FuXi: A cascade machine learning forecasting system for 15-day global weather forecast

Understand

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.