights rapidly build in the northwestern Atlantic, allowing the polar vortex to slide southeastwards and the next arctic front to surge into the US. Powerful upper circulations are moving out of the Desert Southwest. The upper dynamics with the first system will allow a low to track west of the Appalachians with a secondary formation off the Mid-Atlantic coast by 24 hours in the area of strongest thermal gradients.\n\nNorthern New England will receive all snow from this system, while parts of the Northeast will experience a brief period of snow and sleet before it turns to rain due to initially dry low level air. A frontal wave will develop in the eastern Gulf of Mexico on day two, prolonging the rainfall threat along the Gulf Coast through the coastal Carolinas on Saturday. Considerable snow showers are expected with the arctic front along the Great Lakes, while the West remains mostly dry.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "67ef1bb2bf7a2489", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42562:42566:1", + "date": "1988-02-19 07:10:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42564:42568:1'} The data starts from February 19 00:00 and ends on February 19 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The upper trough over the Mississippi Valley moves rapidly northeastward through the next 24 hours as the associated surface system moves up the Ohio Valley, with only minor redevelopment along the Mid-Atlantic coast later today and tonight. Mild temperatures in the eastern US suggest that only substantial snow will occur in interior New England later tonight.\n\nThe southwestern US upper system moves eastward and weakens through the forecast period. No major cyclogenesis is expected off the southeastern US coast in 48 hours. Cold air will move into the eastern US within the next 36-48 hours, bringing cold, dry conditions for most of the eastern two-thirds of the US.\n\nPrecipitation is expected in the southeastern US in 48 hours. The southwestern US system may bring precipitation to southern Texas in the first 24 hours before its effects shift to the Gulf of Mexico.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "2713998a40564d6e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42564:42568:1", + "date": "1988-02-19 19:14:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42566:42570:1'} The data starts from February 19 12:00 and ends on February 20 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong spoke of the polar vortex is driving the next batch of arctic air into the northern Plains. With the strong northern stream of the westerlies now reestablished, this cold should quickly move southeastward into most of the eastern US by 36 hours. The tight gradient at all upper levels and a weak trailing surface ridge suggest much of the eastern US will remain windy through the period. In the shorter range, some drying is expected between the polar and arctic fronts for interior regions along the eastern seaboard. The exception is Florida, where overrunning should linger until the trough axis finally passes eastward. Farther west, upper ridging and a general flow from the northwest should keep the western half of the country dry.\n\nDuring day 2, frontal contrast across the northern US and southern Canada should markedly increase as Chinook-warmed air and low-level southerly flow make progress toward the Great Lakes. This strong flow will translate into warm advection precipitation, mostly snow, in advance of the warm front. Upstream, the next Pacific front will have some arctic air behind it, reaching southern British Columbia by 48 hours via Alaska and the Gulf of Alaska.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "354fca32dce1683b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42566:42570:1", + "date": "1988-02-20 07:12:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42568:42572:1'} The data starts from February 20 00:00 and ends on February 20 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong upper trough is located west of coastal Alaska and extends south-southeastward across 50N, 150W. This system is forecast to consolidate somewhat over the next 24 hours and dig southeastward to the Canadian/US continental divide. The subsequent cold advection should drive substantially colder air southeastward over western Canada, eastern Washington, northern Idaho, and most of Montana than has occurred recently. Intense upper dynamics straddling the continental divide should generate mainly mountain snows. A cold front is expected across the northern Plains, Wyoming, northern Utah, and Nevada westward. Precipitation remains back in the cold air where upper dynamics dominate.\n\nThe above pattern will push out the initial vortex complex over the Great Lakes and northeastern states, which will drive a moderately arctic boundary across the eastern states and subsequently weaken while adding thermal support to the leading cold front now offshore. The best precipitation will be in the cold advection in the form of snow squalls across the eastern Great Lakes.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "9908a099958befb6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42568:42572:1", + "date": "1988-02-20 19:13:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42570:42574:1'} The data starts from February 20 12:00 and ends on February 21 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Effects from the deep Hudson's Bay vortex are expected to continue to expand, controlling the weather of the entire continent by 48 hours. In the East, one branch of this vortex is driving the next blast of cold air eastward. So far, this cold air has struggled to move southward and eastward, so the timing should be slowed a bit during day 1. By 48 hours, temperatures should already be on the rise from the Rockies eastward as the best of the cold advection will be well into the Atlantic.\n\nUpstream, a more sustainable turn to arctic air is in store as the huge vortex lowers heights substantially in western Canada and the flow shifts to one with origins in the far northern latitudes. This process will occur in two steps. The first will be to usher in the height falls behind a shearing short wave digging southward from Alaska and British Columbia. These significant height falls support the deep and expansive surface system centered in southern Canada. This system will be highlighted by strong winds, but does not preclude widespread warm advection snow ahead of the warm front.\n\nWith the upper pattern set, the next spoke of the polar vortex should bring more cold air southward, along with moisture and frontal snow.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "405a69ec58199d1a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42570:42574:1", + "date": "1988-02-21 07:19:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42572:42576:1'} The data starts from February 21 00:00 and ends on February 21 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong mid-latitude polar jet over the mid-Pacific moves eastward, compressing and amplifying the upper ridge along the west coast of North America. This allows a complex dynamic pattern over western Canada and Alaska to dig southeastward under the strong polar jet. By 24 hours, this surge of energy aloft will support a strengthening polar front across the northern Plains to the western Great Lakes. The deep polar vortex initially over northern Hudson Bay will move southward, with an associated vortex lobe rotating about its western flank, supporting a weak arctic boundary near the US-Canadian border in 24 hours and a subsequent double frontal structure. Arctic air will move further south and east, tightening the thermal gradient and leading to a stronger polar front.\n\nGuidance suggests the front will penetrate southward across northern Texas, with a thrust of the upper jet and dynamic surge becoming more easterly by 48 hours, placing the cold front along or just east of the Appalachians.\n\nThe only significant precipitation will be across the Great Lakes, where cold advection, positive vorticity advection, and low-level cyclonic flow will cause snow squalls. Some light snow is possible near the triple point northward over northeastern New York and northern New England into Canada.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "251fa9fe540805b9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42572:42576:1", + "date": "1988-02-21 18:48:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42574:42578:1'} The data starts from February 21 12:00 and ends on February 22 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Eastern two thirds of the country will experience another period of winter cold as a polar vortex moves southeastward toward James Bay over the next two days. An upper level ridge in northwestern Canada and over Iceland is blocking the vortex, causing it to move southward while it slowly weakens. Arctic and polar fronts will remain separate for about 36 hours, with a slower movement of the polar front.\n\nA strong short wave trough near Calgary tonight is expected to rotate under the polar vortex and produce a weak wave on the polar front. Any surface development should be weak due to decreasing upper dynamics over time. Initially, dry air and limited moisture inflow will restrict precipitation to a narrow area. Deep cyclonic flow and weak low level convergence will allow snow squalls to persist in the Great Lakes region long after the arctic front passes. The west will remain warm and dry for now, but a southern jet stream is expected to bring changes to the west coast later this week.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "91f1c8e0aa5e2df8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42574:42578:1", + "date": "1988-02-22 07:16:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42576:42580:1'} The data starts from February 22 00:00 and ends on February 22 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A deepening, massive, near full-latitude trough will dominate the eastern two-thirds of North America within 48 hours, bringing a return to wintry weather and colder temperatures for most areas east of the Rockies within 36-48 hours. Despite the weakening of the Arctic boundary, strong cold air advection at all levels will strengthen the polar front as it moves off the East Coast and becomes stationary across the northern Gulf under westerly flow aloft. Most of the frontal passage will bring only light showers. The main wintry weather will be confined to the vicinity of the Great Lakes, where cyclonic cold advection will generate snow squalls to the lee of the lakes.\n\nOver the Pacific, a strong polar jet near 40N will move eastward then northward, shifting the upper ridge eastward to the far western US and Canada. A weakening Pacific weather front will approach the coast by 48 hours, but with the upper dynamics moving northward, only clouds are expected. The remainder of the western states will be dry and unseasonably warm for this time of year.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "16b7e50f99d23e47", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42576:42580:1", + "date": "1988-02-22 19:06:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42578:42582:1'} The data starts from February 22 12:00 and ends on February 23 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A basically dry pattern across the country should continue, but a strong short wave now in the lower Great Lakes will bring frontal and post-frontal precipitation during Day 1. There is some uncertainty in the timing of the front in the East, but current trends favor a slower progression. Light snow is expected to graze the upper Great Lakes. Despite significant ice cover on much of the Great Lakes, strongly cyclonic flow and sustained cold temperatures should keep lake effect snow active. In the West, the progression of a large blocking ridge should allow the next eastern Pacific front to approach the California coast within 48 hours. After that, there is a good chance that Pacific westerlies may begin to undercut the ridge and reactivate the southern stream.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "30fe43bf2aa3e86c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42578:42582:1", + "date": "1988-02-23 07:00:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42580:42584:1'} The data starts from February 23 00:00 and ends on February 23 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: High winds and isolated areas of moderate to heavy snow are associated with an eastward moving cold front. Otherwise, conditions are dry coast to coast except in the southern states, where an isolated shower may occur in the vicinity of the frontal passage. A weakening Pacific weather front well off the west coast is struggling to survive due to a strong upper ridge over western North America. The boundary front dissipates by the end of the period as upper support splits, with a weak southern portion off the southern California coast, but this has little weather consequence.\n\nA strong upper jet and lower level dynamics are driving the cold front rapidly eastward, especially from the southern Appalachians northward. Most precipitation will occur behind the front as the best dynamics are well west of the front.\n\nThe front drops to extreme southern Florida or just south of Miami by 48 hours as a major trough axis moves eastward to the Florida peninsula position and upper flow becomes slightly north of west upstream.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7d56969d9b94b453", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42580:42584:1", + "date": "1988-02-23 18:32:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42582:42586:1'} The data starts from February 23 12:00 and ends on February 24 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: With this morning’s exit of the East Coast cold front, dry weather will again dominate. East of the Rockies, this dryness will be marked by seasonably cold air as the link to far northern latitudes remains unbroken. During day 2, some progression of the western ridge and lowering pressures in Canada should allow warmer western air to work eastward into the Plains, aided by some downslope winds. Exceptions to this dry scenario include Florida, where the cold front may take a day or so to clear the state, and the Great Lakes, where only a slight relaxation of the cyclonic flow and cold air is expected, but not enough to shut down the lake effect snow showers. In the far West, dry conditions will continue for the next two days. Immediate precipitation hopes are pinned on the eastward progression of a strong system near 150W as the western US ridge edges eastward.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7f40eb1f317bf1a5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42582:42586:1", + "date": "1988-02-24 07:15:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42584:42588:1'} The data starts from February 24 00:00 and ends on February 24 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A short wave associated with an upper low near 30N/130W is forecast to move southeastward to Baja, taking any rain with it south of the U.S. A strong upper low in the Gulf of Alaska is forecast to send a short wave southeastward to near 135W in the next 48 hours, carving a sharp upper trough and pushing the upper ridge eastward to the northern Rockies and Plateau. With the emphasis on the digging of the upper trough, precipitation accompanying the cold front should be delayed and remain well off the West Coast. The frontal boundary preceding the new cold front is forecast to dissipate in the next 12-24 hours as it encounters the upper ridge along the West Coast. Any precipitation associated with this boundary should be forced into western Canada. A broad upper trough over the Ohio Valley and Great Lakes is forecast to move northeastward to the northeastern U.S. and eastern Canada in the next 48 hours. This will allow for a long stretch of northwesterly flow aloft from the northern Rockies to the southeastern U.S. Dry and cold air is forecast to dominate the eastern half of the U.S., while a warming trend is likely across the eastern Rockies and central Plains. Though the upper trough is forecast to exit the Great Lakes, lake effect snows should be slow to dissipate. Near the end of the second 24-hour period, overrunning snow is likely as a short wave moves southeastward from central Canada and warm advection occurs from a retreating warm front. Southern Florida is forecast to have scattered precipitation in the next 12-18 hours as a cold front moves through the region.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "0ebc6365ae861881", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42584:42588:1", + "date": "1988-02-24 18:44:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42586:42590:1'} The data starts from February 24 12:00 and ends on February 25 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Events underway in the central Pacific indicate that a progression of the mean weather pattern is occurring. The Gulf of Alaska low is lifting and filling, and the leading edge of the Pacific westerlies is pressing eastward. Though progress will be slow, this change in pattern should bring the best chance for far western precipitation so far this month. Coastal precipitation is expected within 48 hours. Scattered convection is expected over the southwestern U.S. at 48 hours. Farther east, the lifting and filling of the Hudson Bay vortex will allow more emphasis on digging Canadian short waves. The next of these should be a milder and slower version of its Alberta counterpart, with scattered or better light snow likely due to sufficient relative humidity and dynamics.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "ebc8b9ec13cf9aa5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42586:42590:1", + "date": "1988-02-25 07:21:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42588:42592:1'} The data starts from February 25 00:00 and ends on February 25 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A short wave near 150W is forecast to move east-southeastward to the vicinity of 130W while developing a closed low. A significant amount of cold air is dropping in behind the short wave, which should enhance the deepening of the upper trough and associated surface low. The surface low is expected to be deeper than previously forecast, increasing easterly flow at the surface over California, resulting in drier air. Significant precipitation is expected to move eastward to the Sierra Range in the next 48 hours, but may be slower to arrive. The developing closed low is forecast to move a weakening upper low near 130W northeastward as an open wave to Baja and the southwestern U.S. Little or no precipitation is expected with this system as it is forecast to lift into a weak upper ridge over the southern Rockies. A strong upper ridge over the northern Rockies is forecast to keep that region dry over the next two days. A short wave is forecast to drop rapidly southeastward from central Canada to New England in the next 48 hours, spreading overrunning snow from the northern Mississippi Valley eastward through the Great Lakes to northern New England. After passage of the surface low and associated cold front, low-level cold advection should bring the return of lake effect snows. During the second 24-hour period, scattered rain could return to southern Texas due to return flow of the surface ridge and weak impulses forecast to move through the southern stream.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "99964be2230cc8b6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42588:42592:1", + "date": "1988-02-25 18:59:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42590:42594:1'} The data starts from February 25 12:00 and ends on February 26 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Upper trough just northwest of 35N, 140W digs in and becomes cutoff west of the California coast by 48 hours. Dryness continues over the western states except portions of California as a vortex rotates about the eventual cutoff. The polar jet remains strong over western Canada, so bundles of wave energy should continue moving steadily along. With the mean trough position remaining southward from eastern Canada to the vicinity of the east coast, a shortwave near Lake Winnipeg will dig to over the northeastern states by 48 hours. The best precipitation with this weather system from the northern Great Lakes east-southeastward should remain well north of the surface low and weak warm boundary where positive vorticity advection and 850 mb warm advection pattern are most favorable. A disturbance aloft drifting northeastward from northwestern Mexico will continue weakening into the mean ridge position, so prospects are dim for any significant precipitation.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c3ec0502ed2ecd95", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42590:42594:1", + "date": "1988-02-26 06:47:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42592:42596:1'} The data starts from February 26 00:00 and ends on February 26 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Closed low near 140W is forecast to drift slowly south-southeastward to vicinity of 30N/130W in the next 48 hours. With movement parallel to the California coast, the brunt of precipitation associated with the cold front and surface low should be slow to reach California. There is the possibility of scattered showers ahead of the main system with some weak vorticity lobes. Significant precipitation should not reach California until 48 hours, with the cold front near the California coast.\n\nIn the East, a clipper-like system is forecast to race rapidly southeastward through the Great Lakes to the Mid-Atlantic coast, then slow during the second 24-hour period as it approaches a block in the central Atlantic. This should spread overrunning snow through the Great Lakes and New England, but with the system moving so fast during day 1, amounts should be light and too far off the coast to have any major impact on day 2.\n\nAnother system is forecast to ride the western Canada upper ridge, then dig southeastward to south-central Saskatchewan. This system should not have any effect on northern tier states through the forecast period except to push a new cold front to northern Montana and northern Plains.\n\nDown south, weak vorticity centers are forecast to move through the southern stream from Baja to Gulf Coastal states.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "0013b475baf06f84", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42592:42596:1", + "date": "1988-02-26 18:57:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42594:42598:1'} The data starts from February 26 12:00 and ends on February 27 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A cutoff low is expected to meander near 130W, then begin a slow movement by 48 hours as another system moves eastward along 40-50N. The southern portion of the trough may remain quasi-stationary for a longer duration. A vorticity lobe on the east side will drift northward and northwestward while weakening. Scattered precipitation will slowly expand northward in California and nearby areas.\n\nAn upper trough over northwestern Canada in the polar jet will move southeast toward Lake Superior by 48 hours, similar to the previous system, with the intensity of the upper trough and surface developments also similar. The associated air mass is primarily of maritime origin with little change expected as it moves across Canada to the north-central states. Arctic air will remain north of the system near Hudson Bay.\n\nA system at the surface and aloft over the eastern Great Lakes is expected to continue off the Jersey coast today, with little deepening expected. Little precipitation is expected due to the absence of favorable warm advection. Some post-frontal snow showers are possible to the east of the eastern Great Lakes and Appalachians, but low-level flow suggests these will be of short duration.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "dc0cb8b947f97dc9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42594:42598:1", + "date": "1988-02-27 06:57:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42596:42600:1'} The data starts from February 27 00:00 and ends on February 27 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A short wave near 165W will push a closed low near 30N/130W northeastward to the California coast. As the upper low lifts northeastward, it is forecast to eject vorticity lobes across California and the southern Great Basin over the next 48 hours. The initial surge during the first 24 hours is likely to produce broken precipitation along the entire California coast to the central Sierra range, then scattered precipitation for the remainder of the forecast period across the Great Basin.\n\nAcross eastern Texas, scattered rain is likely as a weak impulse drops southeastward from New Mexico to the northern Gulf during day 1. Scattered convection is possible along the Texas coast as a cold front approaches on day 2.\n\nA clipper-like system continues to drop rapidly southeastward to the Mid-Atlantic coast, then slowly eastward. On its trek southeastward, it should spread snow from southern New England to the Delmarva area over the next 12-18 hours.\n\nAnother clipper is forecast to drop southeastward from northern Alberta to just north of the Great Lakes over the next 48 hours. It is forecast to spread a new area of broken snow across the upper Mississippi Valley and Great Lakes during the 24-48 hour period while dragging a cold front rapidly southeastward through the central Plains to the Ohio Valley and southern Mississippi Valley.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d67082b50363410d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42596:42600:1", + "date": "1988-02-27 18:46:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42598:42602:1'} The data starts from February 27 12:00 and ends on February 28 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A strong upper trough and associated jet at 250 mb near 40N, 160-170W is forecast to move east over the eastern Pacific. The southern part of the trough and jet will dislodge a closed low off the California coast, forcing it northeast then north while weakening late as the stronger northern portion of the Pacific trough approaches. A series of vortices and troughs will drift north over the western states, keeping the region cloudy and unsettled with scattered precipitation.\n\nMeanwhile, the next shortwave over south-central Canada will plunge southeast into the eastern US and Canada. The air mass in the wake of the front is moderately maritime, but within 24 hours, a secondary cold front is expected over Lake Superior as arctic air moves south from Hudson Bay across the Great Lakes to the central Appalachians by 48 hours. Snow activity will break out across the Great Lakes and Appalachians due to strong upper-level dynamics. Over the mid-Atlantic region, snow activity is possible, especially as a vortex center is forecast across West Virginia and central Virginia. Tonight’s threat is moving out to sea as upstream features provide the necessary push.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "8ba81188aac671f7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42598:42602:1", + "date": "1988-02-28 07:11:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42600:42604:1'} The data starts from February 28 00:00 and ends on February 28 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Double vortex structure between 160W-155W is forecast to push eastward, with the northern portion rotating around the Aleutian closed low and the southern vortex digging southeastward to replace the closed low near 130W. The initial upper low near 130W is forecast to lift slowly northward and parallel to the California coast as it weakens. It will be replaced by a new closed low during the second 24-hour period, maintaining split flow across the Pacific Northwest. Although the initial upper low is expected to weaken, it is still expected to eject lobes of vorticity across California and the Great Basin regions over the next 48 hours. Most precipitation is likely to remain along the California coast and western slopes of the Sierra Range.\n\nA train of Alberta clippers is forecast to continue over the next 48 hours as a short wave in Manitoba drops rapidly southeastward to southern New England. The main surface low associated with this system is forecast to rotate around the polar vortex, while minor surface waves could develop along the frontal boundary in eastern Canada and off the Mid-Atlantic coast. No significant development is expected from any of these systems, as the surface low presently in the eastern Atlantic blocks any inflow of Atlantic moisture. Once surface waves enter the eastern Atlantic, they should begin northward movement due to strong blocking upper ridge in the central Atlantic.\n\nBroken snow associated with this short wave should remain across the Great Lakes and northeastern U.S. in the vicinity of best upper dynamics. Otherwise, lake effect snows should occur after the passage of polar and arctic frontal boundaries.\n\nA third in a series of short waves is forecast to move through the Gulf of Alaska and over the upper ridge to central Saskatchewan in the next 48 hours. This should not have any effect on the U.S. during this forecast period but is likely to spread snow into the northern Plains in the following period.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7ef596b5743e033c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42600:42604:1", + "date": "1988-02-28 18:59:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42602:42606:1'} The data starts from February 28 12:00 and ends on February 29 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Split flow over the eastern Pacific will be maintained as wave energy in the southern branch moves east-southeastward, replacing the filling closed low now off the California coast. As the closed low drifts northeastward over the western states and Great Basin region, vorticity spokes will drift about the upper feature, keeping most of the area cloudy and unsettled with some scattered precipitation.\n\nA shortwave over Yukon of Pacific origin, with a maritime surface air mass, will dig southeastward to south of Lake Winnipeg. In 48 hours, the open surface wave structure is likely to be in southern Minnesota at the base of the thermal ridge. Important precipitation is likely to remain well north of the surface center, deep over cold air where upper support and thermal gradient are most favorable.\n\nA short ridge in advance will drive out the upper trough now over the Great Lakes, with no appreciable development along or off the East Coast northeastward to over the Maritimes. Cold advection will be across the Mid-Atlantic region northward over the northeastern states.\n\nElsewhere across the southern tier of states, west-northwesterly flow aloft becoming more zonal with time will keep the entire region basically dry for most of the period. A weak vorticity feature over the west-central Plains by 48 hours hints at some shower activity but is not highly convincing at this time.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "1e892c81d116cb47", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42602:42606:1", + "date": "1988-02-29 06:53:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42604:42608:1'} The data starts from February 29 00:00 and ends on February 29 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: Split flow over the eastern Pacific is forecast to break down in the next 48 hours as a short wave near 130W lifts northeastward to the Gulf of Alaska and a closed low near 125W weakens and lifts into the Great Basin as an open wave. This will allow zonal flow over the central and eastern Pacific to amplify and develop an upper ridge just off the West Coast. The northern short wave should not develop any precipitation across the Pacific Northwest, though the weakening upper low and associated vortices are forecast to spread broken to scattered rain into California. Precipitation could change to or mix with snow at higher elevations of the Sierra Range and Plateau region. As the upper low moves into the Great Basin, expect minor impulses to move ahead of the main system in the southern stream to the lower Plains. This should combine with the return flow of Gulf moisture during the second 24-hour period to develop convection from the Texas coast to the central Plains and Mississippi Valley. Two areas of concentrated convection are expected: one near the short wave forecast to move through Kansas and along the cold front, and the other along the Texas coast where moisture convergence should be the best. Up north, the third in a series of Alberta clippers is forecast to drop southeastward to southern Saskatchewan, then curve east-northeastward through the base of the upper trough and around the polar vortex. The bulk of snow associated with this system is forecast to remain north of the border, with only scattered precipitation in the vicinity of the cold front at the end of day two.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "17f04742b442cc81", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42604:42608:1", + "date": "1988-02-29 18:51:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42606:42610:1'} The data starts from February 29 12:00 and ends on March 01 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A split flow pattern aloft off the West Coast is shifting slowly eastward over western North America over the next 48 hours. A shortwave in the southern branch at 37N,127W is digging to extreme northwestern Mexico, driving an upper trough and embedded vorticity maxima over the southwestern states eastward. A stronger shortwave in the northern branch is weakening over southeastern Alaska due to the entrenched upper ridge over western Canada and the Hudson Bay polar vortex. Only scattered precipitation is expected for the Pacific Northwest and surrounding areas. A weak upper trough drifting eastward over the western states keeps the region unsettled along and west of the Divide, with precipitation emphasis shifting slowly eastward.\n\nA shortwave over Alberta is digging in along the US-Canadian border and eastward over the northern Great Lakes, caused in part by a short ridge moving over the eastern states by 48 hours. An associated cold front should drop over the central Plains and then northeastward to the Ohio Valley. There will be little precipitation along the front across the northern tier due to lack of upper support and unfavorable low-level conditions.\n\nLater in the period, as the western trough shifts flow aloft to southwesterly from Texas northeastward to the Mississippi-Ohio confluence, the combination of upper diffluence and low-level moisture from the western Gulf will focus on the eastern half of Oklahoma and most of Arkansas by late in the period. This will result in a sizeable area of broken precipitation activity, including some convective activity.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "6edb3535e5f5f72b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42606:42610:1", + "date": "1988-03-01 07:10:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42608:42612:1'} The data starts from March 01 00:00 and ends on March 01 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: The short wave currently moving through California will significantly impact weather over the next two days and beyond. This system is stronger than previously indicated and is causing notable warm advection as Gulf inflow clashes with another blast of Canadian air. Colder air is expected to move more easily into Texas, allowing the cold front to extend farther southwest in the eastern US and reducing the northern edge of post-frontal overrunning.\n\nPrecipitation is expected to develop in Oklahoma and Texas and spread eastward during the period. Strong Gulf inflow will be supported by upper-level difluence and a series of weak short wave ripples moving from Mexico.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d9ab5b708d586743", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42608:42612:1", + "date": "1988-03-01 19:09:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42610:42614:1'} The data starts from March 01 12:00 and ends on March 02 06:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: An upper low moves eastward to the vicinity of the Gulf of Alaska by 48 hours as a series of shortwaves traverse the area ahead of the main feature, continuing to bring more surges of Pacific maritime air into Alaska and northwestern Canada. The tail-end of these shortwaves brushes southwestern Canada and the Pacific Northwest eastward but brings only a slight dose of precipitation to the region. Meanwhile, a twin shortwave complex near Lake Winnipeg drops east-southeastward just north of the Great Lakes to the Canadian Maritimes by 48 hours, pushing a cold front clear of New England and back west-southwestward over the Mid-Atlantic area as a thrust of cold air moves over the Great Lakes and Ohio Valley. The western end of the cold front drops early to Oklahoma and Texas, then plunges to the Gulf Coast–Rio Grande Valley position as a shortwave complex over northwestern Mexico and New Mexico–western Texas is pushed east-northeastward by a strong California shortwave, inducing a frontal wave near Arkansas. In the interim, there will be plenty of convective activity over the warm sector and overrunning over the cold sector due to upper dynamics and increasing low-level moisture inflow from the Gulf. Most of Oklahoma, northern and northeastern Texas, and all of Arkansas and vicinity are expected to experience the brunt of convective activity and precipitation. After the lead impulse clears the area, not much is left for the California shortwave to affect later in the period over the southern Four Corners region.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b021840361eaf958", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42610:42614:1", + "date": "1988-03-02 07:11:00" + } + }, + { + "prompt": "The following data shows the global weather fields for 24 hours, sampled at 6 hour intervals. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42612:42616:1'} The data starts from March 02 00:00 and ends on March 02 18:00. Based on the above data, answer the following question in a detailed report:", + "question": "Provide a detailed meteorological discussion and forecast for the continental United States based on the given atmospheric data. Your analysis should include: (1) current weather system positions and movements affecting the US, (2) temperature trends and expected changes over the coming days, (3) precipitation patterns and likelihood of significant weather events, (4) pressure system evolution and their impacts on US weather, (5) any notable atmospheric features (fronts, jet stream position, etc.) that will influence US weather patterns. Focus on translating the observational data into actionable meteorological insights and forecasts for the continental US.", + "response": "The meteorological discussion and forecast for the continental United States is: A southern stream is becoming the focus for active weather as two major shortwaves are involved with the trough over the southwestern United States and northern Mexico. The first wave is expected to weaken as it tracks northeastward to the Ohio Valley. Before it weakens, it is expected to aid in the development of significant convection over east Texas and the lower Mississippi Valley. An active precipitation pattern over the southeastern United States will also be associated with the second, stronger wave, with stronger cyclogenesis expected. The position of the rain/snow line is uncertain for the Mid-Atlantic region, with a tendency toward warmer conditions.\n\nA healthy upper trough and stronger onshore flow are expected for the Pacific Northwest. Weather over the southwestern states should become quieter beyond 12 hours under drier northwesterly flow aloft, with lingering precipitation over eastern Colorado likely to diminish.", + "metadata": { + "prompt_id": "UXG5NN", + "question_id": "IGzIt0", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "9189a170fac82651", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42612:42616:1", + "date": "1988-03-02 19:12:00" + } + } +] \ No newline at end of file diff --git a/level2c_part2.json b/level2c_part2.json new file mode 100644 index 0000000000000000000000000000000000000000..2808c87e5692009050f11a6b58ecbdb95f81c2fc --- /dev/null +++ b/level2c_part2.json @@ -0,0 +1,1282 @@ +[ + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61488:61599:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Mature cold episode (La Niña) conditions continued during January 2001, as sea surface temperatures (SSTs) remained more than 1.0°C below average across the central equatorial Pacific between the date line and 160°W. The SST anomaly pattern for January is similar to, but weaker than, the patterns observed during January 1999 and January 2000. The January patterns of anomalous 850-hPa zonal wind and precipitation also show remarkable similarity among the three years, with low-level easterly anomalies and below normal precipitation over the central and western equatorial Pacific.\n\nSince the demise of the 1997-98 El Niño, many ENSO indices have shown distinct annual cycles, with the northern winter seasons featuring minima in the SST, maxima in the OLR anomalies, and maxima in the low-level easterly winds over the central equatorial Pacific. The slope of the oceanic thermocline has been greater than normal throughout this period, with positive (negative) subsurface temperature anomalies in the west-central (eastern) equatorial Pacific. The strength of this anomalous subsurface pattern has also displayed an annual cycle since mid-1998. The evolution of the atmospheric and oceanic anomaly patterns since mid-1998 is similar to, but stronger than, that observed during 1984-1986, which followed the strong 1982-83 El Niño. During both of these post-strong El Niño periods the anomalous annual cycles were accompanied by an enhanced Australasian monsoon system.\n\nOver the past two years there has been a gradual expansion of the area of positive equatorial subsurface temperature anomalies into the central Pacific. This evolution is consistent with a slow decay of the subsurface thermal structure that characterizes the mature phase of cold episodes. It is likely that cold episode conditions will gradually weaken over the next several months, with near-normal conditions likely during the summer of 2001. Thereafter, near-normal or slightly warmer-than-normal conditions are expected during the second half of 2001.\n\nWetter-than-normal conditions are expected to prevail over Indonesia, northern Australia, Northeast Brazil and portions of southern Africa during the remainder of the NH winter. Over the United States warmer-than-normal conditions are expected along the southern tier of states from southern California eastward to Florida, while cooler-than-average conditions are likely over western and central Canada and in the upper Midwest and Great Lakes.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "4ee175791d2d02b1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61488:61599:1", + "start_date": "2001-02-01", + "end_date": "2001-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61844:61967:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Cold episode (La Niña) conditions weakened during April 2001, as sea surface temperature (SST) anomalies trended toward 0°C throughout the tropical Pacific. However, the persistent pattern of stronger-than-normal low-level easterlies over the central equatorial Pacific continued during March-April 2001. Beginning in early February 2001, SSTs became anomalously warm in many sections of the eastern tropical Pacific, while remaining below normal in the central equatorial Pacific. Similar conditions were observed in the eastern equatorial Pacific during March-April 1999 and 2000. In both of those years the anomalous warming of the eastern equatorial Pacific SSTs lasted until late April or early May and then rapidly disappeared as cross-equatorial flow from the Southern Hemisphere into the Northern Hemisphere became established and seasonal rainfall began to increase over Central America, southern Mexico and the southeastern tropical North Pacific. As in the last two years, the positive SST anomalies rapidly dissipated during late April-early May 2001, as the low-level easterlies became anomalously strong over the eastern tropical Pacific.\n\nSince the demise of the 1997-98 El Niño, the northern winter seasons have featured minima in the SST, maxima in the OLR anomalies, and maxima in the low-level easterly winds over the central equatorial Pacific. The slope of the oceanic thermocline has been greater than normal throughout this period, with positive subsurface temperature anomalies in the west-central equatorial Pacific and negative subsurface temperature anomalies in the eastern equatorial Pacific. The strength of this anomalous subsurface pattern has also displayed an annual cycle since mid-1998. The evolution of the atmospheric and oceanic anomaly patterns since mid-1998 is similar to, but stronger than, that observed during 1984-1986, which followed the strong 1982-83 El Niño. During both of these post-strong El Niño periods the anomalous annual cycles were accompanied by an enhanced Australasian monsoon system.\n\nOver the past two years there has been a gradual expansion of the area of positive equatorial subsurface temperature anomalies into the central Pacific and a gradual decrease in the strength of the negative SST anomalies. It is likely that cold episode conditions will continue to weaken over the next few months, with near-normal conditions likely during the summer of 2001. Thereafter, near-normal or slightly warmer-than-normal conditions are expected during late 2001 and early 2002.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "58f2e53e58165b2d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61844:61967:1", + "start_date": "2001-05-01", + "end_date": "2001-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62212:62335:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Sea surface temperature (SST) anomalies continued to increase in the central equatorial Pacific during July 2001. Since February 2001 SSTs and SST anomalies have steadily increased in the central equatorial Pacific Niño 4 region rising to their highest levels since the 1997-98 warm (El Niño) episode. By late July equatorial SST anomalies between 0.5°C and 1°C were observed between 165°E and 135°W.\nOver the past two years there has been a gradual expansion of the area of positive equatorial subsurface temperature anomalies into the central Pacific and a gradual decrease in the strength and areal extent of the negative subsurface temperature anomalies in the eastern Pacific. This evolution is consistent with the decay of the subsurface thermal structure that characterizes the mature phase of cold episodes and the development of conditions usually found just prior to warm episodes. Accompanying this evolution has been a gradual transition from negative to positive SST anomalies between 160°E and 130°W.\nPositive SST anomalies are likely to continue in the equatorial Pacific during the remainder of 2001 and into the first half of 2002. Warmer than normal oceanic conditions are indicated through early 2002. A weak or moderate warm episode (El Niño) is indicated by the end of 2001 and the beginning of 2002.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "073b84773eadb7a6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62212:62335:1", + "start_date": "2001-08-01", + "end_date": "2001-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62580:62699:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Most oceanic and atmospheric indices reflect ENSO-neutral conditions. However, there are indications of a slow evolution towards a warm episode. Since late June 2001 sea surface temperatures (SSTs) have become anomalously warm in the central equatorial Pacific, with anomalies near 1°C just to the west of the date line. During the same period, subsurface temperature anomalies have remained positive in the central equatorial Pacific between 160°E and 120°W, indicating a deeper-than-normal thermocline in that region.\n\nIn recent months, many tropical Pacific atmospheric and oceanic variables have been modulated by intraseasonal (30-60 day) fluctuations, associated with the Madden-Julian Oscillation (MJO). Low-level wind fluctuations over the central and western tropical Pacific have been consistent with this activity. A significant westerly wind burst occurred over the western equatorial Pacific during mid-October. This event resulted in additional deepening of the oceanic thermocline and an increase in subsurface temperature anomalies in the central equatorial Pacific by the end of the month.\n\nWeak warm or near-normal conditions are predicted for the equatorial Pacific during the remainder of 2001 and into the first half of 2002. A gradual evolution to warm episode conditions will continue in the tropical Pacific over the next several months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b38c978ad6209f07", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62580:62699:1", + "start_date": "2001-11-01", + "end_date": "2001-11-30" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62948:63059:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Warm episode (El Niño) conditions will develop in the tropical Pacific during the next 3 months.\n\nThe evolution towards a warm episode in the tropical Pacific continued during January 2002. By late January equatorial SST anomalies exceeding +1°C were observed in the vicinity of the date line from 170° E to 160 °W. Warmer-than-normal subsurface waters continued to expand eastward beyond the date line during the month.\n\nIn recent months many tropical Pacific atmospheric and oceanic variables have been influenced by intraseasonal (30-60 day) fluctuations associated with the Madden-Julian Oscillation (MJO). Alternating periods of low-level easterly and westerly wind anomalies over the western and central Pacific have been consistent with this activity. December 2001 featured significant low-level westerly anomalies over the western and central equatorial Pacific. This activity generated a strong eastward propagating oceanic Kelvin wave that contributed to a deepening of the oceanic thermocline and warming of the sea-surface temperatures in the vicinity of the date line during January. Due to the ongoing Kelvin wave, an increase in subsurface temperature anomalies and SST anomalies is occurring in the eastern tropical Pacific. Localized warming of SSTs is expected along the coasts of Ecuador and Peru with the arrival of the ongoing Kelvin wave. This warming represents the early stages of El Niño development and that mature El Niño conditions will take several months to develop.\n\nStrong MJO activity observed over the Indian Ocean and west Pacific during late January may contribute to another period of westerly low-level wind anomalies over the central and western equatorial Pacific during February. This may be the impetus for additional Kelvin wave activity that could arrive in the eastern equatorial Pacific by late March.\n\nA spread from near-normal to moderate warm-episode conditions is predicted during the next 3-6 months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "3c97f0058bac16e3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62948:63059:1", + "start_date": "2002-02-01", + "end_date": "2002-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63304:63427:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Warmer-than-normal sea surface and subsurface temperatures were observed throughout most of the equatorial Pacific during April 2002. Sea surface temperature anomalies were up to 2°C warmer than average in the region between the Galapagos Islands and the South American coast, and greater than 1°C warmer than average immediately to the west of 180°W.\nAlthough there was considerable warming in the eastern equatorial Pacific during February-April, which resulted in locally heavy rainfall along the coasts of Ecuador and northern Peru, there was little change in SSTs or subsurface temperature anomalies in regions father west during this period. Consistent with this lack of evolution in the central equatorial Pacific, atmospheric indices for low-level winds, sea level pressure and precipitation continue to indicate near-normal conditions.\nAn eastward-propagating oceanic Kelvin wave, initiated by strong MJO activity in late 2001, resulted in the rapid warming that was observed along the coasts of Ecuador and northern Peru in early February. Since that time MJO activity has weakened and there has been no additional significant Kelvin wave activity. Without such activity a slow evolution towards El Niño conditions is possible through the remainder of 2002. A gradual warming is expected over the next several months, with weak-to-moderate El Niño conditions by the end of 2002. A weak or moderate El Niño would feature considerably weaker global impacts than were experienced during the very strong 1997-98 El Niño.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "cf49663308c10090", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63304:63427:1", + "start_date": "2002-05-01", + "end_date": "2002-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63672:63795:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Warm episode (El Niño) conditions prevailed during July, as SST anomalies remained greater than +1°C throughout the central equatorial Pacific between 170°E and 120°W.\nThe Southern Oscillation Index (SOI) has been consistently negative since March 2002, and weaker-than-average low-level easterly winds occurred during May-July 2002 throughout the equatorial Pacific.\nThe Madden-Julian Oscillation (MJO) contributed to a substantial weakening of the low-level easterly winds throughout the equatorial Pacific during July. As a consequence, drier-than-average conditions were observed over Indonesia and portions of Southeast Asia/ India during the month. In addition, the weaker-than-average easterly winds contributed to a deepening of the oceanic thermocline in the central equatorial Pacific, an increase in subsurface temperature anomalies, and an increase in SST anomalies in the central equatorial Pacific during July.\nWeak-to-moderate El Niño conditions are present. El Niño conditions are likely to continue through the end of 2002 and into early 2003. This warm episode will be much weaker than the 1997-98 El Niño. The global impacts of this warm episode should be correspondingly weaker than those observed during the very strong 1997-98 El Niño.\nDrier-than-average conditions are expected to continue over Indonesia and eastern Australia during the next several months, and wetter-than-average conditions over southeastern South America during the next three months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "742eaa13d42d3701", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63672:63795:1", + "start_date": "2002-08-01", + "end_date": "2002-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64040:64159:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Further evolution toward basin-wide mature El Niño conditions occurred during October, as sea surface temperature (SST) anomalies increased in all of the Niño regions. SST anomalies were greater than +1oC throughout most of the equatorial Pacific between 180oW and the South American coast, and SST anomalies exceeded +2oC between 175oW and 140oW. Positive subsurface temperature departures and a deeper-than-average oceanic thermocline prevailed throughout most of the equatorial Pacific. Atmospheric indicators of El Niño include consistently negative values of the Southern Oscillation Index (SOI) since March 2002, and weaker-than-average low-level easterly winds since May 2002 throughout the equatorial Pacific. In addition, above-average precipitation has been observed over the tropical Pacific, especially in the vicinity of the date line (180oW) since August 2002, while drier-than-average conditions prevailed over many sections of Indonesia, India, Mexico and Central America. These oceanic and atmospheric conditions indicate the presence of El Niño.\n\nEl Niño conditions will continue through spring 2003. SST anomalies are expected to increase further in the eastern equatorial Pacific (Niño 3 and Niño 1+2 regions), with the establishment of basin-wide mature El Niño conditions during December 2002-February 2003. This event is expected to be weaker than the 1997-98 El Niño.\n\nExpected global impacts include drier-than-average conditions over Indonesia and eastern Australia continuing during the next several months, and wetter-than-average conditions over southeastern South America (Uruguay, northeastern Argentina, and southern Brazil) through the end of 2002. Drier-than-average conditions are expected over southeastern Africa during December 2002-February 2003. Drier-than-average conditions are expected over Northeast Brazil and northern South America during December 2002-April 2003. Wetter-than-average conditions are expected over coastal sections of Ecuador and northern Peru during December 2002-April 2003.\n\nOver the United States and Canada, drier-than-average conditions are expected in the Ohio Valley states and northern U.S. Rockies during winter 2002-2003. Wetter-than-average conditions are expected along much of the southern tier of the U.S. during winter 2002-2003. Warmer-than-average conditions are expected in the northern tier states, southern and southeastern Alaska, and western and central Canada during late fall 2002 and winter 2002-2003.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "69e4ce07870c5720", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64040:64159:1", + "start_date": "2002-11-01", + "end_date": "2002-11-30" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64408:64519:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Warm episode (El Niño) conditions continued during January 2003, as equatorial SST anomalies remained greater than +1°C in the central equatorial Pacific (175°E-125°W). In addition, enhanced precipitation and cloudiness were observed over the central tropical Pacific, and positive subsurface temperature departures and a deeper-than-average oceanic thermocline were observed throughout the equatorial Pacific east of 180°W.\n\nDuring January 2003 the warm episode began to weaken. Sea-surface temperature anomalies decreased throughout the eastern equatorial Pacific by as much as 1.5°C during the month, while equatorial easterly winds were near normal throughout the central and eastern equatorial Pacific. Over the past several weeks there has also been a steady eastward progression of negative subsurface temperature anomalies, indicating a gradual depletion of the excess warmth in the upper ocean of the equatorial Pacific.\n\nThe warming associated with the current event has been greatest in the central equatorial Pacific (Niño 4 and Niño 3.4 regions). Regions farther east (e.g., Niño 3 and especially Niño 1+2) have warmed much less. For the equatorial Pacific as a whole, the current event is moderate in intensity.\n\nEl Niño conditions will continue to weaken through April 2003. Thereafter, near-normal conditions are forecast during May-October 2003. Those areas of the world usually affected by El Niño may continue to experience related impacts during the next 2-3 months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "692437a29986b530", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64408:64519:1", + "start_date": "2003-02-01", + "end_date": "2003-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64764:64887:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Warm episode (El Niño) conditions rapidly dissipated in the tropical Pacific during March and April 2003, as sea-surface temperature anomalies continued to decrease across the central and eastern equatorial Pacific and drier-than-average conditions developed over the central equatorial Pacific. Significant deceases in SST anomalies occurred in all of the Niño regions during April and early May. By mid-May equatorial SSTs were near or below normal between 165°W and the South American coast, with only a small area of residual positive SST anomalies west of the date line between 155°E and 175°E.\nConsistent with the cooling trend in SSTs, the equatorial easterlies have been stronger than average over the central and west-central equatorial Pacific since late February, and the equatorial SOI has switched from negative to positive. In recent months the depth of the oceanic thermocline has steadily decreased across the central and eastern equatorial Pacific, and negative subsurface temperature departures have developed and intensified in the upper ocean of this region. By late-April subsurface temperatures at thermocline depth were below average throughout the eastern Pacific, with negative anomalies ranging between -1°C and -3°C. A transition to La Niña is underway and La Nina conditions are likely to develop over the next few months.\nSome forecasts indicate the possibility that La Niña will develop during the second half of 2003, while others indicate a resurgence of El Niño conditions by the end of the year. However, cold episode (La Niña) conditions will likely develop in the tropical Pacific during the next few months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "152ca5728211f8fe", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64764:64887:1", + "start_date": "2003-05-01", + "end_date": "2003-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65132:65255:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Current atmospheric and oceanic conditions in the tropical Pacific are near average and do not support the development of either La Niña or El Niño in the next few months. Equatorial sea-surface temperature anomalies greater than +0.5°C persisted in the region west of the date line, while negative anomalies remained in the eastern Pacific, near the South American coast. During July very little net change was observed in the SST anomalies in the Niño regions. Since late May positive equatorial upper-ocean temperature departures have spread eastward into the central and eastern Pacific. This evolving subsurface pattern is associated with an eastward propagating oceanic Kelvin wave, resulting from a period of weaker-than-average easterlies in the central equatorial Pacific that occurred during late May and early June. SST anomalies in the Niño 3.4 and Niño 3 regions increased during early-June through early July, but then decreased during the last half of July, as the equatorial easterlies strengthened. Some atmospheric indices, such as the Tahiti-Darwin SOI, and central equatorial Pacific low-level (850-hPa) zonal wind and OLR, have displayed considerable month-to-month variability since May 2003 and no consistent trend towards either La Niña or El Niño. Near neutral conditions (Niño 3.4 SST anomalies between -0.5°C and +0.5°C) are forecast for the remainder of 2003 and early 2004.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "fe8dc1b3f8407828", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65132:65255:1", + "start_date": "2003-08-01", + "end_date": "2003-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65500:65619:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Equatorial surface and subsurface temperatures were warmer than average throughout most of the Pacific during October. SST anomalies greater than +0.5°C (~1°F) were observed in most areas along the equator between Indonesia and the South American coast. By the end of the month, positive SST anomalies were observed in all of the Niño regions. However, the 850-hPa zonal wind indices (central and western equatorial Pacific values near zero), OLR index (near zero), 200-hPa zonal wind index (near zero), SOI and EQSOI (near zero) all indicate ENSO-neutral conditions. These indices do not show any significant trends that would support either additional large-scale increases or decreases of SST anomalies in equatorial Pacific.\n\nNear neutral conditions (Niño 3.4 SST anomalies between -0.5°C and +0.5°C) are forecast for the remainder of 2003 and early 2004. If the observed Nino 3.4 SST anomaly for October 2003 (+0.6°C) persists through November, the three-month (September-November) running mean value of this index would reach the threshold (+0.5°C) for El Niño. Thus, it is likely that borderline El Niño/ ENSO-neutral conditions will persist in the equatorial Pacific through the Northern Hemisphere winter of 2003-04. Further evolution of warm-episode conditions is possible if persistent enhanced equatorial convection (cloudiness and rainfall) develops in the vicinity of the date line (180°W), accompanied by weaker-than-average equatorial low-level easterly winds over the central and western Pacific.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7ee4e051dad5649d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65500:65619:1", + "start_date": "2003-11-01", + "end_date": "2003-11-30" + } + }, + { + "prompt": "The following data shows global data for 29 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65868:65983:1'}. The data starts from February 01 00:00 and ends on February 29 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Sea surface temperatures remained warmer than average in the central and western equatorial Pacific and near average in the eastern equatorial Pacific during January. Equatorial ocean surface temperatures greater than +0.5°C above average were found between Indonesia and 165°W, and departures greater than +1°C were found between 160°E and 175°W. Since early December 2003, SST anomalies have decreased in all of the Niño regions. The monthly 850-hPa zonal wind indices, OLR index, 200-hPa zonal wind index, SOI and EQSOI have not shown any significant trends over the last few months that would support a transition to either El Niño or La Niña. However, many of these indices have exhibited considerable week-to-week variability since late November in response to tropical intraseasonal (Madden-Julian Oscillation) activity. Wetter-than-average conditions (enhanced convection) over the tropical Indian Ocean in late November, shifted eastward to the western Pacific by late December and into the central Pacific by early January. As the convective activity shifted eastward, the equatorial easterlies weakened over the western and central Pacific and westerlies developed near the date line (180°W). During the last half of January the equatorial easterlies intensified, becoming stronger than average over the central and western equatorial Pacific, as the convectively inactive phase of the MJO shifted eastward over the region. During late January the convectively active phase of the MJO was over the Indian Ocean. At the current rate of propagation, enhanced convection should shift into the western and central equatorial Pacific during February, accompanied by another period of weaker-than-average easterlies.\n\nThe weakening of the equatorial easterlies in late December 2003-early January 2004 initiated an eastward propagating oceanic Kelvin wave. This Kelvin wave is propagating eastward at about 8-10 degrees of longitude per week. At that rate, the Kelvin wave is expected to reach the vicinity of the west coast of South America around the end of February.\n\nNear neutral conditions are forecast in the tropical Pacific (Niño 3.4 SST anomalies between -0.5°C and +0.5°C) through March 2004.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "14308b793132eb0b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65868:65983:1", + "start_date": "2004-02-01", + "end_date": "2004-02-29" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66228:66351:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected to continue during the next three months.\n\nFor the Pacific basin as a whole, oceanic and atmospheric conditions continue to reflect the neutral phase of the ENSO cycle. However, sea surface temperature anomalies in the equatorial Pacific increased during April 2004 in the Niño 3.4 and 4 regions, and decreased in the eastern Pacific (Niño 3 and 1+2 regions), as the equatorial cold tongue strengthened. By the end of the month, positive sea surface temperature anomalies greater than +0.5°C were observed in the region between Indonesia and 180°W, and negative anomalies (less than -2°C in some places) were observed between 120°W and the South American coast. Since January 2004 equatorial Pacific sea surface temperature anomalies have been largest in the western portion of the basin. This has resulted in an enhanced east-west gradient of sea surface temperature, which has been associated with stronger-than-average easterly winds over the central equatorial Pacific, enhanced precipitation over the western equatorial Pacific and a steeper-than-average thermocline slope in the central equatorial Pacific, as represented by positive (negative) subsurface temperature departures in the western (eastern) portion of the basin.\n\nENSO-neutral conditions will continue for the next 3 months (through July 2004).", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "63121b01d59d44ae", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66228:66351:1", + "start_date": "2004-05-01", + "end_date": "2004-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66596:66719:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: El Niño conditions are expected to develop during the next 3 months.\n\nSea surface temperature anomalies increased substantially in the central equatorial Pacific (Niño 3.4 region) during July 2004, while anomalies greater than +0.5°C persisted in the Niño 4 region. The recent increase and eastward expansion of positive SST anomalies in the central equatorial Pacific indicate the possible early stages of a warm episode. SST anomalies greater than +0.5°C (~1°F) were found between 160°E and 120°W, with anomalies greater than +1°C extending from 180°W eastward to 125°W. In spite of the anomalous warmth in the central equatorial Pacific during July, there appears to be little or no reflection of that warmth in the pattern of deep convection (precipitation) over the region.\n\nConsiderable intraseasonal variability (MJO activity) in recent months has resulted in week-to-week and month-to-month variability in many atmospheric and oceanic indices. During mid-June through early July the easterlies weakened in many areas of the equatorial Pacific, as enhanced convection shifted eastward from the Indian Ocean to the western tropical Pacific. The greatest wind and convection anomalies occurred north of the equator in the western Pacific, associated with two typhoons. By mid-July the low-level winds and equatorial convection returned to near average in many areas of the equatorial Pacific. However, a strong oceanic Kelvin wave, initiated by the weaker-than-average easterly winds in June, has propagated eastward resulting in a substantial deepening of the oceanic thermocline and an increase in the subsurface temperature anomalies in the central and east-central equatorial Pacific. This Kelvin wave is expected to reach the South American coast during August.\n\nThere is about a 50% chance that El Niño conditions will be satisfied for the period June-August 2004. It seems most likely that SST anomalies in the Niño 3.4 region will remain positive, at or above +0.5°C, through the end of 2004. At this time it is not clear what, if any, impacts this event will have on ocean temperatures in the classical El Niño region (Niño 1+2) along the west coast of South America.\n\nNear neutral conditions in the tropical Pacific (Niño 3.4 SST anomalies between -0.5°C and +0.5°C) are forecast through the end of 2004. El Niño conditions (Niño 3.4 SST anomalies greater than or equal to +0.5°C) are also forecast to develop within the next 3-6 months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b7a53f94ed3884a8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66596:66719:1", + "start_date": "2004-08-01", + "end_date": "2004-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66964:67083:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: Warm-episode conditions are expected to continue into early 2005.\nPositive sea surface temperature (SST) anomalies greater than +0.5°C (~1°F) persisted across most of the equatorial Pacific during October 2004. By early November, positive equatorial SST anomalies greater than +1°C (~2°F) were found from 160°E eastward to 150°W and locally in the area around 120°W. The increase and eastward expansion of the area of anomalous warmth in the central and east-central equatorial Pacific during July-October indicates the early stages of a warm (El Niño) episode.\nSince late 2003 MJO activity has resulted in week-to-week and month-to-month variability. In the past few months the warmth in the central equatorial Pacific has supported eastward shifts of enhanced convection associated with the convectively active phase of the Madden-Julian Oscillation (MJO) across the western equatorial Pacific. This activity has been associated with periods of weaker-than-average easterlies that initiated eastward-propagating oceanic Kelvin waves. This intraseasonal variability has been superposed on an upward trend in SST anomalies east of the date line and a gradual increase in the upper-ocean heat content during the last year. Warm episode (El Niño) conditions will persist through early 2005.\nExpected global impacts include drier-than-average conditions over Indonesia (through early 2005), northern and northeastern Australia (November 2004-February 2005), and southeastern Africa (November 2004-March 2005). If the warming in the tropical Pacific strengthens and spreads eastward to the South American coast, then wetter-than-average conditions would be expected in coastal sections of Ecuador and northern Peru during the first few months of 2005, and drier-than-average conditions would be expected to develop in the eastern Amazon late this year and spread to Northeast Brazil during February through April 2005.\nThe current warming in the tropical Pacific is expected to continue through the upcoming winter. Warmer-than-average conditions are expected in the West and in the northern Plains, while cooler and wetter-than-average conditions are expected for portions of the South and Southeast.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7ab90e50bcb6874f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66964:67083:1", + "start_date": "2004-11-01", + "end_date": "2004-11-30" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67332:67443:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: A transition from weak warm-episode (El Niño) conditions to ENSO-neutral conditions is expected during the next three months.\n\nSea surface temperature (SST) anomalies decreased in the equatorial Pacific everywhere east of the date line during January 2005. However, positive sea surface temperature (SST) anomalies greater than +1°C (~1.8°F) persisted in portions of the central and western equatorial Pacific. By early February 2005, positive equatorial SST anomalies greater than +0.5°C (~0.9°F) were found from 140°E eastward to 155°W. The pattern of anomalous warmth in the equatorial Pacific in recent months indicates that a weak warm (mid-Pacific El Niño) episode is in progress. However, through December there was a lack of persistent enhanced convection over the anomalously warm waters of the central equatorial Pacific, which limited El Niño-related impacts.