MWMs
Collection
Multitask Weather Models by Harley-ml • 2 items • Updated
Hweh-446k
Summary
Task: Weather Forecasting
Inputs: 72 hours time-series
Outputs: 12h multivariate forecast
Params: 446k
Framework: PyTorch
Author: Paul Courneya (Harley-ml)
Description
Hweh-446k is a 446-thousand-parameter LSTM model distillation of Hweh-6M (a ~92% reduction in params!!), trained to predict the next 12 hours of weather, including temperature, humidity, pressure, precipitation, and more, using the previous 72 hours of weather context.
We recommend using this model as a backup to a weather API or for fast offline forecasting when internet access is unavailable.
We would also like to give a shoutout to Open-Meteo for providing a free-to-use weather forecasting API.
Why “Hweh”?
In Proto-Indo-European, the root *h₂weh₁- means “to blow.” We chose it as the name for a weather forecasting model because of its connection to wind and air.
Architecture
The model uses a multitask LSTM setup:
| Parameter | Value |
|---|---|
input_dim |
22 |
seq_len |
72 |
num_predict |
12 |
hidden_dim |
128 |
num_layers |
3 |
dropout |
0.1 |
encoder_type |
lstm |
num_locations |
82 |
location_emb_dim |
32 |
num_weather_classes |
7 |
Training
We trained Hweh-446k on 4.06 million rows of weather data from 82 locations with the supervision of Hweh-6M for one epoch, using a batch size of 16 and gradient accumulation of 5. Training ran for 4.3 hours on an RTX 2060 6GB GPU.
Input Features
temperature_2m_normrelative_humidity_2m_normapparent_temperature_normprecipitation_log_normsea_level_pressure_normsurface_pressure_normcloud_cover_total_normvisibility_normwind_speed_10m_normwind_direction_10m_sinwind_direction_10m_coshour_sinhour_cosday_of_year_sinday_of_year_cosweather_code_onehot_clearweather_code_onehot_cloudyweather_code_onehot_fogweather_code_onehot_drizzleweather_code_onehot_rainweather_code_onehot_snowweather_code_onehot_thunderstorm
Output Features
y_temp_c: continuous regressiony_humidity: continuous regressiony_apparent_temperature: continuous regressiony_precipitation_mm: continuous regressiony_sea_level_pressure_hpa: continuous regressiony_surface_pressure_hpa: continuous regressiony_cloud_cover_total: continuous regressiony_wind_speed_10m: continuous regressiony_wind_direction_sin: continuous regressiony_wind_direction_cos: continuous regressiony_rain_prob: binary classificationy_weather_class: multiclass classification
Training Results
Training & Evaluation Metrics
| Step | Train Loss | Eval Loss | Weather Acc | Rain Acc | Rain Recall | Weather Recall |
|---|---|---|---|---|---|---|
| 1k | 8.4609 | 8.8471 | 0.6317 | 0.7451 | 0.7640 | 0.2574 |
| 5k | 5.1420 | 5.0602 | 0.6247 | 0.7531 | 0.8025 | 0.5648 |
| 10k | 4.1733 | 3.9198 | 0.6117 | 0.7876 | 0.8016 | 0.6297 |
| 15k | 3.8354 | 3.6310 | 0.6140 | 0.7920 | 0.8009 | 0.6187 |
| 20k | 3.6206 | 3.4365 | 0.6083 | 0.7881 | 0.8140 | 0.6179 |
| 25k | 3.5378 | 3.3251 | 0.6083 | 0.7859 | 0.8173 | 0.6245 |
| 30k | 3.4534 | 3.2846 | 0.6041 | 0.7812 | 0.8272 | 0.6398 |
| 35k | 3.4272 | 3.2324 | 0.6061 | 0.7860 | 0.8194 | 0.6289 |
| 40k | 3.4143 | 3.2230 | 0.6080 | 0.7862 | 0.8200 | 0.6339 |
| 42.6k | — | 3.2180 | 0.6081 | 0.7857 | 0.8212 | 0.6340 |
Note: Loss looks higher than Hweh-6M's because of KL + train/val loss.
Regression Error Metrics (MAE)
| Step | Apparent | Cloud | Humidity | Precip (mm) | Sea Level P | Surface P | Temp | Wind |
|---|---|---|---|---|---|---|---|---|
| 1k | 212.80 | 2179.70 | 1476.42 | 0.140 | 7571.45 | 83590.98 | 172.79 | 60.49 |
| 5k | 2.28 | 25.58 | 9.04 | 0.107 | 3.50 | 14.55 | 1.90 | 3.78 |
| 10k | 2.06 | 25.31 | 8.08 | 0.100 | 3.31 | 9.63 | 1.72 | 3.37 |
| 15k | 1.91 | 25.00 | 7.88 | 0.101 | 3.18 | 7.93 | 1.61 | 3.25 |
| 20k | 1.88 | 25.12 | 7.60 | 0.101 | 3.13 | 7.41 | 1.56 | 3.18 |
| 25k | 1.84 | 25.01 | 7.53 | 0.102 | 3.09 | 6.61 | 1.53 | 3.13 |
| 30k | 1.81 | 25.03 | 7.45 | 0.102 | 3.12 | 6.60 | 1.51 | 3.12 |
| 35k | 1.81 | 24.94 | 7.42 | 0.101 | 3.07 | 6.39 | 1.52 | 3.12 |
| 40k | 1.79 | 24.94 | 7.39 | 0.101 | 3.06 | 6.37 | 1.50 | 3.11 |
| 42.6k | 1.79 | 24.92 | 7.39 | 0.101 | 3.06 | 6.38 | 1.50 | 3.11 |
This model did better than the teacher on MAE and accuracy, but the real-world accuracy is 5-10% worse.
