  Probabilistic forecasts of wind speed are important for a wide range of
applications, ranging from operational decision making in connection with wind
power generation to storm warnings, ship routing and aviation. We present a
statistical method that provides locally calibrated, probabilistic wind speed
forecasts at any desired place within the forecast domain based on the output
of a numerical weather prediction (NWP) model. Three approaches for wind speed
post-processing are proposed, which use either truncated normal, gamma or
truncated logistic distributions to make probabilistic predictions about future
observations conditional on the forecasts of an ensemble prediction system
(EPS). In order to provide probabilistic forecasts on a grid, predictive
distributions that were calibrated with local wind speed observations need to
be interpolated. We study several interpolation schemes that combine
geostatistical methods with local information on annual mean wind speeds, and
evaluate the proposed methodology with surface wind speed forecasts over
Germany from the COSMO-DE (Consortium for Small-scale Modelling) ensemble
prediction system.
