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FINAL-Bench
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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'observed_era5_precip_mm'}) and 1 missing columns ({'observed_era5_tmax_c'}).
This happened while the csv dataset builder was generating data using
hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive/forecasts_vs_observed_2026_precip.csv (at revision 5b437e95226829cbc2a8fa1841440db5b522d3be), ['hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive@5b437e95226829cbc2a8fa1841440db5b522d3be/forecasts_vs_observed_2026.csv', 'hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive@5b437e95226829cbc2a8fa1841440db5b522d3be/forecasts_vs_observed_2026_precip.csv', 'hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive@5b437e95226829cbc2a8fa1841440db5b522d3be/forecasts_vs_observed_2026_tmin.csv', 'hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive@5b437e95226829cbc2a8fa1841440db5b522d3be/forecasts_vs_observed_2026_wind.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
pa_table = table_cast(pa_table, self._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 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
date: string
city: string
lead_days: int64
observed_era5_precip_mm: double
ecmwf_ifs025: double
ecmwf_aifs025_single: double
gfs_seamless: double
ncep_aigfs025: double
icon_seamless: double
jma_seamless: double
gem_seamless: double
metno_seamless: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1783
to
{'date': Value('string'), 'city': Value('string'), 'lead_days': Value('int64'), 'observed_era5_tmax_c': Value('float64'), 'ecmwf_ifs025': Value('float64'), 'ecmwf_aifs025_single': Value('float64'), 'gfs_seamless': Value('float64'), 'ncep_aigfs025': Value('float64'), 'icon_seamless': Value('float64'), 'jma_seamless': Value('float64'), 'gem_seamless': Value('float64'), 'metno_seamless': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'observed_era5_precip_mm'}) and 1 missing columns ({'observed_era5_tmax_c'}).
This happened while the csv dataset builder was generating data using
hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive/forecasts_vs_observed_2026_precip.csv (at revision 5b437e95226829cbc2a8fa1841440db5b522d3be), ['hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive@5b437e95226829cbc2a8fa1841440db5b522d3be/forecasts_vs_observed_2026.csv', 'hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive@5b437e95226829cbc2a8fa1841440db5b522d3be/forecasts_vs_observed_2026_precip.csv', 'hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive@5b437e95226829cbc2a8fa1841440db5b522d3be/forecasts_vs_observed_2026_tmin.csv', 'hf://datasets/FINAL-Bench/Weather-Model-Forecast-Archive@5b437e95226829cbc2a8fa1841440db5b522d3be/forecasts_vs_observed_2026_wind.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
date string | city string | lead_days int64 | observed_era5_tmax_c float64 | ecmwf_ifs025 float64 | ecmwf_aifs025_single float64 | gfs_seamless float64 | ncep_aigfs025 null | icon_seamless float64 | jma_seamless float64 | gem_seamless float64 | metno_seamless float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|
