Datasets:

deepguess
/

severe-weather-env-gfs-forecast-v1

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
dataset_name: string
source: string
source_file: string
rows: int64
columns: int64
source_manifest: struct<rows: int64, columns: int64, meta_columns: int64, feature_columns: int64, point_table: string (... 276 chars omitted)
  child 0, rows: int64
  child 1, columns: int64
  child 2, meta_columns: int64
  child 3, feature_columns: int64
  child 4, point_table: string
  child 5, source_matrix: string
  child 6, source_model_dir: string
  child 7, renamed_features: struct<surface_native_era5_cape_jkg: string, surface_native_era5_cin_jkg: string>
      child 0, surface_native_era5_cape_jkg: string
      child 1, surface_native_era5_cin_jkg: string
  child 8, prefixed_feature_duplicates_with: string
  child 9, outputs: struct<parquet: string, summary_csv: string, html: string>
      child 0, parquet: string
      child 1, summary_csv: string
      child 2, html: string
  child 10, note: string
created_utc: string
checksums_sha256: struct<data/gfs_point_forecast.parquet: string, data/preview_1000.csv: string, metadata/columns.json (... 60 chars omitted)
  child 0, data/gfs_point_forecast.parquet: string
  child 1, data/preview_1000.csv: string
  child 2, metadata/columns.json: string
  child 3, metadata/manifest.json: string
  child 4, README.md: string
to
{'text': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1821, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              dataset_name: string
              source: string
              source_file: string
              rows: int64
              columns: int64
              source_manifest: struct<rows: int64, columns: int64, meta_columns: int64, feature_columns: int64, point_table: string (... 276 chars omitted)
                child 0, rows: int64
                child 1, columns: int64
                child 2, meta_columns: int64
                child 3, feature_columns: int64
                child 4, point_table: string
                child 5, source_matrix: string
                child 6, source_model_dir: string
                child 7, renamed_features: struct<surface_native_era5_cape_jkg: string, surface_native_era5_cin_jkg: string>
                    child 0, surface_native_era5_cape_jkg: string
                    child 1, surface_native_era5_cin_jkg: string
                child 8, prefixed_feature_duplicates_with: string
                child 9, outputs: struct<parquet: string, summary_csv: string, html: string>
                    child 0, parquet: string
                    child 1, summary_csv: string
                    child 2, html: string
                child 10, note: string
              created_utc: string
              checksums_sha256: struct<data/gfs_point_forecast.parquet: string, data/preview_1000.csv: string, metadata/columns.json (... 60 chars omitted)
                child 0, data/gfs_point_forecast.parquet: string
                child 1, data/preview_1000.csv: string
                child 2, metadata/columns.json: string
                child 3, metadata/manifest.json: string
                child 4, README.md: string
              to
              {'text': Value('string')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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text string
sample_key
sample_id
event_id
sample_lat
sample_lon
target_time_utc
target_year
target_month
target_hour_utc
source_package
label_family
source_report_type
sample_role
hazard
label
label_column
valid_time_utc
lead
forecast_hour
variant
recommendation_source
severe_weight
map_reports
map_report_hit_rate
map_grid_fraction_above
prediction_npy
grid_features_dir
nearest_grid_pred
nearest_grid_index
nearest_grid_dist_deg
window_minutes
feature_sample_lat
feature_sample_lon
feature_target_month
target_dayofyear
feature_target_hour_utc
target_dayofyear_sin
target_dayofyear_cos
target_hour_sin
target_hour_cos
surface_native_model_cape_jkg
surface_native_model_cin_jkg
surface_sp_pa
surface_t2m_k
surface_tcc_fraction
surface_tcwv_kgm2
surface_td2m_k
surface_u10_ms
surface_v10_ms
hrrr_native_srh_0_3km_m2s2
hrrr_wind_gust_ms
profile_levels_t_k_50hpa
profile_levels_t_k_100hpa
profile_levels_t_k_150hpa
profile_levels_t_k_200hpa
profile_levels_t_k_250hpa
profile_levels_t_k_300hpa
profile_levels_t_k_350hpa
profile_levels_t_k_400hpa
profile_levels_t_k_450hpa
profile_levels_t_k_500hpa
profile_levels_t_k_550hpa
profile_levels_t_k_600hpa
profile_levels_t_k_650hpa
profile_levels_t_k_700hpa
profile_levels_t_k_750hpa
profile_levels_t_k_800hpa
profile_levels_t_k_850hpa
profile_levels_t_k_900hpa
profile_levels_t_k_925hpa
profile_levels_t_k_950hpa
profile_levels_t_k_975hpa
profile_levels_t_k_1000hpa
profile_levels_rh_pct_50hpa
profile_levels_rh_pct_100hpa
profile_levels_rh_pct_150hpa
profile_levels_rh_pct_200hpa
profile_levels_rh_pct_250hpa
profile_levels_rh_pct_300hpa
profile_levels_rh_pct_350hpa
profile_levels_rh_pct_400hpa
profile_levels_rh_pct_450hpa
profile_levels_rh_pct_500hpa
profile_levels_rh_pct_550hpa
profile_levels_rh_pct_600hpa
profile_levels_rh_pct_650hpa
profile_levels_rh_pct_700hpa
profile_levels_rh_pct_750hpa
profile_levels_rh_pct_800hpa
profile_levels_rh_pct_850hpa
profile_levels_rh_pct_900hpa
profile_levels_rh_pct_925hpa
profile_levels_rh_pct_950hpa
profile_levels_rh_pct_975hpa
profile_levels_rh_pct_1000hpa
profile_levels_q_kgkg_50hpa
profile_levels_q_kgkg_100hpa
profile_levels_q_kgkg_150hpa
profile_levels_q_kgkg_200hpa
profile_levels_q_kgkg_250hpa

