 Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal componentsSándor Baran Computational Statistics & Data Analysis, 2014, vol. 75, issue C, 227-238 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 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. Keywords: Continuous ranked probability score; Ensemble calibration; Truncated normal distribution (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:75:y:2014:i:c:p:227-238 DOI: 10.1016/j.csda.2014.02.013 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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