\n\nThe MJO activity weakened considerably during early November 2004 and remained weak through mid-December. During the last half of December the MJO strengthened, as enhanced convection and precipitation over the Indian Ocean shifted eastward across Indonesia into the western tropical Pacific. Since early January enhanced convection has persisted in the western equatorial Pacific and expanded eastward into the central equatorial Pacific, accompanied by a weakening of the low-level easterly winds over the region. At this time it is not clear whether the recent enhanced convection and weakening of the easterly winds in the central equatorial Pacific are transient features (related to the MJO) or perhaps evidence of a coupling between the anomalously warm waters and the overlying atmospheric circulation.\n\nWeak warm episode (El Niño) conditions will gradually weaken during the next three months and ENSO-neutral conditions will prevail during the last half of 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "52b9368ff57c298d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67332:67443:1", + "start_date": "2005-02-01", + "end_date": "2005-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67688:67811:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected to prevail during the northern summer (June-August), in spite of recent increases in SST anomalies associated with strong Madden-Julian Oscillation (MJO) activity.\n\nSurface and subsurface water temperatures increased substantially in the eastern equatorial Pacific during April, associated with the arrival of the downwelling phase of a strong oceanic Kelvin wave. Sea surface temperature (SST) anomalies increased by more than 2°C in the extreme eastern equatorial Pacific during April, and by the end of the month, positive equatorial SST anomalies greater than +0.5°C (~0.9°F) were observed in most areas from Indonesia eastward to the South American coast. The increase in SST anomalies in the eastern equatorial Pacific during April was reflected by an increase in the SST anomalies in the Niño 3 and Niño 1+2 regions and by an increase in the upper-ocean heat content in the eastern half of the equatorial Pacific. Subsurface cooling and a decrease in upper-ocean heat content have been evident in the central equatorial Pacific, associated with the upwelling phase of the Kelvin wave. This cooling is expected to propagate eastward, reaching the eastern equatorial Pacific during May. Thus, the effects of the warming along the west coast of South America should be brief.\n\nCloudiness, precipitation and low-level winds displayed considerable week-to-week variability during the month, associated with strong MJO activity. During the first ten days of April enhanced precipitation was observed over Indonesia, while stronger-than-average easterlies prevailed over the central equatorial Pacific. The enhanced precipitation moved eastward into the western tropical Pacific during mid-April, accompanied by anomalous westerly low-level winds over the extreme western equatorial Pacific. However, during the last ten days of April the low-level wind anomalies weakened over the central equatorial Pacific and drier-than-average conditions developed over Indonesia. Continued strong week-to-week variability in the patterns of tropical atmospheric circulation and precipitation is likely during May.\n\nENSO-neutral conditions will prevail during the northern summer (June-August). The forecast becomes increasingly uncertain during the last half of 2005.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "e12508bc303d8cad", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67688:67811:1", + "start_date": "2005-05-01", + "end_date": "2005-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68056:68179:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected during the next 3-6 months.\nSea surface temperature (SST) anomalies decreased throughout the eastern equatorial Pacific during July. By early August, equatorial SSTs were near average in most areas between 180°W and the South American coast, while positive anomalies persisted between Indonesia and 180°W. The decrease in SST anomalies in the eastern equatorial Pacific during July was reflected by a decrease in the SST departures in the Niño 3, Niño 3.4, and Niño 4 regions. By the end of July the patterns of tropical convection, atmospheric circulation, SST and subsurface ocean temperatures were near average, indicating ENSO-neutral conditions.\nCurrent conditions and recent trends support the continuation of ENSO-neutral conditions for the next 3-6 months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "aa3b73b95898771d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68056:68179:1", + "start_date": "2005-08-01", + "end_date": "2005-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68424:68543:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral or weak La Niña conditions are likely during the next 6-9 months.\n\nBy the end of October, equatorial SST anomalies greater than +0.5ºC were found between Indonesia and 175ºW, while negative anomalies less than –0.5ºC were observed at most locations between 130ºW and the South American coast. The SST departures in the Niño 3, Niño 3.4, and Niño 1+2 regions were negative, while weak positive departures were observed in the Niño 4 region. During the last three months surface and subsurface temperature anomalies decreased, especially in the eastern equatorial Pacific, and the Southern Oscillation Index (SOI) increased. During the same period persistent stronger-than-average low-level equatorial easterly winds were observed over the central Pacific, while near-average patterns of convection and sea level pressure occurred over most of the tropical Pacific. The present oceanic and atmospheric anomalies are consistent with ENSO-neutral conditions in the tropical Pacific.\n\nCurrent conditions (stronger-than-average easterly winds over the central equatorial Pacific) and recent observed trends (decreasing SST anomalies throughout the central and eastern equatorial Pacific) do not support the development of El Niño. Rather, they support either a continuation of ENSO-neutral conditions or the development of weak La Niña conditions during the next 6-9 months.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c7b49a0e614fc1f2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68424:68543:1", + "start_date": "2005-11-01", + "end_date": "2005-11-30" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68792:68903:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: La Niña conditions are expected to continue during the next 3-6 months.\n\nThe patterns of anomalous ocean temperatures, atmospheric circulation and precipitation are consistent in indicating La Niña conditions in the tropical Pacific. During January negative equatorial SST anomalies less than –0.5ºC were observed at most locations between the date line and the South American coast, while anomalies greater than +0.5ºC were restricted to the region between Indonesia and 160ºE. Negative SST departures increased in magnitude in the Niño 4 and Niño 3.4 regions, as the oceanic cold tongue strengthened in the central equatorial Pacific. During January above-average precipitation was observed over Indonesia, the Philippines and northern Australia, while below-average precipitation was observed over the central equatorial Pacific. Stronger-than-average low-level (850-hPa) easterly winds persisted over the central equatorial Pacific, and anomalous upper-level (200-hPa) cyclonic circulation centers were observed in both hemispheres. These patterns are similar to those observed during previous La Niña episodes. Cooler conditions are forecasted for the tropical Pacific through mid-2006. Stronger-than-average easterly winds over the central equatorial Pacific and recent cooling trends in observed oceanic conditions support continuation of La Nina conditions in the tropical Pacific during the next 3-6 months.\n\nWetter-than-normal conditions are expected to prevail over Indonesia/Philippines and drier-than-normal conditions over the central equatorial Pacific during the remainder of the NH winter. That pattern of tropical precipitation favors a northward shift in the position of the jet stream over the eastern North Pacific during winter, which is usually accompanied by drier-than-normal conditions over southern California and Arizona. The recent patterns of anomalous temperature and precipitation for the United States are similar to wintertime patterns observed during previous La Niña episodes, except for temperature over the northern Plains and in the Pacific Northwest, which are normally colder than average.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "31c7d75318dd22cf", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68792:68903:1", + "start_date": "2006-02-01", + "end_date": "2006-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69148:69271:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected to prevail during the next 3-6 months.\nThe current patterns of anomalous ocean temperatures indicate a return to ENSO-neutral conditions in the tropical Pacific. During April SSTs were close to average at most locations between Indonesia and 90ºW. During the month, negative SST departures developed in the extreme eastern equatorial Pacific, which is a reversal from conditions observed during February-March.\nDuring April above-average precipitation was observed over portions of Indonesia and northern Australia, while below-average precipitation was observed over the central equatorial Pacific and the eastern tropical Pacific between the equator and 20ºN. Slightly stronger-than-average low-level easterly winds persisted over the central equatorial Pacific, and anomalous upper-level cyclonic circulation centers were observed in both hemispheres. Although these atmospheric features are lingering effects of La Nina, they are weaker than in previous months. Since February the basin-wide upper ocean heat content has increased, becoming slightly positive in April. Collectively, these atmospheric and oceanic features signal the demise of La Niña and a return to ENSO-neutral conditions.\nENSO-neutral conditions are predicted in the tropical Pacific through the end of 2006.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "843fdfe374b10f0d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69148:69271:1", + "start_date": "2006-05-01", + "end_date": "2006-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69516:69639:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected to continue for the next one to three months, with a 50% chance that weak El Niño conditions will develop by the end of 2006.\nEquatorial surface and subsurface temperature anomalies increased during July 2006, with SST anomalies greater than +0.5C observed in most of the equatorial Pacific between 130ºE and 140ºW. Positive SST anomalies were observed in all of the Niño regions. During July, low-level (850-hPa) easterly winds were weaker than average across most of the equatorial Pacific, and the Southern Oscillation Index (SOI) was negative for the third consecutive month. Beginning in February the basin-wide upper ocean heat content increased, and since early April positive anomalies have been observed. Forecasts range from ENSO-neutral to weak warm (El Niño) episode conditions for the remainder of 2006 and into early 2007. In the absence of any strong intraseasonal (Madden-Julian Oscillation – MJO) activity, a continued slow trend toward warm-episode conditions is expected.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "b46aebfb829553cd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69516:69639:1", + "start_date": "2006-08-01", + "end_date": "2006-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69884:70003:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: El Niño conditions are likely to continue into early 2007.\nEquatorial Pacific SST anomalies greater than +0.5ºC were observed in most of the equatorial Pacific, with anomalies exceeding +1.0ºC between 170ºE and 145ºW and between 130ºW and the South American coast. The latest SST departures in the Niño regions are all near +1.0. Beginning in February the basin-wide upper ocean heat content increased, and since early April positive anomalies have been observed. Since early July weaker-than-average low-level equatorial easterly winds have been observed across most of the equatorial Pacific. In October the Southern Oscillation Index (SOI) was negative for the sixth consecutive month. Collectively, these oceanic and atmospheric anomalies are consistent with the early stages of El Niño in the tropical Pacific.\nEl Niño conditions are predicted for the remainder of 2006 and into the spring of 2007.\nTypical El Niño effects are likely to develop over North America during the upcoming winter season, including warmer-than-average temperatures over western and central Canada, and over the western and northern United States, wetter-than-average conditions over portions of the U.S. Gulf Coast and Florida, and drier-than-average conditions in the Ohio Valley and the Pacific Northwest. Global effects that can be expected during November-March include drier-than-average conditions over most of Malaysia, Indonesia, some of the U.S.-affiliated islands in the tropical North Pacific, northern South America and southeastern Africa, and wetter-than-average conditions over equatorial East Africa, central South America (Uruguay, northeastern Argentina, and southern Brazil) and along the coasts of Ecuador and northern Peru.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "6178ac5585e82475", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69884:70003:1", + "start_date": "2006-11-01", + "end_date": "2006-11-30" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70252:70363:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: EL NIÑO/SOUTHERN OSCILLATION (ENSO)\n\nA transition from weak El Niño conditions to ENSO-neutral conditions is expected by March-May 2007.\n\nSST anomalies decreased across the entire equatorial Pacific during January. Positive anomalies between +0.5ºC and 1ºC remain in most of the equatorial Pacific between 170ºE and the South American coast. The latest SST departures in the Niño regions are around 0.5ºC. The equatorial upper-ocean heat content peaked in late November and has been decreasing rapidly since that time, with the latest values being negative for the first time since early April 2006. These trends in surface and subsurface ocean temperatures indicate that the warm episode (El Niño) is weakening. It is still possible for some areas to experience El Niño-related effects during the next month, primarily in the region of the central tropical Pacific. SST anomalies will continue to decrease and ENSO-neutral conditions are likely to develop during March-May 2007.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "efb31718cd51d2d7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70252:70363:1", + "start_date": "2007-02-01", + "end_date": "2007-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70608:70731:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: A transition from ENSO-neutral to La Niña conditions is possible within the next 2-3 months.\nThe pattern of anomalous sea surface temperatures (SSTs) during April 2007 was consistent with ENSO-neutral conditions in the tropical Pacific, with average to slightly below-average SSTs extending from the date line to the west coast of South America. The latest weekly SST departures in the Niño regions are -1.2ºC in Niño 1+2, -0.3ºC in the Niño 3, zero in Niño 3.4, and +0.1ºC in Niño 4.\nThe upper-ocean heat content remained below average across the central and east-central equatorial Pacific, with temperatures at thermocline depth generally 2°-5°C below average. Consistent with the surface and sub-surface ocean temperature patterns, stronger than-average low-level easterly winds persisted over the central equatorial Pacific. Also, convection was enhanced over the western equatorial Pacific and suppressed east of the date line. Collectively, these atmospheric and oceanic conditions continue to indicate the possibility that La Niña conditions will develop over the next 2-3 months.\nForecasts indicate below-average SSTs during the next several months. A rapid transition to La Niña is predicted during May-July 2007.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "698c5a829341d413", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70608:70731:1", + "start_date": "2007-05-01", + "end_date": "2007-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70976:71099:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected to continue through August 2007, with a slightly greater than 50% chance of La Niña developing during the next couple of months.\n\nENSO-neutral conditions continued in the tropical Pacific during July 2007, with average to below-average sea surface temperatures (SSTs) extending from the date line to the west coast of South America. The latest weekly SST departures remained negative in the Niño 1+2 (−1.7ºC), Niño 3 (−1.2 ºC), and Niño 3.4 (−0.5 ºC) regions, and positive in the Niño 4 (+0.2ºC) region. While SSTs in the eastern equatorial Pacific have been cooler than average for the last six months, the departures continue to fall short of the threshold for La Niña.\n\nRecent atmospheric circulation and tropical convection patterns are consistent with the evolution toward La Niña conditions. The low-level easterly winds remained stronger than average in the west-central equatorial Pacific, convection remained suppressed across most of the equatorial Pacific, and a weak area of enhanced convection covered parts of Indonesia and the far western equatorial Pacific. Also, the upper-ocean heat content in the central and east-central equatorial Pacific remained below-average, but the magnitude of the departures continued to exhibit intraseasonal fluctuations. Collectively, the oceanic and atmospheric conditions reflect a continuation of ENSO-neutral conditions.\n\nBelow-average SSTs are predicted in the Niño 3.4 region for the remainder of the year. Forecasts range from ENSO-neutral to La Niña, with an indication of a more immediate transition to La Niña. A continuation of ENSO-neutral conditions is also indicated, but some forecasts show weak La Niña conditions during the fall or winter. Recent atmospheric conditions suggest a slightly greater than 50% chance of La Niña developing during the next couple of months. Historically, the early fall season (August-September-October) has been a critical period for the onset of La Niña events.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "164423b66466fc24", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70976:71099:1", + "start_date": "2007-08-01", + "end_date": "2007-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71344:71463:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: La Niña will likely continue into early 2008.\nLa Niña continued to strengthen during October 2007, as equatorial sea surface temperature (SST) anomalies became increasingly negative from 170ºE to the South American coast. The largest SST departures (−2ºC to −3ºC) are located between 140ºW and the South American coast, with departures of −0.5°C to −1°C observed near the Date Line. All of the Niño region indices, except for Niño-4, remained lower than −1.0°C indicating that La Niña is approaching moderate-strength.\nDuring October, the upper-ocean heat content in the central and east-central equatorial Pacific remained below average, with temperatures ranging from 2°C to 6°C below average at thermocline depth. The low-level easterly winds and upper-level westerly winds remained stronger than average across the central equatorial Pacific, convection remained suppressed throughout the central and eastern equatorial Pacific, and an area of slightly enhanced convection covered parts of the far western Pacific.\nSST forecasts for the Niño 3.4 region indicate a continuation of La Niña into early 2008. At least a moderate La Niña is forecast through December, followed by gradual weakening thereafter.\nExpected La Niña impacts during November – January include a continuation of above-average precipitation over Indonesia and below-average precipitation over the central equatorial Pacific. For the contiguous United States, potential impacts include above average precipitation in the Northern Rockies, Northern California, and in southern and eastern regions of the Pacific Northwest. Below-average precipitation is expected across the southern tier, particularly in the southwestern and southeastern states.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "03519c2b5dd5f210", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71344:71463:1", + "start_date": "2007-11-01", + "end_date": "2007-11-30" + } + }, + { + "prompt": "The following data shows global data for 29 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71712:71827:1'}. The data starts from February 01 00:00 and ends on February 29 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: La Niña is expected to continue through the Northern Hemisphere spring 2008.\nCurrent atmospheric and oceanic conditions indicate that La Niña has continued to strengthen in the tropical Pacific. By the end of January 2008, equatorial SST anomalies were more than 2.0°C below average across parts of the central and east-central equatorial Pacific. Other than the far eastern Niño-1+2 region, the magnitude of the cold anomalies in the Niño region indices increased during the past month with the latest weekly values near −1.5°C. The upper-ocean heat content also decreased further during January, and negative subsurface anomalies between −2°C to −5°C expanded westward towards the Date Line. Consistent with these oceanic conditions, stronger-than-average low-level easterly and upper-level westerly winds persisted across the central equatorial Pacific, convection remained suppressed throughout the central equatorial Pacific, and enhanced convection covered the far western Pacific. Collectively, these oceanic and atmospheric conditions are similar to those accompanying the last strong La Niña episode in 1998-2000.\nA moderate-to-strong La Niña is forecast through the rest of the Northern Hemisphere winter, with the likely continuation of a weaker La Niña through April-May-June. Thereafter, La Niña could continue well into the Northern Hemisphere summer. Current atmospheric and oceanic conditions and recent trends are consistent with the likely continuation of La Niña through the Northern Hemisphere spring 2008.\nExpected La Niña impacts during February-April include a continuation of above-average precipitation over Indonesia and below-average precipitation over the central equatorial Pacific. For the contiguous United States, potential impacts include above-average precipitation in the Northern Rockies, the Pacific Northwest, and the Ohio and Tennessee Valleys. Below-average precipitation is expected across the South, particularly in the southeastern states.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "2d6d19b225f70f2e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71712:71827:1", + "start_date": "2008-02-01", + "end_date": "2008-02-29" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72072:72195:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: A transition from La Niña to ENSO-neutral conditions is possible during June-July 2008. \n\nLa Niña continued to weaken during April 2008. Negative SST anomalies in the central and east-central equatorial Pacific have weakened, while positive SST anomalies are confined to parts of the eastern equatorial Pacific. The latest weekly SSTs in the westernmost Niño-4 and Niño-3.4 regions are between 0.6°C and 0.8°C below average, while departures in the easternmost Niño-3 and Niño-1+2 regions are 0°C and −0.3°C respectively. \n\nPositive subsurface ocean temperatures at thermocline depth have continued to increase in central and east-central equatorial Pacific. While this increase has resulted in positive heat content anomalies, a shallow layer of negative anomalies in the central Pacific continues to persist between the surface and 100m. SSTs remain sufficiently cool to maintain the persistent atmospheric anomalies associated with La Niña. Enhanced low-level easterly winds and upper-level westerly winds continued across the central equatorial Pacific, convection remained suppressed throughout the central equatorial Pacific, and enhanced convection covered the far western Pacific. Collectively, these atmospheric and oceanic conditions indicate an ongoing La Niña. \n\nLa Niña will persist through May-June-July 2008. Thereafter, ENSO-neutral conditions are expected during the second half of the year. However, a return to La Niña or even an El Niño by the end of 2008 is possible. \n\nExpected La Niña impacts during May-July 2008 include a continuation of above-average precipitation over Indonesia and below-average precipitation over the central equatorial Pacific.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "48a7c4e1615cee4f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72072:72195:1", + "start_date": "2008-05-01", + "end_date": "2008-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72440:72563:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected to continue through the Northern Hemisphere Fall 2008.\n\nENSO-neutral conditions continued during July 2008, as sea surface temperatures (SSTs) in the central equatorial Pacific Ocean remained near-average. Atmospheric and oceanic indicators were mixed, with certain areas in the equatorial Pacific Ocean suggesting a lingering influence of La Niña and others reflecting an increase in above-average temperatures, particularly in the eastern Pacific.\n\nFrom west to east, the latest weekly SST index values range from −0.3°C in the Niño-4 region to +0.9°C in the Niño 1+2 region. The subsurface oceanic heat content has also increased in response to positive temperature anomalies along the thermocline. However, a weak, shallow region of below-average temperatures still remains near the International Date Line.\n\nThe atmospheric circulation over the western and central tropical Pacific continues to reflect some aspects of La Niña. Enhanced low-level easterly winds and upper-level westerly winds persist in this region, while convection remains generally suppressed over the central Pacific. In contrast, the eastern equatorial Pacific features weak-to-average low-level easterly winds and average precipitation. Despite recent increases in SST anomalies, the actual SSTs are not warm enough to support convection.\n\nENSO-neutral conditions will continue into the Northern Hemisphere Spring 2009. However, due to the positive heat content anomalies in the Pacific Ocean, the development of El Niño cannot be ruled out during the later part of the year, although chances remain low. ENSO-neutral conditions are expected to continue through the Northern Hemisphere Fall 2008.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "8ae06d93822aa10b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72440:72563:1", + "start_date": "2008-08-01", + "end_date": "2008-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72808:72927:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected to continue into early 2009.\n\nENSO-neutral conditions continued during October 2008, as equatorial sea surface temperatures (SSTs) were near-average across much of the Pacific Ocean, except for small areas of below-average SSTs in the east-central Pacific and off the coast of South America. The latest weekly SST index values were near-average in all Niño regions except for Niño-1+2 (−0.8°C). Subsurface oceanic heat content anomalies became less negative due to the eastward shift of positive temperature anomalies at thermocline depth to ~160°W, but anomalies remained negative in the eastern half of the Pacific.\n\nThe atmospheric winds and convection patterns exhibited a high degree of week-to-week variability across the tropical Pacific during October in response to the Madden-Julian Oscillation (MJO). The cumulative effects of the MJO were above-average convection over Indonesia, and enhanced low-level easterly winds, enhanced upper-level westerly winds, and suppressed convection over the western equatorial Pacific. Overall, the ocean-atmosphere system remains consistent with ENSO-neutral conditions.\n\nENSO-neutral conditions (−0.5°C to 0.5°C in the Niño-3.4 region) are forecast to continue into the first half of 2009. A La Niña may develop during Northern Hemisphere Winter 2008-09. This outcome becomes more likely if the current MJO were to stall in a location that favors enhanced low-level easterlies and increased upwelling in the east-central and eastern Pacific. However, it is rare for La Niña to develop late in the year.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7d865fe988d83d32", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72808:72927:1", + "start_date": "2008-11-01", + "end_date": "2008-11-30" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73176:73287:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: La Niña is expected to continue into Northern Hemisphere Spring 2009.\nLa Niña continued during January 2009, with below-average equatorial sea surface temperatures across the central and east-central Pacific Ocean. Negative subsurface oceanic heat content anomalies persisted east of the International Date Line, but weakened as positive subsurface temperature anomalies from the western Pacific expanded eastward into the central Pacific. Convection remained suppressed near the Date Line, and enhanced across Indonesia. Low-level easterly winds and upper-level westerly winds also continued across the equatorial Pacific Ocean.\nA gradual weakening of La Niña is forecast through February-April 2009, with an eventual transition to ENSO-neutral conditions.\nExpected La Niña impacts during February-April 2009 include above-average precipitation over Indonesia, and below-average precipitation over the central equatorial Pacific. For the contiguous United States, potential impacts include above-average precipitation in the Ohio and Tennessee Valleys and below-average precipitation in the southwestern and southeastern states. Other potential impacts include below-average temperatures in the Pacific Northwest and above-average temperatures across much of the southern United States.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "f18d0eea9b053cb9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73176:73287:1", + "start_date": "2009-02-01", + "end_date": "2009-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73532:73655:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO-neutral conditions are expected to continue into the Northern Hemisphere Summer.\n\nDuring April 2009, the equatorial Pacific Ocean transitioned from La Niña to ENSO-neutral conditions, ending the 2008-09 La Niña. Negative sea surface temperature (SST) anomalies weakened across the equatorial Pacific Ocean and positive anomalies developed in areas of the eastern Pacific. The latest weekly SST indices were near zero in all Niño regions, except for the easternmost Niño-1+2 region. Subsurface oceanic heat content anomalies became positive for the first time since mid-August 2008, reflecting an eastward spread of above-average temperatures near thermocline depth.\n\nAtmospheric anomalies consistent with La Niña weakened during April, with enhanced convection decreasing over Indonesia, although convection remained suppressed near the International Date Line. Also, Madden Julian Oscillation (MJO) activity strongly influenced the atmospheric circulation across the global tropics, and contributed to the periodic fluctuation in the strength of the low-level easterly winds and upper-level westerly winds over the equatorial Pacific Ocean. Collectively, these oceanic and atmospheric anomalies are consistent with a transition to ENSO-neutral conditions.\n\nENSO-neutral conditions are forecast to continue through the remainder of 2009. Above-average temperatures are increasingly favored in the Niño-3.4 region, while below- or near-average temperatures are also predicted.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "c329432aeb74afe0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73532:73655:1", + "start_date": "2009-05-01", + "end_date": "2009-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73900:74023:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: El Niño is expected to strengthen and last through the Northern Hemisphere Winter 2009-2010.\n\nA weak El Niño was present during July 2009, as monthly sea surface temperatures (SST) departures ranged from +0.5°C to +1.5°C across the equatorial Pacific Ocean, with the largest anomalies in the eastern half of the basin. All of the Niño-region SST indices were between +0.6°C to +1.0°C throughout the month. Subsurface oceanic heat content anomalies reflected a deep layer of anomalous warmth between the ocean surface and thermocline. Convection was suppressed over Indonesia and enhanced across the western Pacific and near the International Date Line. A westerly wind burst occurred over the western equatorial Pacific at the end of July.\n\nEl Niño will continue to strengthen. A weak-to-moderate strength El Niño is expected to continue developing into the Northern Hemisphere Fall 2009. A moderate-to-strong El Niño is predicted during the Northern Hemisphere Winter 2009-10.\n\nExpected El Niño impacts during August-October 2009 include enhanced precipitation over the central and west-central Pacific Ocean and the continuation of drier than average conditions over Indonesia. Temperature and precipitation impacts over the United States are typically weak during the Northern Hemisphere Summer and early Fall, and generally strengthen during the late Fall and Winter. El Niño can help to suppress Atlantic hurricane activity by increasing the vertical wind shear over the Caribbean Sea and tropical Atlantic Ocean.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "8d86a233dfae23da", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73900:74023:1", + "start_date": "2009-08-01", + "end_date": "2009-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74268:74387:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: El Niño is expected to continue strengthening and last through at least the Northern Hemisphere winter 2009-2010. \n \nDuring October 2009, sea surface temperature (SST) anomalies increased across the central and eastern equatorial Pacific Ocean. The Niño-3.4 index increased nearly a degree with the most recent weekly value at +1.5°C. Above-average subsurface temperature anomalies increased across a large region of the central and east-central Pacific, with anomalies ranging between +1 to +5°C by the end of the month. Subsurface oceanic heat content anomalies also increased during the month. In addition, low-level westerly and upper-level easterly wind anomalies strengthened over much of the equatorial Pacific. The pattern of tropical convection also remained consistent with El Niño, with enhanced convection over the west-central Pacific and suppressed convection over Indonesia. Collectively, these oceanic and atmospheric anomalies reflect a strengthening El Niño. \n\nThe three-month average Niño-3.4 SST index value will range between +1.0°C and +1.5°C during the Northern Hemisphere winter. A peak in SST anomalies is expected sometime during November-January. This event will last through March-May 2010. The most likely outcome is that El Niño will peak at least at moderate strength and last through at least the Northern Hemisphere winter 2009-10. \n\nExpected El Niño impacts during November 2009-January 2010 include enhanced precipitation over the central tropical Pacific Ocean and a continuation of drier-than-average conditions over Indonesia. For the contiguous United States, potential impacts include above-average precipitation for Florida, central and eastern Texas, and California, with below-average precipitation for parts of the Pacific Northwest. Above-average temperatures and below-average snowfall is most likely for the Northern Rockies, Northern Plains, and Upper Midwest, while below-average temperatures are expected for the southeastern states.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "d8a69cbcd648a940", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74268:74387:1", + "start_date": "2009-11-01", + "end_date": "2009-11-30" + } + }, + { + "prompt": "The following data shows global data for 28 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74636:74747:1'}. The data starts from February 01 00:00 and ends on February 28 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: El Niño is expected to continue at least into the Northern Hemisphere spring 2010.\nA significant El Niño persisted throughout the equatorial Pacific Ocean during January 2010. Although sea surface temperature (SST) departures in the Niño-3.4 region decreased to +1.2°C in late January, SSTs continued to be sufficiently warm to support deep tropical convection. Equatorial convection over the central Pacific remained enhanced during the month, while convection over Indonesia exhibited considerable week-to-week variability. Low-level westerly and upper-level easterly wind anomalies generally prevailed during January.\nDecreasing SST anomalies in the Niño-3.4 region are predicted through 2010. The 3-month Niño-3.4 SST anomaly will drop below +0.5°C around April-May-June 2010, indicating a transition to ENSO-neutral conditions during Northern Hemisphere spring.\nEl Niño impacts are expected to last into the Northern Hemisphere spring, even as equatorial SST departures decrease. Expected impacts during February-April 2010 include drier-than-average conditions over Indonesia and enhanced convection over the central equatorial Pacific Ocean, which will likely expand eastward and influence portions of the eastern tropical Pacific, as well as coastal sections of Peru and Ecuador. For the contiguous United States, potential El Niño impacts include above-average precipitation for the southern tier of the country, with below-average precipitation in the Pacific Northwest and Ohio Valley. Below-average snowfall and above-average temperatures are most likely across the northern tier of states (excluding New England), while below-average temperatures are favored for the south-central and southeastern states.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "f0a9ceb6c82b1c71", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74636:74747:1", + "start_date": "2010-02-01", + "end_date": "2010-02-28" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74992:75115:1'}. The data starts from May 01 00:00 and ends on May 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: EL NIÑO/SOUTHERN OSCILLATION (ENSO) DIAGNOSTIC DISCUSSION\n6 May 2010\n\nENSO Alert System Status: El Niño Advisory\n\nSynopsis: A transition to ENSO-neutral conditions is expected by June 2010, which will continue into the Northern Hemisphere summer 2010.\n\nEl Niño weakened during April 2010 as positive surface temperature (SST) anomalies decreased across the equatorial Pacific Ocean. However, SST anomalies still exceeded +0.5°C across most of the Pacific at the end of the month. Since the end of February, subsurface heat content anomalies have decreased steadily in association with the expansion and strengthening of below-average temperatures at depth (25-200m). Also, enhanced convection developed over Indonesia, while suppressed convection strengthened and expanded over the tropical Pacific, south of the equator. The low-level equatorial trade winds remained near-average, and anomalous upper-level westerly winds prevailed over the central Pacific during much of April. Collectively, these oceanic and atmospheric anomalies reflect a weakening El Niño.\n\nDecreasing SST anomalies in the Niño-3.4 region are predicted through the Northern Hemisphere summer 2010. A transition to ENSO-neutral conditions is predicted during April-June 2010, followed by ENSO-neutral conditions through the end of the year. However, by July-September 2010, there is a possibility of the onset of La Niña conditions. ENSO-neutral conditions are most likely during the second half of the year, but there is a tendency toward increasingly negative SST anomalies in the Niño-3.4 region. There is a growing possibility of La Niña developing during the second half of 2010.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "eddbdd563efb8114", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74992:75115:1", + "start_date": "2010-05-01", + "end_date": "2010-05-31" + } + }, + { + "prompt": "The following data shows global data for 31 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75360:75483:1'}. The data starts from August 01 00:00 and ends on August 31 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: ENSO Alert System Status: La Niña Advisory\n\nLa Niña conditions are expected to strengthen and last through the Northern Hemisphere winter 2010-11.\n\nDuring July 2010 La Niña conditions developed, as negative sea surface temperature (SST) anomalies strengthened across the central and eastern equatorial Pacific Ocean. All of the Niño indices decreased with values less than -1.0°C in Niño 1+2, 3, and 3.4 regions at the end of the month. The subsurface heat content continued to reflect a deep layer of below-average temperatures east of the Date Line. Also convection was enhanced over Indonesia, while remaining suppressed over the western and central tropical Pacific. Enhanced low-level easterly trade winds and anomalous upper-level westerly winds continued over the western and central equatorial Pacific. Collectively, these oceanic and atmospheric anomalies reflect the development and strengthening of La Niña conditions.\n\nLa Niña is predicted to continue through early 2011. There is disagreement over the eventual strength of La Niña. A moderate-to-strong La Niña is predicted, while some forecasts indicate a weaker episode. Given the strong cooling observed over the last several months and the apparent ocean-atmosphere coupling, a moderate-to-strong episode is favored at this time. Therefore, La Niña conditions are expected to strengthen and last through Northern Hemisphere Winter 2010-11.\n\nExpected La Niña impacts during August-October 2010 include suppressed convection over the central tropical Pacific Ocean, and enhanced convection over Indonesia. Temperature and precipitation impacts over the United States are typically weak during the Northern Hemisphere summer and early fall, but strengthen considerably during late fall and winter. Also, La Niña can contribute to increased Atlantic hurricane activity by decreasing the vertical wind shear over the Caribbean Sea and tropical Atlantic Ocean.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "7356f22ed9821141", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75360:75483:1", + "start_date": "2010-08-01", + "end_date": "2010-08-31" + } + }, + { + "prompt": "The following data shows global data for 30 days, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75728:75847:1'}. The data starts from November 01 00:00 and ends on November 30 18:00. Given the data, answer the following question in a detailed report.", + "question": "Generate an ENSO (El Niño Southern Oscillation) climate update and outlook based on the provided atmospheric data. Analyze pressure patterns, temperature distributions, and circulation features that indicate current ENSO state and provide a forecast outlook. Your update should include: (1) assessment of current ENSO conditions (El Niño, La Niña, or neutral), (2) strength and persistence indicators, (3) expected evolution over the next 3-6 months, (4) confidence levels in the forecast, and (5) implications for global climate patterns. Use standard ENSO terminology and probability-based language. If the provided data lacks sufficient Pacific Ocean coverage for full ENSO analysis, focus on global atmospheric patterns consistent with known ENSO signatures.", + "response": "The ENSO update and outlook is: La Niña Advisory\n\nLa Niña is expected to last at least into the Northern Hemisphere spring 2011.\n\nLa Niña continued during October 2010, with below-average sea surface temperatures (SSTs) across most of the equatorial Pacific Ocean. The weekly Niño SST index values remained nearly unchanged, and were all –1.4°C at the end of the month. The subsurface heat content also changed little during October, and remained well below-average with a shallower-than-average thermocline across the central and eastern Pacific. Convection remained enhanced over Indonesia and suppressed over the western and central equatorial Pacific. This pattern was linked to a continuation of enhanced low-level easterly trade winds and anomalous upper-level westerly winds over the western and central equatorial Pacific.\n\nLa Niña is predicted to become a strong episode by the November-January season before gradually weakening. La Niña could persist into the Northern Hemisphere summer 2011.\n\nLikely La Niña impacts during November 2010-January 2011 include suppressed convection over the central tropical Pacific Ocean, and enhanced convection over Indonesia. Expected impacts in the United States include an enhanced chance of above-average precipitation in the Pacific Northwest, Northern Rockies (along with a concomitant increase in snowfall), and Ohio Valley, while below-average precipitation is most likely across the south-central and southeastern states. An increased chance of below-average temperatures is predicted for coastal and near-coastal regions of the northern West Coast, and a higher possibility of above-average temperatures is expected for much of the southern and central U.S.", + "metadata": { + "prompt_id": "jFhdSJ", + "question_id": "E5kiNs", + "level": "2c", + "eval_type": "discussion", + "forced_extreme_window": false, + "task_id": "fb86e54f935bf473", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75728:75847:1", + "start_date": "2010-11-01", + "end_date": "2010-11-30" + } + } +] \ No newline at end of file diff --git a/level2d_part0.json b/level2d_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..81964e4a0bc3006ce91b800eb1e3cd2c95aa67d2 --- /dev/null +++ b/level2d_part0.json @@ -0,0 +1,2462 @@ +[ + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70087:70091:1', 'start_idx': 70087}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A consolidated zonal flow across the lower 48 will result in a predominantly stable and quiet weather pattern over the next few days.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A pronounced split flow across the lower 48 will make for an active weather pattern the next few days.", + "final_claim": "A consolidated zonal flow across the lower 48 will result in a predominantly stable and quiet weather pattern over the next few days.", + "expected_answer": "False", + "date": "2006-12-22", + "met_entry_idx": 13668, + "true_value": "False", + "mode": "boolean", + "time_indices": "70087:70091:1", + "start_idx": 70087, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "189fd049674efd1a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70087:70091:1", + "start_idx": 70087 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68175:68179:1', 'start_idx': 68175}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The frontal boundary extending southward from the remnants of Katrina will stall north of the Gulf Coast region, resulting in dry air intrusion and suppressed convection along the Gulf Coast and in Florida.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The frontal boundary extending southward from the remnants of Katrina will move southward to the Gulf Coast region, keeping deep moisture and daily diurnal convection along the Gulf Coast and in Florida.", + "final_claim": "The frontal boundary extending southward from the remnants of Katrina will stall north of the Gulf Coast region, resulting in dry air intrusion and suppressed convection along the Gulf Coast and in Florida.", + "expected_answer": "False", + "date": "2005-08-31", + "met_entry_idx": 12719, + "true_value": "False", + "mode": "boolean", + "time_indices": "68175:68179:1", + "start_idx": 68175, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3536ea3256ea1bdc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68175:68179:1", + "start_idx": 68175 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51749:51753:1', 'start_idx': 51749}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Some precipitation will occur across the northwestern border states from these height falls, with the most organized convection developing in a warm advection zone over eastern portions of the Dakotas this evening.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Some precipitation will occur across the northwestern border states from these height falls, with the most organized convection developing in a warm advection zone over eastern portions of the Dakotas this evening.", + "final_claim": "Some precipitation will occur across the northwestern border states from these height falls, with the most organized convection developing in a warm advection zone over eastern portions of the Dakotas this evening.", + "expected_answer": "True", + "date": "1994-06-04", + "met_entry_idx": 4548, + "true_value": "True", + "mode": "boolean", + "time_indices": "51749:51753:1", + "start_idx": 51749, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b0ae3dc44dde4d50", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51749:51753:1", + "start_idx": 51749 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77275:77279:1', 'start_idx': 77275}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Temperatures will be 15 to 20 degrees below average for the Northern/Central Plains and the Upper/Middle Mississippi Valley.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Temperatures will be 15 to 20 degrees above average for the Northern/Central Plains and the Upper/Middle Mississippi Valley.", + "final_claim": "Temperatures will be 15 to 20 degrees below average for the Northern/Central Plains and the Upper/Middle Mississippi Valley.", + "expected_answer": "False", + "date": "2011-11-23", + "met_entry_idx": 17084, + "true_value": "False", + "mode": "boolean", + "time_indices": "77275:77279:1", + "start_idx": 77275, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "785605bf3de5a270", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77275:77279:1", + "start_idx": 77275 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72443:72447:1', 'start_idx': 72443}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A deep upper level trough over the center of the country will result in below-normal temperatures across the central and southern Plains and portions of the lower and middle Mississippi Valleys, preventing highs from reaching 100 Fahrenheit.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A large upper level ridge over the center of the country will keep high temperatures across the central and southern Plains and portions of the lower and middle Mississippi Valleys above 100 Fahrenheit.", + "final_claim": "A deep upper level trough over the center of the country will result in below-normal temperatures across the central and southern Plains and portions of the lower and middle Mississippi Valleys, preventing highs from reaching 100 Fahrenheit.", + "expected_answer": "False", + "date": "2008-08-02", + "met_entry_idx": 14839, + "true_value": "False", + "mode": "boolean", + "time_indices": "72443:72447:1", + "start_idx": 72443, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b7f68879bab0e23b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72443:72447:1", + "start_idx": 72443 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51789:51793:1', 'start_idx': 51789}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The northern portion of an unseasonably strong Pacific warm front will advance inland to the Upper Midwest and north-central Plains within 24 hours, then dissipate as supporting dynamics weaken over the region.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The northern portion of an unseasonably strong Pacific cold front will push inland to the Upper Midwest and north-central Plains within 24 hours, then stall as supporting dynamics lift into Canada.", + "final_claim": "The northern portion of an unseasonably strong Pacific warm front will advance inland to the Upper Midwest and north-central Plains within 24 hours, then dissipate as supporting dynamics weaken over the region.", + "expected_answer": "False", + "date": "1994-06-14", + "met_entry_idx": 4568, + "true_value": "False", + "mode": "boolean", + "time_indices": "51789:51793:1", + "start_idx": 51789, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "44caa795b855e3a4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51789:51793:1", + "start_idx": 51789 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89841:89845:1', 'start_idx': 89841}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry conditions are expected to persist in the higher elevations of the Northern Rockies, with no additional precipitation anticipated.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Snow is also forecast to continue in higher elevations of the Northern Rockies, with a few additional inches possible.", + "final_claim": "Dry conditions are expected to persist in the higher elevations of the Northern Rockies, with no additional precipitation anticipated.", + "expected_answer": "False", + "date": "2020-06-30", + "met_entry_idx": 23132, + "true_value": "False", + "mode": "boolean", + "time_indices": "89841:89845:1", + "start_idx": 89841, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "7caae4055406143d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89841:89845:1", + "start_idx": 89841 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72235:72239:1', 'start_idx': 72235}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A high-pressure system over the north central part of the country results in dry and stable conditions with clear skies and calm winds along and ahead of the cold front from the central Plains to the upper Mississippi Valley.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A storm over the north central part of the country brings heavy rain and thunderstorms developing along and ahead of the cold front from the central Plains to the upper Mississippi Valley.", + "final_claim": "A high-pressure system over the north central part of the country results in dry and stable conditions with clear skies and calm winds along and ahead of the cold front from the central Plains to the upper Mississippi Valley.", + "expected_answer": "False", + "date": "2008-06-11", + "met_entry_idx": 14736, + "true_value": "False", + "mode": "boolean", + "time_indices": "72235:72239:1", + "start_idx": 72235, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b9af0f9f72632cba", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72235:72239:1", + "start_idx": 72235 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48301:48305:1', 'start_idx': 48301}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Strong surface development is moving northeast through New England with moderate to heavy precipitation ending early as a cold front sweeps east, pushing the warm air and dynamics offshore.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Strong surface development is moving northeast through New England with moderate to heavy precipitation ending early as a cold front sweeps east, pushing the warm air and dynamics offshore.", + "final_claim": "Strong surface development is moving northeast through New England with moderate to heavy precipitation ending early as a cold front sweeps east, pushing the warm air and dynamics offshore.", + "expected_answer": "True", + "date": "1992-01-24", + "met_entry_idx": 2827, + "true_value": "True", + "mode": "boolean", + "time_indices": "48301:48305:1", + "start_idx": 48301, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e3ff3a9ece133504", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48301:48305:1", + "start_idx": 48301 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66505:66509:1', 'start_idx': 66505}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: An upper-level ridge near the Washington/British Columbia border will result in dry and stable conditions across the Pacific Northwest as it shifts northeastward.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A closed upper-level low near the Washington/British Columbia border will bring scattered showers into the Pacific Northwest as it moves northeastward.", + "final_claim": "An upper-level ridge near the Washington/British Columbia border will result in dry and stable conditions across the Pacific Northwest as it shifts northeastward.", + "expected_answer": "False", + "date": "2004-07-10", + "met_entry_idx": 11891, + "true_value": "False", + "mode": "boolean", + "time_indices": "66505:66509:1", + "start_idx": 66505, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4fa6821545aef445", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66505:66509:1", + "start_idx": 66505 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48549:48553:1', 'start_idx': 48549}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Broken showers are also likely across the southern third of California as the center of the upper low tracks along the southern border of the state over the next 24 hours.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Broken showers are also likely across the southern third of California as the center of the upper low tracks along the southern border of the state over the next 24 hours.", + "final_claim": "Broken showers are also likely across the southern third of California as the center of the upper low tracks along the southern border of the state over the next 24 hours.", + "expected_answer": "True", + "date": "1992-03-26", + "met_entry_idx": 2950, + "true_value": "True", + "mode": "boolean", + "time_indices": "48549:48553:1", + "start_idx": 48549, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b2321293a5a2802a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48549:48553:1", + "start_idx": 48549 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52004:52008:1', 'start_idx': 52004}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The mean upper flow pattern over North America will persist through the period.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The mean upper flow pattern over North America will persist through the period.", + "final_claim": "The mean upper flow pattern over North America will persist through the period.", + "expected_answer": "True", + "date": "1994-08-07", + "met_entry_idx": 4674, + "true_value": "True", + "mode": "boolean", + "time_indices": "52004:52008:1", + "start_idx": 52004, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "807c3f60e8f7bf26", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52004:52008:1", + "start_idx": 52004 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89379:89383:1', 'start_idx': 89379}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and mild conditions are expected for the Central Appalachians, Lower Great Lakes, and the Cascades/Sierra Nevada Mountains.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Snow is forecast for the Central Appalachians, Lower Great Lakes, and the Cascades/Sierra Nevada Mountains.", + "final_claim": "Dry and mild conditions are expected for the Central Appalachians, Lower Great Lakes, and the Cascades/Sierra Nevada Mountains.", + "expected_answer": "False", + "date": "2020-03-06", + "met_entry_idx": 22935, + "true_value": "False", + "mode": "boolean", + "time_indices": "89379:89383:1", + "start_idx": 89379, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "07fe8baeabad033f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89379:89383:1", + "start_idx": 89379 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73877:73881:1', 'start_idx': 73877}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A broad, anomalously strong upper level ridge will continue to dominate the pattern across the lower 48 through the short term.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A broad, anomalously deep upper level trough will continue to dominate the pattern across the lower 48 through the short term.", + "final_claim": "A broad, anomalously strong upper level ridge will continue to dominate the pattern across the lower 48 through the short term.", + "expected_answer": "False", + "date": "2009-07-27", + "met_entry_idx": 15555, + "true_value": "False", + "mode": "boolean", + "time_indices": "73877:73881:1", + "start_idx": 73877, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "8be5a5959f421295", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73877:73881:1", + "start_idx": 73877 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75785:75789:1', 'start_idx': 75785}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Light to moderate showers and thunderstorms will also develop along the central portion of the Gulf Coast moving into the Mid-Atlantic/Ohio Valley and the Southeast.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Light to moderate showers and thunderstorms will also develop along the central portion of the Gulf Coast moving into the Mid-Atlantic/Ohio Valley and the Southeast.", + "final_claim": "Light to moderate showers and thunderstorms will also develop along the central portion of the Gulf Coast moving into the Mid-Atlantic/Ohio Valley and the Southeast.", + "expected_answer": "True", + "date": "2010-11-16", + "met_entry_idx": 16442, + "true_value": "True", + "mode": "boolean", + "time_indices": "75785:75789:1", + "start_idx": 75785, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "613dcd9b4acea321", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75785:75789:1", + "start_idx": 75785 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66905:66909:1', 'start_idx': 66905}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A subdued upper level pattern will persist over the CONUS during the period.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "An amplified upper level pattern will set up over the CONUS during the period.", + "final_claim": "A subdued upper level pattern will persist over the CONUS during the period.", + "expected_answer": "False", + "date": "2004-10-18", + "met_entry_idx": 12090, + "true_value": "False", + "mode": "boolean", + "time_indices": "66905:66909:1", + "start_idx": 66905, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1b9c4de8ec82509e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66905:66909:1", + "start_idx": 66905 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43443:43447:1', 'start_idx': 43443}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: As the eastern Pacific upper trough moves inland, it is forecast to deepen in response to the building upstream upper ridge.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "As the eastern Pacific upper trough moves inland, it is forecast to deepen in response to the building upstream upper ridge.", + "final_claim": "As the eastern Pacific upper trough moves inland, it is forecast to deepen in response to the building upstream upper ridge.", + "expected_answer": "True", + "date": "1988-09-26", + "met_entry_idx": 508, + "true_value": "True", + "mode": "boolean", + "time_indices": "43443:43447:1", + "start_idx": 43443, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f67ccd138ca83793", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43443:43447:1", + "start_idx": 43443 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87397:87401:1', 'start_idx': 87397}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry conditions and clear skies will persist over the Upper Midwest as a weakening high pressure system settles in from the west.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Rain and isolated thunderstorms will continue to move east ahead of a strengthening area of low pressure over the Upper Midwest.", + "final_claim": "Dry conditions and clear skies will persist over the Upper Midwest as a weakening high pressure system settles in from the west.", + "expected_answer": "False", + "date": "2018-10-28", + "met_entry_idx": 22027, + "true_value": "False", + "mode": "boolean", + "time_indices": "87397:87401:1", + "start_idx": 87397, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "cc97311c717ea08e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87397:87401:1", + "start_idx": 87397 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61461:61465:1', 'start_idx': 61461}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: DRY CONDITIONS WILL PERSIST FROM THE NORTHERN MISSISSIPPI VALLEY EASTWARD ACROSS THE SOUTHERN GREAT LAKES, OHIO VALLEY, AND NORTHERN APPALACHIANS.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "SNOW WILL SPREAD FROM THE NORTHERN MISSISSIPPI VALLEY EASTWARD ACROSS THE SOUTHERN GREAT LAKES, OHIO VALLEY, AND NORTHERN APPALACHIANS.", + "final_claim": "DRY CONDITIONS WILL PERSIST FROM THE NORTHERN MISSISSIPPI VALLEY EASTWARD ACROSS THE SOUTHERN GREAT LAKES, OHIO VALLEY, AND NORTHERN APPALACHIANS.", + "expected_answer": "False", + "date": "2001-01-26", + "met_entry_idx": 9379, + "true_value": "False", + "mode": "boolean", + "time_indices": "61461:61465:1", + "start_idx": 61461, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "7e4358bc808606a5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61461:61465:1", + "start_idx": 61461 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45479:45483:1', 'start_idx': 45479}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A persistent northeastward moving upper ridge will be the dominating feature for California and the Desert Southwest for the forecast period.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A slow southwestward sinking upper low will be the dominating feature for California and the Desert Southwest for the forecast period.", + "final_claim": "A persistent northeastward moving upper ridge will be the dominating feature for California and the Desert Southwest for the forecast period.", + "expected_answer": "False", + "date": "1990-02-17", + "met_entry_idx": 1507, + "true_value": "False", + "mode": "boolean", + "time_indices": "45479:45483:1", + "start_idx": 45479, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "99f09cdc0fd8f65b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45479:45483:1", + "start_idx": 45479 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45981:45985:1', 'start_idx': 45981}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: This will maintain widespread precipitation potential across the eastern quarter of the U.S., with persistent, locally excessive rainfall likely in the deformation zone north of the upper low track through northern Michigan and southern Ontario.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "This will maintain widespread precipitation potential across the eastern quarter of the U.S., with persistent, locally excessive rainfall likely in the deformation zone north of the upper low track through northern Michigan and southern Ontario.", + "final_claim": "This will maintain widespread precipitation potential across the eastern quarter of the U.S., with persistent, locally excessive rainfall likely in the deformation zone north of the upper low track through northern Michigan and southern Ontario.", + "expected_answer": "True", + "date": "1990-06-23", + "met_entry_idx": 1756, + "true_value": "True", + "mode": "boolean", + "time_indices": "45981:45985:1", + "start_idx": 45981, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "645beaca244f553c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45981:45985:1", + "start_idx": 45981 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50511:50515:1', 'start_idx': 50511}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The main weather action over the next 48 hours will be confined largely to both coasts and the northern plains as the westerlies gradually become established along the U.S./Canadian border.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The main weather action over the next 48 hours will be confined largely to both coasts and the northern plains as the westerlies gradually become established along the U.S./Canadian border.", + "final_claim": "The main weather action over the next 48 hours will be confined largely to both coasts and the northern plains as the westerlies gradually become established along the U.S./Canadian border.", + "expected_answer": "True", + "date": "1993-07-29", + "met_entry_idx": 3929, + "true_value": "True", + "mode": "boolean", + "time_indices": "50511:50515:1", + "start_idx": 50511, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "40d6aca16c7ac40f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50511:50515:1", + "start_idx": 50511 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67053:67057:1', 'start_idx': 67053}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: This energy will combine with a northern stream trough moving through the upper Mississippi Valley, resulting in a broad trough over most of the continental United States by 48 hours.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "This energy will combine with a northern stream trough moving through the upper Mississippi Valley, resulting in a broad trough over most of the continental United States by 48 hours.", + "final_claim": "This energy will combine with a northern stream trough moving through the upper Mississippi Valley, resulting in a broad trough over most of the continental United States by 48 hours.", + "expected_answer": "True", + "date": "2004-11-24", + "met_entry_idx": 12164, + "true_value": "True", + "mode": "boolean", + "time_indices": "67053:67057:1", + "start_idx": 67053, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ea9806f373be551f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67053:67057:1", + "start_idx": 67053 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55785:55789:1', 'start_idx': 55785}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: One rather strong short wave currently over the Plains States will move across the eastern half of the U.S. this period.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "One rather strong short wave currently over the Plains States will move across the eastern half of the U.S. this period.", + "final_claim": "One rather strong short wave currently over the Plains States will move across the eastern half of the U.S. this period.", + "expected_answer": "True", + "date": "1997-03-09", + "met_entry_idx": 6557, + "true_value": "True", + "mode": "boolean", + "time_indices": "55785:55789:1", + "start_idx": 55785, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9cf452847fe75da7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55785:55789:1", + "start_idx": 55785 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64165:64169:1', 'start_idx': 64165}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A significant winter storm will affect the Southeast and Mid-Atlantic.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A significant winter storm will affect the Southeast and Mid-Atlantic.", + "final_claim": "A significant winter storm will affect the Southeast and Mid-Atlantic.", + "expected_answer": "True", + "date": "2002-12-03", + "met_entry_idx": 10725, + "true_value": "True", + "mode": "boolean", + "time_indices": "64165:64169:1", + "start_idx": 64165, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2e3683b5ba804910", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64165:64169:1", + "start_idx": 64165 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55167:55171:1', 'start_idx': 55167}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Weak westerly flow across the northern Gulf and southeast Atlantic will limit moisture advection into the region, resulting in drier conditions.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Strong easterly flow across the northern Gulf and southeast Atlantic will bring abundant moisture into the region.", + "final_claim": "Weak westerly flow across the northern Gulf and southeast Atlantic will limit moisture advection into the region, resulting in drier conditions.", + "expected_answer": "False", + "date": "1996-10-05", + "met_entry_idx": 6249, + "true_value": "False", + "mode": "boolean", + "time_indices": "55167:55171:1", + "start_idx": 55167, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "83a40540c317e18a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55167:55171:1", + "start_idx": 55167 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75531:75535:1', 'start_idx': 75531}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A return flow is expected to develop in the western Gulf, allowing some moisture to spread northward into the Great Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A return flow is expected to develop in the western Gulf, allowing some moisture to spread northward into the Great Plains.", + "final_claim": "A return flow is expected to develop in the western Gulf, allowing some moisture to spread northward into the Great Plains.", + "expected_answer": "True", + "date": "2010-09-13", + "met_entry_idx": 16328, + "true_value": "True", + "mode": "boolean", + "time_indices": "75531:75535:1", + "start_idx": 75531, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "58c637128e366fdf", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75531:75535:1", + "start_idx": 75531 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52711:52715:1', 'start_idx': 52711}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: In the West, building heights will direct the jet stream southward over the next 24 hours away from the Pacific Northwest, where dry and stable conditions are expected to persist ahead of a weakening warm front that should dissipate before reaching the Pacific Northwest within 24 hours.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "In the West, building heights will direct the jet stream northward over the next 24 hours toward the Pacific Northwest, where heavy rains are expected to develop ahead of an approaching cold front that should move slowly into the Pacific Northwest by 24 hours.", + "final_claim": "In the West, building heights will direct the jet stream southward over the next 24 hours away from the Pacific Northwest, where dry and stable conditions are expected to persist ahead of a weakening warm front that should dissipate before reaching the Pacific Northwest within 24 hours.", + "expected_answer": "False", + "date": "1995-01-30", + "met_entry_idx": 5024, + "true_value": "False", + "mode": "boolean", + "time_indices": "52711:52715:1", + "start_idx": 52711, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c7ef5cd3735d3d27", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52711:52715:1", + "start_idx": 52711 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50033:50037:1', 'start_idx': 50033}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Intense low off the west coast early this morning continues to move almost due east with some weakening occurring.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Intense low off the west coast early this morning continues to move almost due east with some weakening occurring.", + "final_claim": "Intense low off the west coast early this morning continues to move almost due east with some weakening occurring.", + "expected_answer": "True", + "date": "1993-04-01", + "met_entry_idx": 3693, + "true_value": "True", + "mode": "boolean", + "time_indices": "50033:50037:1", + "start_idx": 50033, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e61664c9262ffcd5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50033:50037:1", + "start_idx": 50033 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63741:63745:1', 'start_idx': 63741}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The upper flow across the US will remain relatively zonal, preventing the development of a significant trough into the Pacific Northwest by later in the period.