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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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tags: |
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- weather |
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- climate |
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- regional |
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- india |
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pretty_name: IndiaWeatherBench_data |
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size_categories: |
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- 1M<n<10M |
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--- |
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# IndiaWeatherBench: A Machine Learning-Ready Regional Forecasting Dataset over India |
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**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. |
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--- |
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## 🌏 Dataset Overview |
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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**. |
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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. |
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--- |
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## 📦 Contents |
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IndiaWeatherBench includes: |
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- **Time range**: 2000–2019 (20 years) |
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- **Interval**: 6-hourly (00, 06, 12, 18 UTC) |
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- **Region**: 6°N–36.72°N, 66.6°E–97.25°E (~256×256 grid) |
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- **Train/Val/Test splits**: |
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- Train: 2000–2017 (~26,500 samples) |
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- Val: 2018 (~1,500 samples) |
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- Test: 2019 (~1,500 samples) |
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- **Variables**: 43 channels (see below) |
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--- |
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## 📑 Variable List |
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| Category | Variables | |
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|------------------------|---------------------------------------------------------------------------| |
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| **Single-level** | TMP (2m temp), UGRD/VGRD (10m wind), APCP (precip), PRMSL (MSLP), TCDCRO (cloud cover) | |
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| **Pressure-level** | TMP_prl, HGT, UGRD_prl, VGRD_prl, RH — at 50, 250, 500, 600, 700, 850, 925 hPa | |
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| **Static fields** | MTERH (terrain height), LAND (land cover) | |
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--- |
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## 💾 Data Formats |
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IndiaWeatherBench is released in two formats: |
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### 🧪 Zarr Format |
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- Chunked, cloud-native array storage |
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- Compatible with `xarray`, `dask` |
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- Suitable for scientific analysis and fast slicing |
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```python |
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import xarray as xr |
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ds = xr.open_zarr("imdaa_bench_incremental.zarr", consolidated=True) |
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``` |
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--- |
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### 🚀 HDF5 Format |
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- Optimized for ML training |
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- Each `.h5` file = one time step with all variables |
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- Pre-split into `train/`, `val/`, and `test/` |
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```python |
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import h5py |
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f = h5py.File("imdaa_bench_h5/train/20010101_00.h5", "r") |
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print(list(f.keys())) |
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``` |
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--- |
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## 📜 License and Terms of Use |
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This dataset is released under the **Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0)** license. |
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- ✅ Free for non-commercial, educational, and research use |
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- ❌ For commercial use, contact: `director@ncmrwf.gov.in` |
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- 📧 Send a copy of any publication using this dataset to the same address |
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--- |
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## 🔗 References |
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- [IMDAA Reanalysis Portal (NCMRWF)](https://rds.ncmrwf.gov.in/) |
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- [CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
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--- |
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For questions, issues, or contributions, please open a Discussion or Issue on the Hugging Face page. |
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