  Ensemble model output statistics (EMOS) is a statistical tool for
post-processing forecast ensembles of weather variables obtained from multiple
runs of numerical weather prediction models in order to produce calibrated
predictive probability density functions (PDFs). The EMOS predictive PDF is
given by a parametric distribution with parameters depending on the ensemble
forecasts. We propose an EMOS model for calibrating wind speed forecasts based
on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions
where model parameters and component weights are estimated by optimizing the
values of proper scoring rules over a rolling training period. The new model is
tested on wind speed forecasts of the 50 member European Centre for
Medium-Range Weather Forecasts ensemble, the 11 member Aire Limit\'ee
Adaptation dynamique D\'eveloppement International-Hungary Ensemble Prediction
System ensemble of the Hungarian Meteorological Service and the eight-member
University of Washington mesoscale ensemble, and its predictive performance is
compared to that of various benchmark EMOS models based on single parametric
families and combinations thereof. The results indicate improved calibration of
probabilistic and accuracy of point forecasts in comparison with the raw
ensemble and climatological forecasts. The mixture EMOS model significantly
outperforms the TN and LN EMOS methods, moreover, it provides better calibrated
forecasts than the TN-LN combination model and offers an increased flexibility
while avoiding covariate selection problems.
