Abstract
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 Medium-Range Weather Forecasts(ECMWF).However,a common limitation of these ML models is their tendency to generate in-creasingly 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 FuXi-Extreme shows superior performance in TC track forecasts compared to HRES,but they show inferior performance in TC intensity forecasts in comparison to HRES.
万方数据