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The upper flow across the US will see amplification of a trough into the Pacific Northwest by later in the period.", + "final_claim": "The upper flow across the US will remain relatively zonal, preventing the development of a significant trough into the Pacific Northwest by later in the period.", + "expected_answer": "False", + "date": "2002-08-19", + "met_entry_idx": 10513, + "true_value": "False", + "mode": "boolean", + "time_indices": "63741:63745:1", + "start_idx": 63741, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0e074dad4ff14060", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63741:63745:1", + "start_idx": 63741 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46279:46283:1', 'start_idx': 46279}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Strengthening warm front over the northeastern US expected to advance into Pennsylvania, then intensify on day 2.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Weakening cold front over the northeastern US expected to sag into Pennsylvania, then gradually dissipate on day 2.", + "final_claim": "Strengthening warm front over the northeastern US expected to advance into Pennsylvania, then intensify on day 2.", + "expected_answer": "False", + "date": "1990-09-05", + "met_entry_idx": 1904, + "true_value": "False", + "mode": "boolean", + "time_indices": "46279:46283:1", + "start_idx": 46279, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ae79f2a0066f443e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46279:46283:1", + "start_idx": 46279 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49613:49617:1', 'start_idx': 49613}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A surface low is developing over the central Appalachians as a shortwave trough moves northeastward from the southern Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A surface low is developing over the central Appalachians as a shortwave trough moves northeastward from the southern Plains.", + "final_claim": "A surface low is developing over the central Appalachians as a shortwave trough moves northeastward from the southern Plains.", + "expected_answer": "True", + "date": "1992-12-17", + "met_entry_idx": 3482, + "true_value": "True", + "mode": "boolean", + "time_indices": "49613:49617:1", + "start_idx": 49613, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "8f22fcafbcddbb34", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49613:49617:1", + "start_idx": 49613 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87283:87287:1', 'start_idx': 87283}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Heavy rain is possible over parts of the Upper and Middle Mississippi Valley into the Upper Great Lakes.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Heavy rain is possible over parts of the Upper and Middle Mississippi Valley into the Upper Great Lakes.", + "final_claim": "Heavy rain is possible over parts of the Upper and Middle Mississippi Valley into the Upper Great Lakes.", + "expected_answer": "True", + "date": "2018-09-29", + "met_entry_idx": 21970, + "true_value": "True", + "mode": "boolean", + "time_indices": "87283:87287:1", + "start_idx": 87283, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c4b5cd98466db00b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87283:87287:1", + "start_idx": 87283 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93539:93543:1', 'start_idx': 93539}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A Pacific storm system pushing into the West Coast will bring locally heavy rain near the coast and heavy high elevation snowfall into the Intermountain West over the next couple of days.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A Pacific storm system pushing into the West Coast will bring locally heavy rain near the coast and heavy high elevation snowfall into the Intermountain West over the next couple of days.", + "final_claim": "A Pacific storm system pushing into the West Coast will bring locally heavy rain near the coast and heavy high elevation snowfall into the Intermountain West over the next couple of days.", + "expected_answer": "True", + "date": "2024-02-14", + "met_entry_idx": 25562, + "true_value": "True", + "mode": "boolean", + "time_indices": "93539:93543:1", + "start_idx": 93539, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "31475364b325aa3e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93539:93543:1", + "start_idx": 93539 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43021:43025:1', 'start_idx': 43021}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A deep trough will persist across the country, while an upper ridge over the Intermountain region will weaken over the northern Plains within 48 hours.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A broad ridge will develop across the country, while an upper trough over the Intermountain region will consolidate over the northern Plains within 48 hours.", + "final_claim": "A deep trough will persist across the country, while an upper ridge over the Intermountain region will weaken over the northern Plains within 48 hours.", + "expected_answer": "False", + "date": "1988-06-13", + "met_entry_idx": 299, + "true_value": "False", + "mode": "boolean", + "time_indices": "43021:43025:1", + "start_idx": 43021, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "18218e69510cf840", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43021:43025:1", + "start_idx": 43021 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78457:78461:1', 'start_idx': 78457}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Temperatures will be 10 to near 20 degrees below average from parts of the Middle Mississippi Valley to the Southern High Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Temperatures will be 10 to near 20 degrees below average from parts of the Middle Mississippi Valley to the Southern High Plains.", + "final_claim": "Temperatures will be 10 to near 20 degrees below average from parts of the Middle Mississippi Valley to the Southern High Plains.", + "expected_answer": "True", + "date": "2012-09-14", + "met_entry_idx": 17655, + "true_value": "True", + "mode": "boolean", + "time_indices": "78457:78461:1", + "start_idx": 78457, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4798e3ee951775c4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78457:78461:1", + "start_idx": 78457 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45471:45475:1', 'start_idx': 45471}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Most of the country is experiencing dry and stable conditions today as a surface high strengthens over the central Plains and a ridge of high pressure dominates the Pacific Northwest coast.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A large portion of the country is experiencing precipitation today as a surface low becomes better organized in the central Plains and another Pacific system crosses the Pacific Northwest coast.", + "final_claim": "Most of the country is experiencing dry and stable conditions today as a surface high strengthens over the central Plains and a ridge of high pressure dominates the Pacific Northwest coast.", + "expected_answer": "False", + "date": "1990-02-15", + "met_entry_idx": 1503, + "true_value": "False", + "mode": "boolean", + "time_indices": "45471:45475:1", + "start_idx": 45471, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "61af17395aae943a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45471:45475:1", + "start_idx": 45471 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78525:78529:1', 'start_idx': 78525}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A high pressure system in the Deep South will begin to bring dry and stable conditions northeastward into the eastern U.S. during the next few days.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A low pressure system in the Deep South will begin to spread rain and thunderstorms northeastward into the eastern U.S. during the next few days.", + "final_claim": "A high pressure system in the Deep South will begin to bring dry and stable conditions northeastward into the eastern U.S. during the next few days.", + "expected_answer": "False", + "date": "2012-10-01", + "met_entry_idx": 17689, + "true_value": "False", + "mode": "boolean", + "time_indices": "78525:78529:1", + "start_idx": 78525, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c06401bb0e35a6b1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78525:78529:1", + "start_idx": 78525 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59668:59672:1', 'start_idx': 59668}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry conditions will persist over a large portion of the nation during this period.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Dry conditions will persist over a large portion of the nation during this period.", + "final_claim": "Dry conditions will persist over a large portion of the nation during this period.", + "expected_answer": "True", + "date": "1999-11-05", + "met_entry_idx": 8492, + "true_value": "True", + "mode": "boolean", + "time_indices": "59668:59672:1", + "start_idx": 59668, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1ef3735914989ad3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59668:59672:1", + "start_idx": 59668 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87831:87835:1', 'start_idx': 87831}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Multiple systems will move across the West Coast and the Intermountain West/Central Great Basin, delivering rainfall and higher elevation snowfall.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Multiple systems will move across the West Coast and the Intermountain West/Central Great Basin, delivering rainfall and higher elevation snowfall.", + "final_claim": "Multiple systems will move across the West Coast and the Intermountain West/Central Great Basin, delivering rainfall and higher elevation snowfall.", + "expected_answer": "True", + "date": "2019-02-13", + "met_entry_idx": 22233, + "true_value": "True", + "mode": "boolean", + "time_indices": "87831:87835:1", + "start_idx": 87831, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "97e5ed061e16d55d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87831:87835:1", + "start_idx": 87831 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82107:82111:1', 'start_idx': 82107}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A strengthening area of high pressure over the Gulf of Maine will continue to promote dry and clear conditions across portions of northern New England, especially Maine, through this evening.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A deepening area of low pressure over the Gulf of Maine will continue to foster an area of heavy snow across portions of northern New England, especially Maine, through this evening.", + "final_claim": "A strengthening area of high pressure over the Gulf of Maine will continue to promote dry and clear conditions across portions of northern New England, especially Maine, through this evening.", + "expected_answer": "False", + "date": "2015-03-15", + "met_entry_idx": 19471, + "true_value": "False", + "mode": "boolean", + "time_indices": "82107:82111:1", + "start_idx": 82107, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "05791b4ffde92472", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82107:82111:1", + "start_idx": 82107 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88487:88491:1', 'start_idx': 88487}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry conditions are expected to persist from Colorado to the Four Corners region through tonight, minimizing any risk of localized flooding.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Monsoonal moisture could lead to localized flooding from Colorado to the Four Corners region through tonight.", + "final_claim": "Dry conditions are expected to persist from Colorado to the Four Corners region through tonight, minimizing any risk of localized flooding.", + "expected_answer": "False", + "date": "2019-07-27", + "met_entry_idx": 22537, + "true_value": "False", + "mode": "boolean", + "time_indices": "88487:88491:1", + "start_idx": 88487, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a4418bd6fe36c6b9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88487:88491:1", + "start_idx": 88487 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57383:57387:1', 'start_idx': 57383}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A large-scale trough will remain over the western states.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A large-scale trough will remain over the western states.", + "final_claim": "A large-scale trough will remain over the western states.", + "expected_answer": "True", + "date": "1998-04-12", + "met_entry_idx": 7351, + "true_value": "True", + "mode": "boolean", + "time_indices": "57383:57387:1", + "start_idx": 57383, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ac88f17c1c925fb6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57383:57387:1", + "start_idx": 57383 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93539:93543:1', 'start_idx': 93539}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Snow is forecast over parts of the Cascades, Sierra Nevada Mountains, and the Northern Rockies.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Snow is forecast over parts of the Cascades, Sierra Nevada Mountains, and the Northern Rockies.", + "final_claim": "Snow is forecast over parts of the Cascades, Sierra Nevada Mountains, and the Northern Rockies.", + "expected_answer": "True", + "date": "2023-02-03", + "met_entry_idx": 24858, + "true_value": "True", + "mode": "boolean", + "time_indices": "93539:93543:1", + "start_idx": 93539, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "02afff46df5bfb2d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93539:93543:1", + "start_idx": 93539 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65525:65529:1', 'start_idx': 65525}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Much of the remainder of the nation will be under the influence of a strong surface high, left in the wake of a frigid northern stream trough sweeping through the Canadian Maritimes.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Much of the remainder of the nation will be under the influence of a strong surface high, left in the wake of a frigid northern stream trough sweeping through the Canadian Maritimes.", + "final_claim": "Much of the remainder of the nation will be under the influence of a strong surface high, left in the wake of a frigid northern stream trough sweeping through the Canadian Maritimes.", + "expected_answer": "True", + "date": "2003-11-08", + "met_entry_idx": 11402, + "true_value": "True", + "mode": "boolean", + "time_indices": "65525:65529:1", + "start_idx": 65525, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b955865bdd41a924", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65525:65529:1", + "start_idx": 65525 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76805:76809:1', 'start_idx': 76805}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Heavy rain and windy conditions are expected along the coasts of Texas and eastern Mexico.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Heavy rain and windy conditions are expected along the coasts of Texas and eastern Mexico.", + "final_claim": "Heavy rain and windy conditions are expected along the coasts of Texas and eastern Mexico.", + "expected_answer": "True", + "date": "2011-07-29", + "met_entry_idx": 16882, + "true_value": "True", + "mode": "boolean", + "time_indices": "76805:76809:1", + "start_idx": 76805, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "33094f72d9612bd7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76805:76809:1", + "start_idx": 76805 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78365:78369:1', 'start_idx': 78365}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: TEMPERATURES WILL BE 10 DEGREES BELOW AVERAGE FOR PARTS OF THE NORTHERN/CENTRAL HIGH PLAINS.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "TEMPERATURES WILL BE 10 DEGREES ABOVE AVERAGE FOR PARTS OF THE NORTHERN/CENTRAL HIGH PLAINS.", + "final_claim": "TEMPERATURES WILL BE 10 DEGREES BELOW AVERAGE FOR PARTS OF THE NORTHERN/CENTRAL HIGH PLAINS.", + "expected_answer": "False", + "date": "2012-08-22", + "met_entry_idx": 17609, + "true_value": "False", + "mode": "boolean", + "time_indices": "78365:78369:1", + "start_idx": 78365, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ea2df9e78a621682", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78365:78369:1", + "start_idx": 78365 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85767:85771:1', 'start_idx': 85767}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Heavy rainfall along with severe weather is possible for portions of the Midwest and Upper Mississippi Valley.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Heavy rainfall along with severe weather is possible for portions of the Midwest and Upper Mississippi Valley.", + "final_claim": "Heavy rainfall along with severe weather is possible for portions of the Midwest and Upper Mississippi Valley.", + "expected_answer": "True", + "date": "2017-09-15", + "met_entry_idx": 21256, + "true_value": "True", + "mode": "boolean", + "time_indices": "85767:85771:1", + "start_idx": 85767, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "bac52affb011cd11", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85767:85771:1", + "start_idx": 85767 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66620:66624:1', 'start_idx': 66620}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Unseasonably cool weather will persist across a large part of the nation as more Canadian impulses move into the northern Plains and Great Lakes regions.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Unseasonably cool weather will persist across a large part of the nation as more Canadian impulses move into the northern Plains and Great Lakes regions.", + "final_claim": "Unseasonably cool weather will persist across a large part of the nation as more Canadian impulses move into the northern Plains and Great Lakes regions.", + "expected_answer": "True", + "date": "2004-08-08", + "met_entry_idx": 11948, + "true_value": "True", + "mode": "boolean", + "time_indices": "66620:66624:1", + "start_idx": 66620, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e5a43821dd880ecc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66620:66624:1", + "start_idx": 66620 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84785:84789:1', 'start_idx': 84785}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A large arctic high pressure area settling across the north-central U.S. and Great Lakes region will provide a steady supply of sub-freezing temperatures in the lowest levels of the atmosphere for this region.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A large arctic high pressure area settling across the north-central U.S. and Great Lakes region will provide a steady supply of sub-freezing temperatures in the lowest levels of the atmosphere for this region.", + "final_claim": "A large arctic high pressure area settling across the north-central U.S. and Great Lakes region will provide a steady supply of sub-freezing temperatures in the lowest levels of the atmosphere for this region.", + "expected_answer": "True", + "date": "2017-01-13", + "met_entry_idx": 20783, + "true_value": "True", + "mode": "boolean", + "time_indices": "84785:84789:1", + "start_idx": 84785, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d4b5b808adec0b60", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84785:84789:1", + "start_idx": 84785 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58447:58451:1', 'start_idx': 58447}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry conditions will persist across New England through this evening as weak low-level northwesterly flow behind the high-pressure system limits moisture influx, with a cold front retreating southward away from the coast.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Heavy precipitation will continue across New England through this evening as strong low-level southeasterly flow ahead of the low brings Atlantic moisture over a warm front lifting northward along the coast.", + "final_claim": "Dry conditions will persist across New England through this evening as weak low-level northwesterly flow behind the high-pressure system limits moisture influx, with a cold front retreating southward away from the coast.", + "expected_answer": "False", + "date": "1999-01-03", + "met_entry_idx": 7881, + "true_value": "False", + "mode": "boolean", + "time_indices": "58447:58451:1", + "start_idx": 58447, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1fe04ad43da4c5f1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58447:58451:1", + "start_idx": 58447 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88181:88185:1', 'start_idx': 88181}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: There is a slight risk of severe thunderstorms over parts of the Eastern Gulf Coast into the Southern Mid-Atlantic.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "There is a slight risk of severe thunderstorms over parts of the Eastern Gulf Coast into the Southern Mid-Atlantic.", + "final_claim": "There is a slight risk of severe thunderstorms over parts of the Eastern Gulf Coast into the Southern Mid-Atlantic.", + "expected_answer": "True", + "date": "2019-05-12", + "met_entry_idx": 22397, + "true_value": "True", + "mode": "boolean", + "time_indices": "88181:88185:1", + "start_idx": 88181, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0c9c0c479b5a3ec6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88181:88185:1", + "start_idx": 88181 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51745:51749:1', 'start_idx': 51745}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Over the next 24 hours, vorticity will gradually lower heights over the northwestern states.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Over the next 24 hours, vorticity will gradually lower heights over the northwestern states.", + "final_claim": "Over the next 24 hours, vorticity will gradually lower heights over the northwestern states.", + "expected_answer": "True", + "date": "1994-06-03", + "met_entry_idx": 4546, + "true_value": "True", + "mode": "boolean", + "time_indices": "51745:51749:1", + "start_idx": 51745, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a900fb54908b260a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51745:51749:1", + "start_idx": 51745 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52483:52487:1', 'start_idx': 52483}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: As this system moves southeast and turns toward the northeast on day 2, dry air will dominate much of the southwestern U.S., resulting in minimal precipitation; temperatures are expected to be cool enough to prevent any significant liquid precipitation, with higher elevations remaining largely snow-free.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "As this system moves southeast and turns toward the northeast on day 2, there will be plenty of tropical moisture pulled north with the likelihood of heavy precipitation over much of the southwestern U.S. Temperatures should be warm enough to keep much of the precipitation liquid, but the higher elevations will have snow, possibly heavy on day 2.", + "final_claim": "As this system moves southeast and turns toward the northeast on day 2, dry air will dominate much of the southwestern U.S., resulting in minimal precipitation; temperatures are expected to be cool enough to prevent any significant liquid precipitation, with higher elevations remaining largely snow-free.", + "expected_answer": "False", + "date": "1994-12-04", + "met_entry_idx": 4911, + "true_value": "False", + "mode": "boolean", + "time_indices": "52483:52487:1", + "start_idx": 52483, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a14915f9ab27ba34", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52483:52487:1", + "start_idx": 52483 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44373:44377:1', 'start_idx': 44373}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Upper-level divergence and continued low-level inflow will support continued precipitation in the southern Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Upper-level divergence and continued low-level inflow will support continued precipitation in the southern Plains.", + "final_claim": "Upper-level divergence and continued low-level inflow will support continued precipitation in the southern Plains.", + "expected_answer": "True", + "date": "1989-05-17", + "met_entry_idx": 962, + "true_value": "True", + "mode": "boolean", + "time_indices": "44373:44377:1", + "start_idx": 44373, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5b261d1196c61e8e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44373:44377:1", + "start_idx": 44373 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48875:48879:1', 'start_idx': 48875}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Low pressure currently in eastern Colorado will lift northward into western Montana by 24 hours.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Low pressure currently in eastern Colorado will lift northward into western Montana by 24 hours.", + "final_claim": "Low pressure currently in eastern Colorado will lift northward into western Montana by 24 hours.", + "expected_answer": "True", + "date": "1992-06-15", + "met_entry_idx": 3111, + "true_value": "True", + "mode": "boolean", + "time_indices": "48875:48879:1", + "start_idx": 48875, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "dd413fc74aef2d7d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48875:48879:1", + "start_idx": 48875 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63183:63187:1', 'start_idx': 63183}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A positively tilted shortwave over the Northeast is expected to weaken as it moves across northern New England overnight.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A negatively tilted shortwave over the Northeast is forecasted to intensify as it moves across northern New England overnight.", + "final_claim": "A positively tilted shortwave over the Northeast is expected to weaken as it moves across northern New England overnight.", + "expected_answer": "False", + "date": "2002-04-01", + "met_entry_idx": 10234, + "true_value": "False", + "mode": "boolean", + "time_indices": "63183:63187:1", + "start_idx": 63183, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "76c0c343ff797f17", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63183:63187:1", + "start_idx": 63183 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49583:49587:1', 'start_idx': 49583}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: There will be a double low structure with one surface low over Ohio and another more significant low along the coast of North Carolina.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "There will be a double low structure with one surface low over Ohio and another more significant low along the coast of North Carolina.", + "final_claim": "There will be a double low structure with one surface low over Ohio and another more significant low along the coast of North Carolina.", + "expected_answer": "True", + "date": "1992-12-09", + "met_entry_idx": 3467, + "true_value": "True", + "mode": "boolean", + "time_indices": "49583:49587:1", + "start_idx": 49583, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1480b8eb8ad077b2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49583:49587:1", + "start_idx": 49583 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49047:49051:1', 'start_idx': 49047}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: An upper ridge will continue to build over the West, while a series of shortwaves will create a broad trough across the central and northeastern U.S. During the first 24 hours, convection will likely be concentrated along an old stationary boundary across the southern states and near a newer front stalled along the southern edge of the westerlies from the central/northern Appalachians to the Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "An upper ridge will continue to build over the West, while a series of shortwaves will create a broad trough across the central and northeastern U.S. During the first 24 hours, convection will likely be concentrated along an old stationary boundary across the southern states and near a newer front stalled along the southern edge of the westerlies from the central/northern Appalachians to the Plains.", + "final_claim": "An upper ridge will continue to build over the West, while a series of shortwaves will create a broad trough across the central and northeastern U.S. During the first 24 hours, convection will likely be concentrated along an old stationary boundary across the southern states and near a newer front stalled along the southern edge of the westerlies from the central/northern Appalachians to the Plains.", + "expected_answer": "True", + "date": "1992-07-28", + "met_entry_idx": 3196, + "true_value": "True", + "mode": "boolean", + "time_indices": "49047:49051:1", + "start_idx": 49047, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "97b3aac3a6387260", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49047:49051:1", + "start_idx": 49047 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72593:72597:1', 'start_idx': 72593}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: As Hurricane Ike moves away from the west northwest of western Cuba, dry conditions with calm winds will persist in southern Florida through today and into tonight.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "As Hurricane Ike moves toward the west northwest from western Cuba, outer rain bands with tropical storm conditions will continue to impact southern Florida through today and into tonight.", + "final_claim": "As Hurricane Ike moves away from the west northwest of western Cuba, dry conditions with calm winds will persist in southern Florida through today and into tonight.", + "expected_answer": "False", + "date": "2008-09-09", + "met_entry_idx": 14914, + "true_value": "False", + "mode": "boolean", + "time_indices": "72593:72597:1", + "start_idx": 72593, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "de6da611351c8b87", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72593:72597:1", + "start_idx": 72593 + } + } +] \ No newline at end of file diff --git a/level2d_part1.json b/level2d_part1.json new file mode 100644 index 0000000000000000000000000000000000000000..fcd5b9f72e58d7841a41a8c680eac83cb68f774c --- /dev/null +++ b/level2d_part1.json @@ -0,0 +1,1560 @@ +[ + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51523:51527:1', 'start_idx': 51523}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Snow is expected at most higher elevations, spreading into southwestern Colorado and northwestern New Mexico.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Snow is expected at most higher elevations, spreading into southwestern Colorado and northwestern New Mexico.", + "final_claim": "Snow is expected at most higher elevations, spreading into southwestern Colorado and northwestern New Mexico.", + "expected_answer": "True", + "date": "1994-04-08", + "met_entry_idx": 4435, + "true_value": "True", + "mode": "boolean", + "time_indices": "51523:51527:1", + "start_idx": 51523, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "236d545216398f77", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51523:51527:1", + "start_idx": 51523 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88489:88493:1', 'start_idx': 88489}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and stable conditions will prevail across the Upper Midwest today, with no significant risk of severe weather or flash flooding.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Showers and thunderstorms are expected across the Upper Midwest today, with a localized threat for some severe weather and flash flooding.", + "final_claim": "Dry and stable conditions will prevail across the Upper Midwest today, with no significant risk of severe weather or flash flooding.", + "expected_answer": "False", + "date": "2019-07-28", + "met_entry_idx": 22538, + "true_value": "False", + "mode": "boolean", + "time_indices": "88489:88493:1", + "start_idx": 88489, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3445349566f7bb69", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88489:88493:1", + "start_idx": 88489 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53717:53721:1', 'start_idx': 53717}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The short wave trough moving into the Upper Midwest will weaken as it moves through the Great Lakes and into New England, with a couple of trailing vortices forming a weak, broad trough over the Great Lakes and Mid Mississippi Valley by the end of the period.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The short wave trough moving into the Upper Midwest will weaken as it moves through the Great Lakes and into New England, with a couple of trailing vortices forming a weak, broad trough over the Great Lakes and Mid Mississippi Valley by the end of the period.", + "final_claim": "The short wave trough moving into the Upper Midwest will weaken as it moves through the Great Lakes and into New England, with a couple of trailing vortices forming a weak, broad trough over the Great Lakes and Mid Mississippi Valley by the end of the period.", + "expected_answer": "True", + "date": "1995-10-09", + "met_entry_idx": 5529, + "true_value": "True", + "mode": "boolean", + "time_indices": "53717:53721:1", + "start_idx": 53717, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "459c974a95970aa5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53717:53721:1", + "start_idx": 53717 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51467:51471:1', 'start_idx': 51467}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The split flow pattern over the U.S. is expected to continue during the short range period.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The split flow pattern over the U.S. is expected to continue during the short range period.", + "final_claim": "The split flow pattern over the U.S. is expected to continue during the short range period.", + "expected_answer": "True", + "date": "1994-03-25", + "met_entry_idx": 4407, + "true_value": "True", + "mode": "boolean", + "time_indices": "51467:51471:1", + "start_idx": 51467, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1ae3f056401886a1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51467:51471:1", + "start_idx": 51467 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62865:62869:1', 'start_idx': 62865}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A progressive, low amplitude flow will be featured across the lower 48 states, with ridging over the western states and a trough in the east.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A progressive, low amplitude flow will be featured across the lower 48 states, with ridging over the western states and a trough in the east.", + "final_claim": "A progressive, low amplitude flow will be featured across the lower 48 states, with ridging over the western states and a trough in the east.", + "expected_answer": "True", + "date": "2002-01-12", + "met_entry_idx": 10076, + "true_value": "True", + "mode": "boolean", + "time_indices": "62865:62869:1", + "start_idx": 62865, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "7555e1cb5c2a8cfb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62865:62869:1", + "start_idx": 62865 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66467:66471:1', 'start_idx': 66467}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A strong ridge building over southeastern Canada will allow a warm air mass to spread across the northern Plains, Great Lakes, and eventually the Northeast during this period.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A vigorous trough moving through southeastern Canada will push a cold air mass across the northern Plains, Great Lakes, and eventually the Northeast during this period.", + "final_claim": "A strong ridge building over southeastern Canada will allow a warm air mass to spread across the northern Plains, Great Lakes, and eventually the Northeast during this period.", + "expected_answer": "False", + "date": "2004-06-30", + "met_entry_idx": 11872, + "true_value": "False", + "mode": "boolean", + "time_indices": "66467:66471:1", + "start_idx": 66467, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4b0461854d32e471", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66467:66471:1", + "start_idx": 66467 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79227:79231:1', 'start_idx': 79227}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Periods of rain and snow will impact Long Island and extreme southeastern New England, with any snow accumulations remaining light, an inch or two at the most.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Periods of rain and snow will impact Long Island and extreme southeastern New England, with any snow accumulations remaining light, an inch or two at the most.", + "final_claim": "Periods of rain and snow will impact Long Island and extreme southeastern New England, with any snow accumulations remaining light, an inch or two at the most.", + "expected_answer": "True", + "date": "2013-03-25", + "met_entry_idx": 18039, + "true_value": "True", + "mode": "boolean", + "time_indices": "79227:79231:1", + "start_idx": 79227, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9cba83963c1bbe37", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79227:79231:1", + "start_idx": 79227 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66407:66411:1', 'start_idx': 66407}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: High pressure over the southwest will strengthen, while the open high over southeast Texas will move steadily southwest through day one, then descend southwest and intensify on day two.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Low pressure over the southwest will weaken, while the closed low over southeast Texas will remain nearly stationary through day one, then lift northeast and weaken on day two.", + "final_claim": "High pressure over the southwest will strengthen, while the open high over southeast Texas will move steadily southwest through day one, then descend southwest and intensify on day two.", + "expected_answer": "False", + "date": "2004-06-15", + "met_entry_idx": 11842, + "true_value": "False", + "mode": "boolean", + "time_indices": "66407:66411:1", + "start_idx": 66407, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3348e342543f004f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66407:66411:1", + "start_idx": 66407 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46409:46413:1', 'start_idx': 46409}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The midsection of the nation will continue to see the brunt of the precipitation over the next two days as a large upper trough moves eastward.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The midsection of the nation will continue to see the brunt of the precipitation over the next two days as a large upper trough moves eastward.", + "final_claim": "The midsection of the nation will continue to see the brunt of the precipitation over the next two days as a large upper trough moves eastward.", + "expected_answer": "True", + "date": "1990-10-08", + "met_entry_idx": 1970, + "true_value": "True", + "mode": "boolean", + "time_indices": "46409:46413:1", + "start_idx": 46409, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c36b5093f96a381e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46409:46413:1", + "start_idx": 46409 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91653:91657:1', 'start_idx': 91653}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: One more day of heavy rain chances will be found across Maine as a low pressure system passes by to the east.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "One more day of heavy rain chances will be found across Maine as a low pressure system passes by to the east.", + "final_claim": "One more day of heavy rain chances will be found across Maine as a low pressure system passes by to the east.", + "expected_answer": "True", + "date": "2021-09-26", + "met_entry_idx": 23952, + "true_value": "True", + "mode": "boolean", + "time_indices": "91653:91657:1", + "start_idx": 91653, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "629b80961e1e6903", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91653:91657:1", + "start_idx": 91653 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93539:93543:1', 'start_idx': 93539}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A mild winter front over the Midwest will gradually intensify throughout the day.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A significant winter storm over the Midwest will slowly start winding down.", + "final_claim": "A mild winter front over the Midwest will gradually intensify throughout the day.", + "expected_answer": "False", + "date": "2023-12-26", + "met_entry_idx": 25468, + "true_value": "False", + "mode": "boolean", + "time_indices": "93539:93543:1", + "start_idx": 93539, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5d3745c1a0d15c3d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93539:93543:1", + "start_idx": 93539 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64191:64195:1', 'start_idx': 64191}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: WEAK NORTHERLY FLOW OUT AHEAD WILL RESULT IN A DRY CONTINENTAL AIR MASS THAT, LACKING SUPPORT FROM SHORTWAVE ENERGY AND UPPER JET DYNAMICS, WILL LEAD TO WIDESPREAD DRY CONDITIONS ACROSS FLORIDA AND THE SOUTHEAST OVER THE NEXT 24 HOURS.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "DEEP SOUTHWESTERLY FLOW OUT AHEAD WILL SET UP A MOIST SUBTROPICAL FEED THAT, WITH THE AID OF WEAK SHORTWAVE ENERGY AND UPPER JET DYNAMICS, WILL GENERATE A SWATH OF MODERATE RAINS ACROSS FLORIDA AND THE SOUTHEAST OVER THE NEXT 24 HOURS.", + "final_claim": "WEAK NORTHERLY FLOW OUT AHEAD WILL RESULT IN A DRY CONTINENTAL AIR MASS THAT, LACKING SUPPORT FROM SHORTWAVE ENERGY AND UPPER JET DYNAMICS, WILL LEAD TO WIDESPREAD DRY CONDITIONS ACROSS FLORIDA AND THE SOUTHEAST OVER THE NEXT 24 HOURS.", + "expected_answer": "False", + "date": "2002-12-09", + "met_entry_idx": 10738, + "true_value": "False", + "mode": "boolean", + "time_indices": "64191:64195:1", + "start_idx": 64191, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "933adc417cf441f4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64191:64195:1", + "start_idx": 64191 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86793:86797:1', 'start_idx': 86793}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Heavy rainfall is likely along the track of Tropical Depression Alberto as it moves toward the Upper Great Lakes and the interior Carolinas.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Heavy rainfall is likely along the track of Tropical Depression Alberto as it moves toward the Upper Great Lakes and the interior Carolinas.", + "final_claim": "Heavy rainfall is likely along the track of Tropical Depression Alberto as it moves toward the Upper Great Lakes and the interior Carolinas.", + "expected_answer": "True", + "date": "2018-05-30", + "met_entry_idx": 21742, + "true_value": "True", + "mode": "boolean", + "time_indices": "86793:86797:1", + "start_idx": 86793, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e85ede536d061635", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86793:86797:1", + "start_idx": 86793 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56915:56919:1', 'start_idx': 56915}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: This will result in predominantly wet and cool conditions east of the Rockies as the area will continue to be influenced by Arctic air masses and upslope flow.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "This will result in mostly dry and mild conditions east of the Rockies as the area will continue to be dominated by Pacific air and effects of down-sloping.", + "final_claim": "This will result in predominantly wet and cool conditions east of the Rockies as the area will continue to be influenced by Arctic air masses and upslope flow.", + "expected_answer": "False", + "date": "1997-12-16", + "met_entry_idx": 7118, + "true_value": "False", + "mode": "boolean", + "time_indices": "56915:56919:1", + "start_idx": 56915, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6d4105007dbb057a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56915:56919:1", + "start_idx": 56915 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86051:86055:1', 'start_idx': 86051}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: DRY CONDITIONS EXPECTED OVER THE PACIFIC NORTHWEST.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "HEAVY RAIN POSSIBLE OVER THE PACIFIC NORTHWEST.", + "final_claim": "DRY CONDITIONS EXPECTED OVER THE PACIFIC NORTHWEST.", + "expected_answer": "False", + "date": "2017-11-25", + "met_entry_idx": 21387, + "true_value": "False", + "mode": "boolean", + "time_indices": "86051:86055:1", + "start_idx": 86051, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "7292c079db834331", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86051:86055:1", + "start_idx": 86051 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83817:83821:1', 'start_idx': 83817}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry conditions are expected over the central Plains into the middle Mississippi Valley and parts of the western Gulf Coast.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Heavy rain is possible over the central Plains into the middle Mississippi Valley and parts of the western Gulf Coast.", + "final_claim": "Dry conditions are expected over the central Plains into the middle Mississippi Valley and parts of the western Gulf Coast.", + "expected_answer": "False", + "date": "2016-05-16", + "met_entry_idx": 20308, + "true_value": "False", + "mode": "boolean", + "time_indices": "83817:83821:1", + "start_idx": 83817, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f303ed89500edc57", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83817:83821:1", + "start_idx": 83817 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81951:81955:1', 'start_idx': 81951}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: HEAVY RAINS ANTICIPATED FOR THE PACIFIC NORTHWEST...", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "HEAVY RAINS ANTICIPATED FOR THE PACIFIC NORTHWEST...", + "final_claim": "HEAVY RAINS ANTICIPATED FOR THE PACIFIC NORTHWEST...", + "expected_answer": "True", + "date": "2015-02-04", + "met_entry_idx": 19393, + "true_value": "True", + "mode": "boolean", + "time_indices": "81951:81955:1", + "start_idx": 81951, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0fe9776732876b99", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81951:81955:1", + "start_idx": 81951 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44627:44631:1', 'start_idx': 44627}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A pronounced vorticity minimum remains stationary near the base of the northwest coast upper trough over the next 24 hours, then drifts southwest away from British Columbia.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Strong vorticity maximum rotates through the base of the northwest coast upper trough in the next 24 hours, then lifts northeast into British Columbia.", + "final_claim": "A pronounced vorticity minimum remains stationary near the base of the northwest coast upper trough over the next 24 hours, then drifts southwest away from British Columbia.", + "expected_answer": "False", + "date": "1989-07-19", + "met_entry_idx": 1089, + "true_value": "False", + "mode": "boolean", + "time_indices": "44627:44631:1", + "start_idx": 44627, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "34173b019391299b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44627:44631:1", + "start_idx": 44627 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54637:54641:1', 'start_idx': 54637}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The primary weather features are a strong upper ridge over the southwestern U.S. and a rapidly moving dryline from Kansas eastward to the Carolinas.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The main weather systems are an upper low over the southwestern U.S. and a nearly stationary front from Kansas eastward to the Carolinas.", + "final_claim": "The primary weather features are a strong upper ridge over the southwestern U.S. and a rapidly moving dryline from Kansas eastward to the Carolinas.", + "expected_answer": "False", + "date": "1996-05-26", + "met_entry_idx": 5988, + "true_value": "False", + "mode": "boolean", + "time_indices": "54637:54641:1", + "start_idx": 54637, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "99f913276d4e9fed", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54637:54641:1", + "start_idx": 54637 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50681:50685:1', 'start_idx': 50681}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Minimal short wave energy is present near the base of the eastern North America upper trough.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A considerable amount of short wave energy is swinging around the bottom of the eastern North America upper trough.", + "final_claim": "Minimal short wave energy is present near the base of the eastern North America upper trough.", + "expected_answer": "False", + "date": "1993-09-10", + "met_entry_idx": 4013, + "true_value": "False", + "mode": "boolean", + "time_indices": "50681:50685:1", + "start_idx": 50681, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9c2ca4d4400bd11b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50681:50685:1", + "start_idx": 50681 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50999:51003:1', 'start_idx': 50999}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A consolidated flow pattern will persist along the West Coast, with northern stream dominance resulting in dry and stable conditions across central and northern California over the next 36-48 hours.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A split flow regime will develop along the West Coast, with southern stream energy affecting central and northern California by 36-48 hours.", + "final_claim": "A consolidated flow pattern will persist along the West Coast, with northern stream dominance resulting in dry and stable conditions across central and northern California over the next 36-48 hours.", + "expected_answer": "False", + "date": "1993-11-28", + "met_entry_idx": 4169, + "true_value": "False", + "mode": "boolean", + "time_indices": "50999:51003:1", + "start_idx": 50999, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c6827770bd760f48", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50999:51003:1", + "start_idx": 50999 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44665:44669:1', 'start_idx': 44665}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Stable air masses embedded in southwesterly flow ahead of the upper low are expected to suppress convection in the northern Rockies, mainly on day 2 along and north of a weak frontal boundary across Montana.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Weak impulses embedded in southwesterly flow ahead of the upper low should help trigger convection in the northern Rockies, mainly on day 2 along and north of a weak frontal boundary across Montana.", + "final_claim": "Stable air masses embedded in southwesterly flow ahead of the upper low are expected to suppress convection in the northern Rockies, mainly on day 2 along and north of a weak frontal boundary across Montana.", + "expected_answer": "False", + "date": "1989-07-29", + "met_entry_idx": 1108, + "true_value": "False", + "mode": "boolean", + "time_indices": "44665:44669:1", + "start_idx": 44665, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4d6603ae6901153f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44665:44669:1", + "start_idx": 44665 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90651:90655:1', 'start_idx': 90651}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The combination of a developing area of low pressure off the southern California coast and strengthening high pressure from the Rockies into the Great Basin will drive increasingly strong and locally damaging winds across much of California.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The combination of a developing area of low pressure off the southern California coast and strengthening high pressure from the Rockies into the Great Basin will drive increasingly strong and locally damaging winds across much of California.", + "final_claim": "The combination of a developing area of low pressure off the southern California coast and strengthening high pressure from the Rockies into the Great Basin will drive increasingly strong and locally damaging winds across much of California.", + "expected_answer": "True", + "date": "2021-01-18", + "met_entry_idx": 23494, + "true_value": "True", + "mode": "boolean", + "time_indices": "90651:90655:1", + "start_idx": 90651, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "97db0485311294bc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90651:90655:1", + "start_idx": 90651 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79167:79171:1', 'start_idx': 79167}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Moderate rainfall is also possible over the Ohio River Valley and Mid-Atlantic as the frontal boundary moves eastward.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Moderate rainfall is also possible over the Ohio River Valley and Mid-Atlantic as the frontal boundary moves eastward.", + "final_claim": "Moderate rainfall is also possible over the Ohio River Valley and Mid-Atlantic as the frontal boundary moves eastward.", + "expected_answer": "True", + "date": "2013-03-10", + "met_entry_idx": 18009, + "true_value": "True", + "mode": "boolean", + "time_indices": "79167:79171:1", + "start_idx": 79167, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c9f8c6d0355bdc3e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79167:79171:1", + "start_idx": 79167 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83869:83873:1', 'start_idx': 83869}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Bonnie will produce widespread showers and thunderstorms today across areas from the Carolinas to the Mid-Atlantic, spreading into the Northeast by late afternoon.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Bonnie will produce widespread showers and thunderstorms today across areas from the Carolinas to the Mid-Atlantic, spreading into the Northeast by late afternoon.", + "final_claim": "Bonnie will produce widespread showers and thunderstorms today across areas from the Carolinas to the Mid-Atlantic, spreading into the Northeast by late afternoon.", + "expected_answer": "True", + "date": "2016-05-29", + "met_entry_idx": 20333, + "true_value": "True", + "mode": "boolean", + "time_indices": "83869:83873:1", + "start_idx": 83869, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f51e7e696852d20e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83869:83873:1", + "start_idx": 83869 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61725:61729:1', 'start_idx': 61725}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Heights will rise over the Northeast as flow overrides a downstream trough over Quebec.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Heights will remain low over the Northeast with flow undercutting a blocking ridge over Quebec.", + "final_claim": "Heights will rise over the Northeast as flow overrides a downstream trough over Quebec.", + "expected_answer": "False", + "date": "2001-04-02", + "met_entry_idx": 9510, + "true_value": "False", + "mode": "boolean", + "time_indices": "61725:61729:1", + "start_idx": 61725, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9d5dfabc816c4205", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61725:61729:1", + "start_idx": 61725 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59249:59253:1', 'start_idx': 59249}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The broad upper trough covering much of the nation will strengthen as the flow begins to flatten.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The strong upper ridge encompassing a large portion of the nation will weaken as the flow begins to amplify.", + "final_claim": "The broad upper trough covering much of the nation will strengthen as the flow begins to flatten.", + "expected_answer": "False", + "date": "1999-07-23", + "met_entry_idx": 8281, + "true_value": "False", + "mode": "boolean", + "time_indices": "59249:59253:1", + "start_idx": 59249, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "001c6cb31f3d41d0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59249:59253:1", + "start_idx": 59249 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69243:69247:1', 'start_idx': 69243}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The weather pattern across the lower 48 states is evolving toward a trough in the West, while heights gradually build in the East.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The weather pattern across the lower 48 states is evolving toward a trough in the West, while heights gradually build in the East.", + "final_claim": "The weather pattern across the lower 48 states is evolving toward a trough in the West, while heights gradually build in the East.", + "expected_answer": "True", + "date": "2006-05-25", + "met_entry_idx": 13252, + "true_value": "True", + "mode": "boolean", + "time_indices": "69243:69247:1", + "start_idx": 69243, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2cf8dfafbc9e8b72", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69243:69247:1", + "start_idx": 69243 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78943:78947:1', 'start_idx': 78943}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Some locations may experience dry conditions with temperatures dropping as cool, dry air from the north suppresses moisture from the Gulf of Mexico.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Some locations may receive 1 to 3 inches of rain as humid air from the Gulf of Mexico interacts with advancing cold air.", + "final_claim": "Some locations may experience dry conditions with temperatures dropping as cool, dry air from the north suppresses moisture from the Gulf of Mexico.", + "expected_answer": "False", + "date": "2013-01-13", + "met_entry_idx": 17899, + "true_value": "False", + "mode": "boolean", + "time_indices": "78943:78947:1", + "start_idx": 78943, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "782855c560680e44", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78943:78947:1", + "start_idx": 78943 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45101:45105:1', 'start_idx': 45101}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A strong cold front is expected to move toward the central Mid-Atlantic states by 48 hours.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A strong cold front is expected to move toward the central Mid-Atlantic states by 48 hours.", + "final_claim": "A strong cold front is expected to move toward the central Mid-Atlantic states by 48 hours.", + "expected_answer": "True", + "date": "1989-11-15", + "met_entry_idx": 1320, + "true_value": "True", + "mode": "boolean", + "time_indices": "45101:45105:1", + "start_idx": 45101, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2d907e751db1cf84", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45101:45105:1", + "start_idx": 45101 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73193:73197:1', 'start_idx': 73193}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The longwave pattern features a ridge over the central U.S., with a trough over the Atlantic and a deeper trough over the Pacific.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The longwave pattern features a ridge over the central U.S., with a trough over the Atlantic and a deeper trough over the Pacific.", + "final_claim": "The longwave pattern features a ridge over the central U.S., with a trough over the Atlantic and a deeper trough over the Pacific.", + "expected_answer": "True", + "date": "2009-02-06", + "met_entry_idx": 15214, + "true_value": "True", + "mode": "boolean", + "time_indices": "73193:73197:1", + "start_idx": 73193, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "11a4001a1bbd9657", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73193:73197:1", + "start_idx": 73193 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85139:85143:1', 'start_idx': 85139}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Heavy rain possible over parts of the Pacific Northwest.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Heavy rain possible over parts of the Pacific Northwest.", + "final_claim": "Heavy rain possible over parts of the Pacific Northwest.", + "expected_answer": "True", + "date": "2017-04-11", + "met_entry_idx": 20946, + "true_value": "True", + "mode": "boolean", + "time_indices": "85139:85143:1", + "start_idx": 85139, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e6f0168f283417b1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85139:85143:1", + "start_idx": 85139 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83515:83519:1', 'start_idx': 83515}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: DRY AND MILD CONDITIONS OVER THE NORTHERN CASCADES.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "HEAVY SNOW OVER THE NORTHERN CASCADES.", + "final_claim": "DRY AND MILD CONDITIONS OVER THE NORTHERN CASCADES.", + "expected_answer": "False", + "date": "2016-03-01", + "met_entry_idx": 20162, + "true_value": "False", + "mode": "boolean", + "time_indices": "83515:83519:1", + "start_idx": 83515, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "827b634d27b58301", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83515:83519:1", + "start_idx": 83515 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64435:64439:1', 'start_idx': 64435}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Positioned away from a shallow polar high south of Hudson Bay and a weakening upper trough east of British Columbia, weak southeasterly flow will diminish the transport of shortwave energy across the northern tier states east of the Rockies, while the southern stream suppresses energy movement from northern Baja California to the Ohio Valley.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Sandwiched between a deep polar low north of Hudson Bay and a persistent upper high west of British Columbia, strong northwesterly flow will continue to direct shortwave energy across the northern tier states east of the Rockies, while the southern stream ushers energy from northern Baja California to the Ohio Valley.", + "final_claim": "Positioned away from a shallow polar high south of Hudson Bay and a weakening upper trough east of British Columbia, weak southeasterly flow will diminish the transport of shortwave energy across the northern tier states east of the Rockies, while the southern stream suppresses energy movement from northern Baja California to the Ohio Valley.", + "expected_answer": "False", + "date": "2003-02-08", + "met_entry_idx": 10860, + "true_value": "False", + "mode": "boolean", + "time_indices": "64435:64439:1", + "start_idx": 64435, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "81f3b83d9aa182be", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64435:64439:1", + "start_idx": 64435 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77453:77457:1', 'start_idx': 77453}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Temperatures will be 10 to 15 degrees above average for most of the eastern two thirds of the country.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Temperatures will be 10 to 15 degrees above average for most of the eastern two thirds of the country.", + "final_claim": "Temperatures will be 10 to 15 degrees above average for most of the eastern two thirds of the country.", + "expected_answer": "True", + "date": "2012-01-07", + "met_entry_idx": 17164, + "true_value": "True", + "mode": "boolean", + "time_indices": "77453:77457:1", + "start_idx": 77453, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "50427a6066075b9e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77453:77457:1", + "start_idx": 77453 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84767:84771:1', 'start_idx': 84767}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Widespread rain and mountain snow is expected to persist across much of the western U.S. as low pressure systems from the Pacific move into the West Coast.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Widespread rain and mountain snow is expected to persist across much of the western U.S. as low pressure systems from the Pacific move into the West Coast.", + "final_claim": "Widespread rain and mountain snow is expected to persist across much of the western U.S. as low pressure systems from the Pacific move into the West Coast.", + "expected_answer": "True", + "date": "2017-01-08", + "met_entry_idx": 20774, + "true_value": "True", + "mode": "boolean", + "time_indices": "84767:84771:1", + "start_idx": 84767, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2670f87d9680f5f3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84767:84771:1", + "start_idx": 84767 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61585:61589:1', 'start_idx': 61585}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Two southern stream vortex centers are moving south along the California coast, drifting inland over northern Baja California, where they will weaken before interacting with a northern stream shortwave moving south into the northern and central Great Basin.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Two southern stream vortex centers are moving south along the California coast, drifting inland over northern Baja California, where they will weaken before interacting with a northern stream shortwave moving south into the northern and central Great Basin.", + "final_claim": "Two southern stream vortex centers are moving south along the California coast, drifting inland over northern Baja California, where they will weaken before interacting with a northern stream shortwave moving south into the northern and central Great Basin.", + "expected_answer": "True", + "date": "2001-02-26", + "met_entry_idx": 9441, + "true_value": "True", + "mode": "boolean", + "time_indices": "61585:61589:1", + "start_idx": 61585, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "11d66bf212244a1a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61585:61589:1", + "start_idx": 61585 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49199:49203:1', 'start_idx': 49199}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A broad upper trough will evolve over the western half of the U.S. as a series of northern stream shortwaves dig into the northern Intermountain region and then lift east-northeast along the Canadian border.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A broad upper trough will evolve over the western half of the U.S. as a series of northern stream shortwaves dig into the northern Intermountain region and then lift east-northeast along the Canadian border.", + "final_claim": "A broad upper trough will evolve over the western half of the U.S. as a series of northern stream shortwaves dig into the northern Intermountain region and then lift east-northeast along the Canadian border.", + "expected_answer": "True", + "date": "1992-09-04", + "met_entry_idx": 3272, + "true_value": "True", + "mode": "boolean", + "time_indices": "49199:49203:1", + "start_idx": 49199, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b6f09dc24a49b319", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49199:49203:1", + "start_idx": 49199 + } + } +] \ No newline at end of file diff --git a/level2d_part2.json b/level2d_part2.json new file mode 100644 index 0000000000000000000000000000000000000000..010015e21f4b8797255055bef595952d3ff56514 --- /dev/null +++ b/level2d_part2.json @@ -0,0 +1,1478 @@ +[ + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80521:80525:1', 'start_idx': 80521}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and stable conditions have persisted to the north of a frontal boundary stretched from the Southeast coast back into the northern Gulf.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Widespread precipitation has developed to the north of a frontal boundary stretched from the Southeast coast back into the northern Gulf.", + "final_claim": "Dry and stable conditions have persisted to the north of a frontal boundary stretched from the Southeast coast back into the northern Gulf.", + "expected_answer": "False", + "date": "2014-02-12", + "met_entry_idx": 18678, + "true_value": "False", + "mode": "boolean", + "time_indices": "80521:80525:1", + "start_idx": 80521, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ba1a788abe65c7d7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80521:80525:1", + "start_idx": 80521 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93539:93543:1', 'start_idx': 93539}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dangerous heat will continue for the Desert Southwest, Southern Plains and Lower Mississippi Valley.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Dangerous heat will continue for the Desert Southwest, Southern Plains and Lower Mississippi Valley.", + "final_claim": "Dangerous heat will continue for the Desert Southwest, Southern Plains and Lower Mississippi Valley.", + "expected_answer": "True", + "date": "2023-08-04", + "met_entry_idx": 25195, + "true_value": "True", + "mode": "boolean", + "time_indices": "93539:93543:1", + "start_idx": 93539, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0ab2c13b1aa29c26", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93539:93543:1", + "start_idx": 93539 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46275:46279:1', 'start_idx': 46275}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Over the next two days, the ridge of high pressure is expected to weaken slightly and sag southward from the Missouri area to near Arkansas in response to short wave troughs moving eastward along the Canadian border.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Over the next two days, the ridge of high pressure is expected to weaken slightly and sag southward from the Missouri area to near Arkansas in response to short wave troughs moving eastward along the Canadian border.", + "final_claim": "Over the next two days, the ridge of high pressure is expected to weaken slightly and sag southward from the Missouri area to near Arkansas in response to short wave troughs moving eastward along the Canadian border.", + "expected_answer": "True", + "date": "1990-09-04", + "met_entry_idx": 1902, + "true_value": "True", + "mode": "boolean", + "time_indices": "46275:46279:1", + "start_idx": 46275, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2abf046b1639097e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46275:46279:1", + "start_idx": 46275 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73047:73051:1', 'start_idx': 73047}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The Cascades and the Northern Rockies can expect dry conditions with little to no snowfall this evening.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The Cascades and the Northern Rockies can anticipate moderate to locally heavy snows this evening.", + "final_claim": "The Cascades and the Northern Rockies can expect dry conditions with little to no snowfall this evening.", + "expected_answer": "False", + "date": "2008-12-31", + "met_entry_idx": 15141, + "true_value": "False", + "mode": "boolean", + "time_indices": "73047:73051:1", + "start_idx": 73047, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "abf60819b9233145", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73047:73051:1", + "start_idx": 73047 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67409:67413:1', 'start_idx': 67409}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Smaller pockets of heavier precipitation will persist after this time, but as the closed low shifts eastward and the upper level energy becomes weaker and more disorganized, the heaviest precipitation affecting the central and southern coastline should begin to subside.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Smaller pockets of heavier precipitation will persist after this time, but as the closed low shifts eastward and the upper level energy becomes weaker and more disorganized, the heaviest precipitation affecting the central and southern coastline should begin to subside.", + "final_claim": "Smaller pockets of heavier precipitation will persist after this time, but as the closed low shifts eastward and the upper level energy becomes weaker and more disorganized, the heaviest precipitation affecting the central and southern coastline should begin to subside.", + "expected_answer": "True", + "date": "2005-02-21", + "met_entry_idx": 12342, + "true_value": "True", + "mode": "boolean", + "time_indices": "67409:67413:1", + "start_idx": 67409, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "aa5273f1a08a8057", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67409:67413:1", + "start_idx": 67409 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53061:53065:1', 'start_idx': 53061}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Significant shifts in the synoptic pattern are anticipated across North America over the next two days.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Little change is expected in the overall pattern over North America for the next two days.", + "final_claim": "Significant shifts in the synoptic pattern are anticipated across North America over the next two days.", + "expected_answer": "False", + "date": "1995-04-28", + "met_entry_idx": 5202, + "true_value": "False", + "mode": "boolean", + "time_indices": "53061:53065:1", + "start_idx": 53061, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "8de3c49223a2dd7a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53061:53065:1", + "start_idx": 53061 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86305:86309:1', 'start_idx': 86305}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and mild conditions are forecasted over the Northern Cascades.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Heavy snow is expected over the Northern Cascades.", + "final_claim": "Dry and mild conditions are forecasted over the Northern Cascades.", + "expected_answer": "False", + "date": "2018-01-28", + "met_entry_idx": 21511, + "true_value": "False", + "mode": "boolean", + "time_indices": "86305:86309:1", + "start_idx": 86305, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4047fb5af75ba471", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86305:86309:1", + "start_idx": 86305 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64477:64481:1', 'start_idx': 64477}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The first shortwave moving southeastward from Washington to Colorado overnight is expected to weaken without generating any significant precipitation, resulting in dry and stable conditions across the Rockies.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The first shortwave moving southeastward from Washington to Colorado overnight should produce light snow in the Rockies as it weakens.", + "final_claim": "The first shortwave moving southeastward from Washington to Colorado overnight is expected to weaken without generating any significant precipitation, resulting in dry and stable conditions across the Rockies.", + "expected_answer": "False", + "date": "2003-02-19", + "met_entry_idx": 10881, + "true_value": "False", + "mode": "boolean", + "time_indices": "64477:64481:1", + "start_idx": 64477, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9a42ad81005283ab", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64477:64481:1", + "start_idx": 64477 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86557:86561:1', 'start_idx': 86557}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Central Nebraska is under a winter storm warning.