FuXi-S2S: An accurate machine learning model for global subseasonal
  forecasts
Lei Chen Xiaohui Zhong Jie Wu Deliang Chen Shangping Xie Qingchen Chao Chensen Lin Zixin Hu Bo Lu Hao Li Yuan Qi
Skillful subseasonal forecasts beyond 2 weeks are crucial for a wide range of
applications across various sectors of society. Recently, state-of-the-art
machine learning based weather forecasting models have made significant
advancements, outperforming the high-resolution forecast (HRES) from the
European Centre for Medium-Range Weather Forecasts (ECMWF). However, the full
potential of machine learning models in subseasonal forecasts has yet to be
fully explored. In this study, we introduce FuXi Subseasonal-to-Seasonal
(FuXi-S2S), a machine learning based subseasonal forecasting model that
provides global daily mean forecasts up to 42 days, covering 5 upper-air
atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S
integrates an enhanced FuXi base model with a perturbation module for
flow-dependent perturbations in hidden features, and incorporates Perlin noise
to perturb initial conditions. The model is developed using 72 years of daily
statistics from ECMWF ERA5 reanalysis data. When compared to the ECMWF
Subseasonal-to-Seasonal (S2S) reforecasts, the FuXi-S2S forecasts demonstrate
superior deterministic and ensemble forecasts for total precipitation (TP),
outgoing longwave radiation (OLR), and geopotential at 500 hPa (Z500). Although
it shows slightly inferior performance in predicting 2-meter temperature (T2M),
it has clear advantages over land area. Regarding the extreme forecasts,
FuXi-S2S outperforms ECMWF S2S globally for TP. Furthermore, FuXi-S2S forecasts
surpass the ECMWF S2S reforecasts in predicting the Madden Julian Oscillation
(MJO), a key source of subseasonal predictability. They extend the skillful
prediction of MJO from 30 days to 36 days.