There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impactweather events. We compare forecasts of Storm Ciar\'an, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numericalweather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciar\'an.

翻译：近期，利用机器学习技术制作业务天气预报的潜力引起了广泛关注。随着机器学习技术成为天气预报工具箱的一部分，当前迫切需要了解这些模型在模拟高影响天气事件方面的表现。我们对比了机器学习模型和数值天气预报模型对风暴西亚兰的预报结果——这一欧洲温带气旋在北欧造成16人死亡及重大财产损失。所采用的四种机器学习模型（FourCastNet、Pangu-Weather、GraphCast和FourCastNet-v2）均能准确捕捉气旋的天气尺度结构，包括云头位置、暖区形态和暖输送带急流分布，以及影响风暴快速发展的关键大尺度动力驱动因素（如风暴相对于高空急流出口区的位置）。然而，这些模型在解析对发布天气预警至关重要的精细结构方面表现参差不齐：所有机器学习模型均低估了风暴相关风速的峰值振幅，仅部分模型能解析暖核隔离结构，且均未能捕获尖锐的弯曲暖锋梯度。本研究表明，通过对风暴西亚兰等高影响天气事件的案例研究，可以深入揭示机器学习天气预报的性能与特性。