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The evolution of the weather can be described by deterministic numericalweather forecasting models. Multiple runs of these models with differentinitial conditions and/or model physics result in forecast ensembles which areused for estimating the distribution of future atmospheric variables. However,these ensembles are usually under-dispersive and uncalibrated, sopost-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 inorder to calibrate 2m temperature and 10m wind speed forecasts of theoperational ALADIN Limited Area Model Ensemble Prediction System of theHungarian Meteorological Service. We show that compared to the raw ensembleboth post-processing methods improve the calibration of probabilistic andaccuracy of point forecasts and that the best BMA method slightly outperformsthe EMOS technique.

Comparison of BMA and EMOS statistical calibration methods for temperature and wind speed ensemble weather prediction