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arXiv · 2408.11438

Benchmarking AI-based data assimilation to advance data-driven global weather forecasting

Wuxin Wang·Weicheng Ni·Ben Fei·Tao Han·Lilan Huang·Taikang Yuan·Xiaoyong Li·Lei Bai·Boheng Duan·Kaijun Ren
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Abstract

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.

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Wuxin Wang, Weicheng Ni, Ben Fei, Tao Han, Lilan Huang, Taikang Yuan, Xiaoyong Li, Lei Bai, Boheng Duan, Kaijun Ren. 2024-08-21. Benchmarking AI-based data assimilation to advance data-driven global weather forecasting. https://arxiv.org/abs/2408.11438

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