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Central Nebraska is under a winter storm warning.", + "final_claim": "Central Nebraska is under a winter storm warning.", + "expected_answer": "True", + "date": "2018-04-01", + "met_entry_idx": 21633, + "true_value": "True", + "mode": "boolean", + "time_indices": "86557:86561:1", + "start_idx": 86557, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ce54ffa9c095e398", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86557:86561:1", + "start_idx": 86557 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92045:92049:1', 'start_idx': 92045}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: There is a Slight Risk of below-normal precipitation leading to dry conditions over parts of the Tennessee Valley, Central/Southern Appalachians, and Southeast through today.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "There is a Slight Risk of excessive rainfall over parts of the Tennessee Valley, Central/Southern Appalachians, and Southeast through today.", + "final_claim": "There is a Slight Risk of below-normal precipitation leading to dry conditions over parts of the Tennessee Valley, Central/Southern Appalachians, and Southeast through today.", + "expected_answer": "False", + "date": "2022-01-02", + "met_entry_idx": 24135, + "true_value": "False", + "mode": "boolean", + "time_indices": "92045:92049:1", + "start_idx": 92045, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9720262c3da3e40c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92045:92049:1", + "start_idx": 92045 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63683:63687:1', 'start_idx': 63683}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Higher heights and the passage of a warm front will bring much warmer and more humid weather northward across the Great Lakes and Northeast.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Lower heights and the passage of a cold front will bring much cooler and drier weather southward across the Great Lakes and Northeast.", + "final_claim": "Higher heights and the passage of a warm front will bring much warmer and more humid weather northward across the Great Lakes and Northeast.", + "expected_answer": "False", + "date": "2002-08-04", + "met_entry_idx": 10484, + "true_value": "False", + "mode": "boolean", + "time_indices": "63683:63687:1", + "start_idx": 63683, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "8041543ede2268db", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63683:63687:1", + "start_idx": 63683 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73053:73057:1', 'start_idx': 73053}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A warm front over the Upper Great Lakes will stall and intensify inland, while high pressure builds offshore over the western Atlantic, preventing the development of any new boundaries along the Northeast Coast.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "An Alberta Clipper over the Upper Great Lakes will move quickly to the Northeast Coast and dissipate as a new boundary develops just off the coast over the western Atlantic.", + "final_claim": "A warm front over the Upper Great Lakes will stall and intensify inland, while high pressure builds offshore over the western Atlantic, preventing the development of any new boundaries along the Northeast Coast.", + "expected_answer": "False", + "date": "2009-01-02", + "met_entry_idx": 15144, + "true_value": "False", + "mode": "boolean", + "time_indices": "73053:73057:1", + "start_idx": 73053, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f8215c2ba42a0cd5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73053:73057:1", + "start_idx": 73053 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78761:78765:1', 'start_idx': 78761}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Flood watches are in effect for northern California and southern Oregon where some locations may receive in excess of six inches of rainfall with this event.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Flood watches are in effect for northern California and southern Oregon where some locations may receive in excess of six inches of rainfall with this event.", + "final_claim": "Flood watches are in effect for northern California and southern Oregon where some locations may receive in excess of six inches of rainfall with this event.", + "expected_answer": "True", + "date": "2012-11-29", + "met_entry_idx": 17808, + "true_value": "True", + "mode": "boolean", + "time_indices": "78761:78765:1", + "start_idx": 78761, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "7133f41a69dbb6c9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78761:78765:1", + "start_idx": 78761 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80673:80677:1', 'start_idx': 80673}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Further south, a gradual return of Gulf moisture will allow for increased precipitation along the low's trailing cold front while it pushes south and east out of the Plains and Midwest.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Further south, a gradual return of Gulf moisture will allow for increased precipitation along the low's trailing cold front while it pushes south and east out of the Plains and Midwest.", + "final_claim": "Further south, a gradual return of Gulf moisture will allow for increased precipitation along the low's trailing cold front while it pushes south and east out of the Plains and Midwest.", + "expected_answer": "True", + "date": "2014-03-22", + "met_entry_idx": 18754, + "true_value": "True", + "mode": "boolean", + "time_indices": "80673:80677:1", + "start_idx": 80673, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4d0a324836da0529", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80673:80677:1", + "start_idx": 80673 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88081:88085:1', 'start_idx': 88081}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Several locations will experience calm and stable weather today as a high-pressure system settles over the Great Plains.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Several locations will see a threat for severe thunderstorms today as a two-part upper level storm system moves into the Great Plains.", + "final_claim": "Several locations will experience calm and stable weather today as a high-pressure system settles over the Great Plains.", + "expected_answer": "False", + "date": "2019-04-17", + "met_entry_idx": 22349, + "true_value": "False", + "mode": "boolean", + "time_indices": "88081:88085:1", + "start_idx": 88081, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9bb9878fb46acc8e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88081:88085:1", + "start_idx": 88081 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89763:89767:1', 'start_idx': 89763}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Severe thunderstorms and heavy rain are likely this evening across the Great Lakes and Ohio Valley, extending into the Mid-Atlantic and Northeast overnight.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Severe thunderstorms and heavy rain are likely this evening across the Great Lakes and Ohio Valley, extending into the Mid-Atlantic and Northeast overnight.", + "final_claim": "Severe thunderstorms and heavy rain are likely this evening across the Great Lakes and Ohio Valley, extending into the Mid-Atlantic and Northeast overnight.", + "expected_answer": "True", + "date": "2020-06-10", + "met_entry_idx": 23100, + "true_value": "True", + "mode": "boolean", + "time_indices": "89763:89767:1", + "start_idx": 89763, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "27fc455d00f50078", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89763:89767:1", + "start_idx": 89763 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46263:46267:1', 'start_idx': 46263}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Warm upper anticyclone will continue to give warm and dry conditions to most of the interior portion of the country.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Warm upper anticyclone will continue to give warm and dry conditions to most of the interior portion of the country.", + "final_claim": "Warm upper anticyclone will continue to give warm and dry conditions to most of the interior portion of the country.", + "expected_answer": "True", + "date": "1990-09-01", + "met_entry_idx": 1896, + "true_value": "True", + "mode": "boolean", + "time_indices": "46263:46267:1", + "start_idx": 46263, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1180652bbcb0ac10", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46263:46267:1", + "start_idx": 46263 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77579:77583:1', 'start_idx': 77579}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: On the northern edge of this precipitation, a band of light snow is likely from West Virginia eastward along the Mason-Dixon line.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "On the northern edge of this precipitation, a band of light snow is likely from West Virginia eastward along the Mason-Dixon line.", + "final_claim": "On the northern edge of this precipitation, a band of light snow is likely from West Virginia eastward along the Mason-Dixon line.", + "expected_answer": "True", + "date": "2012-02-07", + "met_entry_idx": 17224, + "true_value": "True", + "mode": "boolean", + "time_indices": "77579:77583:1", + "start_idx": 77579, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9d8e7cccdacc8a80", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77579:77583:1", + "start_idx": 77579 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44771:44775:1', 'start_idx': 44771}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A ridge in the western U.S. upper atmosphere is expected to build southwestward away from eastern Saskatchewan over the next 48 hours.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A short wave in the western U.S. upper trough is forecast to shear northeastward to eastern Saskatchewan over the next 48 hours.", + "final_claim": "A ridge in the western U.S. upper atmosphere is expected to build southwestward away from eastern Saskatchewan over the next 48 hours.", + "expected_answer": "False", + "date": "1989-08-24", + "met_entry_idx": 1160, + "true_value": "False", + "mode": "boolean", + "time_indices": "44771:44775:1", + "start_idx": 44771, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b2d300bff00f04cd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44771:44775:1", + "start_idx": 44771 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58019:58023:1', 'start_idx': 58019}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The southwestern portion of the trough will eventually close off over the Intermountain Region.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The southwestern portion of the trough will eventually close off over the Intermountain Region.", + "final_claim": "The southwestern portion of the trough will eventually close off over the Intermountain Region.", + "expected_answer": "True", + "date": "1998-09-18", + "met_entry_idx": 7668, + "true_value": "True", + "mode": "boolean", + "time_indices": "58019:58023:1", + "start_idx": 58019, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "67df538d56fd76b9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58019:58023:1", + "start_idx": 58019 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84867:84871:1', 'start_idx': 84867}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: ABSENCE OF LAKE EFFECT SNOW DOWNWIND FROM LAKES SUPERIOR, HURON, AND ONTARIO.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "LAKE EFFECT SNOW DOWNWIND FROM THE LAKES SUPERIOR, HURON AND ONTARIO.", + "final_claim": "ABSENCE OF LAKE EFFECT SNOW DOWNWIND FROM LAKES SUPERIOR, HURON, AND ONTARIO.", + "expected_answer": "False", + "date": "2017-02-02", + "met_entry_idx": 20824, + "true_value": "False", + "mode": "boolean", + "time_indices": "84867:84871:1", + "start_idx": 84867, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a74b92837e648799", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84867:84871:1", + "start_idx": 84867 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71113:71117:1', 'start_idx': 71113}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A persistent ridge of high pressure across the Mississippi Valley will still yield some locally hot weather across the Midwest and Plains, but this ridge will weaken over the next couple of days as energy from the southern Plains and lower Mississippi Valley lifts northeastward.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A persistent ridge of high pressure across the Mississippi Valley will still yield some locally hot weather across the Midwest and Plains, but this ridge will weaken over the next couple of days as energy from the southern Plains and lower Mississippi Valley lifts northeastward.", + "final_claim": "A persistent ridge of high pressure across the Mississippi Valley will still yield some locally hot weather across the Midwest and Plains, but this ridge will weaken over the next couple of days as energy from the southern Plains and lower Mississippi Valley lifts northeastward.", + "expected_answer": "True", + "date": "2007-09-05", + "met_entry_idx": 14179, + "true_value": "True", + "mode": "boolean", + "time_indices": "71113:71117:1", + "start_idx": 71113, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "44f5ff270518f606", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71113:71117:1", + "start_idx": 71113 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89149:89153:1', 'start_idx': 89149}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Light snow and freezing rain are possible for portions of the Plains, Mississippi Valley, and Great Lakes.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Light snow and freezing rain are possible for portions of the Plains, Mississippi Valley, and Great Lakes.", + "final_claim": "Light snow and freezing rain are possible for portions of the Plains, Mississippi Valley, and Great Lakes.", + "expected_answer": "True", + "date": "2020-01-09", + "met_entry_idx": 22838, + "true_value": "True", + "mode": "boolean", + "time_indices": "89149:89153:1", + "start_idx": 89149, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e5771a9df6b3898d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89149:89153:1", + "start_idx": 89149 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91347:91351:1', 'start_idx': 91347}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry conditions with below-average precipitation and minimal flood risk are anticipated for the Northeast, Midwest, and the South.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Heavy rain and potential flooding are expected for the Northeast, Midwest, and the South.", + "final_claim": "Dry conditions with below-average precipitation and minimal flood risk are anticipated for the Northeast, Midwest, and the South.", + "expected_answer": "False", + "date": "2021-07-11", + "met_entry_idx": 23812, + "true_value": "False", + "mode": "boolean", + "time_indices": "91347:91351:1", + "start_idx": 91347, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6a551f779c3c3f7a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91347:91351:1", + "start_idx": 91347 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79415:79419:1', 'start_idx': 79415}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: LATE SEASON DRY AND WARM CONDITIONS ARE EXPECTED OVER THE UPPER GREAT LAKES.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "LATE SEASON SNOW IS FORECAST OVER THE UPPER GREAT LAKES.", + "final_claim": "LATE SEASON DRY AND WARM CONDITIONS ARE EXPECTED OVER THE UPPER GREAT LAKES.", + "expected_answer": "False", + "date": "2013-05-11", + "met_entry_idx": 18130, + "true_value": "False", + "mode": "boolean", + "time_indices": "79415:79419:1", + "start_idx": 79415, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1dff6f90ed607944", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79415:79419:1", + "start_idx": 79415 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93539:93543:1', 'start_idx': 93539}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Heavy rain, flash flooding, and severe weather are likely across large portions of the Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Heavy rain, flash flooding, and severe weather are likely across large portions of the Plains.", + "final_claim": "Heavy rain, flash flooding, and severe weather are likely across large portions of the Plains.", + "expected_answer": "True", + "date": "2023-05-12", + "met_entry_idx": 25038, + "true_value": "True", + "mode": "boolean", + "time_indices": "93539:93543:1", + "start_idx": 93539, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "7c4c0bd60d1f0446", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93539:93543:1", + "start_idx": 93539 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86013:86017:1', 'start_idx': 86013}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: High wind, heavy rain, and mountain snows will persist across the Pacific Northwest and California.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "High wind, heavy rain, and mountain snows will persist across the Pacific Northwest and California.", + "final_claim": "High wind, heavy rain, and mountain snows will persist across the Pacific Northwest and California.", + "expected_answer": "True", + "date": "2017-11-16", + "met_entry_idx": 21369, + "true_value": "True", + "mode": "boolean", + "time_indices": "86013:86017:1", + "start_idx": 86013, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "cff9dd7c92b1f15e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86013:86017:1", + "start_idx": 86013 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85925:85929:1', 'start_idx': 85925}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The driest conditions are expected to prevail across Maine with little to no measurable precipitation, as high pressure builds along this front and suppresses convective activity.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The heaviest rainfall is likely to materialize across Maine with 1 to 3 inch rainfall amounts, with isolated higher totals possible as a wave of low pressure develops along this front and intensifies.", + "final_claim": "The driest conditions are expected to prevail across Maine with little to no measurable precipitation, as high pressure builds along this front and suppresses convective activity.", + "expected_answer": "False", + "date": "2017-10-25", + "met_entry_idx": 21332, + "true_value": "False", + "mode": "boolean", + "time_indices": "85925:85929:1", + "start_idx": 85925, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c462b6d191988cb0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85925:85929:1", + "start_idx": 85925 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81163:81167:1', 'start_idx': 81163}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and stable conditions ahead of the boundary will persist from the Upper Great Lakes this evening into the Ohio Valley and Mid-Atlantic/Northeast by tomorrow.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Showers and thunderstorms associated with the boundary will spread from the Upper Great Lakes this evening into the Ohio Valley and Mid-Atlantic/Northeast by tomorrow.", + "final_claim": "Dry and stable conditions ahead of the boundary will persist from the Upper Great Lakes this evening into the Ohio Valley and Mid-Atlantic/Northeast by tomorrow.", + "expected_answer": "False", + "date": "2014-07-22", + "met_entry_idx": 19001, + "true_value": "False", + "mode": "boolean", + "time_indices": "81163:81167:1", + "start_idx": 81163, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1bc7045190a1ec9b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81163:81167:1", + "start_idx": 81163 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62579:62583:1', 'start_idx': 62579}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A lack of Gulf moisture combined with unfavorable jet dynamics will result in a weakening boundary over time, especially from the mid Mississippi Valley northeast into the Canadian Maritimes.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A feed of Gulf moisture surging ahead of the cold front and favorable jet dynamics will lead to a more active boundary over time, especially from the mid Mississippi Valley northeast into the Canadian Maritimes.", + "final_claim": "A lack of Gulf moisture combined with unfavorable jet dynamics will result in a weakening boundary over time, especially from the mid Mississippi Valley northeast into the Canadian Maritimes.", + "expected_answer": "False", + "date": "2001-11-01", + "met_entry_idx": 9933, + "true_value": "False", + "mode": "boolean", + "time_indices": "62579:62583:1", + "start_idx": 62579, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f1a72b723b6a658a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62579:62583:1", + "start_idx": 62579 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45281:45285:1', 'start_idx': 45281}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A potent system moving into the Gulf of Alaska is building a downstream ridge over British Columbia, resulting in the sharpening of the upper trough in the northern stream on day 2, with the axis extending from the Great Lakes to eastern Texas.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A potent system moving into the Gulf of Alaska is building a downstream ridge over British Columbia, resulting in the sharpening of the upper trough in the northern stream on day 2, with the axis extending from the Great Lakes to eastern Texas.", + "final_claim": "A potent system moving into the Gulf of Alaska is building a downstream ridge over British Columbia, resulting in the sharpening of the upper trough in the northern stream on day 2, with the axis extending from the Great Lakes to eastern Texas.", + "expected_answer": "True", + "date": "1989-12-30", + "met_entry_idx": 1409, + "true_value": "True", + "mode": "boolean", + "time_indices": "45281:45285:1", + "start_idx": 45281, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9cb030c5d676a455", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45281:45285:1", + "start_idx": 45281 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93179:93183:1', 'start_idx': 93179}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry conditions expected over southern California, the central Gulf Coast, and the Southeast through tonight, with high pressure building and the threat of precipitation diminishing northeastward.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Flash flood risk over southern California, the central Gulf Coast, and the Southeast through tonight, with the threat shifting northeast.", + "final_claim": "Dry conditions expected over southern California, the central Gulf Coast, and the Southeast through tonight, with high pressure building and the threat of precipitation diminishing northeastward.", + "expected_answer": "False", + "date": "2022-10-12", + "met_entry_idx": 24648, + "true_value": "False", + "mode": "boolean", + "time_indices": "93179:93183:1", + "start_idx": 93179, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "49e40d819f4e4781", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93179:93183:1", + "start_idx": 93179 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50107:50111:1', 'start_idx': 50107}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Late season snows are unlikely along the northern fringe of the precipitation shield from northern Iowa into central Michigan, with conditions expected to be drier and warmer than the previous system due to the weakening upper low.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Some late season snows are likely along the northern fringe of the precipitation shield from northern Iowa into central Michigan, but amounts are not likely to be as heavy as with the last system due to the weakening upper low.", + "final_claim": "Late season snows are unlikely along the northern fringe of the precipitation shield from northern Iowa into central Michigan, with conditions expected to be drier and warmer than the previous system due to the weakening upper low.", + "expected_answer": "False", + "date": "1993-04-19", + "met_entry_idx": 3728, + "true_value": "False", + "mode": "boolean", + "time_indices": "50107:50111:1", + "start_idx": 50107, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "07be2e0a7e9e9fb8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50107:50111:1", + "start_idx": 50107 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56697:56701:1', 'start_idx': 56697}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The lead Pacific shortwave will move through the Great Basin today, sparking scattered rains and snows in Idaho, Wyoming, and northern Utah this morning, and late today in the Four Corners area and the Colorado Rockies.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The lead Pacific shortwave will move through the Great Basin today, sparking scattered rains and snows in Idaho, Wyoming, and northern Utah this morning, and late today in the Four Corners area and the Colorado Rockies.", + "final_claim": "The lead Pacific shortwave will move through the Great Basin today, sparking scattered rains and snows in Idaho, Wyoming, and northern Utah this morning, and late today in the Four Corners area and the Colorado Rockies.", + "expected_answer": "True", + "date": "1997-10-23", + "met_entry_idx": 7010, + "true_value": "True", + "mode": "boolean", + "time_indices": "56697:56701:1", + "start_idx": 56697, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "43a2b909d8867eb0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56697:56701:1", + "start_idx": 56697 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63589:63593:1', 'start_idx': 63589}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The unseasonably cool temperatures will persist across regions as far north as Washington State.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The oppressive heat will continue to affect regions as far north as Washington State.", + "final_claim": "The unseasonably cool temperatures will persist across regions as far north as Washington State.", + "expected_answer": "False", + "date": "2002-07-12", + "met_entry_idx": 10437, + "true_value": "False", + "mode": "boolean", + "time_indices": "63589:63593:1", + "start_idx": 63589, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9e947ef9403ada53", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63589:63593:1", + "start_idx": 63589 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79029:79033:1', 'start_idx': 79029}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A parade of fast moving systems will continue to deliver cold air and light snows from the Upper Midwest to the Northeast and Mid-Atlantic.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A parade of fast moving systems will continue to deliver cold air and light snows from the Upper Midwest to the Northeast and Mid-Atlantic.", + "final_claim": "A parade of fast moving systems will continue to deliver cold air and light snows from the Upper Midwest to the Northeast and Mid-Atlantic.", + "expected_answer": "True", + "date": "2013-02-04", + "met_entry_idx": 17942, + "true_value": "True", + "mode": "boolean", + "time_indices": "79029:79033:1", + "start_idx": 79029, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "8d652ff936b8a7b8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79029:79033:1", + "start_idx": 79029 + } + } +] \ No newline at end of file diff --git a/level2d_part3.json b/level2d_part3.json new file mode 100644 index 0000000000000000000000000000000000000000..fd28a2932fd224c22a4c5085610a75d234e10d98 --- /dev/null +++ b/level2d_part3.json @@ -0,0 +1,2667 @@ +[ + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89249:89253:1', 'start_idx': 89249}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The heaviest snow is expected across the higher terrain of central Wyoming and north central Colorado, with up to a foot of accumulation possible.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The heaviest snow is expected across the higher terrain of central Wyoming and north central Colorado, with up to a foot of accumulation possible.", + "final_claim": "The heaviest snow is expected across the higher terrain of central Wyoming and north central Colorado, with up to a foot of accumulation possible.", + "expected_answer": "True", + "date": "2020-02-03", + "met_entry_idx": 22879, + "true_value": "True", + "mode": "boolean", + "time_indices": "89249:89253:1", + "start_idx": 89249, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "db1df2801d956e2f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89249:89253:1", + "start_idx": 89249 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64341:64345:1', 'start_idx': 64341}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A deep trough will continue to dominate the eastern two thirds of the country with cold air plunging well south to the Gulf Coast states.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A deep trough will continue to dominate the eastern two thirds of the country with cold air plunging well south to the Gulf Coast states.", + "final_claim": "A deep trough will continue to dominate the eastern two thirds of the country with cold air plunging well south to the Gulf Coast states.", + "expected_answer": "True", + "date": "2003-01-16", + "met_entry_idx": 10813, + "true_value": "True", + "mode": "boolean", + "time_indices": "64341:64345:1", + "start_idx": 64341, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d3e15a5d44be717e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64341:64345:1", + "start_idx": 64341 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78537:78541:1', 'start_idx': 78537}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A weakening high-pressure system is expected to bring a period of dry and calm conditions with light winds in northwestern Minnesota.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A rapidly intensifying storm is expected to bring a period of snow along with strong gusty winds in northwestern Minnesota.", + "final_claim": "A weakening high-pressure system is expected to bring a period of dry and calm conditions with light winds in northwestern Minnesota.", + "expected_answer": "False", + "date": "2012-10-04", + "met_entry_idx": 17695, + "true_value": "False", + "mode": "boolean", + "time_indices": "78537:78541:1", + "start_idx": 78537, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "86412bb72410a9b1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78537:78541:1", + "start_idx": 78537 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64081:64085:1', 'start_idx': 64081}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: AT THE SURFACE, WAVY NORTH-SOUTH FRONTAL BOUNDARY WILL MOVE EASTWARD FROM OFF THE ATLANTIC SEABOARD.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "AT THE SURFACE, WAVY NORTH-SOUTH FRONTAL BOUNDARY WILL MOVE EASTWARD FROM OFF THE ATLANTIC SEABOARD.", + "final_claim": "AT THE SURFACE, WAVY NORTH-SOUTH FRONTAL BOUNDARY WILL MOVE EASTWARD FROM OFF THE ATLANTIC SEABOARD.", + "expected_answer": "True", + "date": "2002-11-12", + "met_entry_idx": 10683, + "true_value": "True", + "mode": "boolean", + "time_indices": "64081:64085:1", + "start_idx": 64081, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6658b6725d6c5777", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64081:64085:1", + "start_idx": 64081 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93539:93543:1', 'start_idx': 93539}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and mild conditions will prevail across portions of the Southern Rockies and High Plains beginning today.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A winter storm will impact portions of the Southern Rockies and High Plains beginning today.", + "final_claim": "Dry and mild conditions will prevail across portions of the Southern Rockies and High Plains beginning today.", + "expected_answer": "False", + "date": "2023-12-13", + "met_entry_idx": 25442, + "true_value": "False", + "mode": "boolean", + "time_indices": "93539:93543:1", + "start_idx": 93539, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1bd1e52698da2c90", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93539:93543:1", + "start_idx": 93539 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59755:59759:1', 'start_idx': 59755}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry air to the northwest of the boundary from West Virginia to western New York is associated with upper-level subsidence featuring a ridge axis extending through the eastern Ohio Valley, which will shift north into eastern Canada tonight.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Moisture to the northwest of the boundary from West Virginia to western New York is associated with upper support featuring an axis of shortwave energy extending through the eastern Ohio Valley, which will lift north into eastern Canada tonight.", + "final_claim": "Dry air to the northwest of the boundary from West Virginia to western New York is associated with upper-level subsidence featuring a ridge axis extending through the eastern Ohio Valley, which will shift north into eastern Canada tonight.", + "expected_answer": "False", + "date": "1999-11-26", + "met_entry_idx": 8535, + "true_value": "False", + "mode": "boolean", + "time_indices": "59755:59759:1", + "start_idx": 59755, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0709fd5ff999b9a6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59755:59759:1", + "start_idx": 59755 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73687:73691:1', 'start_idx': 73687}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A quasi-stationary front extending from the Northeast to the Central Plains and into the Rockies will result in predominantly dry and stable conditions near the boundary as high pressure ridges build along the front from west to east.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A quasi-stationary front extending from the Northeast to the Central Plains and into the Rockies will be the focus for showers and thunderstorms near the boundary as waves of low pressure move along the front from west to east.", + "final_claim": "A quasi-stationary front extending from the Northeast to the Central Plains and into the Rockies will result in predominantly dry and stable conditions near the boundary as high pressure ridges build along the front from west to east.", + "expected_answer": "False", + "date": "2009-06-09", + "met_entry_idx": 15461, + "true_value": "False", + "mode": "boolean", + "time_indices": "73687:73691:1", + "start_idx": 73687, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "1b87072f1594cd5e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73687:73691:1", + "start_idx": 73687 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50571:50575:1', 'start_idx': 50571}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The upper trough in the Northeast weakens as it moves into the western Atlantic, while an upper low drops into the Pacific Northwest.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "The upper trough in the Northeast weakens as it moves into the western Atlantic, while an upper low drops into the Pacific Northwest.", + "final_claim": "The upper trough in the Northeast weakens as it moves into the western Atlantic, while an upper low drops into the Pacific Northwest.", + "expected_answer": "True", + "date": "1993-08-13", + "met_entry_idx": 3959, + "true_value": "True", + "mode": "boolean", + "time_indices": "50571:50575:1", + "start_idx": 50571, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "43f492193232a96d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50571:50575:1", + "start_idx": 50571 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52181:52185:1', 'start_idx': 52181}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: In the east, the polar vortex east of Hudson Bay will weaken while remaining stationary and not extending toward Greenland.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "In the east, the polar vortex east of Hudson Bay will fill while moving eastward to Greenland.", + "final_claim": "In the east, the polar vortex east of Hudson Bay will weaken while remaining stationary and not extending toward Greenland.", + "expected_answer": "False", + "date": "1994-09-20", + "met_entry_idx": 4762, + "true_value": "False", + "mode": "boolean", + "time_indices": "52181:52185:1", + "start_idx": 52181, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9ef65dcb07a62916", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52181:52185:1", + "start_idx": 52181 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65880:65884:1', 'start_idx': 65880}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A deep upper trough will slowly progress across the nation's midsection during the next two days, sending another winter storm into the eastern states.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A deep upper trough will slowly progress across the nation's midsection during the next two days, sending another winter storm into the eastern states.", + "final_claim": "A deep upper trough will slowly progress across the nation's midsection during the next two days, sending another winter storm into the eastern states.", + "expected_answer": "True", + "date": "2004-02-05", + "met_entry_idx": 11580, + "true_value": "True", + "mode": "boolean", + "time_indices": "65880:65884:1", + "start_idx": 65880, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d58d6b29de9e0faf", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65880:65884:1", + "start_idx": 65880 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57071:57075:1', 'start_idx': 57071}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A series of shortwaves will lift into the Pacific Northwest, move eastward through the northern Rockies and Plains, and shear northeast across the eastern seaboard.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A series of shortwaves will lift into the Pacific Northwest, move eastward through the northern Rockies and Plains, and shear northeast across the eastern seaboard.", + "final_claim": "A series of shortwaves will lift into the Pacific Northwest, move eastward through the northern Rockies and Plains, and shear northeast across the eastern seaboard.", + "expected_answer": "True", + "date": "1998-01-24", + "met_entry_idx": 7196, + "true_value": "True", + "mode": "boolean", + "time_indices": "57071:57075:1", + "start_idx": 57071, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "cf509818e5282f0e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57071:57075:1", + "start_idx": 57071 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66673:66677:1', 'start_idx': 66673}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: No significant short wave disturbances are expected to move across the Great Lakes today.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Another short wave will move across the Great Lakes today.", + "final_claim": "No significant short wave disturbances are expected to move across the Great Lakes today.", + "expected_answer": "False", + "date": "2004-08-21", + "met_entry_idx": 11974, + "true_value": "False", + "mode": "boolean", + "time_indices": "66673:66677:1", + "start_idx": 66673, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f558af59ef2ae2f1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66673:66677:1", + "start_idx": 66673 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59527:59531:1', 'start_idx': 59527}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A rather uneventful weather pattern is in store for the Lower 48 states following a potent cold front sweeping off the New England and Mid-Atlantic coasts.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A rather uneventful weather pattern is in store for the Lower 48 states following a potent cold front sweeping off the New England and Mid-Atlantic coasts.", + "final_claim": "A rather uneventful weather pattern is in store for the Lower 48 states following a potent cold front sweeping off the New England and Mid-Atlantic coasts.", + "expected_answer": "True", + "date": "1999-09-30", + "met_entry_idx": 8421, + "true_value": "True", + "mode": "boolean", + "time_indices": "59527:59531:1", + "start_idx": 59527, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4d48fb3aca8f4fbb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59527:59531:1", + "start_idx": 59527 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48025:48029:1', 'start_idx': 48025}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The first system is the upper ridge over the Southwest states that will remain stationary on Day 1.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The first system is the upper low over the Southwest states that will track steadily eastward on Day 1.", + "final_claim": "The first system is the upper ridge over the Southwest states that will remain stationary on Day 1.", + "expected_answer": "False", + "date": "1991-11-16", + "met_entry_idx": 2689, + "true_value": "False", + "mode": "boolean", + "time_indices": "48025:48029:1", + "start_idx": 48025, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "420889dc9f19244a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48025:48029:1", + "start_idx": 48025 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72737:72741:1', 'start_idx': 72737}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and stable conditions are expected to prevail mostly across the southern half of the system from the South Central U.S. to the central Gulf Coast.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Showers and thunderstorms will stay mostly with the southern half of the system from the South Central U.S. to the central Gulf Coast.", + "final_claim": "Dry and stable conditions are expected to prevail mostly across the southern half of the system from the South Central U.S. to the central Gulf Coast.", + "expected_answer": "False", + "date": "2008-10-15", + "met_entry_idx": 14986, + "true_value": "False", + "mode": "boolean", + "time_indices": "72737:72741:1", + "start_idx": 72737, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "77a14c2611022d42", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72737:72741:1", + "start_idx": 72737 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63693:63697:1', 'start_idx': 63693}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A trough in the West will move toward the northern Rockies, pushing a cold front into the northern Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A trough in the West will move toward the northern Rockies, pushing a cold front into the northern Plains.", + "final_claim": "A trough in the West will move toward the northern Rockies, pushing a cold front into the northern Plains.", + "expected_answer": "True", + "date": "2002-08-07", + "met_entry_idx": 10489, + "true_value": "True", + "mode": "boolean", + "time_indices": "63693:63697:1", + "start_idx": 63693, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0af947688b24448b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63693:63697:1", + "start_idx": 63693 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65685:65689:1', 'start_idx': 65685}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Meridional flow will prevail across the US, with an upper ridge over the West and an upper trough over the East.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Meridional flow will prevail across the US, with an upper ridge over the West and an upper trough over the East.", + "final_claim": "Meridional flow will prevail across the US, with an upper ridge over the West and an upper trough over the East.", + "expected_answer": "True", + "date": "2003-12-18", + "met_entry_idx": 11482, + "true_value": "True", + "mode": "boolean", + "time_indices": "65685:65689:1", + "start_idx": 65685, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e9ab3f16de511206", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65685:65689:1", + "start_idx": 65685 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72155:72159:1', 'start_idx': 72155}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A predominantly stable pattern is expected for much of the Central and Western U.S. over the next few days as a strong upper level ridge builds over the West.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A very active pattern is expected for much of the Central and Western U.S. over the next few days as a strong upper level trough continues over the West.", + "final_claim": "A predominantly stable pattern is expected for much of the Central and Western U.S. over the next few days as a strong upper level ridge builds over the West.", + "expected_answer": "False", + "date": "2008-05-22", + "met_entry_idx": 14696, + "true_value": "False", + "mode": "boolean", + "time_indices": "72155:72159:1", + "start_idx": 72155, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "425f806e44b7dd91", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72155:72159:1", + "start_idx": 72155 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50505:50509:1', 'start_idx': 50505}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: This will prevent a strong Pacific front from reaching the Pacific Northwest on day 1 and will keep the northern Rockies under stable, high-pressure conditions on day 2.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "This will push a strong Pacific front into the Pacific Northwest on day 1 and into the northern Rockies on day 2.", + "final_claim": "This will prevent a strong Pacific front from reaching the Pacific Northwest on day 1 and will keep the northern Rockies under stable, high-pressure conditions on day 2.", + "expected_answer": "False", + "date": "1993-07-28", + "met_entry_idx": 3926, + "true_value": "False", + "mode": "boolean", + "time_indices": "50505:50509:1", + "start_idx": 50505, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4d3b799f7fc6e1b8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50505:50509:1", + "start_idx": 50505 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54819:54823:1', 'start_idx': 54819}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: This boundary will provide the focusing mechanism for a prolonged period of wet weather across the southern Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "This boundary will provide the focusing mechanism for a prolonged period of wet weather across the southern Plains.", + "final_claim": "This boundary will provide the focusing mechanism for a prolonged period of wet weather across the southern Plains.", + "expected_answer": "True", + "date": "1996-07-10", + "met_entry_idx": 6077, + "true_value": "True", + "mode": "boolean", + "time_indices": "54819:54823:1", + "start_idx": 54819, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "8d76df291db31122", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54819:54823:1", + "start_idx": 54819 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43947:43951:1', 'start_idx': 43947}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: NO SIGNIFICANT POLAR FRONT IS EXPECTED ACROSS NORTHERN STATES FROM WEST COAST TO SAINT LAWRENCE RIVER VALLEY IN 48 HOURS, WITH STABLE HIGH PRESSURE DOMINATING THE REGION.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "POLAR FRONT WILL BE LOCATED ACROSS NORTHERN STATES FROM WEST COAST TO SAINT LAWRENCE RIVER VALLEY IN 48 HOURS.", + "final_claim": "NO SIGNIFICANT POLAR FRONT IS EXPECTED ACROSS NORTHERN STATES FROM WEST COAST TO SAINT LAWRENCE RIVER VALLEY IN 48 HOURS, WITH STABLE HIGH PRESSURE DOMINATING THE REGION.", + "expected_answer": "False", + "date": "1989-01-30", + "met_entry_idx": 753, + "true_value": "False", + "mode": "boolean", + "time_indices": "43947:43951:1", + "start_idx": 43947, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c65a63d85e68c475", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43947:43951:1", + "start_idx": 43947 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48267:48271:1', 'start_idx': 48267}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Significant changes in the large scale pattern are expected over the next 2 days as an upper ridge builds across much of the nation east of the Rockies while an upper trough develops along the West Coast.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "There will be very little change in the large scale pattern during the next 2 days as a broad upper trough covers much of the nation east of the Rockies while an upper ridge remains along the West Coast.", + "final_claim": "Significant changes in the large scale pattern are expected over the next 2 days as an upper ridge builds across much of the nation east of the Rockies while an upper trough develops along the West Coast.", + "expected_answer": "False", + "date": "1992-01-15", + "met_entry_idx": 2810, + "true_value": "False", + "mode": "boolean", + "time_indices": "48267:48271:1", + "start_idx": 48267, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "93be93d7385575dc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48267:48271:1", + "start_idx": 48267 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64965:64969:1', 'start_idx': 64965}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The tail end of a surface front associated with this system will clear out of the eastern Gulf states, resulting in subsidence and dry conditions there.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The tail end of a surface front associated with this system will remain over the eastern Gulf states, focusing convection there.", + "final_claim": "The tail end of a surface front associated with this system will clear out of the eastern Gulf states, resulting in subsidence and dry conditions there.", + "expected_answer": "False", + "date": "2003-06-21", + "met_entry_idx": 11125, + "true_value": "False", + "mode": "boolean", + "time_indices": "64965:64969:1", + "start_idx": 64965, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0c11935c76777cf0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64965:64969:1", + "start_idx": 64965 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79923:79927:1', 'start_idx': 79923}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Scattered showers and thunderstorms are expected from the spine of the Rockies eastward into the central Plains and western Texas.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Scattered showers and thunderstorms are expected from the spine of the Rockies eastward into the central Plains and western Texas.", + "final_claim": "Scattered showers and thunderstorms are expected from the spine of the Rockies eastward into the central Plains and western Texas.", + "expected_answer": "True", + "date": "2013-09-15", + "met_entry_idx": 18383, + "true_value": "True", + "mode": "boolean", + "time_indices": "79923:79927:1", + "start_idx": 79923, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "e954761c27260c10", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79923:79927:1", + "start_idx": 79923 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85951:85955:1', 'start_idx': 85951}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and mild conditions are expected over parts of the central and northern Rockies.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Heavy snow likely over parts of the central and northern Rockies.", + "final_claim": "Dry and mild conditions are expected over parts of the central and northern Rockies.", + "expected_answer": "False", + "date": "2017-10-31", + "met_entry_idx": 21344, + "true_value": "False", + "mode": "boolean", + "time_indices": "85951:85955:1", + "start_idx": 85951, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "dc49abbaf8394a2d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85951:85955:1", + "start_idx": 85951 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56887:56891:1', 'start_idx': 56887}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A strong southern stream from the southern Plains into the Ohio Valley and then eastward across the northern Mid-Atlantic states will provide for a very active storm track and variety of precipitation.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A strong southern stream from the southern Plains into the Ohio Valley and then eastward across the northern Mid-Atlantic states will provide for a very active storm track and variety of precipitation.", + "final_claim": "A strong southern stream from the southern Plains into the Ohio Valley and then eastward across the northern Mid-Atlantic states will provide for a very active storm track and variety of precipitation.", + "expected_answer": "True", + "date": "1997-12-09", + "met_entry_idx": 7104, + "true_value": "True", + "mode": "boolean", + "time_indices": "56887:56891:1", + "start_idx": 56887, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a72732f5ee100d56", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56887:56891:1", + "start_idx": 56887 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84263:84267:1', 'start_idx': 84263}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: There is a slight risk of severe thunderstorms over parts of the northern and central Plains and parts of the upper Mississippi Valley.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "There is a slight risk of severe thunderstorms over parts of the northern and central Plains and parts of the upper Mississippi Valley.", + "final_claim": "There is a slight risk of severe thunderstorms over parts of the northern and central Plains and parts of the upper Mississippi Valley.", + "expected_answer": "True", + "date": "2016-09-04", + "met_entry_idx": 20528, + "true_value": "True", + "mode": "boolean", + "time_indices": "84263:84267:1", + "start_idx": 84263, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "cf2f5a86990dbcc5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84263:84267:1", + "start_idx": 84263 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72647:72651:1', 'start_idx': 72647}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A stationary warm front, currently over the Northern Plains, will result in stable and dry conditions this evening and overnight across the Dakotas as the upper ridge builds east.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A slow moving cold front, currently over the Northern Plains, will be the focus for potentially strong to severe storms this evening and overnight across the Dakotas as the upper troughing edges east.", + "final_claim": "A stationary warm front, currently over the Northern Plains, will result in stable and dry conditions this evening and overnight across the Dakotas as the upper ridge builds east.", + "expected_answer": "False", + "date": "2008-09-22", + "met_entry_idx": 14941, + "true_value": "False", + "mode": "boolean", + "time_indices": "72647:72651:1", + "start_idx": 72647, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d9b004a1cdbce8b4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72647:72651:1", + "start_idx": 72647 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62473:62477:1', 'start_idx': 62473}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: In the Southwest, a weak closed circulation develops at the base of a disintegrating trough moving through the West into the northern Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "In the Southwest, a weak closed circulation develops at the base of a disintegrating trough moving through the West into the northern Plains.", + "final_claim": "In the Southwest, a weak closed circulation develops at the base of a disintegrating trough moving through the West into the northern Plains.", + "expected_answer": "True", + "date": "2001-10-06", + "met_entry_idx": 9880, + "true_value": "True", + "mode": "boolean", + "time_indices": "62473:62477:1", + "start_idx": 62473, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5cf2748a355324b0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62473:62477:1", + "start_idx": 62473 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82519:82523:1', 'start_idx': 82519}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Severe thunderstorms and flash flooding are possible for portions of the Ohio Valley, Mid-Atlantic, and Southeast.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Severe thunderstorms and flash flooding are possible for portions of the Ohio Valley, Mid-Atlantic, and Southeast.", + "final_claim": "Severe thunderstorms and flash flooding are possible for portions of the Ohio Valley, Mid-Atlantic, and Southeast.", + "expected_answer": "True", + "date": "2015-06-26", + "met_entry_idx": 19677, + "true_value": "True", + "mode": "boolean", + "time_indices": "82519:82523:1", + "start_idx": 82519, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a78c24cb812f6495", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82519:82523:1", + "start_idx": 82519 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87091:87095:1', 'start_idx': 87091}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A mid-level closed low centered over Texas will continue the wet pattern for most of the state and other parts of the southern and central Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A mid-level closed low centered over Texas will continue the wet pattern for most of the state and other parts of the southern and central Plains.", + "final_claim": "A mid-level closed low centered over Texas will continue the wet pattern for most of the state and other parts of the southern and central Plains.", + "expected_answer": "True", + "date": "2018-08-12", + "met_entry_idx": 21877, + "true_value": "True", + "mode": "boolean", + "time_indices": "87091:87095:1", + "start_idx": 87091, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "3a28c3c547334d84", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87091:87095:1", + "start_idx": 87091 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89520:89524:1', 'start_idx': 89520}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Mild temperatures and dry conditions will prevail across the northern and central Rockies, extending into the Plains.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Significant cold and snowfall will occur across the northern and central Rockies, moving into the Plains.", + "final_claim": "Mild temperatures and dry conditions will prevail across the northern and central Rockies, extending into the Plains.", + "expected_answer": "False", + "date": "2020-04-11", + "met_entry_idx": 22997, + "true_value": "False", + "mode": "boolean", + "time_indices": "89520:89524:1", + "start_idx": 89520, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c84368369f9541bf", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89520:89524:1", + "start_idx": 89520 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51351:51355:1', 'start_idx': 51351}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Dry and stable conditions will prevail tonight across northern New York into eastern Quebec as the upper low weakens and loses influence.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Lighter wrap around snowfall associated with the upper low will continue tonight from northern New York into eastern Quebec.", + "final_claim": "Dry and stable conditions will prevail tonight across northern New York into eastern Quebec as the upper low weakens and loses influence.", + "expected_answer": "False", + "date": "1994-02-24", + "met_entry_idx": 4348, + "true_value": "False", + "mode": "boolean", + "time_indices": "51351:51355:1", + "start_idx": 51351, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9475291bdab9dcce", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51351:51355:1", + "start_idx": 51351 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43525:43529:1', 'start_idx': 43525}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: This shifts the current zonal jet near 45-50N across Washington and Oregon to a northwesterly component over western Canada.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "This shifts the current zonal jet near 45-50N across Washington and Oregon to a northwesterly component over western Canada.", + "final_claim": "This shifts the current zonal jet near 45-50N across Washington and Oregon to a northwesterly component over western Canada.", + "expected_answer": "True", + "date": "1988-10-17", + "met_entry_idx": 548, + "true_value": "True", + "mode": "boolean", + "time_indices": "43525:43529:1", + "start_idx": 43525, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "bc36cb928fbee275", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43525:43529:1", + "start_idx": 43525 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44627:44631:1', 'start_idx': 44627}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: This should induce frontogenesis in central Montana by 48 hours as the main upper trough is forced inland along the Pacific Northwest coast.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "This should induce frontogenesis in central Montana by 48 hours as the main upper trough is forced inland along the Pacific Northwest coast.", + "final_claim": "This should induce frontogenesis in central Montana by 48 hours as the main upper trough is forced inland along the Pacific Northwest coast.", + "expected_answer": "True", + "date": "1989-07-19", + "met_entry_idx": 1089, + "true_value": "True", + "mode": "boolean", + "time_indices": "44627:44631:1", + "start_idx": 44627, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "ff0ef82722e6e925", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44627:44631:1", + "start_idx": 44627 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78523:78527:1', 'start_idx': 78523}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A broad upper ridge establishing itself across the lower Mississippi Valley has resulted in an extensive area of dry and stable conditions with minimal convective activity.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A broad upper trough setting up across the lower Mississippi Valley has fueled an expansive region of moderate to heavy precipitation with embedded thunderstorm activity.", + "final_claim": "A broad upper ridge establishing itself across the lower Mississippi Valley has resulted in an extensive area of dry and stable conditions with minimal convective activity.", + "expected_answer": "False", + "date": "2012-09-30", + "met_entry_idx": 17688, + "true_value": "False", + "mode": "boolean", + "time_indices": "78523:78527:1", + "start_idx": 78523, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "b4c904b58a23b178", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78523:78527:1", + "start_idx": 78523 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68801:68805:1', 'start_idx': 68801}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: THE SYSTEM PREVENTS ATLANTIC MOISTURE FROM REACHING THE OHIO VALLEY/GREAT LAKES RESULTING IN DRY CONDITIONS OVER THE OHIO VALLEY BY MORNING AND CLEAR SKIES WITH COLD TEMPERATURES OVER THE LOWER GREAT LAKES BY EVENING.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "THE SYSTEM DRAWS ATLANTIC MOISTURE INTO THE OHIO VALLEY/GREAT LAKES PRODUCING MODERATE TO HEAVY RAIN OVER THE OHIO VALLEY BY MORNING AND LIGHT TO MODERATE SNOW OVER THE LOWER GREAT LAKES BY EVENING.", + "final_claim": "THE SYSTEM PREVENTS ATLANTIC MOISTURE FROM REACHING THE OHIO VALLEY/GREAT LAKES RESULTING IN DRY CONDITIONS OVER THE OHIO VALLEY BY MORNING AND CLEAR SKIES WITH COLD TEMPERATURES OVER THE LOWER GREAT LAKES BY EVENING.", + "expected_answer": "False", + "date": "2006-02-04", + "met_entry_idx": 13031, + "true_value": "False", + "mode": "boolean", + "time_indices": "68801:68805:1", + "start_idx": 68801, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "7850ea549e94f27f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68801:68805:1", + "start_idx": 68801 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68099:68103:1', 'start_idx': 68099}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: An upper level low persists over the western Atlantic near 32 N 65 W.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "An upper level high remains over the western Atlantic near 32 N 65 W.", + "final_claim": "An upper level low persists over the western Atlantic near 32 N 65 W.", + "expected_answer": "False", + "date": "2005-08-12", + "met_entry_idx": 12681, + "true_value": "False", + "mode": "boolean", + "time_indices": "68099:68103:1", + "start_idx": 68099, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "9a7e19e9b8d7e8ab", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68099:68103:1", + "start_idx": 68099 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79221:79225:1', 'start_idx': 79221}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: DRY CONDITIONS EXPECTED OVER PARTS OF THE MIDDLE MISSISSIPPI VALLEY TO THE OHIO VALLEY...", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "SNOW OVER PARTS OF THE MIDDLE MISSISSIPPI VALLEY TO THE OHIO VALLEY...", + "final_claim": "DRY CONDITIONS EXPECTED OVER PARTS OF THE MIDDLE MISSISSIPPI VALLEY TO THE OHIO VALLEY...", + "expected_answer": "False", + "date": "2013-03-24", + "met_entry_idx": 18036, + "true_value": "False", + "mode": "boolean", + "time_indices": "79221:79225:1", + "start_idx": 79221, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4117aae29bc70afd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79221:79225:1", + "start_idx": 79221 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65101:65105:1', 'start_idx': 65101}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: NO SHORT WAVE IS EXPECTED TO MOVE FROM SOUTHWEST CANADA ACROSS SOUTH CENTRAL CANADA, AND DRY, STABLE CONDITIONS WILL PERSIST THROUGH THE END OF THE DAY TWO PERIOD INTO THE UPPER GREAT LAKES REGION.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "ANOTHER SHORT WAVE CURRENTLY OVER SOUTHWEST CANADA IS FORECAST TO TRACK EAST ACROSS SOUTH CENTRAL CANADA, THEN REACH THE UPPER GREAT LAKES BY THE END OF THE DAY TWO PERIOD.", + "final_claim": "NO SHORT WAVE IS EXPECTED TO MOVE FROM SOUTHWEST CANADA ACROSS SOUTH CENTRAL CANADA, AND DRY, STABLE CONDITIONS WILL PERSIST THROUGH THE END OF THE DAY TWO PERIOD INTO THE UPPER GREAT LAKES REGION.", + "expected_answer": "False", + "date": "2003-07-25", + "met_entry_idx": 11193, + "true_value": "False", + "mode": "boolean", + "time_indices": "65101:65105:1", + "start_idx": 65101, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6ff2ca61047c6a96", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65101:65105:1", + "start_idx": 65101 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89897:89901:1', 'start_idx': 89897}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Below-average temperatures across the south-central U.S. will gradually intensify over the next couple of days as a strong upper level trough strengthens.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Excessive heat across the south-central U.S. will gradually become less intense during the next couple of days as a strong upper level ridge begins to weaken.", + "final_claim": "Below-average temperatures across the south-central U.S. will gradually intensify over the next couple of days as a strong upper level trough strengthens.", + "expected_answer": "False", + "date": "2020-07-14", + "met_entry_idx": 23152, + "true_value": "False", + "mode": "boolean", + "time_indices": "89897:89901:1", + "start_idx": 89897, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "acdcbd74cbae3ce3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89897:89901:1", + "start_idx": 89897 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42439:42443:1', 'start_idx': 42439}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A trailing band of snow showers will extend southwest under the upper trough through the Great Lakes into the Plains after the main low passes.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A trailing band of snow showers will extend southwest under the upper trough through the Great Lakes into the Plains after the main low passes.", + "final_claim": "A trailing band of snow showers will extend southwest under the upper trough through the Great Lakes into the Plains after the main low passes.", + "expected_answer": "True", + "date": "1988-01-19", + "met_entry_idx": 13, + "true_value": "True", + "mode": "boolean", + "time_indices": "42439:42443:1", + "start_idx": 42439, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "5655ca9cac75dcc5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42439:42443:1", + "start_idx": 42439 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65883:65887:1', 'start_idx': 65883}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: No significant mid-level trough is expected to approach the Pacific Northwest from the eastern Pacific, with stable and dry conditions prevailing inland over the next 48 hours.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Another mid-level trough is approaching the Pacific Northwest from the eastern Pacific and should move inland by 48 hours.", + "final_claim": "No significant mid-level trough is expected to approach the Pacific Northwest from the eastern Pacific, with stable and dry conditions prevailing inland over the next 48 hours.", + "expected_answer": "False", + "date": "2004-02-05", + "met_entry_idx": 11581, + "true_value": "False", + "mode": "boolean", + "time_indices": "65883:65887:1", + "start_idx": 65883, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "884d1aaef5eb2a7c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65883:65887:1", + "start_idx": 65883 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88579:88583:1', 'start_idx': 88579}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Below-average temperatures are anticipated for the Desert Southwest and across the mid-section of the country, while cool and dry conditions persist over parts of the Northeast.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Excessive heat is expected for the Desert Southwest and across the mid-section of the country, while heat and humidity continue over parts of the Northeast.", + "final_claim": "Below-average temperatures are anticipated for the Desert Southwest and across the mid-section of the country, while cool and dry conditions persist over parts of the Northeast.", + "expected_answer": "False", + "date": "2019-08-19", + "met_entry_idx": 22579, + "true_value": "False", + "mode": "boolean", + "time_indices": "88579:88583:1", + "start_idx": 88579, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "d44567acf3f875f6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88579:88583:1", + "start_idx": 88579 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81067:81071:1', 'start_idx': 81067}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Scattered showers and thundershowers will affect the Mississippi Valley, Midwest, Tennessee Valley, Gulf Coast, and South Atlantic states, with a few locales possibly getting severe weather or flash flooding.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Scattered showers and thundershowers will affect the Mississippi Valley, Midwest, Tennessee Valley, Gulf Coast, and South Atlantic states, with a few locales possibly getting severe weather or flash flooding.", + "final_claim": "Scattered showers and thundershowers will affect the Mississippi Valley, Midwest, Tennessee Valley, Gulf Coast, and South Atlantic states, with a few locales possibly getting severe weather or flash flooding.", + "expected_answer": "True", + "date": "2014-06-28", + "met_entry_idx": 18954, + "true_value": "True", + "mode": "boolean", + "time_indices": "81067:81071:1", + "start_idx": 81067, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a72bb91046cc4742", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81067:81071:1", + "start_idx": 81067 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69831:69835:1', 'start_idx': 69831}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A series of strengthening shortwave ridges, one over the Great Lakes and another over the southern Plains, will move southwest within a broad ridge centered over the central United States on day one.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A series of weakening shortwave troughs, one over the Great Lakes and another over the southern Plains, will move northeast within a broad trough centered over the central United States on day one.", + "final_claim": "A series of strengthening shortwave ridges, one over the Great Lakes and another over the southern Plains, will move southwest within a broad ridge centered over the central United States on day one.", + "expected_answer": "False", + "date": "2006-10-19", + "met_entry_idx": 13540, + "true_value": "False", + "mode": "boolean", + "time_indices": "69831:69835:1", + "start_idx": 69831, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "aaa47ae4e40830be", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69831:69835:1", + "start_idx": 69831 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60677:60681:1', 'start_idx': 60677}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: As this occurs, the western trough weakens and retrogrades, allowing the ridge in the eastern Rockies to spread northwest.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "As this occurs, the western trough weakens and retrogrades, allowing the ridge in the eastern Rockies to spread northwest.", + "final_claim": "As this occurs, the western trough weakens and retrogrades, allowing the ridge in the eastern Rockies to spread northwest.", + "expected_answer": "True", + "date": "2000-07-14", + "met_entry_idx": 8994, + "true_value": "True", + "mode": "boolean", + "time_indices": "60677:60681:1", + "start_idx": 60677, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "582daedad2c6cee7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60677:60681:1", + "start_idx": 60677 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74883:74887:1', 'start_idx': 74883}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: As the frontal boundary stalls offshore this evening, weakened offshore flow will limit Pacific moisture, resulting in dry conditions and little to no snowfall across the Sierra Nevadas.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "As the frontal boundary pushes inland this evening, enhanced onshore flow will spread Pacific moisture into the West Coast, fueling moderate to heavy snowfall across the Sierra Nevadas.", + "final_claim": "As the frontal boundary stalls offshore this evening, weakened offshore flow will limit Pacific moisture, resulting in dry conditions and little to no snowfall across the Sierra Nevadas.", + "expected_answer": "False", + "date": "2010-04-04", + "met_entry_idx": 16036, + "true_value": "False", + "mode": "boolean", + "time_indices": "74883:74887:1", + "start_idx": 74883, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "fbbe558cdf40100a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74883:74887:1", + "start_idx": 74883 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45659:45663:1', 'start_idx': 45659}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A secondary surface high is gradually developing off the Mid Atlantic coast, and this system will remain a minor feature without overtaking the primary surface high within 24 hours.