Chronos19/IndianMonsoon_ENSO_Impact

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.

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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

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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.

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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:

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