  The description of wind phenomena is frequently based on data obtained from
anemometers, which usually report the wind speed and direction only in a
horizontal plane. Such measurements are commonly used either to develop wind
generation farms or to forecast weather conditions in a geographical region.
Beyond these standard applications, the information contained in the data may
be richer than expected and may lead to a better understanding of the wind
dynamics in a geographical area. In this work we propose a statistical analysis
based on the wind velocity vectors, which we propose may be grouped in "wind
states" associated to binormal distribution functions. We found that the
velocity plane defined by the anemometric velocity data may be used as a phase
space, where a finite number of states may be found and sorted using standard
clustering methods. The main result is a discretization technique useful to
model the wind with Markov chains. We applied such ideas in anemometric data
for two different sites in Mexico where the wind resource is considered
reliable. The approximated Markov chains of both places give a set of values
for transition probabilities and residence times that may be regarded as a
signature of the dynamics of the site. We consider the results may impact
either the technical decisions for the design of wind generators or the
detailed resource assessment in sites with non-standard features.
