Original paper

Statistical post-processing of probabilistic wind speed forecasting in Hungary

Baran, Sándor; Horányi, András; Nemoda, Dóra

Meteorologische Zeitschrift Vol. 22 No. 3 (2013), p. 273 - 282

published: Jul 1, 2013

DOI: 10.1127/0941-2948/2013/0428

BibTeX file

ArtNo. ESP025012203003, Price: 29.00 €

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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.


bayesian model averagingcontinuous ranked probability scoregamma distribution