Comparison of Postprocessing Methods for the Calibration of 100-m Wind Ensemble Forecasts at Off- and Onshore Sites

Abstract

nsemble forecasts are a valuable addition to deterministic wind forecasts since they allow the quantification of forecast uncertainties. To remove common deficiencies of ensemble forecasts such as biases and ensemble spread deficits, various postprocessing methods for the calibration of wind speed (univariate calibration) and wind vector (bivariate calibration) ensemble forecasts have been developed in recent years. The objective of this paper is to compare the performance of state-of-the-art calibration methods at distinct off- and onshore sites in central Europe. The aim is to identify calibration- and site-dependent improvements in forecast skill over uncalibrated 100-m ensemble forecasts from the ECMWF Ensemble Prediction System. The ensemble forecasts were evaluated at four onshore and three offshore measurement towers in central Europe at 100-m height for lead times up to 5 days. The results show that the recursive and adaptive wind vector calibration (AUV) outperforms calibration methods such as univariate ensemble model output statistics (EMOS), bivariate EMOS, variance deficit calibration, and ensemble copula coupling in terms of the root-mean-square error and continuous ranked probability score at almost all sites. It was found that exponential downweighting of past measurements in AUV contributes to higher forecast skill since similar downweighting approaches in the other calibration methods improved forecast skill. Proposing a bidimensional bias correction in bivariate EMOS similar to the approach taken in AUV yields bivariate EMOS skill at onshore sites that is similar to AUV skill. Deterministic and probabilistic improvements are usually much lower at offshore sites and increase with increasing complexity of the site characteristics since systematic forecast errors and ensemble underdispersion are larger at high-roughness sites.