Generation Examples
| ID | Class |
|---|---|
| 0 | clear |
| 1 | cloudy |
| 2 | fog |
| 3 | drizzle |
| 4 | rain |
| 5 | snow |
| 6 | thunderstorm |
City=Seattle
{
"city": "Seattle",
"location_id": "1",
"model_location_id": 0,
"data_source": "open-meteo forecast api (past-hours context only)",
"requested_at_utc": "2026-05-08T19:57:14.429521+00:00",
"context": {
"hours": 72,
"start_utc": "2026-05-05T19:00:00+00:00",
"end_utc": "2026-05-08T18:00:00+00:00",
"start_local": "2026-05-05T12:00:00-07:00",
"end_local": "2026-05-08T11:00:00-07:00"
},
"model": {
"encoder_type": "lstm",
"seq_len": 72,
"input_dim": 22,
"num_weather_classes": 7
},
"forecast": [
{
"lead_hours": 1,
"target_utc": "2026-05-08T19:00:00+00:00",
"target_local": "2026-05-08T12:00:00-07:00",
"temperature_2m_c": 12.21396255493164,
"relative_humidity_2m_pct": 72.33454895019531,
"apparent_temperature_c": 10.097986221313477,
"precipitation_mm": 0.015628309920430183,
"pressure_msl_hpa": 1022.0569458007812,
"surface_pressure_hpa": 1014.205078125,
"cloud_cover_pct": 94.34225463867188,
"wind_speed_10m_kmh": 12.568346977233887,
"rain_probability": 0.19356799125671387,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.020174680277705193,
"class_1": 0.9282320737838745,
"class_2": 0.0022441258188337088,
"class_3": 0.04022064805030823,
"class_4": 0.008552632294595242,
"class_5": 0.0005501594278030097,
"class_6": 2.556406798248645e-05
}
},
{
"lead_hours": 2,
"target_utc": "2026-05-08T20:00:00+00:00",
"target_local": "2026-05-08T13:00:00-07:00",
"temperature_2m_c": 12.8738374710083,
"relative_humidity_2m_pct": 70.51017761230469,
"apparent_temperature_c": 10.80291748046875,
"precipitation_mm": 0.011432276107370853,
"pressure_msl_hpa": 1022.0043334960938,
"surface_pressure_hpa": 1014.2881469726562,
"cloud_cover_pct": 89.5630111694336,
"wind_speed_10m_kmh": 12.822803497314453,
"rain_probability": 0.2689012587070465,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.04770936816930771,
"class_1": 0.8698592185974121,
"class_2": 0.0019157826900482178,
"class_3": 0.057819921523332596,
"class_4": 0.02183685451745987,
"class_5": 0.0008237561560235918,
"class_6": 3.5158725950168446e-05
}
},
{
"lead_hours": 3,
"target_utc": "2026-05-08T21:00:00+00:00",
"target_local": "2026-05-08T14:00:00-07:00",
"temperature_2m_c": 13.51952075958252,
"relative_humidity_2m_pct": 68.44591522216797,
"apparent_temperature_c": 11.500271797180176,
"precipitation_mm": 0.006943895947188139,
"pressure_msl_hpa": 1021.80859375,
"surface_pressure_hpa": 1014.2529296875,
"cloud_cover_pct": 84.49480438232422,
"wind_speed_10m_kmh": 12.941960334777832,
"rain_probability": 0.30342426896095276,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.07170139998197556,
"class_1": 0.8286910057067871,
"class_2": 0.0013093570014461875,
"class_3": 0.06433302909135818,
"class_4": 0.03300558775663376,
"class_5": 0.0009036734118126333,
"class_6": 5.590655928244814e-05
}
},
{
"lead_hours": 4,
"target_utc": "2026-05-08T22:00:00+00:00",
"target_local": "2026-05-08T15:00:00-07:00",
"temperature_2m_c": 13.970871925354004,
"relative_humidity_2m_pct": 66.8187026977539,
"apparent_temperature_c": 11.959537506103516,
"precipitation_mm": 0.009790810756385326,
"pressure_msl_hpa": 1021.4691162109375,
"surface_pressure_hpa": 1014.1052856445312,
"cloud_cover_pct": 80.34271240234375,
"wind_speed_10m_kmh": 13.050889015197754,
"rain_probability": 0.33110707998275757,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.10919982939958572,
"class_1": 0.7759758830070496,
"class_2": 0.0011831748997792602,
"class_3": 0.07015678286552429,
"class_4": 0.042580746114254,
"class_5": 0.000857028178870678,
"class_6": 4.6529316023224965e-05
}
},
{
"lead_hours": 5,
"target_utc": "2026-05-08T23:00:00+00:00",
"target_local": "2026-05-08T16:00:00-07:00",
"temperature_2m_c": 14.132287979125977,
"relative_humidity_2m_pct": 66.1208267211914,
"apparent_temperature_c": 12.156023025512695,
"precipitation_mm": 0.008600466884672642,
"pressure_msl_hpa": 1021.0518188476562,
"surface_pressure_hpa": 1013.7891845703125,
"cloud_cover_pct": 76.06925201416016,
"wind_speed_10m_kmh": 12.926268577575684,
"rain_probability": 0.3409281373023987,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.1305426061153412,
"class_1": 0.7395681142807007,
"class_2": 0.0007046378450468183,
"class_3": 0.07573042809963226,
"class_4": 0.052331216633319855,
"class_5": 0.0010767169296741486,
"class_6": 4.629969771485776e-05
}
},
{
"lead_hours": 6,
"target_utc": "2026-05-09T00:00:00+00:00",
"target_local": "2026-05-08T17:00:00-07:00",
"temperature_2m_c": 13.963343620300293,
"relative_humidity_2m_pct": 66.67638397216797,
"apparent_temperature_c": 11.971813201904297,
"precipitation_mm": 0.010375693440437317,
"pressure_msl_hpa": 1020.6505126953125,
"surface_pressure_hpa": 1013.4520874023438,
"cloud_cover_pct": 73.21341705322266,
"wind_speed_10m_kmh": 12.721481323242188,
"rain_probability": 0.35606324672698975,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.15235546231269836,
"class_1": 0.7133304476737976,
"class_2": 0.0007980632944963872,
"class_3": 0.07519367337226868,
"class_4": 0.05724283307790756,
"class_5": 0.0010369947412982583,
"class_6": 4.249428093316965e-05
}
},
{
"lead_hours": 7,
"target_utc": "2026-05-09T01:00:00+00:00",
"target_local": "2026-05-08T18:00:00-07:00",
"temperature_2m_c": 13.449448585510254,
"relative_humidity_2m_pct": 68.29602813720703,
"apparent_temperature_c": 11.426795959472656,
"precipitation_mm": 0.012202607467770576,
"pressure_msl_hpa": 1020.3434448242188,
"surface_pressure_hpa": 1013.139892578125,
"cloud_cover_pct": 70.92017364501953,
"wind_speed_10m_kmh": 12.359071731567383,
"rain_probability": 0.3651714026927948,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.16448773443698883,
"class_1": 0.695494532585144,
"class_2": 0.0008648976217955351,
"class_3": 0.07075871527194977,
"class_4": 0.06722015887498856,
"class_5": 0.0011408632853999734,
"class_6": 3.3135267585748807e-05
}
},
{
"lead_hours": 8,
"target_utc": "2026-05-09T02:00:00+00:00",
"target_local": "2026-05-08T19:00:00-07:00",
"temperature_2m_c": 12.755823135375977,
"relative_humidity_2m_pct": 70.44146728515625,
"apparent_temperature_c": 10.662925720214844,
"precipitation_mm": 0.014662384055554867,
"pressure_msl_hpa": 1020.1875610351562,
"surface_pressure_hpa": 1012.8895874023438,
"cloud_cover_pct": 69.15129852294922,
"wind_speed_10m_kmh": 11.787208557128906,
"rain_probability": 0.3489035665988922,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.19570188224315643,
"class_1": 0.6735588908195496,
"class_2": 0.000978420372121036,
"class_3": 0.06184739992022514,
"class_4": 0.0664038360118866,
"class_5": 0.0014616982080042362,
"class_6": 4.789666854776442e-05
}
},
{
"lead_hours": 9,
"target_utc": "2026-05-09T03:00:00+00:00",
"target_local": "2026-05-08T20:00:00-07:00",
"temperature_2m_c": 11.955390930175781,
"relative_humidity_2m_pct": 73.05667877197266,
"apparent_temperature_c": 9.77428913116455,
"precipitation_mm": 0.015376528725028038,
"pressure_msl_hpa": 1020.2242431640625,
"surface_pressure_hpa": 1012.7984619140625,
"cloud_cover_pct": 67.46344757080078,
"wind_speed_10m_kmh": 11.127586364746094,
"rain_probability": 0.36496502161026,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.20349998772144318,
