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.

机器学习在天气预报领域取得了显著进展，其中 FuXi 模型及其改进版 FuXi-Extreme 在预测极端天气事件方面表现出色，但面临着预报提前时间增加时预测结果变得平滑从而低估极端天气事件强度的普遍挑战。

富西 - 极端：基于扩散模型改进极端降雨和风力预报

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

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. Therefore, we present FuXi, a cascaded ML
weather forecasting system that provides 15-day global forecasts with a
temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi is
developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance
evaluation, based on latitude-weighted root mean square error (RMSE) and
anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable
forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first
ML-based weather forecasting system to accomplish this achievement.

使用机器学习技术开发的 FuXi 天气预报系统，在全球 15 天天气预报方面表现与 ECMWF EM 相当，是第一个取得这一成就的 ML-based 天气预报系统。