A machine learning model that outperforms conventional global subseasonal forecast models
A machine learning model that outperforms conventional global subseasonal forecast models
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Author / Creator
Chen, Lei , Zhong, Xiaohui , Li, Hao , Wu, Jie , Lu, Bo , Chen, Deliang , Xie, Shang-Ping , Wu, Libo , Chao, Qingchen , Lin, Chensen , Hu, Zixin and Qi, Yuan
Publisher
London: Nature Publishing Group UK
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Language
English
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Publisher
London: Nature Publishing Group UK
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Contents
Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional...
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Full title
A machine learning model that outperforms conventional global subseasonal forecast models
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_4fb0d812e0774d9faa1648e9dc01dcf4
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4fb0d812e0774d9faa1648e9dc01dcf4
Other Identifiers
ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-024-50714-1
