Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence
Abstract O (10 km) because higher resolutions would impede the creation of the ensembles that are needed for model calibration and uncertainty quantification, for sampling atmospheric and oceanic internal variability, and for broadly exploring and quantifying climate risks. By synergizing decades of scientific development with advanced AI techniques, our approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence ; volume:24 ; number:12 ; year:2024 ; pages:7041-7062 ; extent:22
Atmospheric chemistry and physics ; 24, Heft 12 (2024), 7041-7062 (gesamt 22)
- Urheber
- DOI
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10.5194/acp-24-7041-2024
- URN
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urn:nbn:de:101:1-2408051436068.121298871129
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:48 MESZ
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Beteiligte
- Schneider, Tapio
- Leung, L. Ruby
- Wills, Robert Jnglin
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