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Ciroc0
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dmi-aarhus-predictions

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Tags:
weather
aarhus
denmark
predictions
forecast
machine-learning
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Auto-converted to Parquet Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
target_timestamp: timestamp[ns, tz=Europe/Copenhagen]
reference_time: timestamp[ns, tz=Europe/Copenhagen]
lead_time_hours: int64
lead_bucket: string
dmi_temperature_2m_pred: double
dmi_apparent_temperature_pred: double
dmi_relative_humidity_2m_pred: int64
dmi_dew_point_2m_pred: double
dmi_pressure_msl_pred: double
dmi_cloud_cover_pred: int64
dmi_cloud_cover_low_pred: int64
dmi_cloud_cover_mid_pred: int64
dmi_cloud_cover_high_pred: int64
dmi_precipitation_pred: double
dmi_rain_pred: double
dmi_snowfall_pred: double
dmi_precipitation_probability_pred: int64
dmi_windspeed_10m_pred: double
dmi_winddirection_10m_pred: int64
dmi_windgusts_10m_pred: double
dmi_visibility_pred: double
dmi_shortwave_radiation_pred: double
dmi_direct_radiation_pred: double
dmi_weather_code_pred: int64
dmi_cape_pred: double
forecast_wind_u: double
forecast_wind_v: double
prediction_made_at: timestamp[us, tz=Europe/Copenhagen]
city: string
ml_rain_amount: double
verified: bool
actual_temp: double
actual_wind_speed: double
actual_wind_gust: double
actual_precipitation: double
hour: double
month: double
day_of_year: double
hour_sin: double
hour_cos: double
month_sin: double
month_cos: double
dmi_temperature_2m_pred_run_delta: double
dmi_windspeed_10m_pred_run_delta: double
dmi_windgusts_10m_pred_run_delta: double
dmi_precipitation_pred_run_delta: double
dmi_pressure_msl_pred_run_delta: double
dmi_relative_humidity_2m_pred_run_delta: double
actual_rain: double
actual_rain_event: double
actual_rain_amount: double
ml_temp: double
ml_wind_speed: double
ml_wind_gust: double
ml_rain_prob: double
observation_context_timestamp: timestamp[us, tz=Europe/Copenhagen]
obs_temp_lag_1h: double
obs_temp_mean_3h: double
obs_temp_mean_6h: double
obs_wind_lag_1h: double
obs_wind_mean_3h: double
obs_wind_mean_6h: double
obs_wind_u_lag_1h: double
obs_wind_u_mean_3h: double
obs_wind_v_lag_1h: double
obs_wind_v_mean_3h: double
obs_pressure_lag_1h: double
obs_pressure_mean_3h: double
obs_humidity_lag_1h: double
obs_humidity_mean_3h: double
obs_precip_lag_1h: double
obs_precip_sum_3h: double
obs_precip_sum_6h: double
obs_precip_sum_12h: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 10575
to
{'target_timestamp': Value('timestamp[ns, tz=Europe/Copenhagen]'), 'reference_time': Value('timestamp[ns, tz=Europe/Copenhagen]'), 'lead_time_hours': Value('int64'), 'lead_bucket': Value('string'), 'dmi_temperature_2m_pred': Value('float64'), 'dmi_apparent_temperature_pred': Value('float64'), 'dmi_relative_humidity_2m_pred': Value('int64'), 'dmi_dew_point_2m_pred': Value('float64'), 'dmi_pressure_msl_pred': Value('float64'), 'dmi_cloud_cover_pred': Value('int64'), 'dmi_cloud_cover_low_pred': Value('int64'), 'dmi_cloud_cover_mid_pred': Value('int64'), 'dmi_cloud_cover_high_pred': Value('int64'), 'dmi_precipitation_pred': Value('float64'), 'dmi_rain_pred': Value('float64'), 'dmi_snowfall_pred': Value('float64'), 'dmi_precipitation_probability_pred': Value('int64'), 'dmi_windspeed_10m_pred': Value('float64'), 'dmi_winddirection_10m_pred': Value('int64'), 'dmi_windgusts_10m_pred': Value('float64'), 'dmi_visibility_pred': Value('float64'), 'dmi_shortwave_radiation_pred': Value('float64'), 'dmi_direct_radiation_pred': Value('float64'), 'dmi_weather_code_pred': Value('int64'), 'dmi_cape_pred': Value('float64'), 'forecast_wind_u': Value('float64'), 'forecast_wind_v': Value('float64'), 'prediction_made_at': Value('timestamp[us, tz=Europe/Copenhagen]'), 'city': Value('string'), 'ml_rain_amount': Value('float64'), 'verified': Value('bool'), 'actual_temp': Value('float64'), 'actual_wind_speed': Value('float64'), 'actual_wind_gust': Value('float64'), 'actual_precipitation': Value('float64'), 'hour': Value('float64'), 'month': Value('float64'), 'day_of_year': Value('float64'), 'hour_sin': Value('float64'), 'hour_cos': Value('float64'), 'month_sin': Value('float64'), 'month_cos': Value('float64'), 'dmi_temperature_2m_pred_run_delta': Value('float64'), 'dmi_windspeed_10m_pred_run_delta': Value('float64'), 'dmi_windgusts_10m_pred_run_delta': Value('float64'), 'dmi_precipitation_pred_run_delta': Value('float64'), 'dmi_pressure_msl_pred_run_delta': Value('float64'), 'dmi_relative_humidity_2m_pred_run_delta': Value('float64'), 'actual_rain': Value('float64'), 'actual_rain_event': Value('float64'), 'actual_rain_amount': Value('float64'), 'ml_temp': Value('float64'), 'ml_wind_speed': Value('float64'), 'ml_wind_gust': Value('float64'), 'ml_rain_prob': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2567, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2102, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2125, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 479, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 380, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 209, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 147, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              target_timestamp: timestamp[ns, tz=Europe/Copenhagen]
              reference_time: timestamp[ns, tz=Europe/Copenhagen]
