Journal of Computational Physics
EM algorithms for multivariate Gaussian mixture models with truncated and censored data
Computational Statistics & Data Analysis
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
Improving mixture tree construction using better EM algorithms
Computational Statistics & Data Analysis
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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 forecast ensembles and validating observations from a given training period. A BMA model for calibrating wind speed forecasts is introduced using truncated normal distributions as conditional PDFs and the method is applied to the ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and to the University of Washington Mesoscale Ensemble. 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.