Abstract
The ERA5 reanalysis dataset, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), provides high-resolution, hourly global climate and weather data from 1950 to the present. However, its massive volume poses substantial storage and distribution challenges. To address this, we introduce CRA5, a highly compressed version of ERA5 generated by the neural network framework Aeolus. CRA5 reduces the 400 TB uncompressed float32 dataset to just 0.85 TB, achieving a 470× compression ratio. Notably, it offers over 100 times higher compression than the lossless GRIB files from the Copernicus Climate Data Store (CDS). Extensive experiments validate its numerical accuracy: CRA5 maintains consistent climatology and comparable power spectral density, yielding a mean absolute error of only 0.17 K for temperature across 37 vertical levels. Furthermore, it faithfully reconstructs extreme weather events and large-scale climatological patterns. By significantly lowering infrastructure barriers, CRA5 accelerates data access and facilitates broader collaboration in large-scale atmospheric research.
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Acknowledgements
This research was supported by funding from the Hong Kong RGC General Research Fund (152228/23E, 162161/24E, 162116/25E, and 162180/25E), the National Natural Science Foundation of China (NSFC) Key Program (No. 62532005), the Collaborative Research Fund (Nos. C1042-23GF and C5097-25G), the NSFC/RGC Collaborative Research Scheme (Grant Nos. 62461160332 and CRS_HKUST602/24), the Research Impact Fund (No. R5011-23F), the Areas of Excellence Scheme (AoE/E-601/22-R), and InnoHK (HKGAI). This work was also supported by the JC STEM Lab of AI for Science and Engineering, funded by The Hong Kong Jockey Club Charities Trust; the MTR Research Funding (MRF) Scheme (CHU-24003); and the Research Grants Council of Hong Kong (Project No. CUHK14213224).
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Han, T., Du, D., Chen, Z. et al. CRA5 a high-fidelity compressed reanalysis atmospheric dataset for weather and climate research. Sci Data (2026). https://doi.org/10.1038/s41597-026-07381-2
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DOI: https://doi.org/10.1038/s41597-026-07381-2
