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Artificial intelligence for modeling and understanding extreme weather and climate events

Camps-Valls, Gustau and Fernández-Torres, Miguel-Ángel and Cohrs, Kai-Hendrik and Höhl, Adrian and Castelletti, Andrea and Pacal, Aytac and Robin, Claire and Martinuzzi, Francesco and Papoutsis, Ioannis and Prapas, Ioannis and Pérez-Aracil, Jorge and Weigel, Katja and Gonzalez-Calabuig, Maria and Reichstein, Markus and Rabel, Martin and Giuliani, Matteo and Mahecha, Miguel D. and Popescu, Oana-Iuliana and Pellicer-Valero, Oscar J. and Ouala, Said and Salcedo-Sanz, Sancho and Sippel, Sebastian and Kondylatos, Spyros and Happé, Tamara and Williams, Tristan (2025) Artificial intelligence for modeling and understanding extreme weather and climate events. Nature Communications, 16 (1). Nature Publishing Group. doi: 10.1038/s41467-025-56573-8. ISSN 2041-1723.

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Official URL: https://dx.doi.org/10.1038/s41467-025-56573-8

Abstract

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes and data with limited annotations. This paper reviews how AI is being used to analyze extreme climate events (like floods, droughts, wildfires, and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating real-time information, and deploying understandable models, all crucial steps for gaining stakeholder trust and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy to enhance disaster readiness and risk reduction.

Item URL in elib:https://elib.dlr.de/213222/
Document Type:Article
Title:Artificial intelligence for modeling and understanding extreme weather and climate events
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Camps-Valls, GustauUniversitat de València, Spainhttps://orcid.org/0000-0003-1683-2138UNSPECIFIED
Fernández-Torres, Miguel-ÁngelUniversitat de València, Spainhttps://orcid.org/0000-0002-0801-199XUNSPECIFIED
Cohrs, Kai-HendrikUniversitat de València, Spainhttps://orcid.org/0000-0002-2286-7487UNSPECIFIED
Höhl, AdrianTU Münchenhttps://orcid.org/0000-0003-3380-4489UNSPECIFIED
Castelletti, AndreaPolitecnico di Milano, Milan, Italyhttps://orcid.org/0000-0002-7923-1498UNSPECIFIED
Pacal, AytacDLR, IPA und Univ. Bremenhttps://orcid.org/0000-0002-1324-2389180325022
Robin, ClaireMPI and ELLIS Unit JenaUNSPECIFIEDUNSPECIFIED
Martinuzzi, FrancescoUniv. Leipzig and ScaDS.AI Leipzighttps://orcid.org/0000-0003-3249-3703UNSPECIFIED
Papoutsis, IoannisUniversity of Athens and National Observatory of Athens and Archimedes/Athena Research Center, GreeceUNSPECIFIEDUNSPECIFIED
Prapas, IoannisUniversitat de València, Spainhttps://orcid.org/0000-0002-9111-4112UNSPECIFIED
Pérez-Aracil, JorgeUniversidad de Alcalá, Madrid, Spainhttps://orcid.org/0000-0002-4456-9886UNSPECIFIED
Weigel, KatjaUniv. Bremen and DLR, IPAhttps://orcid.org/0000-0001-6133-7801UNSPECIFIED
Gonzalez-Calabuig, MariaUniversitat de València, Spainhttps://orcid.org/0000-0003-1254-2387UNSPECIFIED
Reichstein, MarkusMPI and ELLIS Unit Jenahttps://orcid.org/0000-0001-5736-1112UNSPECIFIED
Rabel, MartinDLR, Institute for Data Science, JenaUNSPECIFIEDUNSPECIFIED
Giuliani, MatteoPolitecnico di Milano, Milan, Italyhttps://orcid.org/0000-0002-4780-9347UNSPECIFIED
Mahecha, Miguel D.Univ. Leipzig and ScaDS.AI Leipzighttps://orcid.org/0000-0003-3031-613XUNSPECIFIED
Popescu, Oana-IulianaDLR, Institute for Data Science, JenaUNSPECIFIEDUNSPECIFIED
Pellicer-Valero, Oscar J.Universitat de València, SpainUNSPECIFIEDUNSPECIFIED
Ouala, SaidIMT Atlantique, Brest, FranceUNSPECIFIEDUNSPECIFIED
Salcedo-Sanz, SanchoUniversidad de Alcalá, Madrid, SpainUNSPECIFIEDUNSPECIFIED
Sippel, SebastianUniversität Leipzig, Germanyhttps://orcid.org/0000-0002-4510-4458UNSPECIFIED
Kondylatos, SpyrosUniversitat de València, Spainhttps://orcid.org/0000-0002-0949-8592UNSPECIFIED
Happé, TamaraVU Amsterdam, The Netherlandshttps://orcid.org/0000-0002-4548-506XUNSPECIFIED
Williams, TristanUniversitat de València, SpainUNSPECIFIEDUNSPECIFIED
Date:24 February 2025
Journal or Publication Title:Nature Communications
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:16
DOI:10.1038/s41467-025-56573-8
Publisher:Nature Publishing Group
ISSN:2041-1723
Status:Published
Keywords:Climate sciences, Natural hazards, artificial intelligence
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Jena , Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Institute of Data Science
Deposited By: Weigel, Katja
Deposited On:18 Mar 2025 14:28
Last Modified:18 Mar 2025 14:28

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