End of preview.

Severe Weather Environment GFS Forecast v1

This dataset contains compact GFS point-feature rows from severe-weather forecast-map experiments. It is meant for lead-time testing, forecast calibration, and medium-range SPC-style probability research.

This is a research dataset, not operational guidance. It is not an official NOAA, NWS, SPC, or GFS product, and it should not be used as a stand-alone warning or forecast system.

Files

data/gfs_point_forecast.parquet: primary GFS point-feature table.
data/preview_1000.csv: small preview.
metadata/manifest.json: counts and source metadata.
metadata/columns.json: column list.
metadata/checksums.sha256: checksums.

Rows: 364,748
Columns: 255

Labels

GFS rows use:

hazard: tornado, hail, wind, or severe.
label: binary event label for the sampled point.
label_column: source label-column name where available.

Suggested Uses

Hazard-specific forecast calibration by hazard, forecast_hour, and lead.
D1-D4 and longer-lead experiments using the valid time and lead metadata.
Post-processing GFS-driven forecast maps into calibrated probabilistic fields.
Comparing medium-range forecast features against the short-range HRRR event/control data.

QC And Construction

Rows preserve GFS valid time, lead metadata, hazard label, and nearest-grid prediction features.
The archive is compact and case-based; it is not a full global GFS grid archive.
Use case/time-based splits. Do not randomly split rows from the same forecast case across train and test.
GFS is lower resolution than HRRR, so use it for lead time and calibration rather than storm-scale detail.

Source Summary

Rows: 364,748
Columns: 255
Feature columns: 224
Metadata columns: 31
Scope: compact existing-case point-feature archive, not a full retrospective gridded GFS archive.

Caveats

These are pointwise forecast-calibration rows, not direct observations and not full-grid forecast fields.
Forecast labels inherit report uncertainty and spatial matching assumptions from the upstream pipeline.
GFS and HRRR/RAP feature distributions should not be mixed without model-system labels or model-specific heads.
Gridpoint probabilities need calibration, smoothing, and verification before being presented as outlook-like maps.

Attribution

This package is derived from public severe-weather reports and public numerical weather prediction data processed through a local rustwx severe-environment pipeline. Cite the original data providers and this dataset when using it in research or downstream model training.

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Total file size:

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