AI-enhanced subseasonal forecasting of extreme temperature risks
Rattachement africain : us, cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Sub-seasonal weather prediction remains a significant scientific challenge due to the chaotic nature of the atmosphere, with current numerical and AI-driven models exhibiting limited skill, particularly at the fine spatial scales for human exposure, agriculture, and infrastructure. Here, we introduce DeepMet, a high-resolution, AI-driven sub-seasonal forecasting system designed to improve the prediction of temperature extremes and their associated health risks, demonstrated successfully over the continental United States. Specifically, DeepMet substantially outperforms the benchmark of European Centre for Medium-Range Weather Forecasts, reducing the root mean square error by 20-60% for key surface variables, including daily maximum and minimum 2-meter temperature, specific humidity, and 10-meter wind speed. The model also improves the detection of extreme heat and cold events by over 40% across all evaluation metrics. By enhancing early warning capabilities, DeepMet enables more accurate identification of extreme weather conditions, potentially improving risk communication to prevent additional extreme-weather related deaths in the United States. Remarkably, such performance is achieved using only a single GPU for training, making the method highly accessible for local agencies to enhance early warning systems and protect public health. This underscores its strong potential to transform long-range forecasting and significantly enhance public health preparedness in a changing climate.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- AI-enhanced subseasonal forecasting of extreme temperature risks
- Date Crossref
- 01/11/2026
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Tennessee at Knoxville pays non établi dans la noticeUniversité ou école supérieure
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Wuhan University Engineering Research Center of Ministry of Education pays non établi dans la noticeUniversité ou école supérieure
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Beijing Academy of Artificial Intelligence pays non établi dans la noticeInstitution
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Cooperative Institute for Research in Environmental Sciences pays non établi dans la noticeStructure de recherche
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University of Colorado Boulder Cooperative Institute for Research in Environmental Sciences (CIRES) pays non établi dans la noticeUniversité ou école supérieure
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the University of Tennessee Department of Civil and Environmental Engineering pays non établi dans la noticeUniversité ou école supérieure
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School of Remote Sensing and Information Engineering pays non établi dans la noticeUniversité ou école supérieure
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Zhongguancun Academy pays non établi dans la noticeOrganisation à but non lucratif
University of Tennessee at Knoxville, Engineering Research Center of Ministry of Education — Wuhan University et Beijing Academy of Artificial Intelligence, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.