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

作　　者：Xiaohui ZHONG Lei CHEN Jun LIU Chensen LIN Yuan QI Hao LI 

机构地区：[1]Artificial Intelligence Innovation and Incubation Institute,Fudan University,Shanghai 200433,China

出　　处：《Science China Earth Sciences》2024年第12期3696-3708,共13页中国科学（地球科学英文版）

基　　金：supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240154)。

摘　　要：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.

关 键 词：FUXI Diffusion model Weather forecast Extreme weather 

分 类 号：P45[天文地球—大气科学及气象学]