  Weather forecasting is mostly based on the outputs of deterministic numerical
weather forecasting models. Multiple runs of these models with different
initial conditions result in forecast ensembles which is 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 Bayesian Model Averaging (BMA) is applied for calibrating
ensembles of temperature forecasts produced by the operational Limited Area
Model Ensemble Prediction System of the Hungarian Meteorological Service (HMS).
  We describe two possible BMA models for temperature data of the HMS and show
that BMA post-processing significantly improves calibration and probabilistic
forecasts although the accuracy of point forecasts is rather unchanged.

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