  Expensive ultrasonic anemometers are usually required to measure wind speed
accurately. The aim of this work is to overcome the loss of accuracy of a low
cost hot-wire anemometer caused by the changes of air temperature, by means of
a probabilistic calibration using Gaussian Process Regression. Gaussian Process
Regression is a non-parametric, Bayesian, and supervised learning method
designed to make predictions of an unknown target variable as a function of one
or more known input variables. Our approach is validated against real datasets,
obtaining a good performance in inferring the actual wind speed values. By
performing, before its real use in the field, a calibration of the hot-wire
anemometer taking into account air temperature, permits that the wind speed can
be estimated for the typical range of ambient temperatures, including a
grounded uncertainty estimation for each speed measure.