"class_1": 0.6480608582496643,
"class_2": 0.0010103220120072365,
"class_3": 0.06373731791973114,
"class_4": 0.08206527680158615,
"class_5": 0.0015911461086943746,
"class_6": 3.510143869789317e-05
}
},
{
"lead_hours": 10,
"target_utc": "2026-05-09T04:00:00+00:00",
"target_local": "2026-05-08T21:00:00-07:00",
"temperature_2m_c": 11.182319641113281,
"relative_humidity_2m_pct": 75.5196304321289,
"apparent_temperature_c": 8.978246688842773,
"precipitation_mm": 0.016823438927531242,
"pressure_msl_hpa": 1020.421875,
"surface_pressure_hpa": 1012.8126831054688,
"cloud_cover_pct": 65.73115539550781,
"wind_speed_10m_kmh": 10.359850883483887,
"rain_probability": 0.35924479365348816,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.21845510601997375,
"class_1": 0.6389062404632568,
"class_2": 0.0018773162737488747,
"class_3": 0.059891991317272186,
"class_4": 0.07879848033189774,
"class_5": 0.002034474164247513,
"class_6": 3.633901724242605e-05
}
},
{
"lead_hours": 11,
"target_utc": "2026-05-09T05:00:00+00:00",
"target_local": "2026-05-08T22:00:00-07:00",
"temperature_2m_c": 10.499757766723633,
"relative_humidity_2m_pct": 77.536865234375,
"apparent_temperature_c": 8.306406021118164,
"precipitation_mm": 0.01860857754945755,
"pressure_msl_hpa": 1020.7318725585938,
"surface_pressure_hpa": 1012.9368896484375,
"cloud_cover_pct": 65.10720825195312,
"wind_speed_10m_kmh": 9.62015151977539,
"rain_probability": 0.3694237470626831,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.22774139046669006,
"class_1": 0.6242526173591614,
"class_2": 0.0028147574048489332,
"class_3": 0.06025313213467598,
"class_4": 0.0822005346417427,
"class_5": 0.0026846849359571934,
"class_6": 5.289047476253472e-05
}
},
{
"lead_hours": 12,
"target_utc": "2026-05-09T06:00:00+00:00",
"target_local": "2026-05-08T23:00:00-07:00",
"temperature_2m_c": 9.956731796264648,
"relative_humidity_2m_pct": 79.21904754638672,
"apparent_temperature_c": 7.87125301361084,
"precipitation_mm": 0.017959173768758774,
"pressure_msl_hpa": 1021.0579833984375,
"surface_pressure_hpa": 1013.093994140625,
"cloud_cover_pct": 64.14817810058594,
"wind_speed_10m_kmh": 8.923616409301758,
"rain_probability": 0.3691202700138092,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.23541118204593658,
"class_1": 0.618242621421814,
"class_2": 0.0044443667866289616,
"class_3": 0.06440076231956482,
"class_4": 0.0741729885339737,
"class_5": 0.003270090091973543,
"class_6": 5.8019890275318176e-05
}
}
],
"sanity": {
"sequence_shape": [
72,
22
],
"finite_features": true
}
}
PS C:\Users\Paulc> python3.12 weather_infer.py --model_dir "C:\Users\Paulc\weather_model\student_distilled" --city Seattle
Warning: unexpected keys while loading checkpoint: ['distill_proj.weight']
{
"city": "Seattle",
"location_id": "1",
"model_location_id": 0,
"data_source": "open-meteo forecast api (past-hours context only)",
"requested_at_utc": "2026-05-08T19:57:47.439276+00:00",
"context": {
"hours": 72,
"start_utc": "2026-05-05T19:00:00+00:00",
"end_utc": "2026-05-08T18:00:00+00:00",
"start_local": "2026-05-05T12:00:00-07:00",
"end_local": "2026-05-08T11:00:00-07:00"
},
"model": {
"encoder_type": "lstm",
"seq_len": 72,
"input_dim": 22,
"num_weather_classes": 7
},
"forecast": [
{
"lead_hours": 1,
"target_utc": "2026-05-08T19:00:00+00:00",
"target_local": "2026-05-08T12:00:00-07:00",
"temperature_2m_c": 13.681238174438477,
"relative_humidity_2m_pct": 69.90876770019531,
"apparent_temperature_c": 11.687149047851562,
"precipitation_mm": 0.0012515264097601175,
"pressure_msl_hpa": 1019.3030395507812,
"surface_pressure_hpa": 1018.5359497070312,
"cloud_cover_pct": 88.76920318603516,
"wind_speed_10m_kmh": 13.126839637756348,
"rain_probability": 0.1457637995481491,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.02097843959927559,
"class_1": 0.9304905533790588,
"class_2": 0.0025352241937071085,
"class_3": 0.03495039418339729,
"class_4": 0.01054275780916214,
"class_5": 0.0004654920194298029,
"class_6": 3.7044908822281286e-05
}
},
{
"lead_hours": 2,
"target_utc": "2026-05-08T20:00:00+00:00",
"target_local": "2026-05-08T13:00:00-07:00",
"temperature_2m_c": 14.486506462097168,
"relative_humidity_2m_pct": 67.52698516845703,
"apparent_temperature_c": 12.55270767211914,
"precipitation_mm": 0.0008608415955677629,
"pressure_msl_hpa": 1019.0198364257812,
"surface_pressure_hpa": 1018.4589233398438,
"cloud_cover_pct": 84.31889343261719,
"wind_speed_10m_kmh": 13.241435050964355,
"rain_probability": 0.19527363777160645,
"weather_class": 1,
"weather_class_name": "class_1",
"weather_class_probabilities": {
"class_0": 0.053049832582473755,
"class_1": 0.8794819712638855,
"class_2": 0.0021074640098959208,
"class_3": 0.0436088852584362,
"class_4": 0.02111213095486164,
"class_5": 0.0005800225771963596,
"class_6": 5.971627979306504e-05
}
},
{
"lead_hours": 3,
"target_utc": "2026-05-08T21:00:00+00:00",
"target_local": "2026-05-08T14:00:00-07:00",
"temperature_2m_c": 15.089279174804688,
"relative_humidity_2m_pct": 65.6773681640625,
"apparent_temperature_c": 13.181319236755371,
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City=Nuuk
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"class_0": 0.014465805143117905,
"class_1": 0.22260957956314087,
"class_2": 0.018675586208701134,
"class_3": 0.15951745212078094,
"class_4": 0.10494350641965866,
"class_5": 0.47975844144821167,
"class_6": 2.9636566978297196e-05
}
},
{
"lead_hours": 11,
"target_utc": "2026-05-09T06:00:00+00:00",
"target_local": "2026-05-09T05:00:00-01:00",
"temperature_2m_c": 3.9785900115966797,
"relative_humidity_2m_pct": 92.22869110107422,
"apparent_temperature_c": 0.24558448791503906,
"precipitation_mm": 0.10196752846240997,
"pressure_msl_hpa": 1006.7659301757812,
"surface_pressure_hpa": 974.8555297851562,
"cloud_cover_pct": 92.74380493164062,
"wind_speed_10m_kmh": 12.71017837524414,
"rain_probability": 0.7881969213485718,
"weather_class": 5,
"weather_class_name": "class_5",
"weather_class_probabilities": {
"class_0": 0.01727476716041565,
"class_1": 0.232261523604393,
"class_2": 0.019182473421096802,
"class_3": 0.15716883540153503,
"class_4": 0.10425136238336563,
"class_5": 0.46981269121170044,
"class_6": 4.833983985008672e-05
}
},
{
"lead_hours": 12,
"target_utc": "2026-05-09T07:00:00+00:00",
"target_local": "2026-05-09T06:00:00-01:00",
"temperature_2m_c": 4.317435264587402,
"relative_humidity_2m_pct": 90.45832824707031,
"apparent_temperature_c": 0.6276102066040039,
"precipitation_mm": 0.09439859539270401,
"pressure_msl_hpa": 1007.0864868164062,
"surface_pressure_hpa": 975.1940307617188,
"cloud_cover_pct": 91.2315673828125,
"wind_speed_10m_kmh": 12.740204811096191,
"rain_probability": 0.7812836170196533,
"weather_class": 5,
"weather_class_name": "class_5",
"weather_class_probabilities": {
"class_0": 0.019589632749557495,
"class_1": 0.23847728967666626,
"class_2": 0.020689163357019424,
"class_3": 0.159035325050354,
"class_4": 0.08879318088293076,
"class_5": 0.47335243225097656,
"class_6": 6.291128374869004e-05
}
}
],
"sanity": {
"sequence_shape": [
72,
22
],
"finite_features": true
}
}
Note
In observed outputs, the model is often within 1°C of the actual value, which is 0.7 more than Hweh-6M.
