%0 Journal Article %1 Chen2024 %A Chen, Lei %A Zhong, Xiaohui %A Li, Hao %A Wu, Jie %A Lu, Bo %A Chen, Deliang %A Xie, Shang-Ping %A Wu, Libo %A Chao, Qingchen %A Lin, Chensen %A Hu, Zixin %A Qi, Yuan %D 2024 %J Nature Communications %K proj:bayklif2 %N 1 %P 6425 %R 10.1038/s41467-024-50714-1 %T A machine learning model that outperforms conventional global subseasonal forecast models %U https://doi.org/10.1038/s41467-024-50714-1 %V 15 %X 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.

@article{Chen2024, 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.}, added-at = {2025-03-13T09:09:36.000+0100}, author = {Chen, Lei and Zhong, Xiaohui and Li, Hao and Wu, Jie and Lu, Bo and Chen, Deliang and Xie, Shang-Ping and Wu, Libo and Chao, Qingchen and Lin, Chensen and Hu, Zixin and Qi, Yuan}, biburl = {https://www.bibsonomy.org/bibtex/2cad9599959dd734e9ea031587b525ea6/annakrause}, day = 30, description = {A machine learning model that outperforms conventional global subseasonal forecast models | Nature Communications}, doi = {10.1038/s41467-024-50714-1}, interhash = {800fa02b9925581b8273037c801d5518}, intrahash = {cad9599959dd734e9ea031587b525ea6}, issn = {2041-1723}, journal = {Nature Communications}, keywords = {proj:bayklif2}, month = jul, number = 1, pages = 6425, timestamp = {2025-03-13T09:17:09.000+0100}, title = {A machine learning model that outperforms conventional global subseasonal forecast models}, url = {https://doi.org/10.1038/s41467-024-50714-1}, volume = 15, year = 2024 }