FuXi-Extreme: Improving extreme rainfall and wind forecasts with
  diffusion model
Xiaohui Zhong Lei Chen Jun Liu Chensen Lin Yuan Qi Hao Li
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, ML models face a common challenge: as forecast lead times increase,
they tend to generate increasingly smooth predictions, leading to an
underestimation of the intensity of extreme weather events. To address this
challenge, we developed the FuXi-Extreme model, which employs a denoising
diffusion probabilistic model (DDPM) to restore finer-scale details in the
surface forecast data generated by the FuXi model in 5-day forecasts. An
evaluation of extreme total precipitation ($\textrm{TP}$), 10-meter wind speed
($\textrm{WS10}$), and 2-meter temperature ($\textrm{T2M}$) 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.