Furthermore, you can pass locations that are not present in the model’s location embedding table. We’ve observed that the model can generalize to out-of-distribution (OOD) cities, with an estimated accuracy drop of only about 2–5%. However, this figure is an estimate and does not reflect a true ground-truth measurement.
Use Cases
Intended for:
- Backup to API
- Offline forecasting if you have the data
- Research
- Or more simply, for fun
Not intended for:
- Safety-critical forecasting (aviation, emergency response)
- Replacing meteorological or API services
Limitations
- The model is not perfectly accurate and will produce approximate forecasts rather than exact real-world weather conditions.
- Prediction accuracy decreases as the forecast horizon increases up to 12 hours.
- Performance may degrade on unseen or underrepresented geographic regions and climate types.
- The model does not enforce physical laws of atmospheric dynamics and may produce physically inconsistent outputs.
- Forecast quality is sensitive to the quality and completeness of input weather data.
- Rare or extreme weather events are underrepresented in training data and may be poorly predicted.
- Weather class outputs are simplified and do not capture fine-grained meteorological distinctions.
Inference
#!/usr/bin/env python3
from __future__ import annotations
import json
import time
from pathlib import Path
from typing import Any
import numpy as np
import pandas as pd
import requests
import torch
from transformers import AutoConfig, AutoModel
from zoneinfo import ZoneInfo
# ----------------------------
# Change these values here
# ----------------------------
MODEL_ID = r"Harley-ml/Hweh-446k" # HF repo id or local path
CITY = "New York"
SEQUENCE_META_PATH = "Harley-ml/Hweh-446k/weather_sequences.metadata.json"
CONTEXT_HOURS = 72
FORECAST_HOURS = 12
DEVICE = None # "cpu", "cuda", "cuda:0", or None for auto
API_BASE_URL = "https://api.open-meteo.com/v1/forecast"
MAX_RETRIES = 6
REQUEST_TIMEOUT_S = 60
HOURLY_VARS = [
"temperature_2m",
"relative_humidity_2m",
"apparent_temperature",
"precipitation",
"weather_code",
"pressure_msl",
"surface_pressure",
"cloud_cover",
"visibility",
"wind_speed_10m",
"wind_direction_10m",
]
WEATHER_CODE_BUCKETS = 7
TEMP_SCALE = 50.0
HUMIDITY_SCALE = 100.0
WIND_SCALE = 100.0
# ----------------------------
# City metadata (82 locations)
# ----------------------------
CITY_SPECS: dict[str, dict[str, Any]] = {
"Seattle": {"location_id": "1", "latitude": 47.6062, "longitude": -122.3321, "continent": "North America", "climate_tag": "temperate_oceanic", "elevation": 56},
"Portland": {"location_id": "2", "latitude": 45.5152, "longitude": -122.6784, "continent": "North America", "climate_tag": "temperate_oceanic", "elevation": 15},
"San Francisco": {"location_id": "3", "latitude": 37.7749, "longitude": -122.4194, "continent": "North America", "climate_tag": "foggy_mediterranean", "elevation": 16},
"Los Angeles": {"location_id": "4", "latitude": 34.0522, "longitude": -118.2437, "continent": "North America", "climate_tag": "sunny_mediterranean", "elevation": 71},
"Denver": {"location_id": "5", "latitude": 39.7392, "longitude": -104.9903, "continent": "North America", "climate_tag": "semi_arid_highland", "elevation": 1609},
"Chicago": {"location_id": "6", "latitude": 41.8781, "longitude": -87.6298, "continent": "North America", "climate_tag": "humid_continental", "elevation": 181},
"Dallas": {"location_id": "7", "latitude": 32.7767, "longitude": -96.7970, "continent": "North America", "climate_tag": "hot_subhumid", "elevation": 131},
"Atlanta": {"location_id": "8", "latitude": 33.7490, "longitude": -84.3880, "continent": "North America", "climate_tag": "humid_subtropical", "elevation": 320},
"New York": {"location_id": "9", "latitude": 40.7128, "longitude": -74.0060, "continent": "North America", "climate_tag": "humid_subtropical", "elevation": 10},
"Miami": {"location_id": "10", "latitude": 25.7617, "longitude": -80.1918, "continent": "North America", "climate_tag": "tropical_humid", "elevation": 2},
"Phoenix": {"location_id": "11", "latitude": 33.4484, "longitude": -112.0740, "continent": "North America", "climate_tag": "hot_arid", "elevation": 331},
"Salt Lake City": {"location_id": "12", "latitude": 40.7608, "longitude": -111.8910, "continent": "North America", "climate_tag": "semi_arid", "elevation": 1288},
"Anchorage": {"location_id": "13", "latitude": 61.2181, "longitude": -149.9003, "continent": "North America", "climate_tag": "subarctic_snowy", "elevation": 31},
"Minneapolis": {"location_id": "14", "latitude": 44.9778, "longitude": -93.2650, "continent": "North America", "climate_tag": "cold_snowy", "elevation": 264},
"Toronto": {"location_id": "15", "latitude": 43.6532, "longitude": -79.3832, "continent": "North America", "climate_tag": "humid_continental", "elevation": 76},
"Montreal": {"location_id": "16", "latitude": 45.5017, "longitude": -73.5673, "continent": "North America", "climate_tag": "cold_snowy", "elevation": 233},
"Vancouver": {"location_id": "17", "latitude": 49.2827, "longitude": -123.1207, "continent": "North America", "climate_tag": "temperate_oceanic", "elevation": 70},
"Mexico City": {"location_id": "18", "latitude": 19.4326, "longitude": -99.1332, "continent": "North America", "climate_tag": "highland_subtropical", "elevation": 2240},
"Havana": {"location_id": "19", "latitude": 23.1136, "longitude": -82.3666, "continent": "North America", "climate_tag": "tropical_humid", "elevation": 59},
"San Juan": {"location_id": "20", "latitude": 18.4655, "longitude": -66.1057, "continent": "North America", "climate_tag": "tropical_humid", "elevation": 8},
"Lima": {"location_id": "21", "latitude": -12.0464, "longitude": -77.0428, "continent": "South America", "climate_tag": "coastal_arid", "elevation": 154},
"Santiago": {"location_id": "22", "latitude": -33.4489, "longitude": -70.6693, "continent": "South America", "climate_tag": "mediterranean", "elevation": 520},
"Buenos Aires": {"location_id": "23", "latitude": -34.6037, "longitude": -58.3816, "continent": "South America", "climate_tag": "humid_subtropical", "elevation": 25},
"Bogotá": {"location_id": "24", "latitude": 4.7110, "longitude": -74.0721, "continent": "South America", "climate_tag": "highland_cool", "elevation": 2640},