              lead_time_hours: int64
              lead_bucket: string
              dmi_temperature_2m_pred: double
              dmi_apparent_temperature_pred: double
              dmi_relative_humidity_2m_pred: int64
              dmi_dew_point_2m_pred: double
              dmi_pressure_msl_pred: double
              dmi_cloud_cover_pred: int64
              dmi_cloud_cover_low_pred: int64
              dmi_cloud_cover_mid_pred: int64
              dmi_cloud_cover_high_pred: int64
              dmi_precipitation_pred: double
              dmi_rain_pred: double
              dmi_snowfall_pred: double
              dmi_precipitation_probability_pred: int64
              dmi_windspeed_10m_pred: double
              dmi_winddirection_10m_pred: int64
              dmi_windgusts_10m_pred: double
              dmi_visibility_pred: double
              dmi_shortwave_radiation_pred: double
              dmi_direct_radiation_pred: double
              dmi_weather_code_pred: int64
              dmi_cape_pred: double
              forecast_wind_u: double
              forecast_wind_v: double
              prediction_made_at: timestamp[us, tz=Europe/Copenhagen]
              city: string
              ml_rain_amount: double
              verified: bool
              actual_temp: double
              actual_wind_speed: double
              actual_wind_gust: double
              actual_precipitation: double
              hour: double
              month: double
              day_of_year: double
              hour_sin: double
              hour_cos: double
              month_sin: double
              month_cos: double
              dmi_temperature_2m_pred_run_delta: double
              dmi_windspeed_10m_pred_run_delta: double
              dmi_windgusts_10m_pred_run_delta: double
              dmi_precipitation_pred_run_delta: double
              dmi_pressure_msl_pred_run_delta: double
              dmi_relative_humidity_2m_pred_run_delta: double
              actual_rain: double
              actual_rain_event: double
              actual_rain_amount: double
              ml_temp: double
              ml_wind_speed: double
              ml_wind_gust: double
              ml_rain_prob: double
              observation_context_timestamp: timestamp[us, tz=Europe/Copenhagen]
              obs_temp_lag_1h: double
              obs_temp_mean_3h: double
              obs_temp_mean_6h: double
              obs_wind_lag_1h: double
              obs_wind_mean_3h: double
              obs_wind_mean_6h: double
              obs_wind_u_lag_1h: double
              obs_wind_u_mean_3h: double
              obs_wind_v_lag_1h: double
              obs_wind_v_mean_3h: double
              obs_pressure_lag_1h: double
              obs_pressure_mean_3h: double
              obs_humidity_lag_1h: double
              obs_humidity_mean_3h: double
              obs_precip_lag_1h: double
              obs_precip_sum_3h: double
              obs_precip_sum_6h: double
              obs_precip_sum_12h: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 10575
              to
              {'target_timestamp': Value('timestamp[ns, tz=Europe/Copenhagen]'), 'reference_time': Value('timestamp[ns, tz=Europe/Copenhagen]'), 'lead_time_hours': Value('int64'), 'lead_bucket': Value('string'), 'dmi_temperature_2m_pred': Value('float64'), 'dmi_apparent_temperature_pred': Value('float64'), 'dmi_relative_humidity_2m_pred': Value('int64'), 'dmi_dew_point_2m_pred': Value('float64'), 'dmi_pressure_msl_pred': Value('float64'), 'dmi_cloud_cover_pred': Value('int64'), 'dmi_cloud_cover_low_pred': Value('int64'), 'dmi_cloud_cover_mid_pred': Value('int64'), 'dmi_cloud_cover_high_pred': Value('int64'), 'dmi_precipitation_pred': Value('float64'), 'dmi_rain_pred': Value('float64'), 'dmi_snowfall_pred': Value('float64'), 'dmi_precipitation_probability_pred': Value('int64'), 'dmi_windspeed_10m_pred': Value('float64'), 'dmi_winddirection_10m_pred': Value('int64'), 'dmi_windgusts_10m_pred': Value('float64'), 'dmi_visibility_pred': Value('float64'), 'dmi_shortwave_radiation_pred': Value('float64'), 'dmi_direct_radiation_pred': Value('float64'), 'dmi_weather_code_pred': Value('int64'), 'dmi_cape_pred': Value('float64'), 'forecast_wind_u': Value('float64'), 'forecast_wind_v': Value('float64'), 'prediction_made_at': Value('timestamp[us, tz=Europe/Copenhagen]'), 'city': Value('string'), 'ml_rain_amount': Value('float64'), 'verified': Value('bool'), 'actual_temp': Value('float64'), 'actual_wind_speed': Value('float64'), 'actual_wind_gust': Value('float64'), 'actual_precipitation': Value('float64'), 'hour': Value('float64'), 'month': Value('float64'), 'day_of_year': Value('float64'), 'hour_sin': Value('float64'), 'hour_cos': Value('float64'), 'month_sin': Value('float64'), 'month_cos': Value('float64'), 'dmi_temperature_2m_pred_run_delta': Value('float64'), 'dmi_windspeed_10m_pred_run_delta': Value('float64'), 'dmi_windgusts_10m_pred_run_delta': Value('float64'), 'dmi_precipitation_pred_run_delta': Value('float64'), 'dmi_pressure_msl_pred_run_delta': Value('float64'), 'dmi_relative_humidity_2m_pred_run_delta': Value('float64'), 'actual_rain': Value('float64'), 'actual_rain_event': Value('float64'), 'actual_rain_amount': Value('float64'), 'ml_temp': Value('float64'), 'ml_wind_speed': Value('float64'), 'ml_wind_gust': Value('float64'), 'ml_rain_prob': Value('float64')}
              because column names don't match

