---
pretty_name: ENSO-Monsoon Historical Analytics Dataset
tags:
- climate
- meteorology
- enso
- monsoon
- india
- geospatial
- ndvi
- sst
license: mit
task_categories:
- time-series-forecasting
- tabular-regression
---

# 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](https://github.com/AG3106/Visualizing-the-Impact-of-ENSO-Phases-on-Indian-Monsoon), bypassing the need to perform heavy geospatial computations (processing massive NetCDF and GeoTIFF files) at runtime.

---

## Dataset Structure

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