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A secondary surface low is rapidly forming off the Mid Atlantic coast, and this system will become the primary surface low within 24 hours.", + "final_claim": "A secondary surface high is gradually developing off the Mid Atlantic coast, and this system will remain a minor feature without overtaking the primary surface high within 24 hours.", + "expected_answer": "False", + "date": "1990-04-03", + "met_entry_idx": 1597, + "true_value": "False", + "mode": "boolean", + "time_indices": "45659:45663:1", + "start_idx": 45659, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "0c28d9011e4c18b0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45659:45663:1", + "start_idx": 45659 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54663:54667:1', 'start_idx': 54663}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A short wave from the Plains will develop low pressure in the Mid Mississippi Valley today as it moves northeast around the east side of the 500mb trough.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A short wave from the Plains will develop low pressure in the Mid Mississippi Valley today as it moves northeast around the east side of the 500mb trough.", + "final_claim": "A short wave from the Plains will develop low pressure in the Mid Mississippi Valley today as it moves northeast around the east side of the 500mb trough.", + "expected_answer": "True", + "date": "1996-06-01", + "met_entry_idx": 6001, + "true_value": "True", + "mode": "boolean", + "time_indices": "54663:54667:1", + "start_idx": 54663, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4991a2e809f894ca", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54663:54667:1", + "start_idx": 54663 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87351:87355:1', 'start_idx': 87351}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Heavy rain is possible over Texas through tonight as moisture from Tropical Storm Tara is drawn northward.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Heavy rain is possible over Texas through tonight as moisture from Tropical Storm Tara is drawn northward.", + "final_claim": "Heavy rain is possible over Texas through tonight as moisture from Tropical Storm Tara is drawn northward.", + "expected_answer": "True", + "date": "2018-10-16", + "met_entry_idx": 22004, + "true_value": "True", + "mode": "boolean", + "time_indices": "87351:87355:1", + "start_idx": 87351, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "f77a90602f15e2d0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87351:87355:1", + "start_idx": 87351 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73547:73551:1', 'start_idx': 73547}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A surface low will develop along the front over the Middle Mississippi Valley as an upper level trough approaches from the west.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A surface low will develop along the front over the Middle Mississippi Valley as an upper level trough approaches from the west.", + "final_claim": "A surface low will develop along the front over the Middle Mississippi Valley as an upper level trough approaches from the west.", + "expected_answer": "True", + "date": "2009-05-05", + "met_entry_idx": 15391, + "true_value": "True", + "mode": "boolean", + "time_indices": "73547:73551:1", + "start_idx": 73547, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "fb03d60c2917a10b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73547:73551:1", + "start_idx": 73547 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72627:72631:1', 'start_idx': 72627}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The upper-level pattern for the next 60 hours will feature ridging over the eastern half of the nation, while troughing dominates the western tier.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The upper-level pattern for the next 60 hours will consist of troughing over the eastern half of the nation, while a ridge remains strong over the western tier.", + "final_claim": "The upper-level pattern for the next 60 hours will feature ridging over the eastern half of the nation, while troughing dominates the western tier.", + "expected_answer": "False", + "date": "2008-09-17", + "met_entry_idx": 14931, + "true_value": "False", + "mode": "boolean", + "time_indices": "72627:72631:1", + "start_idx": 72627, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "57f0117daeedc0d4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72627:72631:1", + "start_idx": 72627 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49133:49137:1', 'start_idx': 49133}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A cooling trend is expected for the Pacific Northwest and Northern Rockies as an upper trough and embedded low move southward to the Pacific Northwest coast.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A cooling trend is expected for the Pacific Northwest and Northern Rockies as an upper trough and embedded low move southward to the Pacific Northwest coast.", + "final_claim": "A cooling trend is expected for the Pacific Northwest and Northern Rockies as an upper trough and embedded low move southward to the Pacific Northwest coast.", + "expected_answer": "True", + "date": "1992-08-19", + "met_entry_idx": 3239, + "true_value": "True", + "mode": "boolean", + "time_indices": "49133:49137:1", + "start_idx": 49133, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "a7d81387ab1f59c3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49133:49137:1", + "start_idx": 49133 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82497:82501:1', 'start_idx': 82497}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: There is a slight risk of dry and stable conditions with minimal cloud cover over parts of the Middle Mississippi Valley to the Ohio Valley and Central Appalachians.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "There is a slight risk of severe thunderstorms over parts of the Middle Mississippi Valley to the Ohio Valley and Central Appalachians.", + "final_claim": "There is a slight risk of dry and stable conditions with minimal cloud cover over parts of the Middle Mississippi Valley to the Ohio Valley and Central Appalachians.", + "expected_answer": "False", + "date": "2015-06-21", + "met_entry_idx": 19666, + "true_value": "False", + "mode": "boolean", + "time_indices": "82497:82501:1", + "start_idx": 82497, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "bd43b5a1c84a3726", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82497:82501:1", + "start_idx": 82497 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50265:50269:1', 'start_idx': 50265}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: No significant upper-level disturbances develop in the eastern Pacific on day 2, with the absence of a northern upper low near the Pacific Northwest resulting in predominantly dry and stable conditions along coastal sections at 24 hours and beyond.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "An elongated double-barreled system evolves in the eastern Pacific on day 2, with the leading (northern) upper low pivoting close enough to the Pacific Northwest to bring organized showers to the region at 24 hours and beyond, especially along coastal sections.", + "final_claim": "No significant upper-level disturbances develop in the eastern Pacific on day 2, with the absence of a northern upper low near the Pacific Northwest resulting in predominantly dry and stable conditions along coastal sections at 24 hours and beyond.", + "expected_answer": "False", + "date": "1993-05-29", + "met_entry_idx": 3806, + "true_value": "False", + "mode": "boolean", + "time_indices": "50265:50269:1", + "start_idx": 50265, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "889cda6c9a6c632a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50265:50269:1", + "start_idx": 50265 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43813:43817:1', 'start_idx': 43813}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A ridge at 50N, 165W will be building in the vicinity of British Columbia, Idaho, and Montana by 48 hours, which should result in weakening leeside pressure gradients and subsidence by 36 hours.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "A shortwave at 50N, 165W will be digging in the vicinity of British Columbia, Idaho, and Montana by 48 hours, which should generate a deepening leeside low by 36 hours.", + "final_claim": "A ridge at 50N, 165W will be building in the vicinity of British Columbia, Idaho, and Montana by 48 hours, which should result in weakening leeside pressure gradients and subsidence by 36 hours.", + "expected_answer": "False", + "date": "1988-12-28", + "met_entry_idx": 686, + "true_value": "False", + "mode": "boolean", + "time_indices": "43813:43817:1", + "start_idx": 43813, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "475d5af571022e33", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43813:43817:1", + "start_idx": 43813 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71557:71561:1', 'start_idx': 71557}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The central and northern Rockies are expected to experience predominantly dry conditions with minimal snowfall due to prevailing downslope winds along the high terrain.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The best chance for significant snowfall will be across the central and northern Rockies due to upslope flow along the high terrain.", + "final_claim": "The central and northern Rockies are expected to experience predominantly dry conditions with minimal snowfall due to prevailing downslope winds along the high terrain.", + "expected_answer": "False", + "date": "2007-12-25", + "met_entry_idx": 14400, + "true_value": "False", + "mode": "boolean", + "time_indices": "71557:71561:1", + "start_idx": 71557, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "96ba4d163550cdcd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71557:71561:1", + "start_idx": 71557 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65913:65917:1', 'start_idx': 65913}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A substantial amount of moisture is expected to move northeastward along this baroclinic band into the eastern Gulf Coast states by 24 hours.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A substantial amount of moisture is expected to move northeastward along this baroclinic band into the eastern Gulf Coast states by 24 hours.", + "final_claim": "A substantial amount of moisture is expected to move northeastward along this baroclinic band into the eastern Gulf Coast states by 24 hours.", + "expected_answer": "True", + "date": "2004-02-13", + "met_entry_idx": 11596, + "true_value": "True", + "mode": "boolean", + "time_indices": "65913:65917:1", + "start_idx": 65913, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "89d9f739be85a4f3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65913:65917:1", + "start_idx": 65913 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77023:77027:1', 'start_idx': 77023}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Anomalously cool temperatures will move across the Upper Mississippi Valley into the Midwest and Ohio Valley.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "Anomalously cool temperatures will move across the Upper Mississippi Valley into the Midwest and Ohio Valley.", + "final_claim": "Anomalously cool temperatures will move across the Upper Mississippi Valley into the Midwest and Ohio Valley.", + "expected_answer": "True", + "date": "2011-09-21", + "met_entry_idx": 16979, + "true_value": "True", + "mode": "boolean", + "time_indices": "77023:77027:1", + "start_idx": 77023, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "6612090d82148d99", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77023:77027:1", + "start_idx": 77023 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71195:71199:1', 'start_idx': 71195}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A front extending from the Upper Great Lakes to the Southern High Plains will advance eastward to the Northeast with a wave over the Lower Great Lakes, then southwestward to the Southern Plains.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A front extending from the Upper Great Lakes to the Southern High Plains will advance eastward to the Northeast with a wave over the Lower Great Lakes, then southwestward to the Southern Plains.", + "final_claim": "A front extending from the Upper Great Lakes to the Southern High Plains will advance eastward to the Northeast with a wave over the Lower Great Lakes, then southwestward to the Southern Plains.", + "expected_answer": "True", + "date": "2007-09-25", + "met_entry_idx": 14220, + "true_value": "True", + "mode": "boolean", + "time_indices": "71195:71199:1", + "start_idx": 71195, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4b48f70a31cfba2a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71195:71199:1", + "start_idx": 71195 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58327:58331:1', 'start_idx': 58327}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A series of upper-level troughs will continue over the western U.S., then move northeast into the central part of the country during this forecast period.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A series of upper-level troughs will continue over the western U.S., then move northeast into the central part of the country during this forecast period.", + "final_claim": "A series of upper-level troughs will continue over the western U.S., then move northeast into the central part of the country during this forecast period.", + "expected_answer": "True", + "date": "1998-12-04", + "met_entry_idx": 7821, + "true_value": "True", + "mode": "boolean", + "time_indices": "58327:58331:1", + "start_idx": 58327, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "56cf84e6528fc48c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58327:58331:1", + "start_idx": 58327 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50503:50507:1', 'start_idx': 50503}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: The weakened trough will shift rapidly westward and settle west of the front range of the Rockies within 48 hours.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "The amplified ridge will slide slowly eastward and end up along the front range of the Rockies by 48 hours.", + "final_claim": "The weakened trough will shift rapidly westward and settle west of the front range of the Rockies within 48 hours.", + "expected_answer": "False", + "date": "1993-07-27", + "met_entry_idx": 3925, + "true_value": "False", + "mode": "boolean", + "time_indices": "50503:50507:1", + "start_idx": 50503, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "4053f33701e5ee51", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50503:50507:1", + "start_idx": 50503 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66279:66283:1', 'start_idx': 66279}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: A strong ridge over the East Coast and western Atlantic is causing summer-like conditions in the East and will be slow to move eastward.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "A strong ridge over the East Coast and western Atlantic is causing summer-like conditions in the East and will be slow to move eastward.", + "final_claim": "A strong ridge over the East Coast and western Atlantic is causing summer-like conditions in the East and will be slow to move eastward.", + "expected_answer": "True", + "date": "2004-05-14", + "met_entry_idx": 11778, + "true_value": "True", + "mode": "boolean", + "time_indices": "66279:66283:1", + "start_idx": 66279, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "2dd1e91a2cbb8bf1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66279:66283:1", + "start_idx": 66279 + } + }, + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84161:84165:1', 'start_idx': 84161}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: HEAVY TO EXCESSIVE RAINFALL POSSIBLE ACROSS PORTIONS OF ARIZONA AND NEW MEXICO.", + "response": "True", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "positive", + "original_claim": "HEAVY TO EXCESSIVE RAINFALL POSSIBLE ACROSS PORTIONS OF ARIZONA AND NEW MEXICO.", + "final_claim": "HEAVY TO EXCESSIVE RAINFALL POSSIBLE ACROSS PORTIONS OF ARIZONA AND NEW MEXICO.", + "expected_answer": "True", + "date": "2016-08-10", + "met_entry_idx": 20477, + "true_value": "True", + "mode": "boolean", + "time_indices": "84161:84165:1", + "start_idx": 84161, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "c2e621654ced9563", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84161:84165:1", + "start_idx": 84161 + } + } +] \ No newline at end of file diff --git a/level2d_part4.json b/level2d_part4.json new file mode 100644 index 0000000000000000000000000000000000000000..37958d4eab69b9ff998a6d6059f22377d182cc62 --- /dev/null +++ b/level2d_part4.json @@ -0,0 +1,43 @@ +[ + { + "prompt": "The following data shows meteorological conditions over a 24-hour period: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88569:88573:1', 'start_idx': 88569}\n Based on the provided data, answer the following question:", + "question": "Does this data support the provided meteorological claim about the United States? Answer with True or False.\n\nClaim: Calm weather and dry conditions are expected across portions of the Northern and Central Plains into the Upper and Middle Mississippi Valley.", + "response": "False", + "metadata": { + "prompt_id": "HIuSnl", + "question_id": "fWzxI0", + "claim_type": "negative", + "original_claim": "Severe weather and flash flooding are possible across portions of the Northern and Central Plains into the Upper and Middle Mississippi Valley.", + "final_claim": "Calm weather and dry conditions are expected across portions of the Northern and Central Plains into the Upper and Middle Mississippi Valley.", + "expected_answer": "False", + "date": "2019-08-17", + "met_entry_idx": 22575, + "true_value": "False", + "mode": "boolean", + "time_indices": "88569:88573:1", + "start_idx": 88569, + "level": "2d", + "eval_type": "boolean", + "forced_extreme_window": false, + "task_id": "bc271ee8be7d02a8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88569:88573:1", + "start_idx": 88569 + } + } +] \ No newline at end of file diff --git a/level3a_part0.json b/level3a_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..5fa5509cb3f9acbfce4d16cb2a4ca046c61fa00f --- /dev/null +++ b/level3a_part0.json @@ -0,0 +1,3602 @@ +[ + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42431:42432:1', 'start_idx': 13211} The data corresponds to a snapshot on January 16 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 500 hPa at Turkmenistan change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 500 hPa at Turkmenistan to increased by 2.637 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 500 hPa will increase by 0.0002851 m/s at Turkmenistan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 500 hPa", + "location": "Turkmenistan", + "target_variable": "v_component_of_wind_500", + "true_value": "0.00028514862060546875", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "951ea90a24d80a20", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42431:42432:1", + "start_idx": 13211 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41194:41195:1', 'start_idx': 11974} The data corresponds to a snapshot on March 13 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 925 hPa at Argentina change in 12 hours if localized Gaussian perturbations cause U (zonal) component of wind at 925 hPa at Argentina to increased by 2.324 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the U (zonal) component of wind at 925 hPa will decrease by 1.722e-07 m/s at Argentina.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "U (zonal) component of wind at 925 hPa", + "location": "Argentina", + "target_variable": "u_component_of_wind_925", + "true_value": "-1.7219119285982742e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a1de76b826e8fac8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41194:41195:1", + "start_idx": 11974 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74224:74225:1', 'start_idx': 45004} The data corresponds to a snapshot on October 21 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 400 hPa at South America change in 30 hours if localized Gaussian perturbations cause U (zonal) component of wind at 400 hPa at South America to increased by 3.811 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the U (zonal) component of wind at 400 hPa will increase by 2.523e-06 m/s at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "U (zonal) component of wind at 400 hPa", + "location": "South America", + "target_variable": "u_component_of_wind_400", + "true_value": "2.523303010093514e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ac72032a821d7a48", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74224:74225:1", + "start_idx": 45004 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84704:84705:1', 'start_idx': 55484} The data corresponds to a snapshot on December 23 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 600 hPa at North Korea change in 30 hours if localized Gaussian perturbations cause U (zonal) component of wind at 600 hPa at North Korea to increased by 3.79 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the U (zonal) component of wind at 600 hPa will increase by 0.003909 m/s at North Korea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "U (zonal) component of wind at 600 hPa", + "location": "North Korea", + "target_variable": "u_component_of_wind_600", + "true_value": "0.0039085522294044495", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ec297977a1760635", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84704:84705:1", + "start_idx": 55484 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82915:82916:1', 'start_idx': 53695} The data corresponds to a snapshot on October 02 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 400 hPa at Sierra Leone change in 12 hours if localized Gaussian perturbations cause V (meridional) component of wind at 400 hPa at Sierra Leone to increased by 4.345 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the V (meridional) component of wind at 400 hPa will decrease by 0 m/s at Sierra Leone.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "V (meridional) component of wind at 400 hPa", + "location": "Sierra Leone", + "target_variable": "v_component_of_wind_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5617cc4d92bce6d7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82915:82916:1", + "start_idx": 53695 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46493:46494:1', 'start_idx': 17273} The data corresponds to a snapshot on October 28 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 250 hPa at Massachusetts Bay change in 48 hours if localized Gaussian perturbations cause U (zonal) component of wind at 250 hPa at Massachusetts Bay to increased by 5.023 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the U (zonal) component of wind at 250 hPa will decrease by 0.002497 m/s at Massachusetts Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "U (zonal) component of wind at 250 hPa", + "location": "Massachusetts Bay", + "target_variable": "u_component_of_wind_250", + "true_value": "-0.0024967193603515625", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0a001364cc8f089a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46493:46494:1", + "start_idx": 17273 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61088:61089:1', 'start_idx': 31868} The data corresponds to a snapshot on October 24 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 400 hPa at Bellingshausen Sea change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 400 hPa at Bellingshausen Sea to increased by 4.286 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 400 hPa will decrease by 0.0003423 m/s at Bellingshausen Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 400 hPa", + "location": "Bellingshausen Sea", + "target_variable": "u_component_of_wind_400", + "true_value": "-0.00034230094752274454", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "43201421e6e2c6ea", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61088:61089:1", + "start_idx": 31868 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69483:69484:1', 'start_idx': 40263} The data corresponds to a snapshot on July 23 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at Africa change in 6 hours if localized Gaussian perturbations cause Temperature at 700 hPa at Africa to increased by 5.183 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 700 hPa will increase by 0.2718 K at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 700 hPa", + "location": "Africa", + "target_variable": "temperature_700", + "true_value": "0.271793931722641", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d9cea8f38f808af6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69483:69484:1", + "start_idx": 40263 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32915:32916:1', 'start_idx': 3695} The data corresponds to a snapshot on July 12 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 200 hPa at Antarctica change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 200 hPa at Antarctica to increased by 3.19 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 200 hPa will increase by 0.6475 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 200 hPa", + "location": "Antarctica", + "target_variable": "v_component_of_wind_200", + "true_value": "0.6474658846855164", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "08dbf9abe49c7626", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32915:32916:1", + "start_idx": 3695 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89177:89178:1', 'start_idx': 59957} The data corresponds to a snapshot on January 15 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 250 hPa at Ivory Coast change in 36 hours if localized Gaussian perturbations cause Specific humidity at 250 hPa at Ivory Coast to increased by 1.544e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Specific humidity at 250 hPa will increase by 1.032e-08 kg/kg at Ivory Coast.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Specific humidity at 250 hPa", + "location": "Ivory Coast", + "target_variable": "specific_humidity_250", + "true_value": "1.0317307896912098e-08", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8a3391b6539621ba", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89177:89178:1", + "start_idx": 59957 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82063:82064:1', 'start_idx': 52843} The data corresponds to a snapshot on March 03 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 850 hPa at Hudson Strait change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 850 hPa at Hudson Strait to increased by 1.621 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 850 hPa will decrease by 0.0008031 m/s at Hudson Strait.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 850 hPa", + "location": "Hudson Strait", + "target_variable": "v_component_of_wind_850", + "true_value": "-0.0008030533790588379", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7fba043eb10da225", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82063:82064:1", + "start_idx": 52843 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65335:65336:1', 'start_idx': 36115} The data corresponds to a snapshot on September 20 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 100 hPa at Oceania change in 12 hours if localized Gaussian perturbations cause U (zonal) component of wind at 100 hPa at Oceania to increased by 4.513 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the U (zonal) component of wind at 100 hPa will increase by 0.1005 m/s at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "U (zonal) component of wind at 100 hPa", + "location": "Oceania", + "target_variable": "u_component_of_wind_100", + "true_value": "0.10051507502794266", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "57e13c7a1db8a6ee", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65335:65336:1", + "start_idx": 36115 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80620:80621:1', 'start_idx': 51400} The data corresponds to a snapshot on March 08 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 150 hPa at United Arab Emirates change in 42 hours if localized Gaussian perturbations cause V (meridional) component of wind at 150 hPa at United Arab Emirates to increased by 2.002 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the V (meridional) component of wind at 150 hPa will increase by 0.0006065 m/s at United Arab Emirates.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "V (meridional) component of wind at 150 hPa", + "location": "United Arab Emirates", + "target_variable": "v_component_of_wind_150", + "true_value": "0.000606536865234375", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e37d08acd446aba7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80620:80621:1", + "start_idx": 51400 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31220:31221:1', 'start_idx': 2000} The data corresponds to a snapshot on May 15 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 700 hPa at Iran change in 6 hours if localized Gaussian perturbations cause Specific humidity at 700 hPa at Iran to increased by 0.0005948 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Specific humidity at 700 hPa will decrease by 8.131e-09 kg/kg at Iran.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Specific humidity at 700 hPa", + "location": "Iran", + "target_variable": "specific_humidity_700", + "true_value": "-8.1309963206877e-09", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e46cb0a278a012fe", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31220:31221:1", + "start_idx": 2000 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54220:54221:1', 'start_idx': 25000} The data corresponds to a snapshot on February 11 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 300 hPa at Somalia change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 300 hPa at Somalia to increased by 5.19 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 300 hPa will decrease by 0.005047 m/s at Somalia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 300 hPa", + "location": "Somalia", + "target_variable": "u_component_of_wind_300", + "true_value": "-0.00504748011007905", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c35a75165d903819", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54220:54221:1", + "start_idx": 25000 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55676:55677:1', 'start_idx': 26456} The data corresponds to a snapshot on February 09 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 600 hPa at Bass Strait change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 600 hPa at Bass Strait to increased by 2.471 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 600 hPa will decrease by 0.003338 m/s at Bass Strait.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 600 hPa", + "location": "Bass Strait", + "target_variable": "u_component_of_wind_600", + "true_value": "-0.003338336944580078", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2bb2f08c3ea01bee", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55676:55677:1", + "start_idx": 26456 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50487:50488:1', 'start_idx': 21267} The data corresponds to a snapshot on July 22 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 300 hPa at Germany change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 300 hPa at Germany to increased by 3.352 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 300 hPa will decrease by 0.001039 m/s at Germany.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 300 hPa", + "location": "Germany", + "target_variable": "v_component_of_wind_300", + "true_value": "-0.00103931431658566", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "726d0d5f8b938846", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50487:50488:1", + "start_idx": 21267 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73482:73483:1', 'start_idx': 44262} The data corresponds to a snapshot on April 18 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 150 hPa at Antarctica change in 42 hours if localized Gaussian perturbations cause Geopotential at 150 hPa at Antarctica to increased by 1664 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Geopotential at 150 hPa will decrease by 0 m²/s² at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Geopotential at 150 hPa", + "location": "Antarctica", + "target_variable": "geopotential_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bdeeecfdb8104da3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73482:73483:1", + "start_idx": 44262 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34648:34649:1', 'start_idx': 5428} The data corresponds to a snapshot on September 19 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 300 hPa at Aruba change in 6 hours if localized Gaussian perturbations cause Geopotential at 300 hPa at Aruba to increased by 1101 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 300 hPa will decrease by 0 m²/s² at Aruba.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 300 hPa", + "location": "Aruba", + "target_variable": "geopotential_300", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a556218d86f5ed81", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34648:34649:1", + "start_idx": 5428 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63238:63239:1', 'start_idx': 34018} The data corresponds to a snapshot on April 14 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 925 hPa at Paraguay change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 925 hPa at Paraguay to increased by 2.4 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 925 hPa will increase by 0.0001508 m/s at Paraguay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 925 hPa", + "location": "Paraguay", + "target_variable": "v_component_of_wind_925", + "true_value": "0.00015076622366905212", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "47af79927223b68b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63238:63239:1", + "start_idx": 34018 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79954:79955:1', 'start_idx': 50734} The data corresponds to a snapshot on September 22 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at Golfo San Matías change in 24 hours if localized Gaussian perturbations cause Temperature at 50 hPa at Golfo San Matías to increased by 3.889 K in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Temperature at 50 hPa will decrease by 0 K at Golfo San Matías.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Temperature at 50 hPa", + "location": "Golfo San Matías", + "target_variable": "temperature_50", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "30e04ff8be6c2126", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79954:79955:1", + "start_idx": 50734 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61209:61210:1', 'start_idx': 31989} The data corresponds to a snapshot on November 23 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 150 hPa at Africa change in 12 hours if localized Gaussian perturbations cause Specific humidity at 150 hPa at Africa to increased by 1.38e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 150 hPa will increase by 7.248e-08 kg/kg at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 150 hPa", + "location": "Africa", + "target_variable": "specific_humidity_150", + "true_value": "7.247839306501191e-08", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5a5619811049dca8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61209:61210:1", + "start_idx": 31989 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53944:53945:1', 'start_idx': 24724} The data corresponds to a snapshot on December 04 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 150 hPa at Dominican Republic change in 6 hours if localized Gaussian perturbations cause U (zonal) component of wind at 150 hPa at Dominican Republic to increased by 3.415 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the U (zonal) component of wind at 150 hPa will decrease by 0.001093 m/s at Dominican Republic.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "U (zonal) component of wind at 150 hPa", + "location": "Dominican Republic", + "target_variable": "u_component_of_wind_150", + "true_value": "-0.0010933876037597656", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ca6f4bb8a3f7c055", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53944:53945:1", + "start_idx": 24724 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62387:62388:1', 'start_idx': 33167} The data corresponds to a snapshot on September 13 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 150 hPa at Burkina Faso change in 42 hours if localized Gaussian perturbations cause Geopotential at 150 hPa at Burkina Faso to increased by 1557 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Geopotential at 150 hPa will decrease by 0 m²/s² at Burkina Faso.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Geopotential at 150 hPa", + "location": "Burkina Faso", + "target_variable": "geopotential_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "201a47a7013c6690", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62387:62388:1", + "start_idx": 33167 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46852:46853:1', 'start_idx': 17632} The data corresponds to a snapshot on January 26 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 700 hPa at Asia change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 700 hPa at Asia to increased by 2.413 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 700 hPa will increase by 0.0002633 m/s at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 700 hPa", + "location": "Asia", + "target_variable": "u_component_of_wind_700", + "true_value": "0.0002632607356645167", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d0f401c6362fd31e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46852:46853:1", + "start_idx": 17632 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67476:67477:1', 'start_idx': 38256} The data corresponds to a snapshot on March 09 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 925 hPa at Cameroon change in 42 hours if localized Gaussian perturbations cause Temperature at 925 hPa at Cameroon to increased by 3.991 K in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Temperature at 925 hPa will decrease by 5.913e-05 K at Cameroon.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Temperature at 925 hPa", + "location": "Cameroon", + "target_variable": "temperature_925", + "true_value": "-5.91278076171875e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1fe4afad908b7c2e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67476:67477:1", + "start_idx": 38256 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66764:66765:1', 'start_idx': 37544} The data corresponds to a snapshot on September 12 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 500 hPa at North America change in 30 hours if localized Gaussian perturbations cause Geopotential at 500 hPa at North America to increased by 902.9 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Geopotential at 500 hPa will decrease by 0 m²/s² at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Geopotential at 500 hPa", + "location": "North America", + "target_variable": "geopotential_500", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ed2786b061c1b918", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66764:66765:1", + "start_idx": 37544 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84604:84605:1', 'start_idx': 55384} The data corresponds to a snapshot on November 28 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 250 hPa at Selat Bali change in 42 hours if localized Gaussian perturbations cause Geopotential at 250 hPa at Selat Bali to increased by 1992 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Geopotential at 250 hPa will decrease by 0 m²/s² at Selat Bali.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Geopotential at 250 hPa", + "location": "Selat Bali", + "target_variable": "geopotential_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "284fde57c2f30906", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84604:84605:1", + "start_idx": 55384 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78731:78732:1', 'start_idx': 49511} The data corresponds to a snapshot on November 20 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 100 hPa at Boca Grande change in 30 hours if localized Gaussian perturbations cause U (zonal) component of wind at 100 hPa at Boca Grande to increased by 4.224 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the U (zonal) component of wind at 100 hPa will decrease by 0.0001445 m/s at Boca Grande.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "U (zonal) component of wind at 100 hPa", + "location": "Boca Grande", + "target_variable": "u_component_of_wind_100", + "true_value": "-0.00014448165893554688", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1e81f547fa3910b3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78731:78732:1", + "start_idx": 49511 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43224:43225:1', 'start_idx': 14004} The data corresponds to a snapshot on August 02 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Maldives change in 42 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Maldives to increased by 4.781 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the V (meridional) component of wind at 250 hPa will decrease by 0 m/s at Maldives.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Maldives", + "target_variable": "v_component_of_wind_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9417afd9a1b3591f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43224:43225:1", + "start_idx": 14004 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89128:89129:1', 'start_idx': 59908} The data corresponds to a snapshot on January 03 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at East Timor change in 36 hours if localized Gaussian perturbations cause Temperature at 700 hPa at East Timor to increased by 4 K in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Temperature at 700 hPa will decrease by 0 K at East Timor.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Temperature at 700 hPa", + "location": "East Timor", + "target_variable": "temperature_700", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "06228735a1cc19f9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89128:89129:1", + "start_idx": 59908 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33548:33549:1', 'start_idx': 4328} The data corresponds to a snapshot on December 18 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 850 hPa at Turks and Caicos Islands change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 850 hPa at Turks and Caicos Islands to increased by 1.78 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 850 hPa will decrease by 0 m/s at Turks and Caicos Islands.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 850 hPa", + "location": "Turks and Caicos Islands", + "target_variable": "v_component_of_wind_850", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "46bb24cc17dac984", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33548:33549:1", + "start_idx": 4328 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35451:35452:1', 'start_idx': 6231} The data corresponds to a snapshot on April 07 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 400 hPa at Greenland Sea change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 400 hPa at Greenland Sea to increased by 4.215 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 400 hPa will increase by 8.45 m/s at Greenland Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 400 hPa", + "location": "Greenland Sea", + "target_variable": "v_component_of_wind_400", + "true_value": "8.450387001037598", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "aa4c90fe126e3d8d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35451:35452:1", + "start_idx": 6231 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72759:72760:1', 'start_idx': 43539} The data corresponds to a snapshot on October 19 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 400 hPa at Colombia change in 48 hours if localized Gaussian perturbations cause Geopotential at 400 hPa at Colombia to increased by 1189 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Geopotential at 400 hPa will decrease by 0 m²/s² at Colombia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Geopotential at 400 hPa", + "location": "Colombia", + "target_variable": "geopotential_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fc764cc98acb38a8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72759:72760:1", + "start_idx": 43539 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58565:58566:1', 'start_idx': 29345} The data corresponds to a snapshot on February 01 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Chile change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Chile to increased by 3.514 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 250 hPa will decrease by 0.0001164 m/s at Chile.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Chile", + "target_variable": "v_component_of_wind_250", + "true_value": "-0.0001163849447038956", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7c059c51cb94ca57", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58565:58566:1", + "start_idx": 29345 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30634:30635:1', 'start_idx': 1414} The data corresponds to a snapshot on December 20 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 150 hPa at Bristol Channel change in 18 hours if localized Gaussian perturbations cause Geopotential at 150 hPa at Bristol Channel to increased by 1721 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Geopotential at 150 hPa will decrease by 0 m²/s² at Bristol Channel.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Geopotential at 150 hPa", + "location": "Bristol Channel", + "target_variable": "geopotential_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b2c7e7edeaf743f7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30634:30635:1", + "start_idx": 1414 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92031:92032:1', 'start_idx': 62811} The data corresponds to a snapshot on December 28 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 50 hPa at Oceania change in 36 hours if localized Gaussian perturbations cause Geopotential at 50 hPa at Oceania to increased by 1775 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Geopotential at 50 hPa will decrease by 0 m²/s² at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Geopotential at 50 hPa", + "location": "Oceania", + "target_variable": "geopotential_50", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3fc6e2c028aa96f8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92031:92032:1", + "start_idx": 62811 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79389:79390:1', 'start_idx': 50169} The data corresponds to a snapshot on May 04 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 1000 hPa at Cumberland Sound change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 1000 hPa at Cumberland Sound to increased by 1.314 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 1000 hPa will decrease by 0 m/s at Cumberland Sound.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 1000 hPa", + "location": "Cumberland Sound", + "target_variable": "u_component_of_wind_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d3433a77b1787326", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79389:79390:1", + "start_idx": 50169 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71267:71268:1', 'start_idx': 42047} The data corresponds to a snapshot on October 12 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 1000 hPa at Macao S.A.R change in 48 hours if localized Gaussian perturbations cause Geopotential at 1000 hPa at Macao S.A.R to increased by 371 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Geopotential at 1000 hPa will decrease by 0 m²/s² at Macao S.A.R.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Geopotential at 1000 hPa", + "location": "Macao S.A.R", + "target_variable": "geopotential_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d810a81381aa31fc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71267:71268:1", + "start_idx": 42047 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57826:57827:1', 'start_idx': 28606} The data corresponds to a snapshot on July 31 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 925 hPa at Oceania change in 6 hours if localized Gaussian perturbations cause Specific humidity at 925 hPa at Oceania to increased by 0.001886 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Specific humidity at 925 hPa will increase by 4.66e-05 kg/kg at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Specific humidity at 925 hPa", + "location": "Oceania", + "target_variable": "specific_humidity_925", + "true_value": "4.6599529014201835e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "68316cbdc809789c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57826:57827:1", + "start_idx": 28606 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83193:83194:1', 'start_idx': 53973} The data corresponds to a snapshot on December 11 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 50 hPa at Asia change in 18 hours if localized Gaussian perturbations cause Specific humidity at 50 hPa at Asia to increased by 1.255e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 50 hPa will decrease by 2.301e-10 kg/kg at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 50 hPa", + "location": "Asia", + "target_variable": "specific_humidity_50", + "true_value": "-2.3005884697280266e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a619f1905aeeee54", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83193:83194:1", + "start_idx": 53973 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66518:66519:1', 'start_idx': 37298} The data corresponds to a snapshot on July 12 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 500 hPa at Benin change in 18 hours if localized Gaussian perturbations cause U (zonal) component of wind at 500 hPa at Benin to increased by 3.405 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the U (zonal) component of wind at 500 hPa will decrease by 0.1479 m/s at Benin.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "U (zonal) component of wind at 500 hPa", + "location": "Benin", + "target_variable": "u_component_of_wind_500", + "true_value": "-0.14791171252727509", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f6f440b5b1788fc5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66518:66519:1", + "start_idx": 37298 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32946:32947:1', 'start_idx': 3726} The data corresponds to a snapshot on July 20 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 400 hPa at Cabo Verde change in 6 hours if localized Gaussian perturbations cause U (zonal) component of wind at 400 hPa at Cabo Verde to increased by 4.1 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the U (zonal) component of wind at 400 hPa will increase by 0.01264 m/s at Cabo Verde.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "U (zonal) component of wind at 400 hPa", + "location": "Cabo Verde", + "target_variable": "u_component_of_wind_400", + "true_value": "0.012641429901123047", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e4d1ef66a4c70c3a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32946:32947:1", + "start_idx": 3726 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29664:29665:1', 'start_idx': 444} The data corresponds to a snapshot on April 22 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 925 hPa at Niue change in 12 hours if localized Gaussian perturbations cause U (zonal) component of wind at 925 hPa at Niue to increased by 2.766 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the U (zonal) component of wind at 925 hPa will decrease by 0 m/s at Niue.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "U (zonal) component of wind at 925 hPa", + "location": "Niue", + "target_variable": "u_component_of_wind_925", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d8c8154ed1f63d89", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29664:29665:1", + "start_idx": 444 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85936:85937:1', 'start_idx': 56716} The data corresponds to a snapshot on October 27 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Aland change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Aland to increased by 2.227 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 850 hPa will decrease by 0 m/s at Aland.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Aland", + "target_variable": "u_component_of_wind_850", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8ed09ee27a254900", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85936:85937:1", + "start_idx": 56716 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80473:80474:1', 'start_idx': 51253} The data corresponds to a snapshot on January 30 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 500 hPa at Asia change in 48 hours if localized Gaussian perturbations cause U (zonal) component of wind at 500 hPa at Asia to increased by 3.116 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the U (zonal) component of wind at 500 hPa will increase by 0.001058 m/s at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "U (zonal) component of wind at 500 hPa", + "location": "Asia", + "target_variable": "u_component_of_wind_500", + "true_value": "0.0010575648630037904", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7f8706115fbf9e8c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80473:80474:1", + "start_idx": 51253 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61588:61589:1', 'start_idx': 32368} The data corresponds to a snapshot on February 26 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at Southern Patagonian Ice Field change in 42 hours if localized Gaussian perturbations cause Temperature at 700 hPa at Southern Patagonian Ice Field to increased by 5.55 K in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Temperature at 700 hPa will decrease by 0 K at Southern Patagonian Ice Field.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Temperature at 700 hPa", + "location": "Southern Patagonian Ice Field", + "target_variable": "temperature_700", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "223a8699768d8834", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61588:61589:1", + "start_idx": 32368 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44548:44549:1', 'start_idx': 15328} The data corresponds to a snapshot on June 29 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 150 hPa at Husky Lakes change in 30 hours if localized Gaussian perturbations cause Temperature at 150 hPa at Husky Lakes to increased by 2.377 K in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Temperature at 150 hPa will decrease by 0 K at Husky Lakes.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Temperature at 150 hPa", + "location": "Husky Lakes", + "target_variable": "temperature_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1f5d3f892f54725c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44548:44549:1", + "start_idx": 15328 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89970:89971:1', 'start_idx': 60750} The data corresponds to a snapshot on July 31 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 250 hPa at Chad change in 12 hours if localized Gaussian perturbations cause Geopotential at 250 hPa at Chad to increased by 1136 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Geopotential at 250 hPa will decrease by 0 m²/s² at Chad.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Geopotential at 250 hPa", + "location": "Chad", + "target_variable": "geopotential_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "886f3e0d7d50b109", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89970:89971:1", + "start_idx": 60750 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80064:80065:1', 'start_idx': 50844} The data corresponds to a snapshot on October 20 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 925 hPa at Ireland change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 925 hPa at Ireland to increased by 2.117 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 925 hPa will increase by 0.0009821 m/s at Ireland.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 925 hPa", + "location": "Ireland", + "target_variable": "v_component_of_wind_925", + "true_value": "0.0009821256389841437", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "822e57ad8db2a2e8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80064:80065:1", + "start_idx": 50844 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49270:49271:1', 'start_idx': 20050} The data corresponds to a snapshot on September 21 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Slovenia change in 12 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Slovenia to increased by 1.686 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the U (zonal) component of wind at 850 hPa will decrease by 0.0009642 m/s at Slovenia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Slovenia", + "target_variable": "u_component_of_wind_850", + "true_value": "-0.0009641647338867188", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f0877160bba7cb0a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49270:49271:1", + "start_idx": 20050 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32349:32350:1', 'start_idx': 3129} The data corresponds to a snapshot on February 21 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at South America change in 42 hours if localized Gaussian perturbations cause Temperature at 700 hPa at South America to increased by 4.425 K in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Temperature at 700 hPa will decrease by 6.653e-05 K at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Temperature at 700 hPa", + "location": "South America", + "target_variable": "temperature_700", + "true_value": "-6.652832234976813e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f32c4b9c69eaea8d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32349:32350:1", + "start_idx": 3129 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64027:64028:1', 'start_idx': 34807} The data corresponds to a snapshot on October 28 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 200 hPa at Chesapeake Bay change in 36 hours if localized Gaussian perturbations cause Specific humidity at 200 hPa at Chesapeake Bay to increased by 8.561e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Specific humidity at 200 hPa will increase by 9.662e-09 kg/kg at Chesapeake Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Specific humidity at 200 hPa", + "location": "Chesapeake Bay", + "target_variable": "specific_humidity_200", + "true_value": "9.66247171163559e-09", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "feeddedc955e4abd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64027:64028:1", + "start_idx": 34807 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45096:45097:1', 'start_idx': 15876} The data corresponds to a snapshot on November 13 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 150 hPa at Austria change in 30 hours if localized Gaussian perturbations cause Specific humidity at 150 hPa at Austria to increased by 1.006e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Specific humidity at 150 hPa will increase by 8.222e-10 kg/kg at Austria.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Specific humidity at 150 hPa", + "location": "Austria", + "target_variable": "specific_humidity_150", + "true_value": "8.221832104027271e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c3a40d6a0e021f3d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45096:45097:1", + "start_idx": 15876 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87184:87185:1', 'start_idx': 57964} The data corresponds to a snapshot on September 04 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 250 hPa at Lago de Maracaibo change in 42 hours if localized Gaussian perturbations cause Temperature at 250 hPa at Lago de Maracaibo to increased by 1.871 K in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Temperature at 250 hPa will decrease by 0 K at Lago de Maracaibo.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Temperature at 250 hPa", + "location": "Lago de Maracaibo", + "target_variable": "temperature_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "645390b649f3c1a0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87184:87185:1", + "start_idx": 57964 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34665:34666:1', 'start_idx': 5445} The data corresponds to a snapshot on September 23 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 700 hPa at Antarctica change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 700 hPa at Antarctica to increased by 2.617 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 700 hPa will increase by 0.5677 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 700 hPa", + "location": "Antarctica", + "target_variable": "v_component_of_wind_700", + "true_value": "0.567676842212677", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ae4b8421742c5c12", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34665:34666:1", + "start_idx": 5445 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72031:72032:1', 'start_idx': 42811} The data corresponds to a snapshot on April 20 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 300 hPa at Sea of Crete change in 24 hours if localized Gaussian perturbations cause Temperature at 300 hPa at Sea of Crete to increased by 3.92 K in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Temperature at 300 hPa will increase by 0.005005 K at Sea of Crete.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Temperature at 300 hPa", + "location": "Sea of Crete", + "target_variable": "temperature_300", + "true_value": "0.0050048828125", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fd04a2e77e45d07b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72031:72032:1", + "start_idx": 42811 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63290:63291:1', 'start_idx': 34070} The data corresponds to a snapshot on April 27 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at Somaliland change in 6 hours if localized Gaussian perturbations cause Temperature at 50 hPa at Somaliland to increased by 2.374 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 50 hPa will decrease by 8.392e-05 K at Somaliland.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 50 hPa", + "location": "Somaliland", + "target_variable": "temperature_50", + "true_value": "-8.392333984375e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b46224b51c24857a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63290:63291:1", + "start_idx": 34070 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35898:35899:1', 'start_idx': 6678} The data corresponds to a snapshot on July 28 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at Uda Bay change in 12 hours if localized Gaussian perturbations cause Temperature at 700 hPa at Uda Bay to increased by 3.483 K in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Temperature at 700 hPa will decrease by 0.001495 K at Uda Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Temperature at 700 hPa", + "location": "Uda Bay", + "target_variable": "temperature_700", + "true_value": "-0.001495361328125", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8e87f5881f423e76", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35898:35899:1", + "start_idx": 6678 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84414:84415:1', 'start_idx': 55194} The data corresponds to a snapshot on October 11 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 500 hPa at Pamlico Sound change in 6 hours if localized Gaussian perturbations cause Geopotential at 500 hPa at Pamlico Sound to increased by 1312 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 500 hPa will decrease by 0 m²/s² at Pamlico Sound.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 500 hPa", + "location": "Pamlico Sound", + "target_variable": "geopotential_500", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9392297653d40c2c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84414:84415:1", + "start_idx": 55194 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34323:34324:1', 'start_idx': 5103} The data corresponds to a snapshot on June 29 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 200 hPa at Labrador Sea change in 36 hours if localized Gaussian perturbations cause Temperature at 200 hPa at Labrador Sea to increased by 1.739 K in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Temperature at 200 hPa will increase by 0.0209 K at Labrador Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Temperature at 200 hPa", + "location": "Labrador Sea", + "target_variable": "temperature_200", + "true_value": "0.0209023617208004", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "636a54265a8e512d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34323:34324:1", + "start_idx": 5103 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33367:33368:1', 'start_idx': 4147} The data corresponds to a snapshot on November 02 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 600 hPa at Africa change in 42 hours if localized Gaussian perturbations cause Temperature at 600 hPa at Africa to increased by 3.537 K in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Temperature at 600 hPa will increase by 0.5029 K at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Temperature at 600 hPa", + "location": "Africa", + "target_variable": "temperature_600", + "true_value": "0.5028814673423767", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3c85c27e8df2fcde", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33367:33368:1", + "start_idx": 4147 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63869:63870:1', 'start_idx': 34649} The data corresponds to a snapshot on September 19 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 150 hPa at Bab el Mandeb change in 48 hours if localized Gaussian perturbations cause Temperature at 150 hPa at Bab el Mandeb to increased by 2.611 K in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Temperature at 150 hPa will decrease by 0 K at Bab el Mandeb.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Temperature at 150 hPa", + "location": "Bab el Mandeb", + "target_variable": "temperature_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b13a869624b74c35", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63869:63870:1", + "start_idx": 34649 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38276:38277:1', 'start_idx': 9056} The data corresponds to a snapshot on March 14 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 50 hPa at Turkmenistan change in 18 hours if localized Gaussian perturbations cause U (zonal) component of wind at 50 hPa at Turkmenistan to increased by 5.947 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the U (zonal) component of wind at 50 hPa will increase by 0.0002827 m/s at Turkmenistan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "U (zonal) component of wind at 50 hPa", + "location": "Turkmenistan", + "target_variable": "u_component_of_wind_50", + "true_value": "0.00028271146584302187", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3012a1e3f6725439", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38276:38277:1", + "start_idx": 9056 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89189:89190:1', 'start_idx': 59969} The data corresponds to a snapshot on January 18 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 700 hPa at Seno de Skyring change in 24 hours if localized Gaussian perturbations cause Geopotential at 700 hPa at Seno de Skyring to increased by 500 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Geopotential at 700 hPa will decrease by 0 m²/s² at Seno de Skyring.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Geopotential at 700 hPa", + "location": "Seno de Skyring", + "target_variable": "geopotential_700", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2210178e1aa128c9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89189:89190:1", + "start_idx": 59969 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32722:32723:1', 'start_idx': 3502} The data corresponds to a snapshot on May 25 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 500 hPa at Togo change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 500 hPa at Togo to increased by 2.753 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 500 hPa will increase by 0.1764 m/s at Togo.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 500 hPa", + "location": "Togo", + "target_variable": "v_component_of_wind_500", + "true_value": "0.1764233112335205", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "31fd2fffea4e4367", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32722:32723:1", + "start_idx": 3502 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50127:50128:1', 'start_idx': 20907} The data corresponds to a snapshot on April 23 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at The North Western Passages change in 24 hours if localized Gaussian perturbations cause Temperature at 50 hPa at The North Western Passages to increased by 2.626 K in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Temperature at 50 hPa will decrease by 3.391e-05 K at The North Western Passages.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Temperature at 50 hPa", + "location": "The North Western Passages", + "target_variable": "temperature_50", + "true_value": "-3.390842175576836e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "280fa63d71fd9fb3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50127:50128:1", + "start_idx": 20907 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88667:88668:1', 'start_idx': 59447} The data corresponds to a snapshot on September 09 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 850 hPa at Vestfjorden change in 12 hours if localized Gaussian perturbations cause Specific humidity at 850 hPa at Vestfjorden to increased by 0.001034 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 850 hPa will decrease by 9.172e-09 kg/kg at Vestfjorden.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 850 hPa", + "location": "Vestfjorden", + "target_variable": "specific_humidity_850", + "true_value": "-9.172310910798842e-09", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f0c243826763e3ca", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88667:88668:1", + "start_idx": 59447 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67737:67738:1', 'start_idx': 38517} The data corresponds to a snapshot on May 13 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 100 hPa at Jamaica change in 48 hours if localized Gaussian perturbations cause U (zonal) component of wind at 100 hPa at Jamaica to increased by 3.85 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the U (zonal) component of wind at 100 hPa will increase by 0.0001787 m/s at Jamaica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "U (zonal) component of wind at 100 hPa", + "location": "Jamaica", + "target_variable": "u_component_of_wind_100", + "true_value": "0.0001786947250366211", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bbfd3621f15991cb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67737:67738:1", + "start_idx": 38517 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64773:64774:1', 'start_idx': 35553} The data corresponds to a snapshot on May 03 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 400 hPa at Brazil change in 6 hours if localized Gaussian perturbations cause U (zonal) component of wind at 400 hPa at Brazil to increased by 5.359 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the U (zonal) component of wind at 400 hPa will decrease by 6.596e-06 m/s at Brazil.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "U (zonal) component of wind at 400 hPa", + "location": "Brazil", + "target_variable": "u_component_of_wind_400", + "true_value": "-6.5957683546002954e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2865cd2a27f0bc98", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64773:64774:1", + "start_idx": 35553 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88988:88989:1', 'start_idx': 59768} The data corresponds to a snapshot on November 29 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 600 hPa at Yangtze River change in 12 hours if localized Gaussian perturbations cause Specific humidity at 600 hPa at Yangtze River to increased by 0.0003579 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 600 hPa will decrease by 1.927e-07 kg/kg at Yangtze River.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 600 hPa", + "location": "Yangtze River", + "target_variable": "specific_humidity_600", + "true_value": "-1.9266735762357712e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "04684ce836d5f31c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88988:88989:1", + "start_idx": 59768 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70368:70369:1', 'start_idx': 41148} The data corresponds to a snapshot on March 02 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Antarctica change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Antarctica to increased by 2.649 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 850 hPa will increase by 0.1393 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Antarctica", + "target_variable": "u_component_of_wind_850", + "true_value": "0.13928325474262238", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7182a2fc98985434", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70368:70369:1", + "start_idx": 41148 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90249:90250:1', 'start_idx': 61029} The data corresponds to a snapshot on October 09 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 200 hPa at US Naval Base Guantanamo Bay change in 18 hours if localized Gaussian perturbations cause Temperature at 200 hPa at US Naval Base Guantanamo Bay to increased by 2.683 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 200 hPa will decrease by 0 K at US Naval Base Guantanamo Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 200 hPa", + "location": "US Naval Base Guantanamo Bay", + "target_variable": "temperature_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4a074c67352750b6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90249:90250:1", + "start_idx": 61029 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56935:56936:1', 'start_idx': 27715} The data corresponds to a snapshot on December 20 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 250 hPa at Vil'kitskogo Strait change in 36 hours if localized Gaussian perturbations cause Temperature at 250 hPa at Vil'kitskogo Strait to increased by 2.068 K in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Temperature at 250 hPa will increase by 0.0003357 K at Vil'kitskogo Strait.