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README.md

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-license: cc-by-nc-sa-4.0
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+# IndiBench: A Machine Learning-Ready Regional Forecasting Dataset over India
+
+**IndiBench** 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. **IndiBench** addresses these limitations by offering a clean, ready-to-use subset for ML applications.
+
+---
+
+## 📦 Contents
+
+IndiBench 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
+
+IndiBench 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.