README.md

metadata

title: CRA5 Extreme ERA5 Compression
emoji: 🌍
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 6.22.0
app_file: app.py
short_description: Decompress ERA5 weather data with the CRA5 VAEformer codec
python_version: '3.12'
startup_duration_timeout: 1h
pinned: false
license: cc-by-4.0

🌍 CRA5 — Extreme Compression of ERA5

This Space demonstrates CRA5, an efficient Variational Transformer (VAEformer) for compressing the ECMWF ERA5 reanalysis dataset.

A single hourly ERA5 snapshot contains 268 atmospheric variables on a 721 × 1440 global grid — roughly 1.1 GB of float32 data. CRA5 compresses it into a ~2.4 MB binary stream (a ~460× compression ratio) while preserving the physical fields well enough to train numerical weather-prediction models.

The demo:

1. Fetches a tiny .bin stream for a chosen timestamp from the CRA5 dataset.
2. Decompresses it on the GPU with the VAEformer decoder.
3. Reconstructs the full 268-variable global field and visualises selected weather variables (geopotential, temperature, humidity, winds, …).

Links

• Model: taohan10200/CRA5-model
• Dataset: taohan10200/CRA5-Dataset (CC-BY-4.0)
• Paper: CRA5: Extreme Compression of ERA5 (arXiv:2405.03376)
• Code: github.com/taohan10200/CRA5

The cra5 package source (VAEformer + entropy models) is vendored from the official repository under its BSD-3-Clause-Clear license; example weather data is served from the CRA5 dataset (CC-BY-4.0), courtesy of Tao Han et al.

Citation

@article{han2024cra5extremecompressionera5,
  title={CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer},
  author={Tao Han and Zhenghao Chen and Song Guo and Wanghan Xu and Lei Bai},
  year={2024}, eprint={2405.03376}, archivePrefix={arXiv}, primaryClass={cs.LG}
}