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Temperature at 250 hPa", + "location": "Vil'kitskogo Strait", + "target_variable": "temperature_250", + "true_value": "0.000335693359375", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "936799380e40d12e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56935:56936:1", + "start_idx": 27715 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91314:91315:1', 'start_idx': 62094} The data corresponds to a snapshot on July 02 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 600 hPa at Africa change in 36 hours if localized Gaussian perturbations cause V (meridional) component of wind at 600 hPa at Africa to increased by 2.651 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the V (meridional) component of wind at 600 hPa will increase by 0.4099 m/s at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "V (meridional) component of wind at 600 hPa", + "location": "Africa", + "target_variable": "v_component_of_wind_600", + "true_value": "0.40991607308387756", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "661407229c188fb5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91314:91315:1", + "start_idx": 62094 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55751:55752:1', 'start_idx': 26531} The data corresponds to a snapshot on February 27 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 500 hPa at Amundsen Gulf change in 18 hours if localized Gaussian perturbations cause Specific humidity at 500 hPa at Amundsen Gulf to increased by 0.0003567 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 500 hPa will decrease by 5.285e-07 kg/kg at Amundsen Gulf.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 500 hPa", + "location": "Amundsen Gulf", + "target_variable": "specific_humidity_500", + "true_value": "-5.284673534333706e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a45d31cd9385b4d3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55751:55752:1", + "start_idx": 26531 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76727:76728:1', 'start_idx': 47507} The data corresponds to a snapshot on July 08 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 300 hPa at Estonia change in 18 hours if localized Gaussian perturbations cause Specific humidity at 300 hPa at Estonia to increased by 3.979e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 300 hPa will increase by 2.747e-08 kg/kg at Estonia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 300 hPa", + "location": "Estonia", + "target_variable": "specific_humidity_300", + "true_value": "2.7466739993542433e-08", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9b3e88538588209f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76727:76728:1", + "start_idx": 47507 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61804:61805:1', 'start_idx': 32584} The data corresponds to a snapshot on April 21 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 600 hPa at Antarctica change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 600 hPa at Antarctica to increased by 3.045 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 600 hPa will increase by 1.673 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 600 hPa", + "location": "Antarctica", + "target_variable": "v_component_of_wind_600", + "true_value": "1.6731327772140503", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d9a896fda3841d16", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61804:61805:1", + "start_idx": 32584 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36963:36964:1', 'start_idx': 7743} The data corresponds to a snapshot on April 19 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 250 hPa at South America change in 30 hours if localized Gaussian perturbations cause Specific humidity at 250 hPa at South America to increased by 1.984e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Specific humidity at 250 hPa will increase by 2.121e-10 kg/kg at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Specific humidity at 250 hPa", + "location": "South America", + "target_variable": "specific_humidity_250", + "true_value": "2.120941616778893e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "009393435dcb4077", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36963:36964:1", + "start_idx": 7743 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79378:79379:1', 'start_idx': 50158} The data corresponds to a snapshot on May 01 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 600 hPa at Hangzhou Bay change in 48 hours if localized Gaussian perturbations cause Specific humidity at 600 hPa at Hangzhou Bay to increased by 0.0006779 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Specific humidity at 600 hPa will increase by 2.21e-07 kg/kg at Hangzhou Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Specific humidity at 600 hPa", + "location": "Hangzhou Bay", + "target_variable": "specific_humidity_600", + "true_value": "2.210226739407517e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dbd3ff238bc29f2c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79378:79379:1", + "start_idx": 50158 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62273:62274:1', 'start_idx': 33053} The data corresponds to a snapshot on August 16 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 600 hPa at Strait of Singapore change in 24 hours if localized Gaussian perturbations cause Specific humidity at 600 hPa at Strait of Singapore to increased by 0.0005327 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Specific humidity at 600 hPa will decrease by 0 kg/kg at Strait of Singapore.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Specific humidity at 600 hPa", + "location": "Strait of Singapore", + "target_variable": "specific_humidity_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8674e11122259798", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62273:62274:1", + "start_idx": 33053 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29459:29460:1', 'start_idx': 239} The data corresponds to a snapshot on March 01 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 700 hPa at Sea of Japan change in 24 hours if localized Gaussian perturbations cause Geopotential at 700 hPa at Sea of Japan to increased by 807 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Geopotential at 700 hPa will decrease by 0 m²/s² at Sea of Japan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Geopotential at 700 hPa", + "location": "Sea of Japan", + "target_variable": "geopotential_700", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e9f161bb889c6ac5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29459:29460:1", + "start_idx": 239 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82847:82848:1', 'start_idx': 53627} The data corresponds to a snapshot on September 15 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 100 hPa at Oceania change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 100 hPa at Oceania to increased by 4.418 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 100 hPa will increase by 0.09727 m/s at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 100 hPa", + "location": "Oceania", + "target_variable": "u_component_of_wind_100", + "true_value": "0.09727410972118378", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f454589fdcc10e05", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82847:82848:1", + "start_idx": 53627 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53903:53904:1', 'start_idx': 24683} The data corresponds to a snapshot on November 23 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 850 hPa at Europe change in 42 hours if localized Gaussian perturbations cause V (meridional) component of wind at 850 hPa at Europe to increased by 1.867 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the V (meridional) component of wind at 850 hPa will increase by 0.001441 m/s at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "V (meridional) component of wind at 850 hPa", + "location": "Europe", + "target_variable": "v_component_of_wind_850", + "true_value": "0.0014413802418857813", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6e84523852b25f26", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53903:53904:1", + "start_idx": 24683 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77659:77660:1', 'start_idx': 48439} The data corresponds to a snapshot on February 26 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 600 hPa at Czechia change in 24 hours if localized Gaussian perturbations cause U (zonal) component of wind at 600 hPa at Czechia to increased by 2.59 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the U (zonal) component of wind at 600 hPa will decrease by 0.002266 m/s at Czechia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "U (zonal) component of wind at 600 hPa", + "location": "Czechia", + "target_variable": "u_component_of_wind_600", + "true_value": "-0.0022655874490737915", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4736fe1253d04814", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77659:77660:1", + "start_idx": 48439 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38083:38084:1', 'start_idx': 8863} The data corresponds to a snapshot on January 24 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 600 hPa at La Pérouse Strait change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 600 hPa at La Pérouse Strait to increased by 2.706 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 600 hPa will decrease by 0 m/s at La Pérouse Strait.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 600 hPa", + "location": "La Pérouse Strait", + "target_variable": "v_component_of_wind_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cb66a21e0711c9d3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38083:38084:1", + "start_idx": 8863 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55299:55300:1', 'start_idx': 26079} The data corresponds to a snapshot on November 06 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 200 hPa at Oceania change in 48 hours if localized Gaussian perturbations cause Specific humidity at 200 hPa at Oceania to increased by 6.787e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Specific humidity at 200 hPa will increase by 9.147e-07 kg/kg at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Specific humidity at 200 hPa", + "location": "Oceania", + "target_variable": "specific_humidity_200", + "true_value": "9.14720033051708e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2e463105a502ea7c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55299:55300:1", + "start_idx": 26079 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41365:41366:1', 'start_idx': 12145} The data corresponds to a snapshot on April 25 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 150 hPa at Surigao Strait change in 36 hours if localized Gaussian perturbations cause Specific humidity at 150 hPa at Surigao Strait to increased by 1.472e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Specific humidity at 150 hPa will decrease by 0 kg/kg at Surigao Strait.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Specific humidity at 150 hPa", + "location": "Surigao Strait", + "target_variable": "specific_humidity_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "02f74353f46f6393", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41365:41366:1", + "start_idx": 12145 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73112:73113:1', 'start_idx': 43892} The data corresponds to a snapshot on January 16 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 100 hPa at Europe change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 100 hPa at Europe to increased by 2.212 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 100 hPa will increase by 0.002178 m/s at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 100 hPa", + "location": "Europe", + "target_variable": "v_component_of_wind_100", + "true_value": "0.0021775783970952034", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f2789ef4baa8828c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73112:73113:1", + "start_idx": 43892 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88577:88578:1', 'start_idx': 59357} The data corresponds to a snapshot on August 18 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 300 hPa at Africa change in 18 hours if localized Gaussian perturbations cause Temperature at 300 hPa at Africa to increased by 2.717 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 300 hPa will increase by 0.5449 K at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 300 hPa", + "location": "Africa", + "target_variable": "temperature_300", + "true_value": "0.5448694825172424", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "98c94b364934ac86", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88577:88578:1", + "start_idx": 59357 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75367:75368:1', 'start_idx': 46147} The data corresponds to a snapshot on August 02 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at Golfo de California change in 48 hours if localized Gaussian perturbations cause Temperature at 700 hPa at Golfo de California to increased by 3.313 K in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Temperature at 700 hPa will decrease by 0.02513 K at Golfo de California.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Temperature at 700 hPa", + "location": "Golfo de California", + "target_variable": "temperature_700", + "true_value": "-0.02512613870203495", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e5e432a8398aeaa3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75367:75368:1", + "start_idx": 46147 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52764:52765:1', 'start_idx': 23544} The data corresponds to a snapshot on February 12 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Lesotho change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Lesotho to increased by 4.055 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 250 hPa will decrease by 0 m/s at Lesotho.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Lesotho", + "target_variable": "v_component_of_wind_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "507d6455430dcade", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52764:52765:1", + "start_idx": 23544 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46972:46973:1', 'start_idx': 17752} The data corresponds to a snapshot on February 25 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 50 hPa at North Korea change in 30 hours if localized Gaussian perturbations cause V (meridional) component of wind at 50 hPa at North Korea to increased by 2.412 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the V (meridional) component of wind at 50 hPa will increase by 7.725e-05 m/s at North Korea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "V (meridional) component of wind at 50 hPa", + "location": "North Korea", + "target_variable": "v_component_of_wind_50", + "true_value": "7.724761962890625e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3914b2bc212e5b08", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46972:46973:1", + "start_idx": 17752 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31619:31620:1', 'start_idx': 2399} The data corresponds to a snapshot on August 22 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 925 hPa at Oceania change in 30 hours if localized Gaussian perturbations cause Temperature at 925 hPa at Oceania to increased by 3.455 K in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Temperature at 925 hPa will increase by 0.08511 K at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Temperature at 925 hPa", + "location": "Oceania", + "target_variable": "temperature_925", + "true_value": "0.08511170744895935", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "aaef80bad50038e1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31619:31620:1", + "start_idx": 2399 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63908:63909:1', 'start_idx': 34688} The data corresponds to a snapshot on September 29 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 150 hPa at Caribbean Sea change in 42 hours if localized Gaussian perturbations cause Temperature at 150 hPa at Caribbean Sea to increased by 2.686 K in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Temperature at 150 hPa will decrease by 2.325e-05 K at Caribbean Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Temperature at 150 hPa", + "location": "Caribbean Sea", + "target_variable": "temperature_150", + "true_value": "-2.325148852833081e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0d39c89c646f9187", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63908:63909:1", + "start_idx": 34688 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68869:68870:1', 'start_idx': 39649} The data corresponds to a snapshot on February 20 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 925 hPa at Gulf of Aqaba change in 6 hours if localized Gaussian perturbations cause Temperature at 925 hPa at Gulf of Aqaba to increased by 4.976 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 925 hPa will decrease by 0 K at Gulf of Aqaba.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 925 hPa", + "location": "Gulf of Aqaba", + "target_variable": "temperature_925", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "159989041bcf1a1c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68869:68870:1", + "start_idx": 39649 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52147:52148:1', 'start_idx': 22927} The data corresponds to a snapshot on September 10 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Netherlands change in 42 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Netherlands to increased by 4.019 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the V (meridional) component of wind at 250 hPa will increase by 0.9962 m/s at Netherlands.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Netherlands", + "target_variable": "v_component_of_wind_250", + "true_value": "0.9962154626846313", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "057ac4d1e39f6151", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52147:52148:1", + "start_idx": 22927 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83019:83020:1', 'start_idx': 53799} The data corresponds to a snapshot on October 28 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 500 hPa at Aegean Sea change in 36 hours if localized Gaussian perturbations cause V (meridional) component of wind at 500 hPa at Aegean Sea to increased by 2.533 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the V (meridional) component of wind at 500 hPa will decrease by 0.00919 m/s at Aegean Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "V (meridional) component of wind at 500 hPa", + "location": "Aegean Sea", + "target_variable": "v_component_of_wind_500", + "true_value": "-0.009189605712890625", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "281d96ff87940115", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83019:83020:1", + "start_idx": 53799 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44368:44369:1', 'start_idx': 15148} The data corresponds to a snapshot on May 15 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 400 hPa at Cordova Bay change in 6 hours if localized Gaussian perturbations cause Geopotential at 400 hPa at Cordova Bay to increased by 1384 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 400 hPa will decrease by 0 m²/s² at Cordova Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 400 hPa", + "location": "Cordova Bay", + "target_variable": "geopotential_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d8c6cd0711fe9d0e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44368:44369:1", + "start_idx": 15148 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36644:36645:1', 'start_idx': 7424} The data corresponds to a snapshot on January 31 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 400 hPa at Saint Lucia change in 30 hours if localized Gaussian perturbations cause Temperature at 400 hPa at Saint Lucia to increased by 3.416 K in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Temperature at 400 hPa will decrease by 0 K at Saint Lucia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Temperature at 400 hPa", + "location": "Saint Lucia", + "target_variable": "temperature_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "73d03d7deff8e4ce", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36644:36645:1", + "start_idx": 7424 + } + } +] \ No newline at end of file diff --git a/level3a_part1.json b/level3a_part1.json new file mode 100644 index 0000000000000000000000000000000000000000..1271ab535c5c62ce369d5a77d97c0ecf43e66cb8 --- /dev/null +++ b/level3a_part1.json @@ -0,0 +1,3602 @@ +[ + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74746:74747:1', 'start_idx': 45526} The data corresponds to a snapshot on February 28 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 400 hPa at Cabo Verde change in 30 hours if localized Gaussian perturbations cause U (zonal) component of wind at 400 hPa at Cabo Verde to increased by 5.628 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the U (zonal) component of wind at 400 hPa will increase by 0.01733 m/s at Cabo Verde.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "U (zonal) component of wind at 400 hPa", + "location": "Cabo Verde", + "target_variable": "u_component_of_wind_400", + "true_value": "0.017333030700683594", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8a668a31964569ee", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74746:74747:1", + "start_idx": 45526 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32837:32838:1', 'start_idx': 3617} The data corresponds to a snapshot on June 23 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 300 hPa at Asia change in 12 hours if localized Gaussian perturbations cause Geopotential at 300 hPa at Asia to increased by 1363 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Geopotential at 300 hPa will decrease by 0 m²/s² at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Geopotential at 300 hPa", + "location": "Asia", + "target_variable": "geopotential_300", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "112817c7f2b2a58f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32837:32838:1", + "start_idx": 3617 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29763:29764:1', 'start_idx': 543} The data corresponds to a snapshot on May 16 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 150 hPa at Great Barrier Reef change in 48 hours if localized Gaussian perturbations cause U (zonal) component of wind at 150 hPa at Great Barrier Reef to increased by 5.836 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the U (zonal) component of wind at 150 hPa will increase by 0.03549 m/s at Great Barrier Reef.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "U (zonal) component of wind at 150 hPa", + "location": "Great Barrier Reef", + "target_variable": "u_component_of_wind_150", + "true_value": "0.03549385070800781", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e23b281c680cd85c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29763:29764:1", + "start_idx": 543 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58425:58426:1', 'start_idx': 29205} The data corresponds to a snapshot on December 28 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at Marshall Islands change in 30 hours if localized Gaussian perturbations cause Temperature at 700 hPa at Marshall Islands to increased by 4.659 K in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Temperature at 700 hPa will increase by 0.005005 K at Marshall Islands.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Temperature at 700 hPa", + "location": "Marshall Islands", + "target_variable": "temperature_700", + "true_value": "0.0050048828125", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "814176118611fdf3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58425:58426:1", + "start_idx": 29205 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77391:77392:1', 'start_idx': 48171} The data corresponds to a snapshot on December 21 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 1000 hPa at Heard Island and McDonald Islands change in 12 hours if localized Gaussian perturbations cause V (meridional) component of wind at 1000 hPa at Heard Island and McDonald Islands to increased by 1.428 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the V (meridional) component of wind at 1000 hPa will decrease by 0 m/s at Heard Island and McDonald Islands.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "V (meridional) component of wind at 1000 hPa", + "location": "Heard Island and McDonald Islands", + "target_variable": "v_component_of_wind_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3fe913ae9047bfbe", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77391:77392:1", + "start_idx": 48171 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75910:75911:1', 'start_idx': 46690} The data corresponds to a snapshot on December 16 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Bermuda change in 12 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Bermuda to increased by 3.95 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the V (meridional) component of wind at 250 hPa will decrease by 0 m/s at Bermuda.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Bermuda", + "target_variable": "v_component_of_wind_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bdc3fb2de85b4a4e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75910:75911:1", + "start_idx": 46690 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58906:58907:1', 'start_idx': 29686} The data corresponds to a snapshot on April 27 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 200 hPa at Europe change in 30 hours if localized Gaussian perturbations cause Geopotential at 200 hPa at Europe to increased by 1638 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Geopotential at 200 hPa will decrease by 0 m²/s² at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Geopotential at 200 hPa", + "location": "Europe", + "target_variable": "geopotential_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "38b64cacf8a2e5c4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58906:58907:1", + "start_idx": 29686 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '78476:78477:1', 'start_idx': 49256} The data corresponds to a snapshot on September 18 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 50 hPa at Disko Bay change in 36 hours if localized Gaussian perturbations cause Specific humidity at 50 hPa at Disko Bay to increased by 1.351e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Specific humidity at 50 hPa will decrease by 2.328e-10 kg/kg at Disko Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Specific humidity at 50 hPa", + "location": "Disko Bay", + "target_variable": "specific_humidity_50", + "true_value": "-2.3283064365386963e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "eb10bf5edca1c3cc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "78476:78477:1", + "start_idx": 49256 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90888:90889:1', 'start_idx': 61668} The data corresponds to a snapshot on March 18 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 250 hPa at Minto Inlet change in 24 hours if localized Gaussian perturbations cause Temperature at 250 hPa at Minto Inlet to increased by 2.488 K in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Temperature at 250 hPa will decrease by 0 K at Minto Inlet.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Temperature at 250 hPa", + "location": "Minto Inlet", + "target_variable": "temperature_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "aee55c591288bdc3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90888:90889:1", + "start_idx": 61668 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65432:65433:1', 'start_idx': 36212} The data corresponds to a snapshot on October 15 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 200 hPa at Baia de Maputo change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 200 hPa at Baia de Maputo to increased by 6.881 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 200 hPa will decrease by 0 m/s at Baia de Maputo.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 200 hPa", + "location": "Baia de Maputo", + "target_variable": "u_component_of_wind_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "57a9ad004f12f9a5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65432:65433:1", + "start_idx": 36212 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49677:49678:1', 'start_idx': 20457} The data corresponds to a snapshot on January 01 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Europe change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Europe to increased by 3.12 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 850 hPa will increase by 0.002359 m/s at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Europe", + "target_variable": "u_component_of_wind_850", + "true_value": "0.0023590815253555775", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6f1376e96e8ca745", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49677:49678:1", + "start_idx": 20457 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73704:73705:1', 'start_idx': 44484} The data corresponds to a snapshot on June 13 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 700 hPa at Oceania change in 24 hours if localized Gaussian perturbations cause V (meridional) component of wind at 700 hPa at Oceania to increased by 1.748 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the V (meridional) component of wind at 700 hPa will increase by 0.3315 m/s at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "V (meridional) component of wind at 700 hPa", + "location": "Oceania", + "target_variable": "v_component_of_wind_700", + "true_value": "0.3315492570400238", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "20c5e04da915e509", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73704:73705:1", + "start_idx": 44484 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34732:34733:1', 'start_idx': 5512} The data corresponds to a snapshot on October 10 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 300 hPa at Sherman Basin change in 12 hours if localized Gaussian perturbations cause Geopotential at 300 hPa at Sherman Basin to increased by 1931 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Geopotential at 300 hPa will decrease by 0 m²/s² at Sherman Basin.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Geopotential at 300 hPa", + "location": "Sherman Basin", + "target_variable": "geopotential_300", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0f26b53dd61ad02d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34732:34733:1", + "start_idx": 5512 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34080:34081:1', 'start_idx': 4860} The data corresponds to a snapshot on April 30 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 600 hPa at Uda Bay change in 6 hours if localized Gaussian perturbations cause Geopotential at 600 hPa at Uda Bay to increased by 770 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 600 hPa will decrease by 0 m²/s² at Uda Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 600 hPa", + "location": "Uda Bay", + "target_variable": "geopotential_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dd09745dcfaf51a3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34080:34081:1", + "start_idx": 4860 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74521:74522:1', 'start_idx': 45301} The data corresponds to a snapshot on January 03 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 300 hPa at Azerbaijan change in 30 hours if localized Gaussian perturbations cause U (zonal) component of wind at 300 hPa at Azerbaijan to increased by 4.046 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the U (zonal) component of wind at 300 hPa will decrease by 0.01411 m/s at Azerbaijan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "U (zonal) component of wind at 300 hPa", + "location": "Azerbaijan", + "target_variable": "u_component_of_wind_300", + "true_value": "-0.014106114394962788", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "42254221657906ff", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74521:74522:1", + "start_idx": 45301 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77580:77581:1', 'start_idx': 48360} The data corresponds to a snapshot on February 07 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 50 hPa at Asia change in 48 hours if localized Gaussian perturbations cause Geopotential at 50 hPa at Asia to increased by 1556 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Geopotential at 50 hPa will decrease by 0 m²/s² at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Geopotential at 50 hPa", + "location": "Asia", + "target_variable": "geopotential_50", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "31b1c7cd5ba29b50", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77580:77581:1", + "start_idx": 48360 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32325:32326:1', 'start_idx': 3105} The data corresponds to a snapshot on February 15 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 200 hPa at Gulf of Ob change in 12 hours if localized Gaussian perturbations cause Temperature at 200 hPa at Gulf of Ob to increased by 1.933 K in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Temperature at 200 hPa will increase by 0.0001678 K at Gulf of Ob.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Temperature at 200 hPa", + "location": "Gulf of Ob", + "target_variable": "temperature_200", + "true_value": "0.0001678466796875", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ac639f52a13cd2e5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32325:32326:1", + "start_idx": 3105 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59439:59440:1', 'start_idx': 30219} The data corresponds to a snapshot on September 07 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 150 hPa at Oceania change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 150 hPa at Oceania to increased by 6.194 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 150 hPa will increase by 0.5202 m/s at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 150 hPa", + "location": "Oceania", + "target_variable": "u_component_of_wind_150", + "true_value": "0.5201967358589172", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b716d501d4569be4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59439:59440:1", + "start_idx": 30219 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72684:72685:1', 'start_idx': 43464} The data corresponds to a snapshot on October 01 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 150 hPa at Antarctica change in 18 hours if localized Gaussian perturbations cause Specific humidity at 150 hPa at Antarctica to increased by 1.123e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 150 hPa will increase by 1.52e-07 kg/kg at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 150 hPa", + "location": "Antarctica", + "target_variable": "specific_humidity_150", + "true_value": "1.5202668635083683e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5dc84982f026cd3d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72684:72685:1", + "start_idx": 43464 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63316:63317:1', 'start_idx': 34096} The data corresponds to a snapshot on May 04 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 600 hPa at South America change in 18 hours if localized Gaussian perturbations cause Temperature at 600 hPa at South America to increased by 4.025 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 600 hPa will decrease by 0.0001477 K at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 600 hPa", + "location": "South America", + "target_variable": "temperature_600", + "true_value": "-0.00014770508278161287", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d3bb0ad8ab7ff6d4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63316:63317:1", + "start_idx": 34096 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57594:57595:1', 'start_idx': 28374} The data corresponds to a snapshot on June 03 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 600 hPa at France change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 600 hPa at France to increased by 1.657 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 600 hPa will increase by 2.292 m/s at France.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 600 hPa", + "location": "France", + "target_variable": "v_component_of_wind_600", + "true_value": "2.292017698287964", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4092579532d1658e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57594:57595:1", + "start_idx": 28374 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38355:38356:1', 'start_idx': 9135} The data corresponds to a snapshot on April 02 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 700 hPa at Tayabas Bay change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 700 hPa at Tayabas Bay to increased by 1.568 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 700 hPa will increase by 0.0007076 m/s at Tayabas Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 700 hPa", + "location": "Tayabas Bay", + "target_variable": "v_component_of_wind_700", + "true_value": "0.0007076263427734375", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "01b7070793194371", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38355:38356:1", + "start_idx": 9135 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52867:52868:1', 'start_idx': 23647} The data corresponds to a snapshot on March 09 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 50 hPa at Husky Lakes change in 6 hours if localized Gaussian perturbations cause Geopotential at 50 hPa at Husky Lakes to increased by 1356 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 50 hPa will decrease by 0 m²/s² at Husky Lakes.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 50 hPa", + "location": "Husky Lakes", + "target_variable": "geopotential_50", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dbe0b80b0d48bced", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52867:52868:1", + "start_idx": 23647 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55832:55833:1', 'start_idx': 26612} The data corresponds to a snapshot on March 20 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 700 hPa at Republic of the Congo change in 42 hours if localized Gaussian perturbations cause V (meridional) component of wind at 700 hPa at Republic of the Congo to increased by 2.137 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the V (meridional) component of wind at 700 hPa will increase by 0.0008729 m/s at Republic of the Congo.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "V (meridional) component of wind at 700 hPa", + "location": "Republic of the Congo", + "target_variable": "v_component_of_wind_700", + "true_value": "0.0008728980901651084", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4fc74084cd56bbbf", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55832:55833:1", + "start_idx": 26612 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38894:38895:1', 'start_idx': 9674} The data corresponds to a snapshot on August 15 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 700 hPa at Panama change in 12 hours if localized Gaussian perturbations cause Specific humidity at 700 hPa at Panama to increased by 0.0009753 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 700 hPa will increase by 8.557e-07 kg/kg at Panama.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 700 hPa", + "location": "Panama", + "target_variable": "specific_humidity_700", + "true_value": "8.557326509617269e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8cb462beaea44cd8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38894:38895:1", + "start_idx": 9674 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73092:73093:1', 'start_idx': 43872} The data corresponds to a snapshot on January 11 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 500 hPa at United States Virgin Islands change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 500 hPa at United States Virgin Islands to increased by 3.04 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 500 hPa will decrease by 0 m/s at United States Virgin Islands.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 500 hPa", + "location": "United States Virgin Islands", + "target_variable": "v_component_of_wind_500", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "42ae4c07f138b3a4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73092:73093:1", + "start_idx": 43872 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31110:31111:1', 'start_idx': 1890} The data corresponds to a snapshot on April 17 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 300 hPa at Antarctica change in 12 hours if localized Gaussian perturbations cause Temperature at 300 hPa at Antarctica to increased by 4.106 K in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Temperature at 300 hPa will increase by 2.64 K at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Temperature at 300 hPa", + "location": "Antarctica", + "target_variable": "temperature_300", + "true_value": "2.6403801441192627", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7c647a4503751b48", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31110:31111:1", + "start_idx": 1890 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84478:84479:1', 'start_idx': 55258} The data corresponds to a snapshot on October 27 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 400 hPa at Akrotiri Sovereign Base Area change in 18 hours if localized Gaussian perturbations cause Specific humidity at 400 hPa at Akrotiri Sovereign Base Area to increased by 0.0001318 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 400 hPa will decrease by 0 kg/kg at Akrotiri Sovereign Base Area.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 400 hPa", + "location": "Akrotiri Sovereign Base Area", + "target_variable": "specific_humidity_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "77cf0feb76b05962", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84478:84479:1", + "start_idx": 55258 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56714:56715:1', 'start_idx': 27494} The data corresponds to a snapshot on October 26 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 600 hPa at North America change in 6 hours if localized Gaussian perturbations cause Temperature at 600 hPa at North America to increased by 4.4 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 600 hPa will increase by 1.843 K at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 600 hPa", + "location": "North America", + "target_variable": "temperature_600", + "true_value": "1.8431624174118042", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1e4c210695e1de06", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56714:56715:1", + "start_idx": 27494 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61275:61276:1', 'start_idx': 32055} The data corresponds to a snapshot on December 09 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 925 hPa at Africa change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 925 hPa at Africa to increased by 2.405 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 925 hPa will increase by 0.02016 m/s at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 925 hPa", + "location": "Africa", + "target_variable": "v_component_of_wind_925", + "true_value": "0.0201583169400692", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e596f30378ca78e2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61275:61276:1", + "start_idx": 32055 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35087:35088:1', 'start_idx': 5867} The data corresponds to a snapshot on January 06 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 925 hPa at North America change in 30 hours if localized Gaussian perturbations cause V (meridional) component of wind at 925 hPa at North America to increased by 2.474 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the V (meridional) component of wind at 925 hPa will increase by 0.05392 m/s at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "V (meridional) component of wind at 925 hPa", + "location": "North America", + "target_variable": "v_component_of_wind_925", + "true_value": "0.05392034351825714", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "85f245f1527a719a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35087:35088:1", + "start_idx": 5867 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93340:93341:1', 'start_idx': 64120} The data corresponds to a snapshot on November 21 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 100 hPa at Indonesia change in 18 hours if localized Gaussian perturbations cause Temperature at 100 hPa at Indonesia to increased by 4.737 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 100 hPa will increase by 9.537e-07 K at Indonesia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 100 hPa", + "location": "Indonesia", + "target_variable": "temperature_100", + "true_value": "9.5367431640625e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "567a3fb418e9d3ef", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93340:93341:1", + "start_idx": 64120 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '39170:39171:1', 'start_idx': 9950} The data corresponds to a snapshot on October 23 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 925 hPa at Africa change in 12 hours if localized Gaussian perturbations cause Temperature at 925 hPa at Africa to increased by 5.807 K in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Temperature at 925 hPa will increase by 0.04439 K at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Temperature at 925 hPa", + "location": "Africa", + "target_variable": "temperature_925", + "true_value": "0.0443895123898983", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "31e38131a030acff", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "39170:39171:1", + "start_idx": 9950 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '54741:54742:1', 'start_idx': 25521} The data corresponds to a snapshot on June 20 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 150 hPa at Oceania change in 48 hours if localized Gaussian perturbations cause Temperature at 150 hPa at Oceania to increased by 2.142 K in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Temperature at 150 hPa will increase by 0.1814 K at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Temperature at 150 hPa", + "location": "Oceania", + "target_variable": "temperature_150", + "true_value": "0.18137940764427185", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8737595d0896fa5f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "54741:54742:1", + "start_idx": 25521 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75528:75529:1', 'start_idx': 46308} The data corresponds to a snapshot on September 12 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 1000 hPa at Latvia change in 42 hours if localized Gaussian perturbations cause Geopotential at 1000 hPa at Latvia to increased by 364 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Geopotential at 1000 hPa will decrease by 0 m²/s² at Latvia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Geopotential at 1000 hPa", + "location": "Latvia", + "target_variable": "geopotential_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6d7b52f28eaab9fe", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75528:75529:1", + "start_idx": 46308 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73253:73254:1', 'start_idx': 44033} The data corresponds to a snapshot on February 20 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 850 hPa at Bahrain change in 12 hours if localized Gaussian perturbations cause Specific humidity at 850 hPa at Bahrain to increased by 0.001187 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 850 hPa will decrease by 0 kg/kg at Bahrain.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 850 hPa", + "location": "Bahrain", + "target_variable": "specific_humidity_850", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "25e41e12d085df22", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73253:73254:1", + "start_idx": 44033 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37580:37581:1', 'start_idx': 8360} The data corresponds to a snapshot on September 21 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 925 hPa at Sri Lanka change in 6 hours if localized Gaussian perturbations cause Geopotential at 925 hPa at Sri Lanka to increased by 440.3 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 925 hPa will decrease by 0 m²/s² at Sri Lanka.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 925 hPa", + "location": "Sri Lanka", + "target_variable": "geopotential_925", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "558ac80e444215ce", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37580:37581:1", + "start_idx": 8360 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43070:43071:1', 'start_idx': 13850} The data corresponds to a snapshot on June 24 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 50 hPa at North Korea change in 6 hours if localized Gaussian perturbations cause U (zonal) component of wind at 50 hPa at North Korea to increased by 5.602 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the U (zonal) component of wind at 50 hPa will increase by 0.0002114 m/s at North Korea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "U (zonal) component of wind at 50 hPa", + "location": "North Korea", + "target_variable": "u_component_of_wind_50", + "true_value": "0.00021141767501831055", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "687d47ea36a0728e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43070:43071:1", + "start_idx": 13850 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51267:51268:1', 'start_idx': 22047} The data corresponds to a snapshot on February 02 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 200 hPa at North America change in 42 hours if localized Gaussian perturbations cause V (meridional) component of wind at 200 hPa at North America to increased by 4.079 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the V (meridional) component of wind at 200 hPa will increase by 0.5344 m/s at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "V (meridional) component of wind at 200 hPa", + "location": "North America", + "target_variable": "v_component_of_wind_200", + "true_value": "0.5343971848487854", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "996f7a93bf9e9316", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51267:51268:1", + "start_idx": 22047 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81363:81364:1', 'start_idx': 52143} The data corresponds to a snapshot on September 09 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 50 hPa at Africa change in 18 hours if localized Gaussian perturbations cause Specific humidity at 50 hPa at Africa to increased by 1.25e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 50 hPa will decrease by 2.108e-10 kg/kg at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 50 hPa", + "location": "Africa", + "target_variable": "specific_humidity_50", + "true_value": "-2.1083434997848371e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "adec44ca0c18def1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81363:81364:1", + "start_idx": 52143 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45108:45109:1', 'start_idx': 15888} The data corresponds to a snapshot on November 16 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 150 hPa at Ligurian Sea change in 18 hours if localized Gaussian perturbations cause Specific humidity at 150 hPa at Ligurian Sea to increased by 8.916e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 150 hPa will increase by 8.149e-10 kg/kg at Ligurian Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 150 hPa", + "location": "Ligurian Sea", + "target_variable": "specific_humidity_150", + "true_value": "8.149072527885437e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ef0215da5f8131b4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45108:45109:1", + "start_idx": 15888 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76703:76704:1', 'start_idx': 47483} The data corresponds to a snapshot on July 02 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 100 hPa at Somalia change in 18 hours if localized Gaussian perturbations cause Geopotential at 100 hPa at Somalia to increased by 1796 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Geopotential at 100 hPa will decrease by 0 m²/s² at Somalia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Geopotential at 100 hPa", + "location": "Somalia", + "target_variable": "geopotential_100", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "60b8e359fa5d466f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76703:76704:1", + "start_idx": 47483 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83336:83337:1', 'start_idx': 54116} The data corresponds to a snapshot on January 16 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Sea of Crete change in 30 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Sea of Crete to increased by 3.837 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the V (meridional) component of wind at 250 hPa will decrease by 0.00608 m/s at Sea of Crete.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Sea of Crete", + "target_variable": "v_component_of_wind_250", + "true_value": "-0.006080150604248047", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "efeb7db86d723b67", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83336:83337:1", + "start_idx": 54116 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34413:34414:1', 'start_idx': 5193} The data corresponds to a snapshot on July 22 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 600 hPa at North America change in 18 hours if localized Gaussian perturbations cause Specific humidity at 600 hPa at North America to increased by 0.0003701 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 600 hPa will increase by 0.0001552 kg/kg at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 600 hPa", + "location": "North America", + "target_variable": "specific_humidity_600", + "true_value": "0.00015518319560214877", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "371f15d79d32590c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34413:34414:1", + "start_idx": 5193 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72682:72683:1', 'start_idx': 43462} The data corresponds to a snapshot on September 30 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 400 hPa at Puerto Rico change in 6 hours if localized Gaussian perturbations cause Temperature at 400 hPa at Puerto Rico to increased by 3.176 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 400 hPa will decrease by 0 K at Puerto Rico.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 400 hPa", + "location": "Puerto Rico", + "target_variable": "temperature_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e3797be5bada953e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72682:72683:1", + "start_idx": 43462 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75941:75942:1', 'start_idx': 46721} The data corresponds to a snapshot on December 24 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 600 hPa at Solomon Islands change in 24 hours if localized Gaussian perturbations cause Geopotential at 600 hPa at Solomon Islands to increased by 911.4 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Geopotential at 600 hPa will decrease by 0 m²/s² at Solomon Islands.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Geopotential at 600 hPa", + "location": "Solomon Islands", + "target_variable": "geopotential_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3d10048af561a596", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75941:75942:1", + "start_idx": 46721 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53436:53437:1', 'start_idx': 24216} The data corresponds to a snapshot on July 30 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 400 hPa at Delaware Bay change in 12 hours if localized Gaussian perturbations cause V (meridional) component of wind at 400 hPa at Delaware Bay to increased by 4.232 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the V (meridional) component of wind at 400 hPa will decrease by 0 m/s at Delaware Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "V (meridional) component of wind at 400 hPa", + "location": "Delaware Bay", + "target_variable": "v_component_of_wind_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1e73473fc5774f51", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53436:53437:1", + "start_idx": 24216 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83200:83201:1', 'start_idx': 53980} The data corresponds to a snapshot on December 13 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 300 hPa at Yellow Sea change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 300 hPa at Yellow Sea to increased by 4.701 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 300 hPa will increase by 0.002662 m/s at Yellow Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 300 hPa", + "location": "Yellow Sea", + "target_variable": "u_component_of_wind_300", + "true_value": "0.002662444021552801", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0d2e68ce8e2f5e94", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83200:83201:1", + "start_idx": 53980 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32684:32685:1', 'start_idx': 3464} The data corresponds to a snapshot on May 16 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at North America change in 6 hours if localized Gaussian perturbations cause Temperature at 50 hPa at North America to increased by 3.603 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 50 hPa will increase by 0.0499 K at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 50 hPa", + "location": "North America", + "target_variable": "temperature_50", + "true_value": "0.04990098625421524", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bf0bcc0fd9487374", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32684:32685:1", + "start_idx": 3464 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '89768:89769:1', 'start_idx': 60548} The data corresponds to a snapshot on June 11 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 250 hPa at Antarctica change in 18 hours if localized Gaussian perturbations cause U (zonal) component of wind at 250 hPa at Antarctica to increased by 5.58 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the U (zonal) component of wind at 250 hPa will increase by 2.2 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "U (zonal) component of wind at 250 hPa", + "location": "Antarctica", + "target_variable": "u_component_of_wind_250", + "true_value": "2.2003986835479736", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cb99b59c9051b654", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "89768:89769:1", + "start_idx": 60548 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30685:30686:1', 'start_idx': 1465} The data corresponds to a snapshot on January 02 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 300 hPa at Niue change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 300 hPa at Niue to increased by 3.955 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 300 hPa will decrease by 0 m/s at Niue.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 300 hPa", + "location": "Niue", + "target_variable": "u_component_of_wind_300", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cd76338445d8f8a9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30685:30686:1", + "start_idx": 1465 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37398:37399:1', 'start_idx': 8178} The data corresponds to a snapshot on August 06 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Benin change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Benin to increased by 2.004 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 850 hPa will decrease by 0.007214 m/s at Benin.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Benin", + "target_variable": "u_component_of_wind_850", + "true_value": "-0.007213751319795847", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c227eecb23d144f4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37398:37399:1", + "start_idx": 8178 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51717:51718:1', 'start_idx': 22497} The data corresponds to a snapshot on May 26 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at eSwatini change in 42 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at eSwatini to increased by 2.839 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the V (meridional) component of wind at 250 hPa will decrease by 0.00551 m/s at eSwatini.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "eSwatini", + "target_variable": "v_component_of_wind_250", + "true_value": "-0.0055103302001953125", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b08970fcc45f3c62", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51717:51718:1", + "start_idx": 22497 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30944:30945:1', 'start_idx': 1724} The data corresponds to a snapshot on March 07 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at North Macedonia change in 48 hours if localized Gaussian perturbations cause Temperature at 50 hPa at North Macedonia to increased by 3.81 K in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Temperature at 50 hPa will increase by 0.0001144 K at North Macedonia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Temperature at 50 hPa", + "location": "North Macedonia", + "target_variable": "temperature_50", + "true_value": "0.00011444091796875", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0f4c7b7e834e8a09", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30944:30945:1", + "start_idx": 1724 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30311:30312:1', 'start_idx': 1091} The data corresponds to a snapshot on September 30 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 1000 hPa at Korea Strait change in 12 hours if localized Gaussian perturbations cause Geopotential at 1000 hPa at Korea Strait to increased by 323.8 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Geopotential at 1000 hPa will decrease by 0 m²/s² at Korea Strait.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Geopotential at 1000 hPa", + "location": "Korea Strait", + "target_variable": "geopotential_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ea8bf3de9bc6721b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30311:30312:1", + "start_idx": 1091 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37312:37313:1', 'start_idx': 8092} The data corresponds to a snapshot on July 16 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 600 hPa at North America change in 12 hours if localized Gaussian perturbations cause Geopotential at 600 hPa at North America to increased by 924.7 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Geopotential at 600 hPa will decrease by 0 m²/s² at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Geopotential at 600 hPa", + "location": "North America", + "target_variable": "geopotential_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8324715c74107d67", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37312:37313:1", + "start_idx": 8092 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32820:32821:1', 'start_idx': 3600} The data corresponds to a snapshot on June 19 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at Singapore change in 30 hours if localized Gaussian perturbations cause Temperature at 700 hPa at Singapore to increased by 3.791 K in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Temperature at 700 hPa will decrease by 0 K at Singapore.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Temperature at 700 hPa", + "location": "Singapore", + "target_variable": "temperature_700", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3b5064c829c8ffbd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32820:32821:1", + "start_idx": 3600 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33610:33611:1', 'start_idx': 4390} The data corresponds to a snapshot on January 02 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 925 hPa at Western Sahara change in 24 hours if localized Gaussian perturbations cause Specific humidity at 925 hPa at Western Sahara to increased by 0.001448 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Specific humidity at 925 hPa will increase by 1.562e-07 kg/kg at Western Sahara.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Specific humidity at 925 hPa", + "location": "Western Sahara", + "target_variable": "specific_humidity_925", + "true_value": "1.56210376189847e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "40c793812a1a57ed", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33610:33611:1", + "start_idx": 4390 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38758:38759:1', 'start_idx': 9538} The data corresponds to a snapshot on July 12 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 300 hPa at Antarctica change in 24 hours if localized Gaussian perturbations cause U (zonal) component of wind at 300 hPa at Antarctica to increased by 5.475 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the U (zonal) component of wind at 300 hPa will increase by 3.539 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "U (zonal) component of wind at 300 hPa", + "location": "Antarctica", + "target_variable": "u_component_of_wind_300", + "true_value": "3.538569450378418", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bb57650366de60da", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38758:38759:1", + "start_idx": 9538 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65096:65097:1', 'start_idx': 35876} The data corresponds to a snapshot on July 23 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 100 hPa at Bosnia and Herzegovina change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 100 hPa at Bosnia and Herzegovina to increased by 1.921 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 100 hPa will decrease by 0.0006039 m/s at Bosnia and Herzegovina.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 100 hPa", + "location": "Bosnia and Herzegovina", + "target_variable": "v_component_of_wind_100", + "true_value": "-0.0006038844585418701", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3536614361de08e5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65096:65097:1", + "start_idx": 35876 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86303:86304:1', 'start_idx': 57083} The data corresponds to a snapshot on January 26 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 200 hPa at Maldives change in 12 hours if localized Gaussian perturbations cause Specific humidity at 200 hPa at Maldives to increased by 7.856e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 200 hPa will decrease by 0 kg/kg at Maldives.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 200 hPa", + "location": "Maldives", + "target_variable": "specific_humidity_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "435094d7a955d8fe", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86303:86304:1", + "start_idx": 57083 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44478:44479:1', 'start_idx': 15258} The data corresponds to a snapshot on June 11 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 600 hPa at Malta change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 600 hPa at Malta to increased by 1.792 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 600 hPa will decrease by 0 m/s at Malta.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 600 hPa", + "location": "Malta", + "target_variable": "v_component_of_wind_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "cc813a7be6985e23", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44478:44479:1", + "start_idx": 15258 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66013:66014:1', 'start_idx': 36793} The data corresponds to a snapshot on March 08 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 700 hPa at Amundsen Sea change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 700 hPa at Amundsen Sea to increased by 2.682 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 700 hPa will increase by 3.338e-06 m/s at Amundsen Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 700 hPa", + "location": "Amundsen Sea", + "target_variable": "v_component_of_wind_700", + "true_value": "3.337860107421875e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d77145ba00dfd985", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66013:66014:1", + "start_idx": 36793 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47780:47781:1', 'start_idx': 18560} The data corresponds to a snapshot on September 15 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 1000 hPa at Golfo de Urabá change in 48 hours if localized Gaussian perturbations cause U (zonal) component of wind at 1000 hPa at Golfo de Urabá to increased by 1.436 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the U (zonal) component of wind at 1000 hPa will increase by 2.384e-06 m/s at Golfo de Urabá.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "U (zonal) component of wind at 1000 hPa", + "location": "Golfo de Urabá", + "target_variable": "u_component_of_wind_1000", + "true_value": "2.384185791015625e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2e06491a6ea35dff", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47780:47781:1", + "start_idx": 18560 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45777:45778:1', 'start_idx': 16557} The data corresponds to a snapshot on May 02 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 600 hPa at North America change in 6 hours if localized Gaussian perturbations cause Geopotential at 600 hPa at North America to increased by 799.5 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 600 hPa will decrease by 0 m²/s² at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 600 hPa", + "location": "North America", + "target_variable": "geopotential_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8607ad6100a11fb9", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45777:45778:1", + "start_idx": 16557 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49906:49907:1', 'start_idx': 20686} The data corresponds to a snapshot on February 27 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 50 hPa at Panama change in 12 hours if localized Gaussian perturbations cause Specific humidity at 50 hPa at Panama to increased by 1.342e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 50 hPa will increase by 2.328e-10 kg/kg at Panama.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 50 hPa", + "location": "Panama", + "target_variable": "specific_humidity_50", + "true_value": "2.3283064365386963e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a899655988785074", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49906:49907:1", + "start_idx": 20686 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67101:67102:1', 'start_idx': 37881} The data corresponds to a snapshot on December 05 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 50 hPa at Gulf of Suez change in 36 hours if localized Gaussian perturbations cause V (meridional) component of wind at 50 hPa at Gulf of Suez to increased by 2.111 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the V (meridional) component of wind at 50 hPa will decrease by 0 m/s at Gulf of Suez.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "V (meridional) component of wind at 50 hPa", + "location": "Gulf of Suez", + "target_variable": "v_component_of_wind_50", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8c4ac197ec705beb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67101:67102:1", + "start_idx": 37881 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83666:83667:1', 'start_idx': 54446} The data corresponds to a snapshot on April 07 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 200 hPa at Japan change in 24 hours if localized Gaussian perturbations cause Geopotential at 200 hPa at Japan to increased by 2092 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Geopotential at 200 hPa will decrease by 0 m²/s² at Japan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Geopotential at 200 hPa", + "location": "Japan", + "target_variable": "geopotential_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "291d9bad6515858b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83666:83667:1", + "start_idx": 54446 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63922:63923:1', 'start_idx': 34702} The data corresponds to a snapshot on October 02 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 300 hPa at Saint Martin change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 300 hPa at Saint Martin to increased by 2.776 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 300 hPa will decrease by 0 m/s at Saint Martin.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 300 hPa", + "location": "Saint Martin", + "target_variable": "v_component_of_wind_300", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4601b6bd603b4edb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63922:63923:1", + "start_idx": 34702 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92448:92449:1', 'start_idx': 63228} The data corresponds to a snapshot on April 12 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 200 hPa at Belgium change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 200 hPa at Belgium to increased by 4.333 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 200 hPa will decrease by 0 m/s at Belgium.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 200 hPa", + "location": "Belgium", + "target_variable": "v_component_of_wind_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b133cac5a8b9774b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92448:92449:1", + "start_idx": 63228 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40903:40904:1', 'start_idx': 11683} The data corresponds to a snapshot on December 30 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 500 hPa at South America change in 12 hours if localized Gaussian perturbations cause V (meridional) component of wind at 500 hPa at South America to increased by 2.079 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the V (meridional) component of wind at 500 hPa will increase by 0.0001372 m/s at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "V (meridional) component of wind at 500 hPa", + "location": "South America", + "target_variable": "v_component_of_wind_500", + "true_value": "0.00013715270324610174", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "64b318901058983f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40903:40904:1", + "start_idx": 11683 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45641:45642:1', 'start_idx': 16421} The data corresponds to a snapshot on March 29 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 850 hPa at Lithuania change in 36 hours if localized Gaussian perturbations cause Specific humidity at 850 hPa at Lithuania to increased by 0.00134 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Specific humidity at 850 hPa will decrease by 3.76e-08 kg/kg at Lithuania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Specific humidity at 850 hPa", + "location": "Lithuania", + "target_variable": "specific_humidity_850", + "true_value": "-3.7597260416077916e-08", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "781c659abedcc310", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45641:45642:1", + "start_idx": 16421 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93260:93261:1', 'start_idx': 64040} The data corresponds to a snapshot on November 01 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 700 hPa at Antarctica change in 30 hours if localized Gaussian perturbations cause U (zonal) component of wind at 700 hPa at Antarctica to increased by 3.301 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the U (zonal) component of wind at 700 hPa will increase by 0.7145 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "U (zonal) component of wind at 700 hPa", + "location": "Antarctica", + "target_variable": "u_component_of_wind_700", + "true_value": "0.7144856452941895", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f8fb766f02f93a2d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93260:93261:1", + "start_idx": 64040 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70842:70843:1', 'start_idx': 41622} The data corresponds to a snapshot on June 28 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 300 hPa at Viscount Melville Sound change in 6 hours if localized Gaussian perturbations cause Specific humidity at 300 hPa at Viscount Melville Sound to increased by 3.467e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Specific humidity at 300 hPa will decrease by 7.464e-08 kg/kg at Viscount Melville Sound.