  We propose a statistical space-time model for predicting atmospheric wind
speed based on deterministic numerical weather predictions and historical
measurements. We consider a Gaussian multivariate space-time framework that
combines multiple sources of past physical model outputs and measurements along
with model predictions in order to produce a probabilistic wind speed forecast
within the prediction window. We illustrate this strategy on a ground wind
speed forecast for several months in 2012 for a region near the Great Lakes in
the United States. The results show that the prediction is improved in the
mean-squared sense relative to the numerical forecasts as well as in
probabilistic scores. Moreover, the samples are shown to produce realistic wind
scenarios based on the sample spectrum.