"Quito": {"location_id": "25", "latitude": -0.1807, "longitude": -78.4678, "continent": "South America", "climate_tag": "highland_equatorial", "elevation": 2850},
"Caracas": {"location_id": "26", "latitude": 10.4806, "longitude": -66.9036, "continent": "South America", "climate_tag": "tropical_humid", "elevation": 900},
"Rio de Janeiro": {"location_id": "27", "latitude": -22.9068, "longitude": -43.1729, "continent": "South America", "climate_tag": "tropical_humid", "elevation": 5},
"São Paulo": {"location_id": "28", "latitude": -23.5505, "longitude": -46.6333, "continent": "South America", "climate_tag": "humid_subtropical", "elevation": 760},
"La Paz": {"location_id": "29", "latitude": -16.4897, "longitude": -68.1193, "continent": "South America", "climate_tag": "highland_cold", "elevation": 3640},
"Cusco": {"location_id": "30", "latitude": -13.5319, "longitude": -71.9675, "continent": "South America", "climate_tag": "highland_cool", "elevation": 3399},
"Montevideo": {"location_id": "31", "latitude": -34.9011, "longitude": -56.1645, "continent": "South America", "climate_tag": "temperate_oceanic", "elevation": 43},
"Asunción": {"location_id": "32", "latitude": -25.2637, "longitude": -57.5759, "continent": "South America", "climate_tag": "humid_subtropical", "elevation": 43},
"Manaus": {"location_id": "33", "latitude": -3.1190, "longitude": -60.0217, "continent": "South America", "climate_tag": "tropical_humid", "elevation": 92},
"Recife": {"location_id": "34", "latitude": -8.0476, "longitude": -34.8770, "continent": "South America", "climate_tag": "tropical_coastal", "elevation": 4},
"Punta Arenas": {"location_id": "35", "latitude": -53.1638, "longitude": -70.9171, "continent": "South America", "climate_tag": "cold_windy", "elevation": 34},
"London": {"location_id": "36", "latitude": 51.5074, "longitude": -0.1278, "continent": "Europe", "climate_tag": "temperate_oceanic", "elevation": 11},
"Paris": {"location_id": "37", "latitude": 48.8566, "longitude": 2.3522, "continent": "Europe", "climate_tag": "temperate_oceanic", "elevation": 35},
"Madrid": {"location_id": "38", "latitude": 40.4168, "longitude": -3.7038, "continent": "Europe", "climate_tag": "hot_summer_mediterranean", "elevation": 667},
"Rome": {"location_id": "39", "latitude": 41.9028, "longitude": 12.4964, "continent": "Europe", "climate_tag": "hot_summer_mediterranean", "elevation": 21},
"Berlin": {"location_id": "40", "latitude": 52.52, "longitude": 13.4050, "continent": "Europe", "climate_tag": "temperate_continental", "elevation": 34},
"Stockholm": {"location_id": "41", "latitude": 59.3293, "longitude": 18.0686, "continent": "Europe", "climate_tag": "cold_marine", "elevation": 28},
"Oslo": {"location_id": "42", "latitude": 59.9139, "longitude": 10.7522, "continent": "Europe", "climate_tag": "cold_snowy", "elevation": 23},
"Helsinki": {"location_id": "43", "latitude": 60.1699, "longitude": 24.9384, "continent": "Europe", "climate_tag": "cold_snowy", "elevation": 25},
"Reykjavik": {"location_id": "44", "latitude": 64.1466, "longitude": -21.9426, "continent": "Europe", "climate_tag": "cold_windy", "elevation": 12},
"Kyiv": {"location_id": "45", "latitude": 50.4501, "longitude": 30.5234, "continent": "Europe", "climate_tag": "humid_continental", "elevation": 179},
"Lisbon": {"location_id": "46", "latitude": 38.7223, "longitude": -9.1393, "continent": "Europe", "climate_tag": "sunny_mediterranean", "elevation": 7},
"Athens": {"location_id": "47", "latitude": 37.9838, "longitude": 23.7275, "continent": "Europe", "climate_tag": "sunny_mediterranean", "elevation": 70},
"Zurich": {"location_id": "48", "latitude": 47.3769, "longitude": 8.5417, "continent": "Europe", "climate_tag": "temperate_continental", "elevation": 408},
"Dublin": {"location_id": "49", "latitude": 53.3498, "longitude": -6.2603, "continent": "Europe", "climate_tag": "temperate_oceanic", "elevation": 20},
"Vienna": {"location_id": "50", "latitude": 48.2082, "longitude": 16.3738, "continent": "Europe", "climate_tag": "temperate_continental", "elevation": 171},
"Dubai": {"location_id": "51", "latitude": 25.2048, "longitude": 55.2708, "continent": "Asia", "climate_tag": "hot_arid", "elevation": 16},
"Riyadh": {"location_id": "52", "latitude": 24.7136, "longitude": 46.6753, "continent": "Asia", "climate_tag": "hot_arid", "elevation": 612},
"Delhi": {"location_id": "53", "latitude": 28.7041, "longitude": 77.1025, "continent": "Asia", "climate_tag": "hot_semi_arid", "elevation": 216},
"Mumbai": {"location_id": "54", "latitude": 19.0760, "longitude": 72.8777, "continent": "Asia", "climate_tag": "tropical_humid", "elevation": 14},
"Bangkok": {"location_id": "55", "latitude": 13.7563, "longitude": 100.5018, "continent": "Asia", "climate_tag": "tropical_monsoon", "elevation": 2},
"Singapore": {"location_id": "56", "latitude": 1.3521, "longitude": 103.8198, "continent": "Asia", "climate_tag": "tropical_humid", "elevation": 15},
"Tokyo": {"location_id": "57", "latitude": 35.6762, "longitude": 139.6503, "continent": "Asia", "climate_tag": "humid_subtropical", "elevation": 40},
"Seoul": {"location_id": "58", "latitude": 37.5665, "longitude": 126.9780, "continent": "Asia", "climate_tag": "humid_continental", "elevation": 38},
"Ulaanbaatar": {"location_id": "59", "latitude": 47.8864, "longitude": 106.9057, "continent": "Asia", "climate_tag": "cold_steppe", "elevation": 1350},
"Kathmandu": {"location_id": "60", "latitude": 27.7172, "longitude": 85.3240, "continent": "Asia", "climate_tag": "highland_subtropical", "elevation": 1400},
"Chiang Mai": {"location_id": "61", "latitude": 18.7883, "longitude": 98.9853, "continent": "Asia", "climate_tag": "tropical_seasonal", "elevation": 300},
"Lhasa": {"location_id": "62", "latitude": 29.6520, "longitude": 91.1721, "continent": "Asia", "climate_tag": "high_altitude_cold", "elevation": 3656},
"Jakarta": {"location_id": "63", "latitude": -6.2088, "longitude": 106.8456, "continent": "Asia", "climate_tag": "tropical_humid", "elevation": 8},
"Manila": {"location_id": "64", "latitude": 14.5995, "longitude": 120.9842, "continent": "Asia", "climate_tag": "tropical_humid", "elevation": 16},
