  This paper presents an initial exploration of high frequency records of
extreme wind speed in two steps. The first consists in finding the suitable
extreme distribution for $120$ measuring stations in Switzerland, by comparing
three known distributions: Weibull, Gamma, and Generalized extreme value. This
comparison serves as a basis for the second step which applies a spatial
modelling by using Extreme Learning Machine. The aim is to model distribution
parameters by employing a high dimensional input space of topographical
information. The knowledge of probability distribution gives a comprehensive
information and a global overview of wind phenomena. Through this study, a
flexible and a simple modelling approach is presented, which can be generalized
to almost extreme environmental data for risk assessment and to model renewable
energy.
