Mixture EMOS model for calibrating ensemble forecasts of wind speed

• Verfasst von: Baran, Sándor [VerfasserIn]
• Verfasst von: Lerch, Sebastian [VerfasserIn]
• Titel: Mixture EMOS model for calibrating ensemble forecasts of wind speed
• Verf.angabe: S. Baran and S. Lerch
• E-Jahr: 2016
• Jahr: 17 January 2016
• Umfang: 5 S.
• Fussnoten: Gesehen am 19.08.2020
• Titel Quelle: Enthalten in: Environmetrics
• Ort Quelle: Chichester, West Sussex : Wiley, 1991
• Jahr Quelle: 2016
• Band/Heft Quelle: 27(2016), 2, Seite 116-130
• ISSN Quelle: 1099-095X
• DOI: doi:10.1002/env.2380
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: continuous ranked probability score
• Sach-SW: ensemble calibration
• Sach-SW: ensemble model output statistics
• Sach-SW: log-normal distribution
• Sach-SW: truncated normal distribution
• K10plus-PPN: 1727457757

Abstract

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.