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.

翻译：机器学习模型在天气预报领域的显著进展已取得卓越成果。诸如FuXi等基于机器学习的先进天气预报模型，在统计预报性能上已超越欧洲中期天气预报中心的高分辨率预报系统（HRES）。然而，机器学习模型面临一个共同挑战：随着预报时效增加，其预测结果趋于平滑，导致对极端天气事件强度的低估。为应对这一挑战，我们开发了FuXi-Extreme模型，该模型采用去噪扩散概率模型（DDPM）对FuXi模型生成的5天预报中的地表预报数据进行精细尺度细节恢复。对极端总降水量（$\textrm{TP}$）、10米风速（$\textrm{WS10}$）和2米温度（$\textrm{T2M}$）的评估表明，FuXi-Extreme在性能上优于FuXi和HRES。此外，基于国际气候管理最佳路径归档数据集（IBTrACS）的热带气旋（TC）预报评估显示，FuXi和FuXi-Extreme在TC路径预报上均优于HRES，但在TC强度预报上逊于HRES。