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Statistical post-processing of probabilistic wind speed forecasting in Hungary

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Abstract:

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 a forecast ensemble which is applied for estimating the future distribution of atmospheric variables. However, as these ensembles are usually under-dispersive and uncalibrated, 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 accuracy of point forecasts.

Keywords: BAYESIAN MODEL AVERAGING; CONTINUOUS RANKED PROBABILITY SCORE; GAMMA DISTRIBUTION

Document Type: Research Article

DOI: http://dx.doi.org/10.1127/0941-2948/2013/0428

Publication date: July 1, 2013

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  • Meteorologische Zeitschrift (originally founded in 1866) is the joint periodical of the meteorological societies of Austria, Germany and Switzerland. It accepts high-quality peer-reviewed manuscripts on all aspects of observational, theoretical and computational research out of the entire field of meteorology, including climatology. Meteorologische Zeitschrift represents a natural forum for the meteorological community of Central Europe and worldwide.
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