  The evolution of the weather can be described by deterministic numerical
weather forecasting models. Multiple runs of these models with different
initial conditions and/or model physics result in forecast ensembles which are
used for estimating the distribution of future atmospheric variables. However,
these ensembles are usually under-dispersive and uncalibrated, so
post-processing is required.
  In the present work we compare different versions of Bayesian Model Averaging
(BMA) and Ensemble Model Output Statistics (EMOS) post-processing methods in
order to calibrate 2m temperature and 10m wind speed forecasts of the
operational ALADIN Limited Area Model Ensemble Prediction System of the
Hungarian Meteorological Service. We show that compared to the raw ensemble
both post-processing methods improve the calibration of probabilistic and
accuracy of point forecasts and that the best BMA method slightly outperforms
the EMOS technique.
