Evidence map›Paper›PMID 42151190›Full record

ArticleScientific data2026

CRA5 a high-fidelity compressed reanalysis atmospheric dataset for weather and climate research.

Tao Han, Dazhao Du, Zhenghao Chen, Song Guo, Wanli Ouyang, Lei Bai

Abstract read
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tao HanThe Hong Kong University of Science and Technology, Computer Science and Engineering, Hong Kong SAR, China.ORCID http://orcid.org/0000-0003-1255-3270
Dazhao DuThe Hong Kong University of Science and Technology, Computer Science and Engineering, Hong Kong SAR, China.
Zhenghao ChenThe University of Newcastle, New South Wales, Australia.
Song GuoThe Hong Kong University of Science and Technology, Computer Science and Engineering, Hong Kong SAR, China. songguo@cse.ust.hk.
Wanli OuyangShanghai Artificial Intelligence Laboratory, No. 701, Yunjin Road, Xuhui District, Shanghai, China.
Lei BaiShanghai Artificial Intelligence Laboratory, No. 701, Yunjin Road, Xuhui District, Shanghai, China. bailei@pjlab.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID42151190
PMCPMC13443884

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.