| --- |
| 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. |
|
|
