Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting

Baran, Sándor and Sebastian, Lerch (2015) Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 141 (691). pp. 2289-2299. ISSN 0035-9009

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Abstract

Ensembles of forecasts are obtained from multiple runs of numerical weather fore- casting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteoro- logical Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods and to the TN- GEV mixture model. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison to the raw ensemble and to climatological forecasts. Further, the TN-LN mixture model outperforms the traditional TN method and its predictive performance is able to keep up with the models utilizing the GEV distribution without assigning mass to negative values.

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