  In recent years, ensemble weather forecasting have become a routine at all
major weather prediction centres. These forecasts are obtained from multiple
runs of numerical weather prediction models with different initial conditions
or model parametrizations. However, ensemble forecasts can often be
underdispersive and also biased, so some kind of post-processing is needed to
account for these deficiencies. One of the most popular state of the art
statistical post-processing techniques is the ensemble model output statistics
(EMOS), which provides a full predictive distribution of the studied weather
quantity. We propose a novel EMOS model for calibrating wind speed ensemble
forecasts, where the predictive distribution is a generalized extreme value
(GEV) distribution left truncated at zero (TGEV). The truncation corrects the
disadvantage of the GEV distribution based EMOS models of occasionally
predicting negative wind speed values, without affecting its favorable
properties. The new model is tested on four data sets of wind speed ensemble
forecasts provided by three different ensemble prediction systems, covering
various geographical domains and time periods. The forecast skill of the TGEV
EMOS model is compared with the predictive performance of the truncated normal,
log-normal and GEV methods and the raw and climatological forecasts as well.
The results verify the advantageous properties of the novel TGEV EMOS approach.