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Specific humidity at 300 hPa", + "location": "Viscount Melville Sound", + "target_variable": "specific_humidity_300", + "true_value": "-7.463677320629358e-08", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "db86096e73fcdd3e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70842:70843:1", + "start_idx": 41622 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44711:44712:1', 'start_idx': 15491} The data corresponds to a snapshot on August 08 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Persian Gulf change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Persian Gulf to increased by 1.719 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 850 hPa will increase by 0.0002108 m/s at Persian Gulf.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Persian Gulf", + "target_variable": "u_component_of_wind_850", + "true_value": "0.00021076202392578125", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "75aafcb44c7b2723", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44711:44712:1", + "start_idx": 15491 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64319:64320:1', 'start_idx': 35099} The data corresponds to a snapshot on January 09 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Europe change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Europe to increased by 4.545 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 250 hPa will increase by 0.02798 m/s at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Europe", + "target_variable": "v_component_of_wind_250", + "true_value": "0.0279818307608366", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2f80f7e01f387338", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64319:64320:1", + "start_idx": 35099 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72830:72831:1', 'start_idx': 43610} The data corresponds to a snapshot on November 06 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 50 hPa at North America change in 36 hours if localized Gaussian perturbations cause V (meridional) component of wind at 50 hPa at North America to increased by 1.748 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the V (meridional) component of wind at 50 hPa will increase by 0.02394 m/s at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "V (meridional) component of wind at 50 hPa", + "location": "North America", + "target_variable": "v_component_of_wind_50", + "true_value": "0.023941906169056892", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ab7081d9d8fc2192", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72830:72831:1", + "start_idx": 43610 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53826:53827:1', 'start_idx': 24606} The data corresponds to a snapshot on November 04 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 850 hPa at Cabo Verde change in 12 hours if localized Gaussian perturbations cause Temperature at 850 hPa at Cabo Verde to increased by 6.081 K in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Temperature at 850 hPa will decrease by 0.0002136 K at Cabo Verde.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Temperature at 850 hPa", + "location": "Cabo Verde", + "target_variable": "temperature_850", + "true_value": "-0.000213623046875", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6b6a939cb5ada256", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53826:53827:1", + "start_idx": 24606 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52575:52576:1', 'start_idx': 23355} The data corresponds to a snapshot on December 26 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 925 hPa at North America change in 6 hours if localized Gaussian perturbations cause Geopotential at 925 hPa at North America to increased by 275.5 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 925 hPa will decrease by 0 m²/s² at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 925 hPa", + "location": "North America", + "target_variable": "geopotential_925", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "14ab83883df67ba0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52575:52576:1", + "start_idx": 23355 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53523:53524:1', 'start_idx': 24303} The data corresponds to a snapshot on August 20 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 100 hPa at Moldova change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 100 hPa at Moldova to increased by 4.604 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 100 hPa will increase by 0.0007842 m/s at Moldova.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 100 hPa", + "location": "Moldova", + "target_variable": "u_component_of_wind_100", + "true_value": "0.0007841587066650391", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e991e8e8199d2397", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53523:53524:1", + "start_idx": 24303 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88049:88050:1', 'start_idx': 58829} The data corresponds to a snapshot on April 08 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 1000 hPa at Cook Islands change in 36 hours if localized Gaussian perturbations cause V (meridional) component of wind at 1000 hPa at Cook Islands to increased by 1.065 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the V (meridional) component of wind at 1000 hPa will increase by 3.815e-06 m/s at Cook Islands.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "V (meridional) component of wind at 1000 hPa", + "location": "Cook Islands", + "target_variable": "v_component_of_wind_1000", + "true_value": "3.814697265625e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0e815b9e065e1276", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88049:88050:1", + "start_idx": 58829 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79769:79770:1', 'start_idx': 50549} The data corresponds to a snapshot on August 07 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 100 hPa at South Georgia and the Islands change in 30 hours if localized Gaussian perturbations cause V (meridional) component of wind at 100 hPa at South Georgia and the Islands to increased by 1.986 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the V (meridional) component of wind at 100 hPa will decrease by 4.256e-05 m/s at South Georgia and the Islands.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "V (meridional) component of wind at 100 hPa", + "location": "South Georgia and the Islands", + "target_variable": "v_component_of_wind_100", + "true_value": "-4.2557716369628906e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "65059a09aa1ba286", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79769:79770:1", + "start_idx": 50549 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48030:48031:1', 'start_idx': 18810} The data corresponds to a snapshot on November 16 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 500 hPa at Beaufort Sea change in 18 hours if localized Gaussian perturbations cause Geopotential at 500 hPa at Beaufort Sea to increased by 1328 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Geopotential at 500 hPa will decrease by 0 m²/s² at Beaufort Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Geopotential at 500 hPa", + "location": "Beaufort Sea", + "target_variable": "geopotential_500", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "eaf502ae75d095be", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48030:48031:1", + "start_idx": 18810 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51222:51223:1', 'start_idx': 22002} The data corresponds to a snapshot on January 22 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 1000 hPa at Syria change in 6 hours if localized Gaussian perturbations cause Temperature at 1000 hPa at Syria to increased by 4.677 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 1000 hPa will increase by 2.035e-05 K at Syria.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 1000 hPa", + "location": "Syria", + "target_variable": "temperature_1000", + "true_value": "2.0345052689663135e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bb10ff1722071c66", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51222:51223:1", + "start_idx": 22002 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43845:43846:1', 'start_idx': 14625} The data corresponds to a snapshot on January 04 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 925 hPa at Peru change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 925 hPa at Peru to increased by 2.579 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 925 hPa will decrease by 1.267e-05 m/s at Peru.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 925 hPa", + "location": "Peru", + "target_variable": "u_component_of_wind_925", + "true_value": "-1.2670244359469507e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "829a50ae64c02fd2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43845:43846:1", + "start_idx": 14625 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74250:74251:1', 'start_idx': 45030} The data corresponds to a snapshot on October 27 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 850 hPa at Liechtenstein change in 36 hours if localized Gaussian perturbations cause Specific humidity at 850 hPa at Liechtenstein to increased by 0.001047 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Specific humidity at 850 hPa will decrease by 0 kg/kg at Liechtenstein.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Specific humidity at 850 hPa", + "location": "Liechtenstein", + "target_variable": "specific_humidity_850", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e4969633f3fb7c31", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74250:74251:1", + "start_idx": 45030 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82965:82966:1', 'start_idx': 53745} The data corresponds to a snapshot on October 15 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 500 hPa at Spain change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 500 hPa at Spain to increased by 2.071 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 500 hPa will increase by 2.221 m/s at Spain.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 500 hPa", + "location": "Spain", + "target_variable": "v_component_of_wind_500", + "true_value": "2.22076416015625", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "34be4fdcb28bd310", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82965:82966:1", + "start_idx": 53745 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93316:93317:1', 'start_idx': 64096} The data corresponds to a snapshot on November 15 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 600 hPa at Gulf of Anadyr' change in 24 hours if localized Gaussian perturbations cause V (meridional) component of wind at 600 hPa at Gulf of Anadyr' to increased by 3.023 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the V (meridional) component of wind at 600 hPa will increase by 0.001367 m/s at Gulf of Anadyr'.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "V (meridional) component of wind at 600 hPa", + "location": "Gulf of Anadyr'", + "target_variable": "v_component_of_wind_600", + "true_value": "0.0013672113418579102", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e439a5a62aca1fd0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93316:93317:1", + "start_idx": 64096 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91281:91282:1', 'start_idx': 62061} The data corresponds to a snapshot on June 24 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 150 hPa at Gulf of Gabès change in 6 hours if localized Gaussian perturbations cause Geopotential at 150 hPa at Gulf of Gabès to increased by 2301 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 150 hPa will decrease by 0 m²/s² at Gulf of Gabès.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 150 hPa", + "location": "Gulf of Gabès", + "target_variable": "geopotential_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6b38e8af38af2c53", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91281:91282:1", + "start_idx": 62061 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45616:45617:1', 'start_idx': 16396} The data corresponds to a snapshot on March 23 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 850 hPa at Philippine Sea change in 24 hours if localized Gaussian perturbations cause Temperature at 850 hPa at Philippine Sea to increased by 5.077 K in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Temperature at 850 hPa will increase by 4.094e-06 K at Philippine Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Temperature at 850 hPa", + "location": "Philippine Sea", + "target_variable": "temperature_850", + "true_value": "4.0938216443464626e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "618bf884f18a40fd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45616:45617:1", + "start_idx": 16396 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31013:31014:1', 'start_idx': 1793} The data corresponds to a snapshot on March 24 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 100 hPa at Europe change in 12 hours if localized Gaussian perturbations cause Geopotential at 100 hPa at Europe to increased by 2030 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Geopotential at 100 hPa will decrease by 0 m²/s² at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Geopotential at 100 hPa", + "location": "Europe", + "target_variable": "geopotential_100", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2aa73b92960a9fc3", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31013:31014:1", + "start_idx": 1793 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53686:53687:1', 'start_idx': 24466} The data corresponds to a snapshot on September 30 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 1000 hPa at East Korea Bay change in 24 hours if localized Gaussian perturbations cause U (zonal) component of wind at 1000 hPa at East Korea Bay to increased by 1.853 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the U (zonal) component of wind at 1000 hPa will decrease by 0 m/s at East Korea Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "U (zonal) component of wind at 1000 hPa", + "location": "East Korea Bay", + "target_variable": "u_component_of_wind_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "156ceb568ab1a8e8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53686:53687:1", + "start_idx": 24466 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30787:30788:1', 'start_idx': 1567} The data corresponds to a snapshot on January 27 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 850 hPa at Bahía Grande change in 36 hours if localized Gaussian perturbations cause V (meridional) component of wind at 850 hPa at Bahía Grande to increased by 1.935 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the V (meridional) component of wind at 850 hPa will decrease by 0 m/s at Bahía Grande.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "V (meridional) component of wind at 850 hPa", + "location": "Bahía Grande", + "target_variable": "v_component_of_wind_850", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "09d18de71a6cce9f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30787:30788:1", + "start_idx": 1567 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66647:66648:1', 'start_idx': 37427} The data corresponds to a snapshot on August 13 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 925 hPa at South America change in 30 hours if localized Gaussian perturbations cause U (zonal) component of wind at 925 hPa at South America to increased by 1.568 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the U (zonal) component of wind at 925 hPa will increase by 1.935e-06 m/s at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "U (zonal) component of wind at 925 hPa", + "location": "South America", + "target_variable": "u_component_of_wind_925", + "true_value": "1.935139380293549e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4adb5b98eec50e2c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66647:66648:1", + "start_idx": 37427 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67834:67835:1', 'start_idx': 38614} The data corresponds to a snapshot on June 06 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at Finland change in 24 hours if localized Gaussian perturbations cause Temperature at 50 hPa at Finland to increased by 3.261 K in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Temperature at 50 hPa will increase by 4.578e-05 K at Finland.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Temperature at 50 hPa", + "location": "Finland", + "target_variable": "temperature_50", + "true_value": "4.57763671875e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7048af61a13bf286", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67834:67835:1", + "start_idx": 38614 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30169:30170:1', 'start_idx': 949} The data corresponds to a snapshot on August 26 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 150 hPa at Gulf of Aqaba change in 24 hours if localized Gaussian perturbations cause V (meridional) component of wind at 150 hPa at Gulf of Aqaba to increased by 2.892 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the V (meridional) component of wind at 150 hPa will decrease by 0 m/s at Gulf of Aqaba.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "V (meridional) component of wind at 150 hPa", + "location": "Gulf of Aqaba", + "target_variable": "v_component_of_wind_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "52c2de1b8f642387", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30169:30170:1", + "start_idx": 949 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93285:93286:1', 'start_idx': 64065} The data corresponds to a snapshot on November 07 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 400 hPa at East Korea Bay change in 18 hours if localized Gaussian perturbations cause U (zonal) component of wind at 400 hPa at East Korea Bay to increased by 4.025 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the U (zonal) component of wind at 400 hPa will decrease by 0 m/s at East Korea Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "U (zonal) component of wind at 400 hPa", + "location": "East Korea Bay", + "target_variable": "u_component_of_wind_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "eab41937017edaec", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93285:93286:1", + "start_idx": 64065 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84474:84475:1', 'start_idx': 55254} The data corresponds to a snapshot on October 26 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 100 hPa at South America change in 42 hours if localized Gaussian perturbations cause Specific humidity at 100 hPa at South America to increased by 2.043e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Specific humidity at 100 hPa will increase by 1.421e-09 kg/kg at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Specific humidity at 100 hPa", + "location": "South America", + "target_variable": "specific_humidity_100", + "true_value": "1.4207326426429745e-09", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "130f9981f1a2491b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84474:84475:1", + "start_idx": 55254 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87069:87070:1', 'start_idx': 57849} The data corresponds to a snapshot on August 06 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 50 hPa at Asia change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 50 hPa at Asia to increased by 3.248 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 50 hPa will increase by 0.0001346 m/s at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 50 hPa", + "location": "Asia", + "target_variable": "u_component_of_wind_50", + "true_value": "0.00013459821639116853", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7dd78fcce0081c3b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87069:87070:1", + "start_idx": 57849 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58115:58116:1', 'start_idx': 28895} The data corresponds to a snapshot on October 11 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 925 hPa at Bosnia and Herzegovina change in 6 hours if localized Gaussian perturbations cause Specific humidity at 925 hPa at Bosnia and Herzegovina to increased by 0.001209 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Specific humidity at 925 hPa will increase by 7.155e-08 kg/kg at Bosnia and Herzegovina.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Specific humidity at 925 hPa", + "location": "Bosnia and Herzegovina", + "target_variable": "specific_humidity_925", + "true_value": "7.154881132009905e-08", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c7f7d3d92a4c7546", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58115:58116:1", + "start_idx": 28895 + } + } +] \ No newline at end of file diff --git a/level3a_part2.json b/level3a_part2.json new file mode 100644 index 0000000000000000000000000000000000000000..c7ff6f28750e9a9469be5f8860a99a2e2752d1e6 --- /dev/null +++ b/level3a_part2.json @@ -0,0 +1,3602 @@ +[ + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37653:37654:1', 'start_idx': 8433} The data corresponds to a snapshot on October 09 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 850 hPa at Taiwan change in 24 hours if localized Gaussian perturbations cause V (meridional) component of wind at 850 hPa at Taiwan to increased by 1.565 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the V (meridional) component of wind at 850 hPa will decrease by 2.384e-05 m/s at Taiwan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "V (meridional) component of wind at 850 hPa", + "location": "Taiwan", + "target_variable": "v_component_of_wind_850", + "true_value": "-2.384185791015625e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "10a5c3e296ee972d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37653:37654:1", + "start_idx": 8433 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57857:57858:1', 'start_idx': 28637} The data corresponds to a snapshot on August 08 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Mauritania change in 24 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Mauritania to increased by 1.836 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the U (zonal) component of wind at 850 hPa will increase by 3.508e-05 m/s at Mauritania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Mauritania", + "target_variable": "u_component_of_wind_850", + "true_value": "3.5075841879006475e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3c6a06c1bf3a49fc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57857:57858:1", + "start_idx": 28637 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81497:81498:1', 'start_idx': 52277} The data corresponds to a snapshot on October 13 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 100 hPa at North America change in 48 hours if localized Gaussian perturbations cause Specific humidity at 100 hPa at North America to increased by 2.041e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Specific humidity at 100 hPa will increase by 5.47e-09 kg/kg at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Specific humidity at 100 hPa", + "location": "North America", + "target_variable": "specific_humidity_100", + "true_value": "5.4698223728166795e-09", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "983dfa649e398b84", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81497:81498:1", + "start_idx": 52277 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47513:47514:1', 'start_idx': 18293} The data corresponds to a snapshot on July 10 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 150 hPa at Yellow Sea change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 150 hPa at Yellow Sea to increased by 2.734 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 150 hPa will increase by 0.0002561 m/s at Yellow Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 150 hPa", + "location": "Yellow Sea", + "target_variable": "v_component_of_wind_150", + "true_value": "0.0002561449946369976", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bf3b108ce56efbff", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47513:47514:1", + "start_idx": 18293 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '29553:29554:1', 'start_idx': 333} The data corresponds to a snapshot on March 25 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 250 hPa at Tyrrhenian Sea change in 18 hours if localized Gaussian perturbations cause Geopotential at 250 hPa at Tyrrhenian Sea to increased by 1967 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Geopotential at 250 hPa will decrease by 0 m²/s² at Tyrrhenian Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Geopotential at 250 hPa", + "location": "Tyrrhenian Sea", + "target_variable": "geopotential_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e3533fe88bddad85", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "29553:29554:1", + "start_idx": 333 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '55615:55616:1', 'start_idx': 26395} The data corresponds to a snapshot on January 24 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 200 hPa at Asia change in 36 hours if localized Gaussian perturbations cause V (meridional) component of wind at 200 hPa at Asia to increased by 2.4 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the V (meridional) component of wind at 200 hPa will increase by 0.0005029 m/s at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "V (meridional) component of wind at 200 hPa", + "location": "Asia", + "target_variable": "v_component_of_wind_200", + "true_value": "0.0005028857267461717", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "62c14d5225c24532", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "55615:55616:1", + "start_idx": 26395 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84034:84035:1', 'start_idx': 54814} The data corresponds to a snapshot on July 08 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 700 hPa at Northern Cyprus change in 36 hours if localized Gaussian perturbations cause Geopotential at 700 hPa at Northern Cyprus to increased by 507.4 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Geopotential at 700 hPa will decrease by 0 m²/s² at Northern Cyprus.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Geopotential at 700 hPa", + "location": "Northern Cyprus", + "target_variable": "geopotential_700", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e5624c2d50213869", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84034:84035:1", + "start_idx": 54814 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69751:69752:1', 'start_idx': 40531} The data corresponds to a snapshot on September 28 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 600 hPa at San Francisco Bay change in 30 hours if localized Gaussian perturbations cause Geopotential at 600 hPa at San Francisco Bay to increased by 801.3 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Geopotential at 600 hPa will decrease by 0 m²/s² at San Francisco Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Geopotential at 600 hPa", + "location": "San Francisco Bay", + "target_variable": "geopotential_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "640470d71465f59b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69751:69752:1", + "start_idx": 40531 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70379:70380:1', 'start_idx': 41159} The data corresponds to a snapshot on March 04 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 250 hPa at Tajikistan change in 36 hours if localized Gaussian perturbations cause Geopotential at 250 hPa at Tajikistan to increased by 2059 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Geopotential at 250 hPa will decrease by 0 m²/s² at Tajikistan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Geopotential at 250 hPa", + "location": "Tajikistan", + "target_variable": "geopotential_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6d761369431c5d61", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70379:70380:1", + "start_idx": 41159 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38956:38957:1', 'start_idx': 9736} The data corresponds to a snapshot on August 31 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 600 hPa at Ireland change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 600 hPa at Ireland to increased by 1.604 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 600 hPa will increase by 0.01443 m/s at Ireland.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 600 hPa", + "location": "Ireland", + "target_variable": "v_component_of_wind_600", + "true_value": "0.014428514055907726", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3831634451dc8c17", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38956:38957:1", + "start_idx": 9736 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65024:65025:1', 'start_idx': 35804} The data corresponds to a snapshot on July 05 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 700 hPa at South America change in 18 hours if localized Gaussian perturbations cause U (zonal) component of wind at 700 hPa at South America to increased by 3.196 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the U (zonal) component of wind at 700 hPa will increase by 9.388e-05 m/s at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "U (zonal) component of wind at 700 hPa", + "location": "South America", + "target_variable": "u_component_of_wind_700", + "true_value": "9.387944737682119e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5d3783f46e2550ea", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65024:65025:1", + "start_idx": 35804 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34057:34058:1', 'start_idx': 4837} The data corresponds to a snapshot on April 24 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 925 hPa at Yangtze River change in 6 hours if localized Gaussian perturbations cause Temperature at 925 hPa at Yangtze River to increased by 3.316 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 925 hPa will decrease by 0.0001068 K at Yangtze River.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 925 hPa", + "location": "Yangtze River", + "target_variable": "temperature_925", + "true_value": "-0.0001068115234375", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f116e7940f9ec342", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34057:34058:1", + "start_idx": 4837 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64316:64317:1', 'start_idx': 35096} The data corresponds to a snapshot on January 09 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 50 hPa at Beaufort Sea change in 24 hours if localized Gaussian perturbations cause Geopotential at 50 hPa at Beaufort Sea to increased by 2210 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Geopotential at 50 hPa will decrease by 0 m²/s² at Beaufort Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Geopotential at 50 hPa", + "location": "Beaufort Sea", + "target_variable": "geopotential_50", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "89ae89c91abce810", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64316:64317:1", + "start_idx": 35096 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80773:80774:1', 'start_idx': 51553} The data corresponds to a snapshot on April 15 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 925 hPa at Morocco change in 36 hours if localized Gaussian perturbations cause Temperature at 925 hPa at Morocco to increased by 6 K in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Temperature at 925 hPa will increase by 2.861e-05 K at Morocco.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Temperature at 925 hPa", + "location": "Morocco", + "target_variable": "temperature_925", + "true_value": "2.86102294921875e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8bdc42296c5ebafb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80773:80774:1", + "start_idx": 51553 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44367:44368:1', 'start_idx': 15147} The data corresponds to a snapshot on May 14 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 150 hPa at Gulf of Sakhalin change in 6 hours if localized Gaussian perturbations cause Specific humidity at 150 hPa at Gulf of Sakhalin to increased by 1.166e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Specific humidity at 150 hPa will decrease by 0 kg/kg at Gulf of Sakhalin.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Specific humidity at 150 hPa", + "location": "Gulf of Sakhalin", + "target_variable": "specific_humidity_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "59323236be680a11", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44367:44368:1", + "start_idx": 15147 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52768:52769:1', 'start_idx': 23548} The data corresponds to a snapshot on February 13 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 100 hPa at La Pérouse Strait change in 24 hours if localized Gaussian perturbations cause Specific humidity at 100 hPa at La Pérouse Strait to increased by 2.143e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Specific humidity at 100 hPa will decrease by 0 kg/kg at La Pérouse Strait.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Specific humidity at 100 hPa", + "location": "La Pérouse Strait", + "target_variable": "specific_humidity_100", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7e1b6ec29c1305c4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52768:52769:1", + "start_idx": 23548 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87432:87433:1', 'start_idx': 58212} The data corresponds to a snapshot on November 05 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 100 hPa at Richard Collinson Inlet change in 18 hours if localized Gaussian perturbations cause Temperature at 100 hPa at Richard Collinson Inlet to increased by 2.821 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 100 hPa will increase by 3.052e-05 K at Richard Collinson Inlet.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 100 hPa", + "location": "Richard Collinson Inlet", + "target_variable": "temperature_100", + "true_value": "3.0517578125e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "dc2a193d8a67d1de", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87432:87433:1", + "start_idx": 58212 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40117:40118:1', 'start_idx': 10897} The data corresponds to a snapshot on June 17 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 1000 hPa at Africa change in 6 hours if localized Gaussian perturbations cause Geopotential at 1000 hPa at Africa to increased by 272.2 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 1000 hPa will decrease by 0 m²/s² at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 1000 hPa", + "location": "Africa", + "target_variable": "geopotential_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d068d719c9b415fc", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40117:40118:1", + "start_idx": 10897 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75472:75473:1', 'start_idx': 46252} The data corresponds to a snapshot on August 29 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 100 hPa at Switzerland change in 48 hours if localized Gaussian perturbations cause Specific humidity at 100 hPa at Switzerland to increased by 1.666e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Specific humidity at 100 hPa will increase by 7.64e-11 kg/kg at Switzerland.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Specific humidity at 100 hPa", + "location": "Switzerland", + "target_variable": "specific_humidity_100", + "true_value": "7.639755494892597e-11", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b8fe1b15698eb349", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75472:75473:1", + "start_idx": 46252 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90557:90558:1', 'start_idx': 61337} The data corresponds to a snapshot on December 25 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 300 hPa at South America change in 48 hours if localized Gaussian perturbations cause Geopotential at 300 hPa at South America to increased by 1817 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Geopotential at 300 hPa will decrease by 0 m²/s² at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Geopotential at 300 hPa", + "location": "South America", + "target_variable": "geopotential_300", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6b46f5dda90ccf06", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90557:90558:1", + "start_idx": 61337 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88960:88961:1', 'start_idx': 59740} The data corresponds to a snapshot on November 22 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 100 hPa at Labrador Sea change in 12 hours if localized Gaussian perturbations cause U (zonal) component of wind at 100 hPa at Labrador Sea to increased by 5.362 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the U (zonal) component of wind at 100 hPa will increase by 0.0104 m/s at Labrador Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "U (zonal) component of wind at 100 hPa", + "location": "Labrador Sea", + "target_variable": "u_component_of_wind_100", + "true_value": "0.010395915247499943", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6b1d8da2ff499516", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88960:88961:1", + "start_idx": 59740 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75396:75397:1', 'start_idx': 46176} The data corresponds to a snapshot on August 10 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at Gulf of Mannar change in 18 hours if localized Gaussian perturbations cause Temperature at 50 hPa at Gulf of Mannar to increased by 3.414 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 50 hPa will increase by 6.104e-05 K at Gulf of Mannar.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 50 hPa", + "location": "Gulf of Mannar", + "target_variable": "temperature_50", + "true_value": "6.103515625e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ffbba5a2eba3317c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75396:75397:1", + "start_idx": 46176 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71216:71217:1', 'start_idx': 41996} The data corresponds to a snapshot on September 30 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 700 hPa at North Macedonia change in 48 hours if localized Gaussian perturbations cause Specific humidity at 700 hPa at North Macedonia to increased by 0.0008479 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Specific humidity at 700 hPa will increase by 4.287e-07 kg/kg at North Macedonia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Specific humidity at 700 hPa", + "location": "North Macedonia", + "target_variable": "specific_humidity_700", + "true_value": "4.287390993340523e-07", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "13513ba9f771454c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71216:71217:1", + "start_idx": 41996 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56289:56290:1', 'start_idx': 27069} The data corresponds to a snapshot on July 12 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 500 hPa at Africa change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 500 hPa at Africa to increased by 2.521 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 500 hPa will increase by 0.464 m/s at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 500 hPa", + "location": "Africa", + "target_variable": "v_component_of_wind_500", + "true_value": "0.4640082120895386", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9c9d3257f455b75a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56289:56290:1", + "start_idx": 27069 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73339:73340:1', 'start_idx': 44119} The data corresponds to a snapshot on March 13 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 500 hPa at South America change in 36 hours if localized Gaussian perturbations cause U (zonal) component of wind at 500 hPa at South America to increased by 2.781 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the U (zonal) component of wind at 500 hPa will increase by 0.0001799 m/s at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "U (zonal) component of wind at 500 hPa", + "location": "South America", + "target_variable": "u_component_of_wind_500", + "true_value": "0.00017992858192883432", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "99ec70c01099ab2c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73339:73340:1", + "start_idx": 44119 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74751:74752:1', 'start_idx': 45531} The data corresponds to a snapshot on March 01 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 400 hPa at South America change in 6 hours if localized Gaussian perturbations cause Temperature at 400 hPa at South America to increased by 3.6 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 400 hPa will increase by 2.319e-06 K at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 400 hPa", + "location": "South America", + "target_variable": "temperature_400", + "true_value": "2.3193358629214345e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a019c5d5ad1807e8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74751:74752:1", + "start_idx": 45531 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92098:92099:1', 'start_idx': 62878} The data corresponds to a snapshot on January 14 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 400 hPa at Oceania change in 36 hours if localized Gaussian perturbations cause Geopotential at 400 hPa at Oceania to increased by 1146 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Geopotential at 400 hPa will decrease by 0 m²/s² at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Geopotential at 400 hPa", + "location": "Oceania", + "target_variable": "geopotential_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1281abeeade4bdd2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92098:92099:1", + "start_idx": 62878 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47844:47845:1', 'start_idx': 18624} The data corresponds to a snapshot on October 01 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 1000 hPa at Hamilton Inlet change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 1000 hPa at Hamilton Inlet to increased by 1.794 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 1000 hPa will decrease by 0 m/s at Hamilton Inlet.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 1000 hPa", + "location": "Hamilton Inlet", + "target_variable": "v_component_of_wind_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8173570f49b1d239", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47844:47845:1", + "start_idx": 18624 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64527:64528:1', 'start_idx': 35307} The data corresponds to a snapshot on March 02 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Dominican Republic change in 12 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Dominican Republic to increased by 2.779 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the V (meridional) component of wind at 250 hPa will decrease by 0.002997 m/s at Dominican Republic.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Dominican Republic", + "target_variable": "v_component_of_wind_250", + "true_value": "-0.002997070550918579", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4cf8a49e61aadf1c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64527:64528:1", + "start_idx": 35307 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49062:49063:1', 'start_idx': 19842} The data corresponds to a snapshot on July 31 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 100 hPa at Asia change in 18 hours if localized Gaussian perturbations cause Geopotential at 100 hPa at Asia to increased by 1651 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Geopotential at 100 hPa will decrease by 0 m²/s² at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Geopotential at 100 hPa", + "location": "Asia", + "target_variable": "geopotential_100", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a37cc7bdaa6aa469", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49062:49063:1", + "start_idx": 19842 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85540:85541:1', 'start_idx': 56320} The data corresponds to a snapshot on July 20 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 700 hPa at Peru change in 30 hours if localized Gaussian perturbations cause Specific humidity at 700 hPa at Peru to increased by 0.0008545 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Specific humidity at 700 hPa will increase by 5.259e-09 kg/kg at Peru.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Specific humidity at 700 hPa", + "location": "Peru", + "target_variable": "specific_humidity_700", + "true_value": "5.259185531514277e-09", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c6fd234b3e7bb73c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85540:85541:1", + "start_idx": 56320 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40592:40593:1', 'start_idx': 11372} The data corresponds to a snapshot on October 14 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 150 hPa at The North Western Passages change in 6 hours if localized Gaussian perturbations cause U (zonal) component of wind at 150 hPa at The North Western Passages to increased by 4.762 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the U (zonal) component of wind at 150 hPa will decrease by 0.0001625 m/s at The North Western Passages.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "U (zonal) component of wind at 150 hPa", + "location": "The North Western Passages", + "target_variable": "u_component_of_wind_150", + "true_value": "-0.00016248226165771484", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ab480d472f93655d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40592:40593:1", + "start_idx": 11372 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83302:83303:1', 'start_idx': 54082} The data corresponds to a snapshot on January 07 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 700 hPa at Tuvalu change in 18 hours if localized Gaussian perturbations cause Temperature at 700 hPa at Tuvalu to increased by 3.255 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 700 hPa will increase by 0.003662 K at Tuvalu.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 700 hPa", + "location": "Tuvalu", + "target_variable": "temperature_700", + "true_value": "0.003662109375", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "aece1260466f053d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83302:83303:1", + "start_idx": 54082 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60327:60328:1', 'start_idx': 31107} The data corresponds to a snapshot on April 16 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at North America change in 36 hours if localized Gaussian perturbations cause Temperature at 50 hPa at North America to increased by 3.564 K in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Temperature at 50 hPa will increase by 0.048 K at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Temperature at 50 hPa", + "location": "North America", + "target_variable": "temperature_50", + "true_value": "0.048004575073719025", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "54903c1314f2421d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60327:60328:1", + "start_idx": 31107 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45825:45826:1', 'start_idx': 16605} The data corresponds to a snapshot on May 14 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 600 hPa at New Caledonia change in 24 hours if localized Gaussian perturbations cause U (zonal) component of wind at 600 hPa at New Caledonia to increased by 2.336 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the U (zonal) component of wind at 600 hPa will decrease by 0 m/s at New Caledonia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "U (zonal) component of wind at 600 hPa", + "location": "New Caledonia", + "target_variable": "u_component_of_wind_600", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b27a43c56348a816", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45825:45826:1", + "start_idx": 16605 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30202:30203:1', 'start_idx': 982} The data corresponds to a snapshot on September 03 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 200 hPa at Minto Inlet change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 200 hPa at Minto Inlet to increased by 3.475 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 200 hPa will decrease by 0 m/s at Minto Inlet.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 200 hPa", + "location": "Minto Inlet", + "target_variable": "v_component_of_wind_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "2f891c0da04be710", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30202:30203:1", + "start_idx": 982 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71365:71366:1', 'start_idx': 42145} The data corresponds to a snapshot on November 06 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 500 hPa at Waddenzee change in 18 hours if localized Gaussian perturbations cause Geopotential at 500 hPa at Waddenzee to increased by 1295 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Geopotential at 500 hPa will decrease by 0 m²/s² at Waddenzee.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Geopotential at 500 hPa", + "location": "Waddenzee", + "target_variable": "geopotential_500", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "36f29e56b59b8d78", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71365:71366:1", + "start_idx": 42145 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43108:43109:1', 'start_idx': 13888} The data corresponds to a snapshot on July 04 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 250 hPa at Ireland change in 36 hours if localized Gaussian perturbations cause Temperature at 250 hPa at Ireland to increased by 1.946 K in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Temperature at 250 hPa will increase by 0.004466 K at Ireland.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Temperature at 250 hPa", + "location": "Ireland", + "target_variable": "temperature_250", + "true_value": "0.0044657387770712376", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "86a812326c84ba55", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43108:43109:1", + "start_idx": 13888 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48424:48425:1', 'start_idx': 19204} The data corresponds to a snapshot on February 23 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 100 hPa at Irish Sea change in 6 hours if localized Gaussian perturbations cause Temperature at 100 hPa at Irish Sea to increased by 4.482 K in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Temperature at 100 hPa will decrease by 0 K at Irish Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Temperature at 100 hPa", + "location": "Irish Sea", + "target_variable": "temperature_100", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "356db73bff1ea77d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48424:48425:1", + "start_idx": 19204 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44965:44966:1', 'start_idx': 15745} The data corresponds to a snapshot on October 11 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 850 hPa at Kangertittivaq change in 42 hours if localized Gaussian perturbations cause Specific humidity at 850 hPa at Kangertittivaq to increased by 0.001186 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Specific humidity at 850 hPa will decrease by 1.901e-06 kg/kg at Kangertittivaq.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Specific humidity at 850 hPa", + "location": "Kangertittivaq", + "target_variable": "specific_humidity_850", + "true_value": "-1.901214773170068e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5ca290862f3fbe2b", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44965:44966:1", + "start_idx": 15745 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57015:57016:1', 'start_idx': 27795} The data corresponds to a snapshot on January 09 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 300 hPa at Guinea change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 300 hPa at Guinea to increased by 3.637 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 300 hPa will decrease by 0.002649 m/s at Guinea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 300 hPa", + "location": "Guinea", + "target_variable": "v_component_of_wind_300", + "true_value": "-0.0026493072509765625", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "54fae5872a31c38d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57015:57016:1", + "start_idx": 27795 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83654:83655:1', 'start_idx': 54434} The data corresponds to a snapshot on April 04 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 150 hPa at Kazakhstan change in 42 hours if localized Gaussian perturbations cause V (meridional) component of wind at 150 hPa at Kazakhstan to increased by 2.76 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the V (meridional) component of wind at 150 hPa will increase by 0.0001875 m/s at Kazakhstan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "V (meridional) component of wind at 150 hPa", + "location": "Kazakhstan", + "target_variable": "v_component_of_wind_150", + "true_value": "0.00018745852867141366", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1dd5aed041758fa2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83654:83655:1", + "start_idx": 54434 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46817:46818:1', 'start_idx': 17597} The data corresponds to a snapshot on January 17 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 400 hPa at Antarctica change in 12 hours if localized Gaussian perturbations cause V (meridional) component of wind at 400 hPa at Antarctica to increased by 2.41 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the V (meridional) component of wind at 400 hPa will increase by 1.687 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "V (meridional) component of wind at 400 hPa", + "location": "Antarctica", + "target_variable": "v_component_of_wind_400", + "true_value": "1.687002420425415", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5f8b68d16a11eb1e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46817:46818:1", + "start_idx": 17597 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32385:32386:1', 'start_idx': 3165} The data corresponds to a snapshot on March 02 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 1000 hPa at Northern Cyprus change in 24 hours if localized Gaussian perturbations cause Specific humidity at 1000 hPa at Northern Cyprus to increased by 0.001648 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Specific humidity at 1000 hPa will decrease by 0 kg/kg at Northern Cyprus.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Specific humidity at 1000 hPa", + "location": "Northern Cyprus", + "target_variable": "specific_humidity_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "52b98d489d3c9a87", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32385:32386:1", + "start_idx": 3165 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64759:64760:1', 'start_idx': 35539} The data corresponds to a snapshot on April 29 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 1000 hPa at South America change in 36 hours if localized Gaussian perturbations cause Geopotential at 1000 hPa at South America to increased by 393.8 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Geopotential at 1000 hPa will decrease by 0 m²/s² at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Geopotential at 1000 hPa", + "location": "South America", + "target_variable": "geopotential_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8f9b35accf6421fa", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64759:64760:1", + "start_idx": 35539 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '69369:69370:1', 'start_idx': 40149} The data corresponds to a snapshot on June 25 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 100 hPa at Boca Grande change in 6 hours if localized Gaussian perturbations cause U (zonal) component of wind at 100 hPa at Boca Grande to increased by 4.801 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the U (zonal) component of wind at 100 hPa will decrease by 0.0001597 m/s at Boca Grande.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "U (zonal) component of wind at 100 hPa", + "location": "Boca Grande", + "target_variable": "u_component_of_wind_100", + "true_value": "-0.00015974044799804688", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f75f453fa7ae31b0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "69369:69370:1", + "start_idx": 40149 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86683:86684:1', 'start_idx': 57463} The data corresponds to a snapshot on May 01 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 250 hPa at Persian Gulf change in 24 hours if localized Gaussian perturbations cause Specific humidity at 250 hPa at Persian Gulf to increased by 2.186e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Specific humidity at 250 hPa will decrease by 5.239e-10 kg/kg at Persian Gulf.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Specific humidity at 250 hPa", + "location": "Persian Gulf", + "target_variable": "specific_humidity_250", + "true_value": "-5.238689482212067e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e3f290d1edfca290", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86683:86684:1", + "start_idx": 57463 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48526:48527:1', 'start_idx': 19306} The data corresponds to a snapshot on March 19 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 250 hPa at Lützow-Holm Bay change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 250 hPa at Lützow-Holm Bay to increased by 4.341 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 250 hPa will increase by 0.6844 m/s at Lützow-Holm Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 250 hPa", + "location": "Lützow-Holm Bay", + "target_variable": "v_component_of_wind_250", + "true_value": "0.6844202876091003", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f562db7e6dee63f8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48526:48527:1", + "start_idx": 19306 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47297:47298:1', 'start_idx': 18077} The data corresponds to a snapshot on May 17 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 925 hPa at East Korea Bay change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 925 hPa at East Korea Bay to increased by 1.544 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 925 hPa will decrease by 0 m/s at East Korea Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 925 hPa", + "location": "East Korea Bay", + "target_variable": "v_component_of_wind_925", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1134861aefa75c93", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47297:47298:1", + "start_idx": 18077 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57272:57273:1', 'start_idx': 28052} The data corresponds to a snapshot on March 15 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 150 hPa at Strait of Gibraltar change in 6 hours if localized Gaussian perturbations cause Geopotential at 150 hPa at Strait of Gibraltar to increased by 1991 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 150 hPa will decrease by 0 m²/s² at Strait of Gibraltar.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 150 hPa", + "location": "Strait of Gibraltar", + "target_variable": "geopotential_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7e17af8dc3234ea7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57272:57273:1", + "start_idx": 28052 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50302:50303:1', 'start_idx': 21082} The data corresponds to a snapshot on June 06 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 1000 hPa at Africa change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 1000 hPa at Africa to increased by 1.526 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 1000 hPa will decrease by 0.001337 m/s at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 1000 hPa", + "location": "Africa", + "target_variable": "v_component_of_wind_1000", + "true_value": "-0.0013368576765060425", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a25afb813b0c3dc8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50302:50303:1", + "start_idx": 21082 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74110:74111:1', 'start_idx': 44890} The data corresponds to a snapshot on September 22 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 600 hPa at Europe change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 600 hPa at Europe to increased by 3.034 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 600 hPa will increase by 0.02455 m/s at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 600 hPa", + "location": "Europe", + "target_variable": "u_component_of_wind_600", + "true_value": "0.024548230692744255", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3d856138d092539c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74110:74111:1", + "start_idx": 44890 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30678:30679:1', 'start_idx': 1458} The data corresponds to a snapshot on December 31 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 250 hPa at Sulu Sea change in 42 hours if localized Gaussian perturbations cause Geopotential at 250 hPa at Sulu Sea to increased by 1775 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Geopotential at 250 hPa will decrease by 0 m²/s² at Sulu Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Geopotential at 250 hPa", + "location": "Sulu Sea", + "target_variable": "geopotential_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fc35af67fea9b163", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30678:30679:1", + "start_idx": 1458 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '36152:36153:1', 'start_idx': 6932} The data corresponds to a snapshot on September 30 00:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 200 hPa at Antarctica change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 200 hPa at Antarctica to increased by 3.554 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 200 hPa will increase by 0.7367 m/s at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 200 hPa", + "location": "Antarctica", + "target_variable": "v_component_of_wind_200", + "true_value": "0.7366898059844971", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7eea47815e5c86c1", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "36152:36153:1", + "start_idx": 6932 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43484:43485:1', 'start_idx': 14264} The data corresponds to a snapshot on October 06 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 600 hPa at Uzbekistan change in 12 hours if localized Gaussian perturbations cause U (zonal) component of wind at 600 hPa at Uzbekistan to increased by 3.478 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the U (zonal) component of wind at 600 hPa will decrease by 0.002448 m/s at Uzbekistan.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "U (zonal) component of wind at 600 hPa", + "location": "Uzbekistan", + "target_variable": "u_component_of_wind_600", + "true_value": "-0.0024475096724927425", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "58955ae21d0d4654", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43484:43485:1", + "start_idx": 14264 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '43199:43200:1', 'start_idx': 13979} The data corresponds to a snapshot on July 26 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 500 hPa at Portugal change in 24 hours if localized Gaussian perturbations cause Geopotential at 500 hPa at Portugal to increased by 1135 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Geopotential at 500 hPa will decrease by 0 m²/s² at Portugal.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Geopotential at 500 hPa", + "location": "Portugal", + "target_variable": "geopotential_500", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "df18e80222a2446a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "43199:43200:1", + "start_idx": 13979 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40550:40551:1', 'start_idx': 11330} The data corresponds to a snapshot on October 03 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 250 hPa at Darnley Bay change in 24 hours if localized Gaussian perturbations cause U (zonal) component of wind at 250 hPa at Darnley Bay to increased by 5.83 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the U (zonal) component of wind at 250 hPa will decrease by 0 m/s at Darnley Bay.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "U (zonal) component of wind at 250 hPa", + "location": "Darnley Bay", + "target_variable": "u_component_of_wind_250", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a61a481768e3311d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40550:40551:1", + "start_idx": 11330 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44004:44005:1', 'start_idx': 14784} The data corresponds to a snapshot on February 13 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 150 hPa at Irish Sea change in 30 hours if localized Gaussian perturbations cause U (zonal) component of wind at 150 hPa at Irish Sea to increased by 5.914 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the U (zonal) component of wind at 150 hPa will decrease by 0 m/s at Irish Sea.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "U (zonal) component of wind at 150 hPa", + "location": "Irish Sea", + "target_variable": "u_component_of_wind_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fbe82ca8516f29fb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44004:44005:1", + "start_idx": 14784 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40372:40373:1', 'start_idx': 11152} The data corresponds to a snapshot on August 20 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 50 hPa at South Pacific Ocean change in 48 hours if localized Gaussian perturbations cause Geopotential at 50 hPa at South Pacific Ocean to increased by 1860 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Geopotential at 50 hPa will decrease by 0 m²/s² at South Pacific Ocean.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Geopotential at 50 hPa", + "location": "South Pacific Ocean", + "target_variable": "geopotential_50", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "b9b959ad96447fa7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40372:40373:1", + "start_idx": 11152 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '45411:45412:1', 'start_idx': 16191} The data corresponds to a snapshot on January 30 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 850 hPa at Asia change in 48 hours if localized Gaussian perturbations cause Specific humidity at 850 hPa at Asia to increased by 0.001359 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Specific humidity at 850 hPa will increase by 5.206e-08 kg/kg at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Specific humidity at 850 hPa", + "location": "Asia", + "target_variable": "specific_humidity_850", + "true_value": "5.2064031308418635e-08", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "149e68fe51499058", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "45411:45412:1", + "start_idx": 16191 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '86335:86336:1', 'start_idx': 57115} The data corresponds to a snapshot on February 03 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 1000 hPa at Antarctica change in 42 hours if localized Gaussian perturbations cause Specific humidity at 1000 hPa at Antarctica to increased by 0.001184 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Specific humidity at 1000 hPa will decrease by 1.696e-06 kg/kg at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Specific humidity at 1000 hPa", + "location": "Antarctica", + "target_variable": "specific_humidity_1000", + "true_value": "-1.695807100077218e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8945d77136c545a8", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "86335:86336:1", + "start_idx": 57115 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48055:48056:1', 'start_idx': 18835} The data corresponds to a snapshot on November 22 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 200 hPa at Gulf of Kamchatka change in 48 hours if localized Gaussian perturbations cause Geopotential at 200 hPa at Gulf of Kamchatka to increased by 1880 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Geopotential at 200 hPa will decrease by 0 m²/s² at Gulf of Kamchatka.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Geopotential at 200 hPa", + "location": "Gulf of Kamchatka", + "target_variable": "geopotential_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8086e1b1af201dc7", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48055:48056:1", + "start_idx": 18835 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88108:88109:1', 'start_idx': 58888} The data corresponds to a snapshot on April 23 00:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 925 hPa at Saudi Arabia change in 42 hours if localized Gaussian perturbations cause Geopotential at 925 hPa at Saudi Arabia to increased by 337.9 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Geopotential at 925 hPa will decrease by 0 m²/s² at Saudi Arabia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Geopotential at 925 hPa", + "location": "Saudi Arabia", + "target_variable": "geopotential_925", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "ef83cbb976b3da5c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88108:88109:1", + "start_idx": 58888 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49504:49505:1', 'start_idx': 20284} The data corresponds to a snapshot on November 19 00:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 200 hPa at Leyte Gulf change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 200 hPa at Leyte Gulf to increased by 5.552 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 200 hPa will decrease by 0 m/s at Leyte Gulf.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 200 hPa", + "location": "Leyte Gulf", + "target_variable": "u_component_of_wind_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "08ebf0232276f7fd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49504:49505:1", + "start_idx": 20284 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41606:41607:1', 'start_idx': 12386} The data corresponds to a snapshot on June 24 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 925 hPa at Europe change in 12 hours if localized Gaussian perturbations cause U (zonal) component of wind at 925 hPa at Europe to increased by 2.852 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the U (zonal) component of wind at 925 hPa will increase by 0.00122 m/s at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "U (zonal) component of wind at 925 hPa", + "location": "Europe", + "target_variable": "u_component_of_wind_925", + "true_value": "0.0012200343189761043", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "fc685d79d5865856", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41606:41607:1", + "start_idx": 12386 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33164:33165:1', 'start_idx': 3944} The data corresponds to a snapshot on September 13 00:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 925 hPa at South America change in 30 hours if localized Gaussian perturbations cause Temperature at 925 hPa at South America to increased by 4.396 K in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Temperature at 925 hPa will increase by 1.709e-06 K at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Temperature at 925 hPa", + "location": "South America", + "target_variable": "temperature_925", + "true_value": "1.7089844277506927e-06", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3e8f6614eccf423e", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33164:33165:1", + "start_idx": 3944 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37695:37696:1', 'start_idx': 8475} The data corresponds to a snapshot on October 19 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 925 hPa at Golfo de Tehuantepec change in 18 hours if localized Gaussian perturbations cause Temperature at 925 hPa at Golfo de Tehuantepec to increased by 3.52 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 925 hPa will decrease by 0 K at Golfo de Tehuantepec.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 925 hPa", + "location": "Golfo de Tehuantepec", + "target_variable": "temperature_925", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bfb79feba3f3d90f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37695:37696:1", + "start_idx": 8475 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66732:66733:1', 'start_idx': 37512} The data corresponds to a snapshot on September 04 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 100 hPa at Cuba change in 36 hours if localized Gaussian perturbations cause Specific humidity at 100 hPa at Cuba to increased by 1.296e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Specific humidity at 100 hPa will increase by 1.552e-10 kg/kg at Cuba.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Specific humidity at 100 hPa", + "location": "Cuba", + "target_variable": "specific_humidity_100", + "true_value": "1.5522043372850902e-10", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "4935103acda3899f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66732:66733:1", + "start_idx": 37512 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32011:32012:1', 'start_idx': 2791} The data corresponds to a snapshot on November 28 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 850 hPa at Oceania change in 36 hours if localized Gaussian perturbations cause Temperature at 850 hPa at Oceania to increased by 4.562 K in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Temperature at 850 hPa will increase by 0.2035 K at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Temperature at 850 hPa", + "location": "Oceania", + "target_variable": "temperature_850", + "true_value": "0.20345550775527954", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "0b3bc5614fe41a1f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32011:32012:1", + "start_idx": 2791 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60967:60968:1', 'start_idx': 31747} The data corresponds to a snapshot on September 23 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 925 hPa at Africa change in 18 hours if localized Gaussian perturbations cause U (zonal) component of wind at 925 hPa at Africa to increased by 1.704 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the U (zonal) component of wind at 925 hPa will increase by 0.01404 m/s at Africa.