Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929225136603136 |
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| author | Beucler, Tom Koch, Erwan Kotlarski, Sven Leutwyler, David Michel, Adrien Koh, Jonathan |
| author_facet | Beucler, Tom Koch, Erwan Kotlarski, Sven Leutwyler, David Michel, Adrien Koh, Jonathan |
| contents | We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations.
Recommendation 1: Develop Hybrid AI-Physical Models: Emphasize the integration of AI and physical modeling for improved reliability, especially for longer prediction horizons, acknowledging the delicate balance between knowledge-based and data-driven components required for optimal performance. Recommendation 2: Emphasize Robustness in AI Downscaling Approaches, favoring techniques that respect physical laws, preserve inter-variable dependencies and spatial structures, and accurately represent extremes at the local scale. Recommendation 3: Promote Inclusive Model Development: Ensure Earth System Model development is open and accessible to diverse stakeholders, enabling forecasters, the public, and AI/statistics experts to use, develop, and engage with the model and its predictions/projections. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13691 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications Beucler, Tom Koch, Erwan Kotlarski, Sven Leutwyler, David Michel, Adrien Koh, Jonathan Atmospheric and Oceanic Physics Artificial Intelligence Computational Physics We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations. Recommendation 1: Develop Hybrid AI-Physical Models: Emphasize the integration of AI and physical modeling for improved reliability, especially for longer prediction horizons, acknowledging the delicate balance between knowledge-based and data-driven components required for optimal performance. Recommendation 2: Emphasize Robustness in AI Downscaling Approaches, favoring techniques that respect physical laws, preserve inter-variable dependencies and spatial structures, and accurately represent extremes at the local scale. Recommendation 3: Promote Inclusive Model Development: Ensure Earth System Model development is open and accessible to diverse stakeholders, enabling forecasters, the public, and AI/statistics experts to use, develop, and engage with the model and its predictions/projections. |
| title | Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence Computational Physics |
| url | https://arxiv.org/abs/2311.13691 |