Benchmarking AI-based data assimilation to advance data-driven global weather forecasting
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910022451068928 |
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| author | Wang, Wuxin Ni, Weicheng Fei, Ben Han, Tao Huang, Lilan Yuan, Taikang Li, Xiaoyong Bai, Lei Duan, Boheng Ren, Kaijun |
| author_facet | Wang, Wuxin Ni, Weicheng Fei, Ben Han, Tao Huang, Lilan Yuan, Taikang Li, Xiaoyong Bai, Lei Duan, Boheng Ren, Kaijun |
| contents | Research on Artificial Intelligence (AI)-based Data Assimilation (DA) is expanding rapidly. However, the absence of an objective, comprehensive, and real-world benchmark hinders the fair comparison of diverse methods. Here, we introduce DABench, a benchmark designed for contributing to the development and evaluation of AI-based DA methods. By integrating real-world observations, DABench provides an objective and fair platform for validating long-term closed-loop DA cycles, supporting both deterministic and ensemble configurations. Furthermore, we assess the efficacy of AI-based DA in generating initial conditions for the advanced AI-based weather forecasting model to produce accurate medium-range global weather forecasting. Our dual-validation, utilizing both reanalysis data and independent radiosonde observations, demonstrates that AI-based DA achieves performance competitive with state-of-the-art AI-driven four-dimensional variational frameworks across both global weather DA and medium-range forecasting metrics. We invite the research community to utilize DABench to accelerate the advancement of AI-based DA for global weather forecasting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11438 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking AI-based data assimilation to advance data-driven global weather forecasting Wang, Wuxin Ni, Weicheng Fei, Ben Han, Tao Huang, Lilan Yuan, Taikang Li, Xiaoyong Bai, Lei Duan, Boheng Ren, Kaijun Machine Learning Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics Research on Artificial Intelligence (AI)-based Data Assimilation (DA) is expanding rapidly. However, the absence of an objective, comprehensive, and real-world benchmark hinders the fair comparison of diverse methods. Here, we introduce DABench, a benchmark designed for contributing to the development and evaluation of AI-based DA methods. By integrating real-world observations, DABench provides an objective and fair platform for validating long-term closed-loop DA cycles, supporting both deterministic and ensemble configurations. Furthermore, we assess the efficacy of AI-based DA in generating initial conditions for the advanced AI-based weather forecasting model to produce accurate medium-range global weather forecasting. Our dual-validation, utilizing both reanalysis data and independent radiosonde observations, demonstrates that AI-based DA achieves performance competitive with state-of-the-art AI-driven four-dimensional variational frameworks across both global weather DA and medium-range forecasting metrics. We invite the research community to utilize DABench to accelerate the advancement of AI-based DA for global weather forecasting. |
| title | Benchmarking AI-based data assimilation to advance data-driven global weather forecasting |
| topic | Machine Learning Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2408.11438 |