"Karachi": {"location_id": "65", "latitude": 24.8607, "longitude": 67.0011, "continent": "Asia", "climate_tag": "hot_arid", "elevation": 10},
"Cairo": {"location_id": "66", "latitude": 30.0444, "longitude": 31.2357, "continent": "Africa", "climate_tag": "hot_arid", "elevation": 23},
"Alexandria": {"location_id": "67", "latitude": 31.2001, "longitude": 29.9187, "continent": "Africa", "climate_tag": "coastal_mediterranean", "elevation": 5},
"Casablanca": {"location_id": "68", "latitude": 33.5731, "longitude": -7.5898, "continent": "Africa", "climate_tag": "coastal_mediterranean", "elevation": 56},
"Marrakech": {"location_id": "69", "latitude": 31.6295, "longitude": -7.9811, "continent": "Africa", "climate_tag": "hot_semi_arid", "elevation": 466},
"Lagos": {"location_id": "70", "latitude": 6.5244, "longitude": 3.3792, "continent": "Africa", "climate_tag": "tropical_humid", "elevation": 41},
"Nairobi": {"location_id": "71", "latitude": -1.2921, "longitude": 36.8219, "continent": "Africa", "climate_tag": "temperate_highland", "elevation": 1795},
"Addis Ababa": {"location_id": "72", "latitude": 8.9806, "longitude": 38.7578, "continent": "Africa", "climate_tag": "temperate_highland", "elevation": 2355},
"Cape Town": {"location_id": "73", "latitude": -33.9249, "longitude": 18.4241, "continent": "Africa", "climate_tag": "mediterranean", "elevation": 25},
"Johannesburg": {"location_id": "74", "latitude": -26.2041, "longitude": 28.0473, "continent": "Africa", "climate_tag": "subtropical_highland", "elevation": 1753},
"Windhoek": {"location_id": "75", "latitude": -22.5609, "longitude": 17.0658, "continent": "Africa", "climate_tag": "semi_arid", "elevation": 1650},
"Accra": {"location_id": "76", "latitude": 5.6037, "longitude": -0.1870, "continent": "Africa", "climate_tag": "tropical_humid", "elevation": 61},
"Kigali": {"location_id": "77", "latitude": -1.9441, "longitude": 30.0619, "continent": "Africa", "climate_tag": "highland_tropical", "elevation": 1567},
"Tunis": {"location_id": "78", "latitude": 36.8065, "longitude": 10.1815, "continent": "Africa", "climate_tag": "mediterranean", "elevation": 4},
"Dakar": {"location_id": "79", "latitude": -14.7167, "longitude": -17.4677, "continent": "Africa", "climate_tag": "hot_coastal", "elevation": 25},
"Mombasa": {"location_id": "80", "latitude": -4.0435, "longitude": 39.6682, "continent": "Africa", "climate_tag": "tropical_coastal", "elevation": 17},
"Sydney": {"location_id": "81", "latitude": -33.8688, "longitude": 151.2093, "continent": "Oceania", "climate_tag": "humid_subtropical", "elevation": 58},
"Melbourne": {"location_id": "82", "latitude": -37.8136, "longitude": 144.9631, "continent": "Oceania", "climate_tag": "temperate_oceanic", "elevation": 31},
}
CITY_TIMEZONES: dict[str, str] = {
"Seattle": "America/Los_Angeles",
"Portland": "America/Los_Angeles",
"San Francisco": "America/Los_Angeles",
"Los Angeles": "America/Los_Angeles",
"Denver": "America/Denver",
"Chicago": "America/Chicago",
"Dallas": "America/Chicago",
"Atlanta": "America/New_York",
"New York": "America/New_York",
"Miami": "America/New_York",
"Phoenix": "America/Phoenix",
"Salt Lake City": "America/Denver",
"Anchorage": "America/Anchorage",
"Minneapolis": "America/Chicago",
"Toronto": "America/Toronto",
"Montreal": "America/Toronto",
"Vancouver": "America/Vancouver",
"Mexico City": "America/Mexico_City",
"Havana": "America/Havana",
"San Juan": "America/Puerto_Rico",
"Lima": "America/Lima",
"Santiago": "America/Santiago",
"Buenos Aires": "America/Argentina/Buenos_Aires",
"Bogotá": "America/Bogota",
"Quito": "America/Guayaquil",
"Caracas": "America/Caracas",
"Rio de Janeiro": "America/Sao_Paulo",
"São Paulo": "America/Sao_Paulo",
"La Paz": "America/La_Paz",
"Cusco": "America/Lima",
"Montevideo": "America/Montevideo",
"Asunción": "America/Asuncion",
"Manaus": "America/Manaus",
"Recife": "America/Recife",
"Punta Arenas": "America/Punta_Arenas",
"London": "Europe/London",
"Paris": "Europe/Paris",
"Madrid": "Europe/Madrid",
"Rome": "Europe/Rome",
"Berlin": "Europe/Berlin",
"Stockholm": "Europe/Stockholm",
"Oslo": "Europe/Oslo",
"Helsinki": "Europe/Helsinki",
"Reykjavik": "Atlantic/Reykjavik",
"Kyiv": "Europe/Kyiv",
"Lisbon": "Europe/Lisbon",
"Athens": "Europe/Athens",
"Zurich": "Europe/Zurich",
"Dublin": "Europe/Dublin",
"Vienna": "Europe/Vienna",
"Dubai": "Asia/Dubai",
"Riyadh": "Asia/Riyadh",
"Delhi": "Asia/Kolkata",
"Mumbai": "Asia/Kolkata",
"Bangkok": "Asia/Bangkok",
"Singapore": "Asia/Singapore",
"Tokyo": "Asia/Tokyo",
"Seoul": "Asia/Seoul",
"Ulaanbaatar": "Asia/Ulaanbaatar",
"Kathmandu": "Asia/Kathmandu",
"Chiang Mai": "Asia/Bangkok",
"Lhasa": "Asia/Shanghai",
"Jakarta": "Asia/Jakarta",
"Manila": "Asia/Manila",
"Karachi": "Asia/Karachi",
"Cairo": "Africa/Cairo",
"Alexandria": "Africa/Cairo",
"Casablanca": "Africa/Casablanca",
"Marrakech": "Africa/Casablanca",
"Lagos": "Africa/Lagos",
"Nairobi": "Africa/Nairobi",
"Addis Ababa": "Africa/Addis_Ababa",
"Cape Town": "Africa/Johannesburg",
"Johannesburg": "Africa/Johannesburg",
"Windhoek": "Africa/Windhoek",
"Accra": "Africa/Accra",
"Kigali": "Africa/Kigali",
"Tunis": "Africa/Tunis",
"Dakar": "Africa/Dakar",
"Mombasa": "Africa/Nairobi",
"Sydney": "Australia/Sydney",
"Melbourne": "Australia/Melbourne",
}
# ----------------------------
# Helpers
# ----------------------------
def weather_code_to_bucket(code) -> int:
if code is None:
return 1
try:
if pd.isna(code):
return 1
except Exception:
pass
code = int(code)
if code == 0:
return 0
if code in (1, 2, 3):
return 1
if code in (45, 48):
return 2
if code in (51, 53, 55, 56, 57):
return 3
if code in (61, 63, 65, 66, 67, 80, 81, 82):
return 4
if code in (71, 73, 75, 77, 85, 86):
return 5
if code in (95, 96, 99):
return 6
return 1
def cyc(x: np.ndarray, period: float) -> tuple[np.ndarray, np.ndarray]:
angle = 2.0 * np.pi * (x / period)
return np.sin(angle), np.cos(angle)
def clamp_array(x: np.ndarray, lo: float | None = None, hi: float | None = None) -> np.ndarray:
return np.clip(x, lo, hi)
def request_with_backoff(session: requests.Session, url: str, params: dict[str, Any]) -> dict[str, Any]:
last_exc: Exception | None = None
for attempt in range(MAX_RETRIES):
try:
resp = session.get(url, params=params, timeout=REQUEST_TIMEOUT_S)