2026-01-01 | Seoul | 1 | -2.6 | -4.2 | -3.3 | -4.6 | null | -3.4 | -6 | -4.8 | -4.7 |
2026-01-01 | Seoul | 2 | -2.6 | -4.1 | -3.1 | -3.5 | null | -5.6 | -6.8 | -5.8 | -4.5 |
2026-01-01 | Seoul | 3 | -2.6 | -4.3 | -4 | -3.5 | null | -5.3 | -4.8 | -6.3 | -4.7 |
2026-01-01 | Seoul | 4 | -2.6 | -5.5 | -4.4 | -4.9 | null | -5.8 | -3.9 | -6.3 | -6 |
2026-01-01 | Seoul | 5 | -2.6 | -6.7 | -4.7 | -4.5 | null | -5.1 | -3.8 | -0.9 | -7.1 |
2026-01-01 | Seoul | 6 | -2.6 | -4.1 | -3.8 | -1.3 | null | -3 | -3.6 | -2.7 | -4.8 |
2026-01-01 | Seoul | 7 | -2.6 | 0.5 | -2.3 | -0.9 | null | null | -1.5 | -1.9 | -0.1 |
2026-01-01 | Seoul | 8 | -2.6 | null | null | null | null | null | null | null | null |
2026-01-01 | Seoul | 9 | -2.6 | null | null | null | null | null | null | null | null |
2026-01-01 | Seoul | 10 | -2.6 | null | null | null | null | null | null | null | null |
2026-01-01 | Busan | 1 | 1.7 | -0.1 | 1.2 | 0.2 | null | 0.1 | -1.1 | 0.3 | -0.4 |
2026-01-01 | Busan | 2 | 1.7 | 0 | 1.3 | 0.5 | null | 0.1 | -0.7 | 0.3 | -0.3 |
2026-01-01 | Busan | 3 | 1.7 | 1 | 1.3 | 1 | null | -0.1 | 2.5 | 1.6 | 0.5 |
2026-01-01 | Busan | 4 | 1.7 | -0.7 | 1 | -0.3 | null | -1.1 | 2.4 | -1.1 | -1.3 |
2026-01-01 | Busan | 5 | 1.7 | -1.2 | 1.2 | 0.1 | null | -0.7 | 3.3 | 2.4 | -1 |
2026-01-01 | Busan | 6 | 1.7 | 1.5 | 1.8 | 1.6 | null | 6.3 | 4 | 2.2 | 1.1 |
2026-01-01 | Busan | 7 | 1.7 | 5.8 | 3.3 | 9 | null | null | 4.5 | 4.5 | 5.6 |
2026-01-01 | Busan | 8 | 1.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Busan | 9 | 1.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Busan | 10 | 1.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Tokyo | 1 | 8.6 | 7.5 | 9.3 | 7.9 | null | 9.3 | 7.8 | 9.8 | 7.5 |
2026-01-01 | Tokyo | 2 | 8.6 | 6.8 | 8.4 | 8.4 | null | 9.2 | 7.6 | 8.6 | 7.5 |
2026-01-01 | Tokyo | 3 | 8.6 | 7.7 | 8.6 | 8.1 | null | 9.4 | 8.5 | 9.6 | 7.8 |
2026-01-01 | Tokyo | 4 | 8.6 | 5.1 | 6.4 | 8.2 | null | 7.7 | 8.9 | 9.4 | 5.3 |
2026-01-01 | Tokyo | 5 | 8.6 | 7.8 | 7.5 | 7 | null | 8.9 | 8.7 | 10.5 | 8.1 |
2026-01-01 | Tokyo | 6 | 8.6 | 8 | 7.1 | 8 | null | 10.6 | 10.5 | 9.8 | 8.3 |
2026-01-01 | Tokyo | 7 | 8.6 | 9.4 | 7.2 | 10.8 | null | null | 11.4 | 9.9 | 9.6 |
2026-01-01 | Tokyo | 8 | 8.6 | null | null | null | null | null | null | null | null |
2026-01-01 | Tokyo | 9 | 8.6 | null | null | null | null | null | null | null | null |
2026-01-01 | Tokyo | 10 | 8.6 | null | null | null | null | null | null | null | null |
2026-01-01 | Beijing | 1 | 2.5 | 0.8 | 1.6 | -0 | null | 0.7 | -0.3 | -0.5 | 0.8 |
2026-01-01 | Beijing | 2 | 2.5 | 1.3 | 1.5 | 0.3 | null | -0 | 0.3 | -0.2 | 1.1 |
2026-01-01 | Beijing | 3 | 2.5 | 0.4 | 0.6 | 0.6 | null | 0.6 | -0.2 | -0.3 | 0.5 |
2026-01-01 | Beijing | 4 | 2.5 | 1.1 | -1.3 | 1.5 | null | 0.3 | 1.1 | 2.2 | 0.8 |
2026-01-01 | Beijing | 5 | 2.5 | 0.7 | 0.3 | 0.4 | null | 0.6 | 0.7 | 3.3 | 0.4 |
2026-01-01 | Beijing | 6 | 2.5 | 0.3 | 0.8 | 1.3 | null | 2.7 | 2 | 2.1 | 0.5 |
2026-01-01 | Beijing | 7 | 2.5 | 2.4 | 0.6 | 1.5 | null | null | 1.3 | 2.5 | 2.1 |
2026-01-01 | Beijing | 8 | 2.5 | null | null | null | null | null | null | null | null |
