Comparison and combination of regional and global ensemble prediction systems for probabilistic predictions of hub-height wind speed

AMS Citation:
Junk, C., S. Späth, L. Von Bremen, and L. Delle Monache, 2015: Comparison and combination of regional and global ensemble prediction systems for probabilistic predictions of hub-height wind speed. Weather and Forecasting, 30, 1234-1253, doi:10.1175/WAF-D-15-0021.1.
Date:2015-10-01
Resource Type:article
Title:Comparison and combination of regional and global ensemble prediction systems for probabilistic predictions of hub-height wind speed
Abstract: The objective of this paper is to compare probabilistic 100-m wind speed forecasts, which are relevant for wind energy applications, from different regional and global ensemble prediction systems (EPSs) at six measurement towers in central Europe and to evaluate the benefits of combining single-model ensembles into multimodel ensembles. The global 51-member EPS from the European Centre for Medium-Range Weather Forecasts (ECMWF EPS) is compared against the Consortium for Small-Scale Modelling’s (COSMO) limited-area 16-member EPS (COSMO-LEPS) and a regional, high-resolution 20-member EPS centered over Germany (COSMO-DE EPS). The ensemble forecasts are calibrated with univariate (wind speed) ensemble model output statistics (EMOS) and bivariate (wind vector) recursive and adaptive calibration (AUV). The multimodel ensembles are constructed by pooling together raw or best-calibrated ensemble forecasts. An additional postprocessing of these multimodel ensembles with both EMOS and AUV is also tested. The best-performing calibration methodology for ECMWF EPS is AUV, while EMOS performs better than AUV for the calibration of COSMO-DE EPS. COSMO-LEPS has similar skill when calibrated with both EMOS and AUV. The AUV ECMWF EPS outperforms the EMOS COSMO-LEPS and COSMO-DE EPS for deterministic and probabilistic wind speed forecast skill. For most thresholds, ECMWF EPS has a comparable reliability and sharpness but higher discrimination ability. Multimodel ensembles, which are constructed by pooling together the best-calibrated EPSs, improve the skill relative to the AUV ECMWF EPS. An analysis of the error correlation among the EPSs indicates that multimodel ensemble skill can be considerably higher when the error correlation is low.
Peer Review:Refereed
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OpenSky citable URL: ark:/85065/d7th8p1h
Publisher's Version: 10.1175/WAF-D-15-0021.1
Author(s):
  • Constantin Junk
  • Stephan Späth
  • Lueder von Bremen
  • Luca Delle Monache - NCAR/UCAR
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