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Darwin-Prediction-Weather-WB2

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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:    TypeError
Message:      Couldn't cast array of type
struct<dtype: string, units: string>
to
{'_FillValue': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2315, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2161, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<dtype: string, units: string>
              to
              {'_FillValue': Value('string')}

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Darwin-Prediction-Weather: WeatherBench 2 forecasts (2020)

Darwin-Prediction-Weather is VIDRAFT's forecast fusion and learned-correction engine. This repository holds its global deterministic forecasts for the WeatherBench 2 evaluation year 2020, in the WeatherBench 2 Zarr layout.

On the WeatherBench 2 deterministic protocol (RMSE vs ERA5, global, latitude-weighted, 1.5 degree, 2020) Darwin-Prediction-Weather records the lowest error in all 15 evaluated cells, ahead of every model on the official scorecard.

Variable	1 d	3 d	5 d	7 d	10 d
2m temperature [K]	0.468	0.743	1.107	1.531	1.984
best on official scorecard	0.488	0.786	1.125	1.552	2.022
Z500 [m2/s2]	33.7	108.5	240.6	400.3	595.4
best on official scorecard	36.8	113.8	250.7	415.1	606.5
T850 [K]	0.503	0.901	1.406	2.000	2.699
best on official scorecard	0.518	0.922	1.435	2.041	2.737

Official bests are WeatherMesh4 (1 to 7 days) and GenCast ensemble mean (10 days), read from gs://weatherbench2/benchmark_results. The same scoring code reproduces the published gencast_mean scores to within 0.4 percent.

File	Variable
`darwin_prediction_weather_2020_240x121_2m_temperature.zarr`	2m_temperature
`darwin_prediction_weather_2020_240x121_geopotential_500.zarr`	geopotential, level 500
`darwin_prediction_weather_2020_240x121_temperature_850.zarr`	temperature, level 850

Dimensions: time (702 initializations, 00/12 UTC, 2020), prediction_timedelta (1, 3, 5, 7, 10 days), latitude (121), longitude (240).

import xarray as xr
ds = xr.open_zarr("darwin_prediction_weather_2020_240x121_geopotential_500.zarr")

Method

For every initialization the engine produces one global forecast by gridpoint bias correction and skill-weighted fusion of its input forecasts. All correction fields and fusion weights use only data whose verification time precedes the initialization time. The fusion strategy per variable and lead time was selected on 2018 data and frozen before the 2020 evaluation.

Inputs and license

Inputs: WeatherBench 2 public forecast archives (GenCast ensemble mean, IFS ENS mean, GraphCast, Pangu-Weather, FuXi, IFS HRES). Ground truth: ERA5 (ECMWF / Copernicus Climate Change Service). Credit to the respective providers under their original licenses. This dataset is released under CC BY-NC-SA 4.0.

VIDRAFT. Contact: vidraft@mail.vidraft.net