Over the past few years, due to the rapid development of machine learning
(ML) models for weather forecasting, state-of-the-art ML models have shown
superior performance compared to the European Centre for Medium-Range Weather
Forecasts (ECMWF)’s high-resolution forecast (HRES) in 10-day forecasts at a
spatial resolution of 0.25 degree. However, the challenge remains to perform
comparably to the ECMWF ensemble mean (EM) in 15-day forecasts. Previous
studies have demonstrated the importance of mitigating the accumulation of
forecast errors for effective long-term forecasts. Despite numerous efforts to
reduce accumulation errors, including autoregressive multi-time step loss,
using a single model is found to be insufficient to achieve optimal performance
in both short and long lead times. Therefore, we present FuXi, a cascaded ML
weather forecasting system that provides 15-day global forecasts with a
temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi is
developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance
evaluation, based on latitude-weighted root mean square error (RMSE) and
anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable
forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first
ML-based weather forecasting system to accomplish this achievement.