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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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A machine learning model that outperforms conventional global subseasonal forecast models.

Lei Chen^1,2, Xiaohui Zhong¹, Hao Li³

¹Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China.

Nature Communications

|July 30, 2024

Summary

A new machine learning model, FuXi Subseasonal-to-Seasonal (FuXi-S2S), significantly improves subseasonal weather forecasts up to 42 days. It enhances predictions for precipitation and outgoing longwave radiation by better capturing forecast uncertainty and the Madden-Julian Oscillation (MJO).

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Area of Science:

Earth System Science
Atmospheric Science
Machine Learning Applications in Meteorology

Background:

Subseasonal forecasts (up to 42 days) are critical for societal sectors but remain a significant scientific challenge.
While machine learning models show promise, they have not yet surpassed conventional numerical weather prediction models at subseasonal timescales.

Purpose of the Study:

To introduce FuXi Subseasonal-to-Seasonal (FuXi-S2S), a novel machine learning model for subseasonal-to-seasonal weather forecasting.
To evaluate FuXi-S2S's performance against the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal-to-Seasonal model.

Main Methods:

Developed FuXi-S2S, a machine learning model providing global daily mean forecasts for atmospheric and surface variables up to 42 days.
Trained FuXi-S2S on 72 years of daily statistics from ECMWF ERA5 reanalysis data.
Compared FuXi-S2S ensemble forecasts against ECMWF's state-of-the-art model for precipitation and outgoing longwave radiation.

Main Results:

FuXi-S2S outperforms the ECMWF Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation.
The model demonstrates enhanced global precipitation forecasting accuracy.
FuXi-S2S improves skillful prediction of the Madden-Julian Oscillation (MJO) from 30 to 36 days and captures associated teleconnections.

Conclusions:

FuXi-S2S represents a significant advancement in subseasonal forecasting, outperforming current leading models.
The model's enhanced ability to predict forecast uncertainty and the MJO is key to its improved performance.
FuXi-S2S offers a valuable tool for Earth system science research, potentially enabling new discoveries and a paradigm shift.