A data-to-forecast machine learning system for global weather

Xiuyu Sun, Xiaohui Zhong, Xiaoze Xu, Yuanqing Huang, Hao Li (), J. David Neelin (), Deliang Chen, Jie Feng, Wei Han (), Libo Wu () and Yuan Qi ()
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Xiuyu Sun: Shanghai Academy of Artificial Intelligence for Science
Xiaohui Zhong: Fudan University
Xiaoze Xu: Shanghai Academy of Artificial Intelligence for Science
Yuanqing Huang: Shanghai Academy of Artificial Intelligence for Science
Hao Li: Shanghai Academy of Artificial Intelligence for Science
J. David Neelin: University of California
Deliang Chen: Tsinghua University
Jie Feng: Shanghai Academy of Artificial Intelligence for Science
Wei Han: China Meteorological Administration
Libo Wu: Shanghai Academy of Artificial Intelligence for Science
Yuan Qi: Shanghai Academy of Artificial Intelligence for Science

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrate global observations, data assimilation (DA), and physics-based models. However, further advances are increasingly constrained by high computational costs, the underutilization of vast observational datasets, and challenges in obtaining finer resolution. Recent advances in machine learning present a promising alternative, but still depend on the initial conditions generated by NWP systems. Here, we introduce FuXi Weather, a machine learning-based global forecasting system that assimilates multi-satellite data and is capable of cycling DA and forecasting. FuXi Weather generates reliable 10-day forecasts at 0.25° resolution using fewer observations than conventional NWP systems. It demonstrates the value of background forecasts in constraining the analysis during DA. FuXi Weather outperforms the European Centre for Medium-Range Weather Forecasts high-resolution forecasts beyond day one in observation-sparse regions such as central Africa, highlighting its potential to improve forecasts where observational infrastructure is limited.

Date: 2025
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DOI: 10.1038/s41467-025-62024-1

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