Bivariate postprocessing of wind vectors

Abstract To quantify the uncertainty in numerical weather prediction (NWP) forecasts, ensemble prediction systems are utilized. Although NWP forecasts are improving continuously, they suffer from systematic bias and dispersion errors. To obtain well‐calibrated and sharp predictive probability distributions, statistical postprocessing methods are applied to NWP output. Recent developments focus on multivariate postprocessing models incorporating dependences into the model directly. We introduce three novel bivariate postprocessing approaches and analyze their performance for joint postprocessing of bivariate wind‐vector components for 60 stations in Germany. Bivariate vine‐copula‐based models, a bivariate gradient‐boosted version of ensemble model output statistics (EMOS), and a bivariate distributional regression network (DRN) are compared with bivariate EMOS. The case study indicates that the novel bivariate methods improve over the bivariate EMOS approaches. The bivariate DRN and the most flexible version of the bivariate vine‐copula approach exhibit the best performance in terms of verification scores and calibration.