Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components

Abstract

Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather prediction models, in order to create calibrated predictive probability density functions (PDFs). The BMA predictive PDF of the future weather quantity is the mixture of the individual PDFs corresponding to the ensemble members and the weights and model parameters are estimated using ensemble members and validating observation from a given training period. In the present paper we introduce a BMA model for calibrating wind speed forecasts, where the components PDFs follow truncated normal distribution with cut-off at zero, and apply it to the ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service. Three parameter estimation methods are proposed and each of the corresponding models outperforms the traditional gamma BMA model both in calibration and in accuracy of predictions. Moreover, since here the maximum likelihood estimation of the parameters does not require numerical optimization, modelling can be performed much faster than in case of gamma mixtures.

arXiv admin note: substantial text overlap with arXiv:1303.2133

References in corpus (3)

• Multivariate probabilistic forecasting using Bayesian model averaging and copulas
• Ensemble model output statistics for wind vectors
• Probabilistic wind speed forecasting in Hungary

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• Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging
• Statistical post-processing of hydrological forecasts using Bayesian model averaging
• Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature
• Combining low-dimensional ensemble postprocessing with reordering methods
• Bivariate Gaussian models for wind vectors in a distributional regression framework
• Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile
• Statistical post-processing of dual-resolution ensemble forecasts
• Comparison of BMA and EMOS statistical calibration methods for temperature and wind speed ensemble weather prediction
• Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical models