Artificial intelligence for modeling and understanding extreme weather and climate events
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 acc...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56573-8 |
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| author | Gustau Camps-Valls Miguel-Ángel Fernández-Torres Kai-Hendrik Cohrs Adrian Höhl Andrea Castelletti Aytac Pacal Claire Robin Francesco Martinuzzi Ioannis Papoutsis Ioannis Prapas Jorge Pérez-Aracil Katja Weigel Maria Gonzalez-Calabuig Markus Reichstein Martin Rabel Matteo Giuliani Miguel D. Mahecha Oana-Iuliana Popescu Oscar J. Pellicer-Valero Said Ouala Sancho Salcedo-Sanz Sebastian Sippel Spyros Kondylatos Tamara Happé Tristan Williams |
| author_facet | Gustau Camps-Valls Miguel-Ángel Fernández-Torres Kai-Hendrik Cohrs Adrian Höhl Andrea Castelletti Aytac Pacal Claire Robin Francesco Martinuzzi Ioannis Papoutsis Ioannis Prapas Jorge Pérez-Aracil Katja Weigel Maria Gonzalez-Calabuig Markus Reichstein Martin Rabel Matteo Giuliani Miguel D. Mahecha Oana-Iuliana Popescu Oscar J. Pellicer-Valero Said Ouala Sancho Salcedo-Sanz Sebastian Sippel Spyros Kondylatos Tamara Happé Tristan Williams |
| author_sort | Gustau Camps-Valls |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a441756dc2304a5fa2aa86c42982e4d5 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-a441756dc2304a5fa2aa86c42982e4d52025-08-20T02:59:32ZengNature PortfolioNature Communications2041-17232025-02-0116111410.1038/s41467-025-56573-8Artificial intelligence for modeling and understanding extreme weather and climate eventsGustau Camps-Valls0Miguel-Ángel Fernández-Torres1Kai-Hendrik Cohrs2Adrian Höhl3Andrea Castelletti4Aytac Pacal5Claire Robin6Francesco Martinuzzi7Ioannis Papoutsis8Ioannis Prapas9Jorge Pérez-Aracil10Katja Weigel11Maria Gonzalez-Calabuig12Markus Reichstein13Martin Rabel14Matteo Giuliani15Miguel D. Mahecha16Oana-Iuliana Popescu17Oscar J. Pellicer-Valero18Said Ouala19Sancho Salcedo-Sanz20Sebastian Sippel21Spyros Kondylatos22Tamara Happé23Tristan Williams24Image Processing Laboratory, Universitat de ValènciaImage Processing Laboratory, Universitat de ValènciaImage Processing Laboratory, Universitat de ValènciaChair of Data Science in Earth Observation, Technical University of MunichDepartment of Electronics, Information, and Bioengineering, Politecnico di MilanoDeutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der AtmosphäreMax Planck Institute of BiogeochemistryInstitute for Earth System Science & Remote Sensing, Leipzig UniversityNational Technical University of AthensImage Processing Laboratory, Universitat de ValènciaDepartment of Signal Processing and Communications, Universidad de AlcaláDeutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der AtmosphäreImage Processing Laboratory, Universitat de ValènciaMax Planck Institute of BiogeochemistryGerman Aerospace Center (DLR), Institute for Data ScienceDepartment of Electronics, Information, and Bioengineering, Politecnico di MilanoInstitute for Earth System Science & Remote Sensing, Leipzig UniversityGerman Aerospace Center (DLR), Institute for Data ScienceImage Processing Laboratory, Universitat de ValènciaDepartment of Mathematical and Electrical Engineering, IMT Atlantique, Lab-STICC, UMR CNRS 6285 & INRIA team odysseyDepartment of Signal Processing and Communications, Universidad de AlcaláInstitute for Meteorology, Leipzig UniversityImage Processing Laboratory, Universitat de ValènciaInstitute for Environmental Studies, VU AmsterdamImage Processing Laboratory, Universitat de ValènciaAbstract 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.https://doi.org/10.1038/s41467-025-56573-8 |
| spellingShingle | Gustau Camps-Valls Miguel-Ángel Fernández-Torres Kai-Hendrik Cohrs Adrian Höhl Andrea Castelletti Aytac Pacal Claire Robin Francesco Martinuzzi Ioannis Papoutsis Ioannis Prapas Jorge Pérez-Aracil Katja Weigel Maria Gonzalez-Calabuig Markus Reichstein Martin Rabel Matteo Giuliani Miguel D. Mahecha Oana-Iuliana Popescu Oscar J. Pellicer-Valero Said Ouala Sancho Salcedo-Sanz Sebastian Sippel Spyros Kondylatos Tamara Happé Tristan Williams Artificial intelligence for modeling and understanding extreme weather and climate events Nature Communications |
| title | Artificial intelligence for modeling and understanding extreme weather and climate events |
| title_full | Artificial intelligence for modeling and understanding extreme weather and climate events |
| title_fullStr | Artificial intelligence for modeling and understanding extreme weather and climate events |
| title_full_unstemmed | Artificial intelligence for modeling and understanding extreme weather and climate events |
| title_short | Artificial intelligence for modeling and understanding extreme weather and climate events |
| title_sort | artificial intelligence for modeling and understanding extreme weather and climate events |
| url | https://doi.org/10.1038/s41467-025-56573-8 |
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