if resp.status_code == 429:
retry_after = resp.headers.get("Retry-After")
sleep_s = float(retry_after) if retry_after else min(60.0, 2**attempt)
print(f"Rate limited. Sleeping {sleep_s:.1f}s and retrying.", flush=True)
time.sleep(sleep_s)
continue
resp.raise_for_status()
return resp.json()
except Exception as e:
last_exc = e
sleep_s = min(60.0, 2**attempt)
print(f"Request failed: {e}. Sleeping {sleep_s:.1f}s and retrying.", flush=True)
time.sleep(sleep_s)
raise RuntimeError(f"Failed after {MAX_RETRIES} retries: {params}") from last_exc
def load_sequence_meta(path: str) -> dict[str, Any]:
p = Path(path)
if not p.exists():
return {"location_to_id": {}}
with open(p, "r", encoding="utf-8") as f:
meta = json.load(f)
meta.setdefault("location_to_id", {})
return meta
def load_model():
config = AutoConfig.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModel.from_pretrained(MODEL_ID, config=config, trust_remote_code=True)
model.eval()
return model, config
def fetch_recent_history(city: str, context_hours: int) -> pd.DataFrame:
if city not in CITY_SPECS:
raise ValueError(f"Unknown city: {city}")
spec = CITY_SPECS[city]
session = requests.Session()
session.headers.update({"User-Agent": "Mozilla/5.0"})
params = {
"latitude": spec["latitude"],
"longitude": spec["longitude"],
"hourly": ",".join(HOURLY_VARS),
"timezone": "UTC",
"temperature_unit": "celsius",
"wind_speed_unit": "kmh",
"precipitation_unit": "mm",
"past_hours": int(context_hours) + 2,
"forecast_hours": 0,
}
data = request_with_backoff(session, API_BASE_URL, params=params)
hourly = data.get("hourly", {})
if "time" not in hourly:
raise ValueError(f"No hourly data returned for {city}: {data}")
df = pd.DataFrame(hourly)
if df.empty:
raise ValueError(f"Empty hourly response for {city}.")
df["time"] = pd.to_datetime(df["time"], errors="coerce", utc=True)
df = df.dropna(subset=["time"]).sort_values("time").drop_duplicates(subset=["time"]).reset_index(drop=True)
needed = HOURLY_VARS
missing = [c for c in needed if c not in df.columns]
if missing:
raise ValueError(f"Missing hourly columns in API response: {missing}")
for c in needed:
df[c] = pd.to_numeric(df[c], errors="coerce")
df["weather_code"] = df["weather_code"].fillna(1)
df["precipitation"] = df["precipitation"].fillna(0.0)
for c in [
"temperature_2m",
"relative_humidity_2m",
"apparent_temperature",
"precipitation",
"pressure_msl",
"surface_pressure",
"cloud_cover",
"visibility",
"wind_speed_10m",
"wind_direction_10m",
]:
df[c] = df[c].interpolate(limit_direction="both").ffill().bfill()
now_utc = pd.Timestamp.now(tz="UTC")
df = df[df["time"] <= now_utc].copy()
if len(df) < context_hours:
raise ValueError(f"Not enough observed rows: got {len(df)}, need {context_hours}")
return df.tail(context_hours).reset_index(drop=True)
def build_single_sequence(df: pd.DataFrame) -> np.ndarray:
hour = df["time"].dt.hour.to_numpy()
doy = df["time"].dt.dayofyear.to_numpy()
hour_sin, hour_cos = cyc(hour.astype(float), 24.0)
doy_sin, doy_cos = cyc(doy.astype(float), 365.25)
temp = np.nan_to_num(df["temperature_2m"].astype(float).to_numpy(), nan=0.0)
humidity = np.nan_to_num(df["relative_humidity_2m"].astype(float).to_numpy(), nan=0.0)
apparent = np.nan_to_num(df["apparent_temperature"].astype(float).to_numpy(), nan=0.0)
precip = np.nan_to_num(df["precipitation"].astype(float).to_numpy(), nan=0.0)
pressure = np.nan_to_num(df["pressure_msl"].astype(float).to_numpy(), nan=0.0)
surface_pressure = np.nan_to_num(df["surface_pressure"].astype(float).to_numpy(), nan=0.0)
cloud_cover = np.nan_to_num(df["cloud_cover"].astype(float).to_numpy(), nan=0.0)
visibility = np.nan_to_num(df["visibility"].astype(float).to_numpy(), nan=0.0)
wind = np.nan_to_num(df["wind_speed_10m"].astype(float).to_numpy(), nan=0.0)
wind_dir = np.nan_to_num(df["wind_direction_10m"].astype(float).to_numpy(), nan=0.0)
humidity = clamp_array(humidity, 0.0, 100.0)
cloud_cover = clamp_array(cloud_cover, 0.0, 100.0)
precip = clamp_array(precip, 0.0, None)
wind = clamp_array(wind, 0.0, None)
visibility = clamp_array(visibility, 0.0, None)
wind_dir_sin, wind_dir_cos = cyc(wind_dir, 360.0)
weather_bucket = df["weather_code"].fillna(1).apply(weather_code_to_bucket).to_numpy(dtype=np.int64)
rows = []
for i in range(len(df)):
wc_oh = np.zeros(WEATHER_CODE_BUCKETS, dtype=np.float32)
wc_oh[weather_bucket[i]] = 1.0
row = np.concatenate(
[
np.array(
[
temp[i] / TEMP_SCALE,
humidity[i] / HUMIDITY_SCALE,
apparent[i] / TEMP_SCALE,
np.log1p(max(precip[i], 0.0)) / 3.0,
pressure[i] / 1100.0,
surface_pressure[i] / 1100.0,
cloud_cover[i] / 100.0,
visibility[i] / 50000.0,
wind[i] / WIND_SCALE,
wind_dir_sin[i],
wind_dir_cos[i],
hour_sin[i],
hour_cos[i],
doy_sin[i],
doy_cos[i],
],
dtype=np.float32,
),
wc_oh,
]
)
rows.append(row)
seq = np.asarray(rows, dtype=np.float32)
if not np.isfinite(seq).all():
bad = np.argwhere(~np.isfinite(seq))
raise ValueError(f"Non-finite values remain in sequence at positions like: {bad[:10].tolist()}")
return seq
def to_iso(ts: pd.Timestamp, tz_name: str | None = None) -> str:
if tz_name:
try:
return ts.tz_convert(ZoneInfo(tz_name)).isoformat()
except Exception:
pass
return ts.isoformat()
def get_logits(out):
if isinstance(out, dict) and "logits" in out:
return out["logits"]
if hasattr(out, "logits"):
return out.logits
return out
def resolve_location_index(seq_meta: dict[str, Any], city_location_id: str) -> int:
location_to_id = seq_meta.get("location_to_id", {})
if city_location_id in location_to_id:
return int(location_to_id[city_location_id])
try:
as_int = int(city_location_id)
if as_int in location_to_id:
return int(location_to_id[as_int])
if str(as_int) in location_to_id:
return int(location_to_id[str(as_int)])
except Exception:
pass
for unk_key in ("UNK", "<UNK>", "unknown", "UNKNOWN"):
if unk_key in location_to_id:
return int(location_to_id[unk_key])
return 0
def predict():
seq_meta = load_sequence_meta(SEQUENCE_META_PATH)
model, config = load_model()
if CITY not in CITY_SPECS:
raise ValueError(f"Unknown city: {CITY}")
if CONTEXT_HOURS <= 0:
raise ValueError("CONTEXT_HOURS must be > 0")
if hasattr(config, "seq_len") and int(config.seq_len) != CONTEXT_HOURS:
raise ValueError(f"Set CONTEXT_HOURS to {int(config.seq_len)} for this model.")