2026-01-01 | Beijing | 9 | 2.5 | null | null | null | null | null | null | null | null |
2026-01-01 | Beijing | 10 | 2.5 | null | null | null | null | null | null | null | null |
2026-01-01 | Singapore | 1 | 29.7 | 31 | 29.9 | 30.1 | null | 31.3 | 27.7 | 29 | 32 |
2026-01-01 | Singapore | 2 | 29.7 | 30.7 | 29.5 | 29.7 | null | 30.4 | 28.6 | 30.4 | 31.3 |
2026-01-01 | Singapore | 3 | 29.7 | 30.5 | 29.4 | 30 | null | 31.7 | 29.6 | 28.5 | 32 |
2026-01-01 | Singapore | 4 | 29.7 | 30.9 | 29.6 | 30.5 | null | 31.3 | 29.7 | 30.2 | 31.7 |
2026-01-01 | Singapore | 5 | 29.7 | 27.8 | 29.4 | 30 | null | 31.6 | 27.2 | 29.3 | 29.5 |
2026-01-01 | Singapore | 6 | 29.7 | 29 | 29.3 | 29.8 | null | 30.4 | 27.2 | 29.3 | 29.5 |
2026-01-01 | Singapore | 7 | 29.7 | 25.5 | 29.3 | 28.3 | null | null | 27.3 | 29.8 | 26.7 |
2026-01-01 | Singapore | 8 | 29.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Singapore | 9 | 29.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Singapore | 10 | 29.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Delhi | 1 | 15.3 | 17 | 15.5 | 22.5 | null | 16.5 | 18 | 16 | 17.9 |
2026-01-01 | Delhi | 2 | 15.3 | 19.4 | 14.5 | 22.9 | null | 19 | 19 | 17.8 | 19.7 |
2026-01-01 | Delhi | 3 | 15.3 | 19.9 | 15.2 | 22.5 | null | 18.5 | 20.6 | 15.5 | 20.7 |
2026-01-01 | Delhi | 4 | 15.3 | 18.9 | 15.5 | 22.8 | null | 18.8 | 21 | 19.7 | 18 |
2026-01-01 | Delhi | 5 | 15.3 | 17.9 | 15.6 | 24.2 | null | 20.5 | 21.4 | 19.9 | 17.5 |
2026-01-01 | Delhi | 6 | 15.3 | 18 | 16.8 | 24.8 | null | 20.2 | 23 | 20.3 | 17.9 |
2026-01-01 | Delhi | 7 | 15.3 | 20.4 | 18.9 | 25.1 | null | null | 22.7 | 21.1 | 20.7 |
2026-01-01 | Delhi | 8 | 15.3 | null | null | null | null | null | null | null | null |
2026-01-01 | Delhi | 9 | 15.3 | null | null | null | null | null | null | null | null |
2026-01-01 | Delhi | 10 | 15.3 | null | null | null | null | null | null | null | null |
2026-01-01 | Dubai | 1 | 23.3 | 22.5 | 23.1 | 21.4 | null | 22.4 | 23.2 | 22.3 | 22.6 |
2026-01-01 | Dubai | 2 | 23.3 | 22.4 | 23.5 | 21.4 | null | 22.4 | 22.8 | 21.9 | 22 |
2026-01-01 | Dubai | 3 | 23.3 | 22 | 23.4 | 21.2 | null | 22 | 23 | 21.9 | 21.7 |
2026-01-01 | Dubai | 4 | 23.3 | 21.7 | 23.4 | 21.1 | null | 22.1 | 22.7 | 22.1 | 21.3 |
2026-01-01 | Dubai | 5 | 23.3 | 21.3 | 23.3 | 21.3 | null | 22.1 | 22.7 | 22.3 | 21.4 |
2026-01-01 | Dubai | 6 | 23.3 | 21.6 | 23.1 | 21.9 | null | 22.1 | 22.5 | 22.5 | 21.4 |
2026-01-01 | Dubai | 7 | 23.3 | 22.4 | 23.3 | 21.9 | null | null | 22.7 | 21.8 | 22.3 |
2026-01-01 | Dubai | 8 | 23.3 | null | null | null | null | null | null | null | null |
2026-01-01 | Dubai | 9 | 23.3 | null | null | null | null | null | null | null | null |
2026-01-01 | Dubai | 10 | 23.3 | null | null | null | null | null | null | null | null |
2026-01-01 | Moscow | 1 | -9.7 | -8.5 | -10.2 | -11.3 | null | -11.3 | -12.4 | -7.9 | -8.5 |
2026-01-01 | Moscow | 2 | -9.7 | -9.1 | -11.1 | -10.5 | null | -11.7 | -13 | -9.5 | -9.2 |
2026-01-01 | Moscow | 3 | -9.7 | -8.7 | -10.9 | -10.4 | null | -12.7 | -11.1 | -9.2 | -8.8 |
2026-01-01 | Moscow | 4 | -9.7 | -8.9 | -11.5 | -8.6 | null | -13.9 | -14.9 | -10.1 | -9 |
2026-01-01 | Moscow | 5 | -9.7 | -10.2 | -10.8 | -9.5 | null | -12.6 | -15.2 | -9.7 | -9.9 |
