Prediction of various weather quantities is mostly based on deterministic
numerical weather forecasting models. Multiple runs of these models with
different initial conditions result ensembles of forecasts which are applied
for estimating the distribution of future weather quantities. However, the
ensembles are usually under-dispersive and uncalibrated, so post-processing is
required.
In the present work Bayesian Model Averaging (BMA) is applied for calibrating
ensembles of wind speed forecasts produced by the operational Limited Area
Model Ensemble Prediction System of the Hungarian Meteorological Service (HMS).
We describe two possible BMA models for wind speed data of the HMS and show
that BMA post-processing significantly improves the calibration and precision
of forecasts.Comment: 17 pages, 10 figure
