FuXi-Extreme:Improving extreme rainfall and wind forecasts with diffusion model

Significant advancements in the development of machine learning(ML) models for weather forecasting have produced remarkable results. State-of-the-art ML-based weather forecast models, such as FuXi, have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts(HRES) of the European Centre for MediumRange Weather Forecasts(ECMWF). However, a common limitation of these ML models is their tendency to generate increasingly smooth predictions as forecast lead times increase, which often results in the underestimation of intensities of extreme weather events. To address this challenge, we developed the FuXi-Extreme model, which employs a denoising diffusion probabilistic model(DDPM) to enhance finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts. An evaluation of extreme total precipitation(TP), 10-meter wind speed(WS10), and 2-meter temperature(T2M)illustrates the superior performance of FuXi-Extreme over both FuXi and HRES. Moreover, when evaluating tropical cyclone(TC) forecasts based on International Best Track Archive for Climate Stewardship(IBTrACS) dataset, both FuXi and FuXiExtreme shows superior performance in TC track forecasts compared to HRES, but they show inferior performance in TC intensity forecasts in comparison to HRES.