2026-01-01 | Moscow | 6 | -9.7 | -8.6 | -10 | -9.5 | null | -11.3 | -10.3 | -8.9 | -8.8 |
2026-01-01 | Moscow | 7 | -9.7 | -8.9 | -11.6 | -7.2 | null | null | -10.1 | -9.7 | -8.7 |
2026-01-01 | Moscow | 8 | -9.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Moscow | 9 | -9.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Moscow | 10 | -9.7 | null | null | null | null | null | null | null | null |
2026-01-01 | Berlin | 1 | 3.8 | 3 | 3 | 3.6 | null | 3.6 | 2.7 | 2.5 | 3 |
2026-01-01 | Berlin | 2 | 3.8 | 3.2 | 3.1 | 3 | null | 3.5 | 2.5 | 2.7 | 3 |
2026-01-01 | Berlin | 3 | 3.8 | 2.8 | 3.4 | 3.1 | null | 3.1 | 3 | 3.3 | 2.7 |
2026-01-01 | Berlin | 4 | 3.8 | 3.1 | 3.6 | 3.1 | null | 2.5 | 3.3 | 2.8 | 3.1 |
2026-01-01 | Berlin | 5 | 3.8 | 3.3 | 4.2 | 2.3 | null | 2.7 | 2.2 | 1.6 | 3 |
2026-01-01 | Berlin | 6 | 3.8 | 2.4 | 2.8 | 1.8 | null | 3.6 | 1.4 | 1.3 | 2.3 |
2026-01-01 | Berlin | 7 | 3.8 | 2.3 | 2.5 | -1 | null | null | -2.7 | 1.7 | 1.9 |
2026-01-01 | Berlin | 8 | 3.8 | null | null | null | null | null | null | null | null |
2026-01-01 | Berlin | 9 | 3.8 | null | null | null | null | null | null | null | null |
2026-01-01 | Berlin | 10 | 3.8 | null | null | null | null | null | null | null | null |
2026-01-01 | London | 1 | 6.1 | 5.8 | 5.6 | 5.6 | null | 5.5 | 4.7 | 5.2 | 5.8 |
2026-01-01 | London | 2 | 6.1 | 5.5 | 5.5 | 5.2 | null | 5.5 | 4.4 | 4.9 | 5.7 |
2026-01-01 | London | 3 | 6.1 | 5.8 | 5.4 | 5.5 | null | 5.3 | 3.6 | 5.1 | 5.9 |
2026-01-01 | London | 4 | 6.1 | 5 | 4.5 | 5.5 | null | 4.2 | 4.2 | 5.6 | 5.1 |
2026-01-01 | London | 5 | 6.1 | 5.6 | 3.8 | 5.6 | null | 4.4 | 3.7 | 5 | 5.6 |
2026-01-01 | London | 6 | 6.1 | 4.6 | 7.1 | 6.7 | null | 5.9 | 6.6 | 3.3 | 4.6 |
2026-01-01 | London | 7 | 6.1 | 2.3 | 6.3 | 4.6 | null | null | 3.6 | 5.9 | 3.4 |
2026-01-01 | London | 8 | 6.1 | null | null | null | null | null | null | null | null |
2026-01-01 | London | 9 | 6.1 | null | null | null | null | null | null | null | null |
2026-01-01 | London | 10 | 6.1 | null | null | null | null | null | null | null | null |
End of preview.
Weather Model Forecast Archive (2026)
Daily record, from 2026-01-01, of what each public global weather model forecast and what was observed, for 24 world cities. It is the data behind the Global Weather Model Leaderboard. Updated every day.
forecasts_vs_observed_2026.csv
| Column | Meaning |
|---|---|
date |
UTC day being verified |
city |
one of 24 cities |
lead_days |
how many days earlier the forecast was issued (1, 3, 5, 7) |
observed_era5_tmax_c |
ERA5 daily maximum 2 m temperature, degrees Celsius |
ecmwf_ifs025 ... metno_seamless |
each model's forecast of the same quantity |
Models: ECMWF IFS, ECMWF AIFS, NOAA GFS, NOAA AIGFS, DWD ICON, JMA GSM, ECCC GEM, MET Norway. Empty cells mean the model issued no forecast for that day and lead.
Weather data by Open-Meteo.com (CC BY 4.0). Verification: ERA5, Copernicus Climate Change Service. Compiled by VIDRAFT and released under CC BY-NC 4.0. Non-commercial use only. For commercial licensing contact vidraft@mail.vidraft.net. Contact: vidraft@mail.vidraft.net
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