A machine learning model that outperforms conventional global subseasonal forecast models

Lei Chen, Xiaohui Zhong, Hao Li (), Jie Wu, Bo Lu (), Deliang Chen, Shang-Ping Xie, Libo Wu, Qingchen Chao, Chensen Lin, Zixin Hu and Yuan Qi ()
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Lei Chen: Fudan University
Xiaohui Zhong: Fudan University
Hao Li: Fudan University
Jie Wu: National Climate Center
Bo Lu: National Climate Center
Deliang Chen: University of Gothenburg
Shang-Ping Xie: University of California San Diego
Libo Wu: Fudan University
Qingchen Chao: National Climate Center
Chensen Lin: Fudan University
Zixin Hu: Fudan University
Yuan Qi: Fudan University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-50714-1

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