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.

On the probabilistic skill of dual‐resolution ensemble forecasts

“…Junk et al . () examined the skill of probabilistic forecasts of 100 m wind for short‐range wind energy prediction based on a combination of regional and global ensemble forecasts. They also used postprocessing techniques to construct a probabilistic forecast and they found that such combinations of forecasts can considerably improve the skill when the correlation of the errors among the different ensembles is low.…”

Intra-hour irradiance forecasting techniques for solar power integration: A review

“…The binned-spread skill diagram compares the standard error (e.g. RMSE) of ensemble mean over binned ensemble spread and therefore is able to assess the statistical consistency at a particular forecast lead time (Junk et al, 2015). The 1:1 diagonal line of the diagram represents the perfect spread-skill line and good statistical consistency.…”

Homogeneity analysis of wind data from 213 m high Cabauw tower

“…Data from Cabauw tower have been used in a vast number of meteorological studies (Monna and Van der Vliet, 1987;Ulden and Wieringa, 1996;Durañona et al, 2007;Baas et al, 2010;Sofiev et al, 2010;Van de Wiel et al, 2010;Monahan et al, 2011;He et al, 2012;Van de Wiel et al, 2012;Drechsel et al, 2012;Monna and Bosveld, 2013a;Junk et al, 2015;Baas et al, 2016;Gualtieri, 2016;Frieß et al, 2016;Román-Cascón et al, 2016; more than 200 peer-reviewed papers), as well as in many research projects and reports (see Monna et al, 2012;Monna and Bosveld, 2013b for more references). The above articles used the data for various research purposes such as the investigation of extreme non-synoptic winds, atmospheric stability, nocturnal low-level jets, atmospheric chemistry, and wind energy studies.…”