  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-impact weather 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 numerical weather 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.
