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---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- weather
- climate
- regional
- india
pretty_name: IndiaWeatherBench_data
size_categories:
- 1M<n<10M
---
# IndiaWeatherBench: A Machine Learning-Ready Regional Forecasting Dataset over India
**IndiaWeatherBench** provides a curated benchmark dataset for machine learning-based regional weather forecasting over the Indian subcontinent. It is built upon the **Indian Monsoon Data Assimilation and Analysis (IMDAA)** reanalysis dataset, produced under the National Monsoon Mission by NCMRWF, UK Met Office, and IMD.
---
## 🌏 Dataset Overview
The original IMDAA dataset is a high-resolution regional reanalysis developed by the **National Centre for Medium Range Weather Forecasting (NCMRWF)**, Ministry of Earth Sciences (MoES), Government of India, in collaboration with the **UK Met Office** and the **India Meteorological Department (IMD)**. It provides hourly weather data from **1979 to 2020** over the Indian subcontinent at a **0.12° (~12 km)** spatial resolution and includes over **57 variables** across **63 pressure levels**.
However, the raw dataset presents several challenges for machine learning workflows, including download difficulty, lack of standardized splits, and storage in meteorological formats. **IndiaWeatherBench** addresses these limitations by offering a clean, ready-to-use subset for ML applications.
---
## 📦 Contents
IndiaWeatherBench includes:
- **Time range**: 2000–2019 (20 years)
- **Interval**: 6-hourly (00, 06, 12, 18 UTC)
- **Region**: 6°N–36.72°N, 66.6°E–97.25°E (~256×256 grid)
- **Train/Val/Test splits**:
- Train: 2000–2017 (~26,500 samples)
- Val: 2018 (~1,500 samples)
- Test: 2019 (~1,500 samples)
- **Variables**: 43 channels (see below)
---
## 📑 Variable List
| Category | Variables |
|------------------------|---------------------------------------------------------------------------|
| **Single-level** | TMP (2m temp), UGRD/VGRD (10m wind), APCP (precip), PRMSL (MSLP), TCDCRO (cloud cover) |
| **Pressure-level** | TMP_prl, HGT, UGRD_prl, VGRD_prl, RH — at 50, 250, 500, 600, 700, 850, 925 hPa |
| **Static fields** | MTERH (terrain height), LAND (land cover) |
---
## 💾 Data Formats
IndiaWeatherBench is released in two formats:
### 🧪 Zarr Format
- Chunked, cloud-native array storage
- Compatible with `xarray`, `dask`
- Suitable for scientific analysis and fast slicing
```python
import xarray as xr
ds = xr.open_zarr("imdaa_bench_incremental.zarr", consolidated=True)
```
---
### 🚀 HDF5 Format
- Optimized for ML training
- Each `.h5` file = one time step with all variables
- Pre-split into `train/`, `val/`, and `test/`
```python
import h5py
f = h5py.File("imdaa_bench_h5/train/20010101_00.h5", "r")
print(list(f.keys()))
```
---
## 📜 License and Terms of Use
This dataset is released under the **Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0)** license.
- ✅ Free for non-commercial, educational, and research use
- ❌ For commercial use, contact: `director@ncmrwf.gov.in`
- 📧 Send a copy of any publication using this dataset to the same address
---
## 🔗 References
- [IMDAA Reanalysis Portal (NCMRWF)](https://rds.ncmrwf.gov.in/)
- [CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/)
---
For questions, issues, or contributions, please open a Discussion or Issue on the Hugging Face page.
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