FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models

• URL: http://arxiv.org/abs/2312.09926v2
• Date: Fri, 5 Jul 2024 08:23:00 GMT
• Title: FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
• Authors: Lei Chen, Xiaohui Zhong, Hao Li, Jie Wu, Bo Lu, Deliang Chen, Shangping Xie, Qingchen Chao, Chensen Lin, Zixin Hu, Yuan Qi,
• Abstract summary: FuXi Subseasonal-to-Seasonal (FuXi-S2S) is a machine learning model that provides global daily mean forecasts up to 42 days. 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.
• Score: 13.852128658186876
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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