  Many environmental processes such as rainfall, wind or snowfall are
inherently spatial and the modelling of extremes has to take into account that
feature. In addition, environmental processes are often attached with an angle,
e.g., wind speed and direction or extreme snowfall and time of occurrence in
year. This article proposes a Bayesian hierarchical model with a conditional
independence assumption that aims at modelling simultaneously spatial extremes
and an angular component. The proposed model relies on the extreme value theory
as well as recent developments for handling directional statistics over a
continuous domain. Working within a Bayesian setting, a Gibbs sampler is
introduced whose performances are analysed through a simulation study. The
paper ends with an application on extreme wind speed in France. Results show
that extreme wind events in France are mainly coming from West apart from the
Mediterranean part of France and the Alps.