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DMI Aarhus Predictions

Prediction and frontend contract dataset for the Aarhus weather pipeline. Maintained by Ciroc0.

Primary files

File Purpose Produced by
predictions_latest.parquet Current future + verified prediction store dmi-collector
frontend_snapshot.json Primary integration contract for the Vercel frontend dmi-collector

Compatibility files

File Status Notes
predictions.parquet Legacy Still read by compatibility code
history_snapshot.json Legacy Only used by older frontend fallback logic

Update pattern

  • predictions_latest.parquet is rewritten on scheduled prediction runs
  • verification updates actual columns once the target hour is in the past
  • frontend_snapshot.json is regenerated after prediction and verification writes

Schema highlights for predictions_latest.parquet

  • target_timestamp
  • reference_time
  • lead_time_hours
  • lead_bucket
  • prediction_made_at
  • city
  • verified
  • dmi_temperature_2m_pred
  • dmi_windspeed_10m_pred
  • dmi_windgusts_10m_pred
  • dmi_precipitation_probability_pred
  • dmi_precipitation_pred
  • ml_temp
  • ml_wind_speed
  • ml_wind_gust
  • ml_rain_prob
  • ml_rain_amount
  • actual_temp
  • actual_wind_speed
  • actual_wind_gust
  • actual_precipitation
  • actual_rain_event
  • actual_rain_amount

Snapshot contract

frontend_snapshot.json is the main public-facing contract and contains:

  • location metadata
  • generated timestamp
  • target labels and explanations
  • current weather block
  • 48-hour forecast rows
  • historical backtest payload
  • verification metrics
  • lead-bucket summary
  • feature importance
  • model info
  • alert rows

Attribution

This dataset is released under CC BY 4.0.

Please preserve attribution to:

  • Ciroc0
  • Open-Meteo - https://open-meteo.com
  • DMI / DMI HARMONIE
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
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Number of rows:
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