  Ensembles of forecasts are obtained from multiple runs of numerical weather
forecasting models with different initial conditions and typically employed to
account for forecast uncertainties. However, biases and dispersion errors often
occur in forecast ensembles, they are usually under-dispersive and uncalibrated
and require statistical post-processing. We present an Ensemble Model Output
Statistics (EMOS) method for calibration of wind speed forecasts based on the
log-normal (LN) distribution, and we also show a regime-switching extension of
the model which combines the previously studied truncated normal (TN)
distribution with the LN.
  Both presented models are applied to wind speed forecasts of the eight-member
University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble
and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological
Service, and their predictive performances are compared to those of the TN and
general extreme value (GEV) distribution based EMOS methods and to the TN-GEV
mixture model. The results indicate improved calibration of probabilistic and
accuracy of point forecasts in comparison to the raw ensemble and to
climatological forecasts. Further, the TN-LN mixture model outperforms the
traditional TN method and its predictive performance is able to keep up with
the models utilizing the GEV distribution without assigning mass to negative
values.
