Datasets:

Chronos19
/

IndianMonsoon_ENSO_Impact

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
state: string
pearson_r: double
p_value: double
regression: struct<slope: double, intercept: double>
  child 0, slope: double
  child 1, intercept: double
points: list<item: struct<year: int64, oni: double, anomaly_pct: double, phase: string>>
  child 0, item: struct<year: int64, oni: double, anomaly_pct: double, phase: string>
      child 0, year: int64
      child 1, oni: double
      child 2, anomaly_pct: double
      child 3, phase: string
states: list<item: string>
  child 0, item: string
to
{'states': List(Value('string')), 'pearson_r': List({'state': Value('string'), 'r': Value('float64'), 'p_value': Value('float64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              state: string
              pearson_r: double
              p_value: double
              regression: struct<slope: double, intercept: double>
                child 0, slope: double
                child 1, intercept: double
              points: list<item: struct<year: int64, oni: double, anomaly_pct: double, phase: string>>
                child 0, item: struct<year: int64, oni: double, anomaly_pct: double, phase: string>
                    child 0, year: int64
                    child 1, oni: double
                    child 2, anomaly_pct: double
                    child 3, phase: string
              states: list<item: string>
                child 0, item: string
              to
              {'states': List(Value('string')), 'pearson_r': List({'state': Value('string'), 'r': Value('float64'), 'p_value': Value('float64')})}
              because column names don't match

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ENSO-Monsoon Historical Analytics Dataset

This dataset contains raw, intermediate SQLite databases, and optimized precomputed JSON files analyzing the impact of the El Niño-Southern Oscillation (ENSO) phases on the Indian Summer Monsoon. The period of study spans from 2000 to 2024.

It is designed to power the ENSO-Monsoon Analytics Dashboard, bypassing the need to perform heavy geospatial computations (processing massive NetCDF and GeoTIFF files) at runtime.

Dataset Structure

data/
├── raw/                 # Original geospatial/meteorological products
│   ├── ersst/           # Extended Reconstructed Sea Surface Temperature NetCDFs
│   ├── ndvi/            # MODIS Satellite-derived NDVI GeoTIFFs (state/regional buckets)
│   ├── oisst/           # Optimum Interpolation Sea Surface Temperature NetCDFs
│   └── rainfall/        # CHIRPS India Daily/Weekly Rainfall 5km TIFs
│
├── db/                  # SQLite Databases for tabular exploration
│   └── climate.db       # Aggregated relational database containing parsed data
│
└── precomputed/         # Pre-rendered JSON data for instant API delivery
    ├── correlation/     # ONI vs. Rainfall Pearson-r coefficients and scatter points
    ├── ndvi/            # Regionally grouped weekly average NDVI profiles (2000-2024)
    ├── oni/             # Climatology and raw Oceanic Niño Index timeseries
    ├── rainfall/        # Annual cumulative and calendar heatmaps per state
    └── sst/             # Spatial average grid anomalies and sea surface temp coordinates

Data Descriptions

1. `raw/`

ERSST / OISST: NOAA global sea surface temperatures used to calculate indices.
CHIRPS Rainfall: Climate Hazards Group InfraRed Precipitation with Station data, subset to the Indian landmass.
NDVI: Normalized Difference Vegetation Index from MODIS satellite sensors, aggregated to assess agricultural vegetation health under El Niño/La Niña events.

2. `db/climate.db`

A unified SQLite database hosting ready-to-query tables:

sst_grid: Cell-by-cell monthly SST anomalies.
weekly_rainfall: Weekly rainfall totals (mm) per Indian state.
weekly_ndvi: Regional vegetation index averages over time.
oni_index: Calculated 3-month running mean of SST anomalies in the Niño 3.4 region.

3. `precomputed/`

Stateless JSON structures optimized for frontend consumption, resolving:

Climatological normals and deviations.
Cumulative rainfall curves (June to September) compared against long-period averages.
Grid-level correlations mapping ENSO parameters directly to state agricultural outputs.
SST Grid (sst/): Spatial grid coordinates mapped to Sea Surface Temperature anomalies. Updated to use high-resolution OISST (0.25° grid) instead of ERSST, fully precomputed for all years from 2000 to 2024.

Processing

All files in db/ and precomputed/ are produced using the data parsing pipeline in the scripts/ directory of the core repository. The pipeline automatically:

Validates coordinate alignments.
Normalizes state boundaries (stripping special characters/diacritics for clean filesystem mapping).
Computes anomalies against long-period climatological means.

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