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "U (zonal) component of wind at 925 hPa", + "location": "Africa", + "target_variable": "u_component_of_wind_925", + "true_value": "0.01404496468603611", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "8fc5ab02c2c2ff70", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60967:60968:1", + "start_idx": 31747 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40303:40304:1', 'start_idx': 11083} The data corresponds to a snapshot on August 02 18:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 600 hPa at Asia change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 600 hPa at Asia to increased by 3.509 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 600 hPa will increase by 0.001002 m/s at Asia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 600 hPa", + "location": "Asia", + "target_variable": "u_component_of_wind_600", + "true_value": "0.0010016378946602345", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "25ffd283b976d738", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40303:40304:1", + "start_idx": 11083 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35589:35590:1', 'start_idx': 6369} The data corresponds to a snapshot on May 12 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at North America change in 24 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at North America to increased by 2.188 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the U (zonal) component of wind at 850 hPa will increase by 0.08727 m/s at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "North America", + "target_variable": "u_component_of_wind_850", + "true_value": "0.08726854622364044", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9a535ff78eb2ab40", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35589:35590:1", + "start_idx": 6369 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30417:30418:1', 'start_idx': 1197} The data corresponds to a snapshot on October 27 06:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 850 hPa at Myanmar change in 6 hours if localized Gaussian perturbations cause Geopotential at 850 hPa at Myanmar to increased by 391.7 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 850 hPa will decrease by 0 m²/s² at Myanmar.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 850 hPa", + "location": "Myanmar", + "target_variable": "geopotential_850", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "755d63ed8962ddb2", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30417:30418:1", + "start_idx": 1197 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91745:91746:1', 'start_idx': 62525} The data corresponds to a snapshot on October 18 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 250 hPa at Antarctica change in 48 hours if localized Gaussian perturbations cause Specific humidity at 250 hPa at Antarctica to increased by 2.731e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Specific humidity at 250 hPa will increase by 1.076e-05 kg/kg at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Specific humidity at 250 hPa", + "location": "Antarctica", + "target_variable": "specific_humidity_250", + "true_value": "1.0761787962110247e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7a80011a251aa54d", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91745:91746:1", + "start_idx": 62525 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57550:57551:1', 'start_idx': 28330} The data corresponds to a snapshot on May 23 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 50 hPa at Yucatan Channel change in 24 hours if localized Gaussian perturbations cause Temperature at 50 hPa at Yucatan Channel to increased by 3.184 K in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Temperature at 50 hPa will increase by 9.155e-05 K at Yucatan Channel.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Temperature at 50 hPa", + "location": "Yucatan Channel", + "target_variable": "temperature_50", + "true_value": "9.1552734375e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "17b7a7bdb69ed218", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57550:57551:1", + "start_idx": 28330 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67702:67703:1', 'start_idx': 38482} The data corresponds to a snapshot on May 04 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 300 hPa at Antarctica change in 12 hours if localized Gaussian perturbations cause Specific humidity at 300 hPa at Antarctica to increased by 6.335e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 300 hPa will increase by 4.082e-05 kg/kg at Antarctica.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 300 hPa", + "location": "Antarctica", + "target_variable": "specific_humidity_300", + "true_value": "4.082434679730795e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "1be49a76f3470424", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67702:67703:1", + "start_idx": 38482 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74045:74046:1', 'start_idx': 44825} The data corresponds to a snapshot on September 06 06:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Europe change in 42 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Europe to increased by 2.386 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the U (zonal) component of wind at 850 hPa will increase by 0.001849 m/s at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Europe", + "target_variable": "u_component_of_wind_850", + "true_value": "0.001849462860263884", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "22b1c4d36791b6bb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74045:74046:1", + "start_idx": 44825 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44549:44550:1', 'start_idx': 15329} The data corresponds to a snapshot on June 29 06:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 600 hPa at Europe change in 48 hours if localized Gaussian perturbations cause V (meridional) component of wind at 600 hPa at Europe to increased by 1.813 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the V (meridional) component of wind at 600 hPa will increase by 0.01474 m/s at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "V (meridional) component of wind at 600 hPa", + "location": "Europe", + "target_variable": "v_component_of_wind_600", + "true_value": "0.014736389741301537", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "01c891a5d686ad32", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44549:44550:1", + "start_idx": 15329 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72051:72052:1', 'start_idx': 42831} The data corresponds to a snapshot on April 25 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 500 hPa at North America change in 6 hours if localized Gaussian perturbations cause Specific humidity at 500 hPa at North America to increased by 0.0002586 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Specific humidity at 500 hPa will increase by 0.000124 kg/kg at North America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Specific humidity at 500 hPa", + "location": "North America", + "target_variable": "specific_humidity_500", + "true_value": "0.00012395290832500905", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "5ad122e14103aefb", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72051:72052:1", + "start_idx": 42831 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50912:50913:1', 'start_idx': 21692} The data corresponds to a snapshot on November 06 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 1000 hPa at South America change in 30 hours if localized Gaussian perturbations cause Specific humidity at 1000 hPa at South America to increased by 0.001858 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Specific humidity at 1000 hPa will decrease by 1.505e-09 kg/kg at South America.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Specific humidity at 1000 hPa", + "location": "South America", + "target_variable": "specific_humidity_1000", + "true_value": "-1.5051518920117246e-09", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "335f2ef6d18d9e67", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50912:50913:1", + "start_idx": 21692 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33355:33356:1', 'start_idx': 4135} The data corresponds to a snapshot on October 30 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 50 hPa at Khatanga Gulf change in 12 hours if localized Gaussian perturbations cause Specific humidity at 50 hPa at Khatanga Gulf to increased by 8.283e-08 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Specific humidity at 50 hPa will decrease by 0 kg/kg at Khatanga Gulf.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Specific humidity at 50 hPa", + "location": "Khatanga Gulf", + "target_variable": "specific_humidity_50", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "346ff813a7aa7e7f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33355:33356:1", + "start_idx": 4135 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '46370:46371:1', 'start_idx': 17150} The data corresponds to a snapshot on September 27 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 850 hPa at Oceania change in 18 hours if localized Gaussian perturbations cause U (zonal) component of wind at 850 hPa at Oceania to increased by 2.366 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the U (zonal) component of wind at 850 hPa will increase by 0.1062 m/s at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "U (zonal) component of wind at 850 hPa", + "location": "Oceania", + "target_variable": "u_component_of_wind_850", + "true_value": "0.10616810619831085", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "79cf9d3102cd3502", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "46370:46371:1", + "start_idx": 17150 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40823:40824:1', 'start_idx': 11603} The data corresponds to a snapshot on December 10 18:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 850 hPa at Tsugaru Strait change in 42 hours if localized Gaussian perturbations cause Specific humidity at 850 hPa at Tsugaru Strait to increased by 0.001128 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 42 hours, the Specific humidity at 850 hPa will decrease by 0 kg/kg at Tsugaru Strait.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 42, + "variable": "Specific humidity at 850 hPa", + "location": "Tsugaru Strait", + "target_variable": "specific_humidity_850", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "56bc5ed264e825a5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40823:40824:1", + "start_idx": 11603 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59037:59038:1', 'start_idx': 29817} The data corresponds to a snapshot on May 30 06:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 300 hPa at Scarborough Reef change in 18 hours if localized Gaussian perturbations cause Specific humidity at 300 hPa at Scarborough Reef to increased by 4.174e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 300 hPa will decrease by 0 kg/kg at Scarborough Reef.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 300 hPa", + "location": "Scarborough Reef", + "target_variable": "specific_humidity_300", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "003ff811e4810291", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59037:59038:1", + "start_idx": 29817 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90875:90876:1', 'start_idx': 61655} The data corresponds to a snapshot on March 14 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 250 hPa at Gulf of Ob change in 18 hours if localized Gaussian perturbations cause Temperature at 250 hPa at Gulf of Ob to increased by 3.111 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 250 hPa will increase by 0.0006104 K at Gulf of Ob.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 250 hPa", + "location": "Gulf of Ob", + "target_variable": "temperature_250", + "true_value": "0.0006103515625", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a2f35adfd0bc4660", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90875:90876:1", + "start_idx": 61655 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38007:38008:1', 'start_idx': 8787} The data corresponds to a snapshot on January 05 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 250 hPa at Europe change in 48 hours if localized Gaussian perturbations cause Temperature at 250 hPa at Europe to increased by 2.258 K in the provided initial conditions data?", + "response": "Based on the provided data, in 48 hours, the Temperature at 250 hPa will increase by 0.006179 K at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 48, + "variable": "Temperature at 250 hPa", + "location": "Europe", + "target_variable": "temperature_250", + "true_value": "0.006179256830364466", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "42fe5b851290116f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38007:38008:1", + "start_idx": 8787 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87389:87390:1', 'start_idx': 58169} The data corresponds to a snapshot on October 25 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 600 hPa at Kangertittivaq change in 30 hours if localized Gaussian perturbations cause Temperature at 600 hPa at Kangertittivaq to increased by 4.885 K in the provided initial conditions data?", + "response": "Based on the provided data, in 30 hours, the Temperature at 600 hPa will decrease by 0.08423 K at Kangertittivaq.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 30, + "variable": "Temperature at 600 hPa", + "location": "Kangertittivaq", + "target_variable": "temperature_600", + "true_value": "-0.084228515625", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "61c21d81423b4503", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87389:87390:1", + "start_idx": 58169 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73542:73543:1', 'start_idx': 44322} The data corresponds to a snapshot on May 03 12:00. Based on the above data, answer the following question:", + "question": "How will the U (zonal) component of wind at 200 hPa at Latvia change in 12 hours if localized Gaussian perturbations cause U (zonal) component of wind at 200 hPa at Latvia to increased by 6.275 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the U (zonal) component of wind at 200 hPa will increase by 0.009663 m/s at Latvia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "U (zonal) component of wind at 200 hPa", + "location": "Latvia", + "target_variable": "u_component_of_wind_200", + "true_value": "0.009662628173828125", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "99e51ce80950b7d6", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73542:73543:1", + "start_idx": 44322 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33863:33864:1', 'start_idx': 4643} The data corresponds to a snapshot on March 06 18:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 500 hPa at Oceania change in 6 hours if localized Gaussian perturbations cause Geopotential at 500 hPa at Oceania to increased by 1297 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the Geopotential at 500 hPa will decrease by 0 m²/s² at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "Geopotential at 500 hPa", + "location": "Oceania", + "target_variable": "geopotential_500", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "9f9dfe9257a38f92", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33863:33864:1", + "start_idx": 4643 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66342:66343:1', 'start_idx': 37122} The data corresponds to a snapshot on May 29 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 100 hPa at Minto Inlet change in 18 hours if localized Gaussian perturbations cause Temperature at 100 hPa at Minto Inlet to increased by 4.203 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 100 hPa will decrease by 0 K at Minto Inlet.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 100 hPa", + "location": "Minto Inlet", + "target_variable": "temperature_100", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "a49f8aba4f5e1e5f", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66342:66343:1", + "start_idx": 37122 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77979:77980:1', 'start_idx': 48759} The data corresponds to a snapshot on May 16 18:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 1000 hPa at Gulf of Kamchatka change in 12 hours if localized Gaussian perturbations cause Temperature at 1000 hPa at Gulf of Kamchatka to increased by 4.155 K in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Temperature at 1000 hPa will decrease by 0 K at Gulf of Kamchatka.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Temperature at 1000 hPa", + "location": "Gulf of Kamchatka", + "target_variable": "temperature_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "d6fb2f8ff9b9697c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77979:77980:1", + "start_idx": 48759 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47098:47099:1', 'start_idx': 17878} The data corresponds to a snapshot on March 28 12:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 1000 hPa at Saint Lucia change in 6 hours if localized Gaussian perturbations cause V (meridional) component of wind at 1000 hPa at Saint Lucia to increased by 1.702 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 6 hours, the V (meridional) component of wind at 1000 hPa will decrease by 0 m/s at Saint Lucia.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 6, + "variable": "V (meridional) component of wind at 1000 hPa", + "location": "Saint Lucia", + "target_variable": "v_component_of_wind_1000", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e171b2236b290dc0", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47098:47099:1", + "start_idx": 17878 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91596:91597:1', 'start_idx': 62376} The data corresponds to a snapshot on September 11 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 150 hPa at Baía de São Marcos change in 36 hours if localized Gaussian perturbations cause Specific humidity at 150 hPa at Baía de São Marcos to increased by 1.017e-06 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 36 hours, the Specific humidity at 150 hPa will decrease by 0 kg/kg at Baía de São Marcos.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 36, + "variable": "Specific humidity at 150 hPa", + "location": "Baía de São Marcos", + "target_variable": "specific_humidity_150", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "6a8247c13070203a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91596:91597:1", + "start_idx": 62376 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65426:65427:1', 'start_idx': 36206} The data corresponds to a snapshot on October 13 12:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 100 hPa at Norfolk Island change in 12 hours if localized Gaussian perturbations cause Temperature at 100 hPa at Norfolk Island to increased by 4.356 K in the provided initial conditions data?", + "response": "Based on the provided data, in 12 hours, the Temperature at 100 hPa will decrease by 0 K at Norfolk Island.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 12, + "variable": "Temperature at 100 hPa", + "location": "Norfolk Island", + "target_variable": "temperature_100", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "7aba31613bd7b8c4", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65426:65427:1", + "start_idx": 36206 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72151:72152:1', 'start_idx': 42931} The data corresponds to a snapshot on May 20 18:00. Based on the above data, answer the following question:", + "question": "How will the V (meridional) component of wind at 50 hPa at Gulf of Anadyr' change in 18 hours if localized Gaussian perturbations cause V (meridional) component of wind at 50 hPa at Gulf of Anadyr' to increased by 2.032 m/s in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the V (meridional) component of wind at 50 hPa will increase by 2.028e-05 m/s at Gulf of Anadyr'.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "V (meridional) component of wind at 50 hPa", + "location": "Gulf of Anadyr'", + "target_variable": "v_component_of_wind_50", + "true_value": "2.0276755094528198e-05", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "177aa20e0194b64c", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72151:72152:1", + "start_idx": 42931 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74010:74011:1', 'start_idx': 44790} The data corresponds to a snapshot on August 28 12:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 300 hPa at Great Australian Bight change in 18 hours if localized Gaussian perturbations cause Specific humidity at 300 hPa at Great Australian Bight to increased by 4.332e-05 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 300 hPa will increase by 1.957e-08 kg/kg at Great Australian Bight.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 300 hPa", + "location": "Great Australian Bight", + "target_variable": "specific_humidity_300", + "true_value": "1.957373108041338e-08", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "f3e3aeb0d17007a5", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74010:74011:1", + "start_idx": 44790 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42014:42015:1', 'start_idx': 12794} The data corresponds to a snapshot on October 04 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 400 hPa at Oceania change in 24 hours if localized Gaussian perturbations cause Geopotential at 400 hPa at Oceania to increased by 1659 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Geopotential at 400 hPa will decrease by 0 m²/s² at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Geopotential at 400 hPa", + "location": "Oceania", + "target_variable": "geopotential_400", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "3d6b2b990ae88101", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42014:42015:1", + "start_idx": 12794 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83872:83873:1', 'start_idx': 54652} The data corresponds to a snapshot on May 29 00:00. Based on the above data, answer the following question:", + "question": "How will the Specific humidity at 100 hPa at Dominican Republic change in 18 hours if localized Gaussian perturbations cause Specific humidity at 100 hPa at Dominican Republic to increased by 1.487e-07 kg/kg in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Specific humidity at 100 hPa will decrease by 0 kg/kg at Dominican Republic.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Specific humidity at 100 hPa", + "location": "Dominican Republic", + "target_variable": "specific_humidity_100", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "e22a6238a63e199a", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83872:83873:1", + "start_idx": 54652 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '56137:56138:1', 'start_idx': 26917} The data corresponds to a snapshot on June 04 06:00. Based on the above data, answer the following question:", + "question": "How will the Temperature at 300 hPa at Europe change in 18 hours if localized Gaussian perturbations cause Temperature at 300 hPa at Europe to increased by 2.963 K in the provided initial conditions data?", + "response": "Based on the provided data, in 18 hours, the Temperature at 300 hPa will increase by 0.01447 K at Europe.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 18, + "variable": "Temperature at 300 hPa", + "location": "Europe", + "target_variable": "temperature_300", + "true_value": "0.014468987472355366", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "c0f52483a7abe5ce", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "56137:56138:1", + "start_idx": 26917 + } + }, + { + "prompt": "The following data shows the global data aggregated over 6 hours. {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58486:58487:1', 'start_idx': 29266} The data corresponds to a snapshot on January 12 12:00. Based on the above data, answer the following question:", + "question": "How will the Geopotential at 200 hPa at Oceania change in 24 hours if localized Gaussian perturbations cause Geopotential at 200 hPa at Oceania to increased by 1854 m²/s² in the provided initial conditions data?", + "response": "Based on the provided data, in 24 hours, the Geopotential at 200 hPa will decrease by 0 m²/s² at Oceania.", + "metadata": { + "question_id": "bTPFmj", + "prompt_id": "UonGHT", + "lead_time": 24, + "variable": "Geopotential at 200 hPa", + "location": "Oceania", + "target_variable": "geopotential_200", + "true_value": "0.0", + "level": "3a", + "eval_type": "numerical", + "forced_extreme_window": false, + "task_id": "bfa288f8950130dd", + "difficulty": "hard" + }, + "data_desc": { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58486:58487:1", + "start_idx": 29266 + } + } +] \ No newline at end of file diff --git a/level3b_part0.json b/level3b_part0.json new file mode 100644 index 0000000000000000000000000000000000000000..04c928caf5f932e5a3c08fa37dcdc765f6369ec3 --- /dev/null +++ b/level3b_part0.json @@ -0,0 +1,5902 @@ +[ + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50964:50965:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 19 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.7536022919125241.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.7536022919125241", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "fbf8b1f1b9f4bb77", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50964:50965:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/07c94afbd6a0442b_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83985:83986:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on June 26 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.14162908679568076.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.14162908679568076", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b1b185c3349afbe4", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83985:83986:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/82a79369052e4630_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49370:49371:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on October 16 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.40948481692460537.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.40948481692460537", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "128fc60ac26fc44b", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49370:49371:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/e86ae0bd3a43406d_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63608:63609:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 16 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.2811814683456657.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.2811814683456657", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "df28724d4d7b3df9", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63608:63609:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/a9f3285bb6a34e26_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '49598:49599:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 12 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.7670368796835922.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.7670368796835922", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "6050c5d8cbcb8adb", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "49598:49599:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/3a776389f26b4b1c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65474:65475:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on October 25 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.7429785769183455.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.7429785769183455", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b61fe2beb758550f", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65474:65475:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/bd1ac9bace5e44fe_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50664:50665:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 05 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.38695890648428455.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.38695890648428455", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "357c9db27e726eb9", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50664:50665:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/ba9b4c11190e4544_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79709:79710:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 23 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.27299307148778573.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.27299307148778573", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "c2450e24542255e2", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79709:79710:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/26d6a5433d3d4ebb_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61204:61205:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 22 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.9075413253512001.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9075413253512001", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "74c76ed96e2dae88", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61204:61205:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/583d5894e4134c68_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42970:42971:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on May 30 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.9881303944630107.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9881303944630107", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "a87e2c34be1c69b9", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42970:42971:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/a0c8ebc0401040be_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31699:31700:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 11 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.5182434665154485.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.5182434665154485", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "a7f3eca26a071773", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31699:31700:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/8c7927242326494c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '59869:59870:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 24 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.6257871490085806.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6257871490085806", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "3198a9887d28e9be", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "59869:59870:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/a77e76eb63f64de2_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51919:51920:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 15 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.740514150914227.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.740514150914227", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b0539f60e5cfce92", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51919:51920:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/917e780505834eee_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91936:91937:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 05 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.35876896616852505.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.35876896616852505", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "14b69dfd796fcc29", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91936:91937:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/41d4c9dc53ad473b_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68751:68752:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 21 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.5651207156186916.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.5651207156186916", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "d4244150af8965b5", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68751:68752:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/7969dd6d91c14770_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72470:72471:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on August 08 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.9752006129652642.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9752006129652642", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "c3e08bb8de3746e9", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72470:72471:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/45a3b0e3d8b44f61_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60863:60864:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on August 28 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.6013661675344246.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6013661675344246", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "fb46990ddb7bc435", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60863:60864:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/6744f222f2654a13_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48423:48424:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on February 22 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.13953423181259006.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.13953423181259006", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "5af83039a9fe1346", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48423:48424:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/5f075ac0fe2c47ba_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42736:42737:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on April 02 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.8485353863386259.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8485353863386259", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "27e3167e47e19cba", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42736:42737:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/26715e1bb95f4893_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '72908:72909:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 26 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.7956554130796067.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.7956554130796067", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "572ba5f8e61aff07", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "72908:72909:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/008cc914f95741fe_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80026:80027:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on October 10 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.062373869957535244.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.062373869957535244", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "3060ac2ca60edfa4", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80026:80027:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/aefb0b5a34264113_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '53643:53644:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 19 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.49260608691330177.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.49260608691330177", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "7afe703325287a4c", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "53643:53644:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/d2e3c07c560a47ef_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87535:87536:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 30 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.7893179217714082.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.7893179217714082", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b204c61198092f68", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87535:87536:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/8ab2c0beaa7b4b97_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52906:52907:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on March 19 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.6899492830933546.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6899492830933546", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "9163a644d57ebdd3", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52906:52907:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/73c611771dd84b7d_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57312:57313:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on March 25 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.42752116599005907.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.42752116599005907", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "57db40ddae0e8753", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57312:57313:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/2f46e0ace49d454b_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70417:70418:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on March 14 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.2504541693237554.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.2504541693237554", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "951c96fe71f3ab39", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70417:70418:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/cecee99707754955_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88545:88546:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on August 10 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.2087811121696802.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.2087811121696802", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "eb75dce5ae3986fe", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88545:88546:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/da8007ada7ac4e54_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '41805:41806:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on August 13 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.2438604645587935.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.2438604645587935", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "8d29a7f8a546793b", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "41805:41806:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/5cf87cb7b96f4ceb_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64758:64759:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on April 29 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.2835495763726841.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.2835495763726841", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "5629d37d7e82db5c", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64758:64759:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/1f6f5cd4cdf143a9_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85555:85556:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 23 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.574290209577886.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.574290209577886", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "1f6cb67c18772047", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85555:85556:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/130d9d1a665c4f25_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '87557:87558:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 06 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.9373396938491771.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9373396938491771", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "de3c8419611571b5", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "87557:87558:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/b456aeaab64d4bde_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80922:80923:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on May 22 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.989684924873364.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.989684924873364", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "d49b4d201fef1c5a", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80922:80923:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/e5fc81c31677463f_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '33671:33672:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 17 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.1706409657741098.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.1706409657741098", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "8007b8a5546e9e02", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "33671:33672:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/b2ec84675c1d48d3_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60919:60920:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 11 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.30737901430504033.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.30737901430504033", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b814e922b7456733", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60919:60920:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/512746764e034e60_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71367:71368:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 06 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.6476421645761462.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6476421645761462", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "70b952d3a4a65c26", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71367:71368:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/a1cee653debd4a3c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93512:93513:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 03 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.42919365835108525.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.42919365835108525", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "ac08a4e15e0f46b4", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93512:93513:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/15ce9c85d5304931_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65721:65722:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 26 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.6858339249207017.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6858339249207017", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "c829bfe4bf81e52c", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65721:65722:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/8afce9e09dd54406_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '32119:32120:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 25 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.5588583820766988.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.5588583820766988", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "2a1ba1657e3296a6", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "32119:32120:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/a0bbbd02171148ac_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42194:42195:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 18 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.5936799024032561.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.5936799024032561", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "9457ad0701179200", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42194:42195:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/6e4453fdbf7e47ba_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '58149:58150:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on October 20 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.007155594034099821.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.007155594034099821", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "cc5f5bc54a71a52d", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "58149:58150:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/41fc2197be90453c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '83132:83133:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 26 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.03264809590974116.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.03264809590974116", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "964e7ca84f53f7fd", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "83132:83133:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/d6f7b9a72c6d4fe6_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70714:70715:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on May 27 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.8095208008395653.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8095208008395653", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b180af2f2f9fb09f", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70714:70715:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/1f1b1a6e55f84a6b_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '91648:91649:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 24 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.28129125626073204.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.28129125626073204", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "e4b804cde1a01656", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "91648:91649:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/048f27cc40fb4e68_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '84311:84312:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 15 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.38753317699185696.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.38753317699185696", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "a5cc2dd03103f351", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "84311:84312:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/663f32d9fc3545bb_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37324:37325:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 19 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.8979012318167191.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8979012318167191", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "2bf8f0e8d84f22bd", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37324:37325:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/992d922e19844b91_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57643:57644:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on June 15 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.6856251068111132.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6856251068111132", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "79b76dfaeab464f3", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57643:57644:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/b985161c5ddc4fe9_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63416:63417:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on May 29 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.41627511445457777.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.41627511445457777", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "ecdc99fd24e697a0", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63416:63417:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/eaa3da172c3e4c84_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48655:48656:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on April 20 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.8637077507166069.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8637077507166069", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "19ef9a6139cec840", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48655:48656:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/716952a58d7b4578_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88721:88722:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 23 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.7119685058835882.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.7119685058835882", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "5f58707cdeeffcd7", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88721:88722:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/446a36f517cd491a_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '42473:42474:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 27 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.31763421676511394.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.31763421676511394", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "e496d0d17bbc889a", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "42473:42474:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/e323d1dbc45c4892_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '44578:44579:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 06 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.7158959016508113.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.7158959016508113", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "e1fed33ff8e863ad", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "44578:44579:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/c972e4d8af854198_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '70402:70403:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on March 10 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.8141971704211786.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8141971704211786", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "c6f0ce06dea3fc52", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "70402:70403:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/51704dd8948949cd_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51803:51804:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on June 16 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.8295203912501486.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8295203912501486", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "1c6df553aa8afded", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51803:51804:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/27f23fc2f9474324_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65380:65381:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on October 02 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.11581692644795993.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.11581692644795993", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "3ad5d4e2638ed235", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65380:65381:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/75e3eb0d61814ec9_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '77821:77822:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on April 07 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.8925189468167233.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8925189468167233", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "aab6726ac19956d9", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "77821:77822:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/962a61c469954caa_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68628:68629:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 22 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.9374976998505189.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9374976998505189", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "c5b4e56bbc73bb3a", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68628:68629:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/7ffedf65266d4b63_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '38483:38484:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on May 04 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.016392534126912328.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.016392534126912328", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b4ea1026d47947a9", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "38483:38484:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/9ec034fbdb0f45c3_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '75527:75528:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 11 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.040523381829932315.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.040523381829932315", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "f66d49eb74c9716f", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "75527:75528:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/df8f3e8dcb924f0e_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64488:64489:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on February 21 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.4568134921129253.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.4568134921129253", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "d7cbe40267c4e662", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64488:64489:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/ce52bc34661f4b71_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '66528:66529:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 15 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.03416102235801821.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.03416102235801821", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "92a5f9f0088d6487", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "66528:66529:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/fafa07cf538c432a_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '31882:31883:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on October 27 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.37213596234611246.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.37213596234611246", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "20253405700ff61f", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "31882:31883:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/e5e4eb8135c145ff_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '64332:64333:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 13 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.9798974704803377.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9798974704803377", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "8ca10c0a24107bca", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "64332:64333:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/34aef6caab1a4aa4_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50891:50892:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on October 31 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.19488555618028014.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.19488555618028014", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b7cabffede91322c", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50891:50892:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/59749f81fd67499c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '92353:92354:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on March 19 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.26184558392027646.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.26184558392027646", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "f18e2ca8a261c304", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "92353:92354:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/0db8543c29c14f47_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '35789:35790:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 01 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.5410628502239629.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.5410628502239629", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "074c9290a4a3b35a", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "35789:35790:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/93d366f3d01b46fe_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37969:37970:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 27 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.1092684498090073.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.1092684498090073", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "bd206bfa73567176", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37969:37970:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/0513ad482e1d455c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40593:40594:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on October 14 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.24071257376674626.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.24071257376674626", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "e8ff0bb44fec013b", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40593:40594:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/4a83378347cb49e1_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '60864:60865:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on August 29 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.3571789807319278.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.3571789807319278", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "138a729814027edc", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "60864:60865:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/c233da88d0914169_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52191:52192:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 21 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.613354445398652.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.613354445398652", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "580b2c992f85086f", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52191:52192:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/114ccab939194d7a_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '30740:30741:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 16 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.9564005081064199.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9564005081064199", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "505778ef5f101c39", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "30740:30741:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/6fa652079ab64ffa_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '57865:57866:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on August 10 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.1933396909020898.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.1933396909020898", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "e8606ed17adc0d71", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "57865:57866:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/baaeee979ffa4771_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '80595:80596:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on March 01 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.12115542181393413.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.12115542181393413", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "4aad5cd37ede5148", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "80595:80596:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/a1bf034c170d4682_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '93429:93430:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 13 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.6427100722105477.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6427100722105477", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b18576ba1b824e88", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "93429:93430:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/26e985b93d41498c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '62114:62115:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 07 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.4767167913240815.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.4767167913240815", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "634494e58e907998", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "62114:62115:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/90aeea2ba6374892_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37795:37796:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 13 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.5443826013624598.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.5443826013624598", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "ab83ab49262bf626", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37795:37796:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/2a0a1a8a0d024546_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '47320:47321:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on May 23 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.4718407138161502.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.4718407138161502", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "bd2fb206ab7fcc88", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "47320:47321:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/17fdec0770f34b9c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '90378:90379:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 10 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.8352827134953387.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8352827134953387", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "723dc07d0d034605", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "90378:90379:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/3ad2a8b317cf45b1_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61192:61193:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 19 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.4912575273344074.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.4912575273344074", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "e7773eb262d74c18", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61192:61193:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/b5de3349e8d545d4_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '63553:63554:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 02 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.8081061068439052.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8081061068439052", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "cb96e5fbb050c28e", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "63553:63554:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/6d21868500bb4b59_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '88281:88282:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on June 05 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.8879053723229267.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8879053723229267", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "88ee951d546e1e15", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "88281:88282:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/8a444fc3b67e4f78_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '52704:52705:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 28 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.17944785542631314.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.17944785542631314", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "4c0eec9ea7295da1", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "52704:52705:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/0bda9468ae8640c4_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '71725:71726:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on February 04 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.6155421308874031.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6155421308874031", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "537ed0b183b5e47b", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "71725:71726:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/fe646421d22843a0_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '85421:85422:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on June 20 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.4395194332111386.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.4395194332111386", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "e7ff290df1e382ae", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "85421:85422:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/d42e33937105469a_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48219:48220:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 02 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.6521940385714512.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6521940385714512", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "de356a7c3e690a16", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48219:48220:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/f9b45b31199c4ad6_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '61487:61488:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on January 31 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.2828938436385131.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.2828938436385131", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "4b6bb5b2d91934a8", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "61487:61488:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/7fcfb634a7a94875_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '68485:68486:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on November 16 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.10435962864467552.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.10435962864467552", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "9ae795af8ead1f76", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "68485:68486:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/af7bbe7b06cf42c8_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '82325:82326:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on May 08 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.8880122484485029.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8880122484485029", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "318ff8ce43656954", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "82325:82326:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/40f40d955fb24298_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '48444:48445:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on February 28 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.6377405509783733.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6377405509783733", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "d2a4ae7e94751a59", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "48444:48445:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/34c47f59db394ece_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '37207:37208:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on June 19 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.08867205808338807.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.08867205808338807", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "daa64579651b8b9d", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "37207:37208:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/1907f199cd5d49b6_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '65256:65257:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 01 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.017004677280634484.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.017004677280634484", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "8ea33cf0b8a5c5ca", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "65256:65257:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/b57e6ee4774a4ab5_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '79508:79509:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on June 03 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.8294960351542287.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8294960351542287", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "620e455f60cbec0e", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "79508:79509:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/ea28a9a49e8e418e_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '76901:76902:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on August 21 06:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.9602978638936465.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9602978638936465", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "06c726c0044139b2", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "76901:76902:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/f3fe5366116e452f_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '34331:34332:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 01 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.9972451792429501.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9972451792429501", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "bae116a43ecfba32", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "34331:34332:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/68609c4aef144e24_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '40184:40185:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on July 04 00:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.2493057210323455.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.2493057210323455", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "31e7490199cbb4b2", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "40184:40185:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/20c1bf21f50f47d4_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '81790:81791:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on December 25 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.9173345372574848.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9173345372574848", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "4b8c3887452b25b6", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "81790:81791:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/c63b28b408ac435c_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '74039:74040:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on September 04 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.7087425416564664.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.7087425416564664", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "1d3a8bfdb3b9f273", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "74039:74040:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/3efc336d759a4cd1_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '51570:51571:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.rhcl2: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on April 19 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.rhcl2 from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.rhcl2, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.rhcl2 used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.rhcl2 will be 0.8787153422274782.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.8787153422274782", + "target_variable": "shortwave_radiation.rhcl2", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "b587c9d201b68332", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "51570:51571:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/ec8993ff892b4e0e_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '50567:50568:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter convection.entmax: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on August 11 18:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of convection.entmax from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except convection.entmax, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter convection.entmax used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the convection.entmax will be 0.6095719467084337.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.6095719467084337", + "target_variable": "convection.entmax", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "fd16e66db5db2352", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "50567:50568:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/b5fe0f65def14d30_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '67546:67547:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter mod_radcon.albsea: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on March 26 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of mod_radcon.albsea from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except mod_radcon.albsea, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter mod_radcon.albsea used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the mod_radcon.albsea will be 0.9650967684841104.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.9650967684841104", + "target_variable": "mod_radcon.albsea", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "0578eadb3238e367", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "67546:67547:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + "normalized_surface_pressure", + "convection.precnv", + "convection.cbmf", + "convection.iptop", + "convection.se", + "condensation.precls", + "condensation.dqlsc", + "condensation.dtlsc", + "humidity.rh", + "humidity.qsat", + "land_model.stl_lm", + "date.model_step", + "date.tyear", + "date.dt_seconds" + ], + "path": "cache/simulation_output/9d0836d182d043c7_simulation_output.nc" + } + ] + }, + { + "prompt": "The following data consists of two parts: (1) recent global data spanning 6 hours, sampled at an interval of 6 hours: {'type': 'wb2', 'variables': ['mean_sea_level_pressure', '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_temperature', 'surface_pressure', 'geopotential', 'specific_humidity', 'temperature', 'u_component_of_wind', 'v_component_of_wind'], 'time_indices': '73214:73215:1'} (2) Data output of running the Simulator for a certain value of the simulator model input parameter shortwave_radiation.qacl: {'variables': ['temperature', 'u_wind', 'v_wind', 'specific_humidity', 'geopotential', 'normalized_surface_pressure', 'convection.precnv', 'convection.cbmf', 'convection.iptop', 'convection.se', 'condensation.precls', 'condensation.dqlsc', 'condensation.dtlsc', 'humidity.rh', 'humidity.qsat', 'land_model.stl_lm', 'date.model_step', 'date.tyear', 'date.dt_seconds']}. The data corresponds to a snapshot on February 10 12:00. Based on the above data, answer the following question:", + "question": "What is the optimal value of shortwave_radiation.qacl from which the simulated output was generated? This is a parameter estimation for the Simulator API. The reference output was generated by running the Simulator with default values for all arguments except shortwave_radiation.qacl, which has a feasible range 0 to 1. By comparing MSE between Simulator outputs, estimate the value of the parameter shortwave_radiation.qacl used to generate the reference simulation. You may make a maximum of 3 simulation calls per code block. You may run multiple code blocks (upto 4 error-free code blocks) to get a more accurate answer.", + "response": "Based on the provided data, the optimal value of the shortwave_radiation.qacl will be 0.11879766921892532.", + "metadata": { + "question_id": "3IO1hr", + "prompt_id": "eZia7s", + "true_value": "0.11879766921892532", + "target_variable": "shortwave_radiation.qacl", + "level": "3b", + "eval_type": "simulation", + "forced_extreme_window": false, + "task_id": "fe7ef3503259a852", + "difficulty": "hard" + }, + "data_desc": [ + { + "type": "wb2", + "variables": [ + "mean_sea_level_pressure", + "10m_u_component_of_wind", + "10m_v_component_of_wind", + "2m_temperature", + "surface_pressure", + "geopotential", + "specific_humidity", + "temperature", + "u_component_of_wind", + "v_component_of_wind" + ], + "time_indices": "73214:73215:1" + }, + { + "type": "wb2_segment", + "variables": [ + "temperature", + "u_wind", + "v_wind", + "specific_humidity", + "geopotential", + 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