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There has been huge recent interest in the potential of making operationalweather forecasts using machine learning techniques. As they become a part ofthe weather forecasting toolbox, there is a pressing need to understand howwell current machine learning models can simulate high-impactweather events. Wecompare forecasts of Storm Ciar\'an, a European windstorm that caused sixteendeaths and extensive damage in Northern Europe, made by machine learning andnumericalweather prediction models. The four machine learning models considered(FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecaststhat accurately capture the synoptic-scale structure of the cyclone includingthe position of the cloud head, shape of the warm sector and location of warmconveyor belt jet, and the large-scale dynamical drivers important for therapid storm development such as the position of the storm relative to theupper-level jet exit. However, their ability to resolve the more detailedstructures important for issuing weather warnings is more mixed. All of themachine learning models underestimate the peak amplitude of winds associatedwith the storm, only some machine learning models resolve the warm coreseclusion and none of the machine learning models capture the sharp bent-backwarm frontal gradient. Our study shows there is a great deal about theperformance and properties of machine learning weather forecasts that can bederived from case studies of high-impact weather events such as Storm Ciar\'an.

Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciar\'an