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- Title
A machine learning model that outperforms conventional global subseasonal forecast models.
- Authors
Chen, Lei; Zhong, Xiaohui; Li, Hao; Wu, Jie; Lu, Bo; Chen, Deliang; Xie, Shang-Ping; Wu, Libo; Chao, Qingchen; Lin, Chensen; Hu, Zixin; Qi, Yuan
- Abstract
Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research. This paper introduces FuXi-S2S, a machine-learning model that outperforms conventional numerical weather prediction models at subseasonal timescales globally, extending the skillful Madden–Julian Oscillation prediction form 30 days to 36 days.
- Subjects
MACHINE learning; PRECIPITATION forecasting; WEATHER forecasting; MADDEN-Julian oscillation; PREDICTION models
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-50714-1