复旦大学李昊团队开发出了新的FuXi-Extreme模型，来改进极端降雨和风能预报。2024年10月15日出版的《中国科学：地球科学》发表了这项成果。

用于天气预报的机器学习（ML）模型的发展取得了重大进展，并取得了显著成果。与欧洲中期天气预报中心（ECMWF）的高分辨率预报（HRES）相比，最先进的基于ML的天气预报模式，如“Fuxi”，已显示出优越的统计预报性能。然而，这些ML模型的一个共同局限性是，随着预测提前期的增加，它们倾向于生成越来越平滑的预测，这通常会造成预测时低估极端天气事件的强度。

为了应对这一挑战，研究人员开发出了FuXi-extreme模型，该模型采用去噪扩散概率模型（DDPM），来增强FuXi模型生成的5天地面预报数据中的精细尺度细节。通过对极端总降水量（TP）、10米风速（WS10）和2米温度（T2M）的评价，结果显示Fuxi-extreme模型的性能优于Fuxi和HRES上。

此外，在评估基于国际气候管理最佳路径方案（IBTrACS）数据集的热带气旋（TC）预报时，“Fuxi”和“FuXi-Extreme”模型在TC路径预报方面的表现优于HRES，但在TC强度预报方面的表现低于HRES。

附：英文原文

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

Author: Xiaohui ZHONG, Lei CHEN, Jun LIU, Chensen LIN, Yuan QI, Hao LI

Issue&Volume: 2024/10/15

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 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 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.

DOI: 10.1007/s11430-023-1427-x

Source: https://www.sciengine.com/SCES/doi/10.1007/s11430-023-1427-x