city_spec = CITY_SPECS[CITY]
city_tz = CITY_TIMEZONES.get(CITY, "UTC")
model_location_id = resolve_location_index(seq_meta, str(city_spec["location_id"]))
df = fetch_recent_history(CITY, CONTEXT_HOURS)
seq = build_single_sequence(df)
X = torch.from_numpy(seq).unsqueeze(0)
loc = torch.tensor([model_location_id], dtype=torch.long)
target_device = torch.device(
DEVICE if DEVICE else ("cuda" if torch.cuda.is_available() else "cpu")
)
model = model.to(target_device)
X = X.to(target_device)
loc = loc.to(target_device)
weather_class_names = getattr(config, "weather_class_names", None)
if not weather_class_names:
weather_class_names = [f"class_{i}" for i in range(int(getattr(config, "num_weather_classes", 7)))]
with torch.no_grad():
out = model(X=X, location_id=loc)
logits = get_logits(out)
(
temp_pred,
humidity_pred,
apparent_pred,
precip_pred,
sea_level_pressure_pred,
surface_pressure_pred,
cloud_cover_pred,
wind_pred,
wind_dir_sin_pred,
wind_dir_cos_pred,
rain_logit,
weather_logits,
) = logits
temp_pred = temp_pred.squeeze(0).detach().cpu().numpy()
humidity_pred = humidity_pred.squeeze(0).detach().cpu().numpy()
apparent_pred = apparent_pred.squeeze(0).detach().cpu().numpy()
precip_pred = precip_pred.squeeze(0).detach().cpu().numpy()
sea_level_pressure_pred = sea_level_pressure_pred.squeeze(0).detach().cpu().numpy()
surface_pressure_pred = surface_pressure_pred.squeeze(0).detach().cpu().numpy()
cloud_cover_pred = cloud_cover_pred.squeeze(0).detach().cpu().numpy()
wind_pred = wind_pred.squeeze(0).detach().cpu().numpy()
rain_prob = torch.sigmoid(rain_logit).squeeze(0).detach().cpu().numpy()
weather_probs = torch.softmax(weather_logits, dim=-1).squeeze(0).detach().cpu().numpy()
weather_idx = np.argmax(weather_probs, axis=-1).astype(np.int64)
humidity_pred = np.clip(humidity_pred, 0.0, 100.0)
cloud_cover_pred = np.clip(cloud_cover_pred, 0.0, 100.0)
precip_pred = np.clip(precip_pred, 0.0, None)
wind_pred = np.clip(wind_pred, 0.0, None)
rain_prob = np.clip(rain_prob, 0.0, 1.0)
context_start = df["time"].iloc[0]
context_end = df["time"].iloc[-1]
requested_at_utc = pd.Timestamp.now(tz="UTC")
horizon = min(
int(FORECAST_HOURS),
int(temp_pred.shape[0]),
int(humidity_pred.shape[0]),
int(weather_idx.shape[0]),
)
forecast = []
for lead in range(1, horizon + 1):
target_time = context_end + pd.Timedelta(hours=lead)
idx = lead - 1
w_idx = int(weather_idx[idx])
forecast.append(
{
"lead_hours": lead,
"target_utc": target_time.isoformat(),
"target_local": to_iso(target_time, city_tz),
"temperature_2m_c": float(temp_pred[idx]),
"relative_humidity_2m_pct": float(humidity_pred[idx]),
"apparent_temperature_c": float(apparent_pred[idx]),
"precipitation_mm": float(precip_pred[idx]),
"pressure_msl_hpa": float(sea_level_pressure_pred[idx]),
"surface_pressure_hpa": float(surface_pressure_pred[idx]),
"cloud_cover_pct": float(cloud_cover_pred[idx]),
"wind_speed_10m_kmh": float(wind_pred[idx]),
"rain_probability": float(rain_prob[idx]),
"weather_class": w_idx,
"weather_class_name": weather_class_names[w_idx] if w_idx < len(weather_class_names) else f"class_{w_idx}",
"weather_class_probabilities": {
name: float(prob) for name, prob in zip(weather_class_names, weather_probs[idx])
},
}
)
result = {
"city": CITY,
"location_id": str(city_spec["location_id"]),
"model_location_id": int(model_location_id),
"data_source": "open-meteo forecast api (past-hours context only)",
"requested_at_utc": requested_at_utc.isoformat(),
"context": {
"hours": int(len(df)),
"start_utc": context_start.isoformat(),
"end_utc": context_end.isoformat(),
"start_local": to_iso(context_start, city_tz),
"end_local": to_iso(context_end, city_tz),
},
"model": {
"model_id": MODEL_ID,
"encoder_type": getattr(config, "encoder_type", None),
"seq_len": int(getattr(config, "seq_len", CONTEXT_HOURS)),
"input_dim": int(getattr(config, "input_dim", seq.shape[1])),
"num_weather_classes": int(getattr(config, "num_weather_classes", len(weather_class_names))),
},
"forecast": forecast,
"sanity": {
"sequence_shape": list(seq.shape),
"finite_features": bool(np.isfinite(seq).all()),
},
}
print(json.dumps(result, indent=2))
if __name__ == "__main__":
predict()
Citation
@misc{distilhweh-446k,
title = {DistilHweh-446k: Knowledge Distillation in Short-Term Multivariate Weather Forecasting},
author = {Paul Courneya; Harley-ml},
year = {2026},
url = {https://huggingface.co/Harley-ml/DistilHweh-446k}
}
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