Regional climate risk assessment from climate models using probabilistic machine learning
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866910106148405248 |
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| author | Wan, Zhong Yi Lopez-Gomez, Ignacio Carver, Robert Schneider, Tapio Anderson, John Sha, Fei Zepeda-Núñez, Leonardo |
| author_facet | Wan, Zhong Yi Lopez-Gomez, Ignacio Carver, Robert Schneider, Tapio Anderson, John Sha, Fei Zepeda-Núñez, Leonardo |
| contents | Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decisions. We introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections, without requiring paired simulated and observed events during training. GenFocal synthesizes complex and long-lived hazards, such as heat waves and tropical cyclones, even when they are not well represented in the coarse climate projections. It also samples high-impact, rare events more accurately than leading methods. By translating large-scale climate projections into actionable, localized information, GenFocal provides a powerful new paradigm to improve climate adaptation and resilience strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08079 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Regional climate risk assessment from climate models using probabilistic machine learning Wan, Zhong Yi Lopez-Gomez, Ignacio Carver, Robert Schneider, Tapio Anderson, John Sha, Fei Zepeda-Núñez, Leonardo Machine Learning Numerical Analysis Atmospheric and Oceanic Physics Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decisions. We introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections, without requiring paired simulated and observed events during training. GenFocal synthesizes complex and long-lived hazards, such as heat waves and tropical cyclones, even when they are not well represented in the coarse climate projections. It also samples high-impact, rare events more accurately than leading methods. By translating large-scale climate projections into actionable, localized information, GenFocal provides a powerful new paradigm to improve climate adaptation and resilience strategies. |
| title | Regional climate risk assessment from climate models using probabilistic machine learning |
| topic | Machine Learning Numerical Analysis Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2412.08079 |