Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression

Statistical postprocessing techniques are commonly used to improve the skill of ensembles from numerical weather forecasts. This paper considers spatial extensions of the well-established nonhomogeneous Gaussian regression (NGR) postprocessing technique for surface temperature and a recent modification thereof in which the local climatology is included in the regression model to permit locally adaptive postprocessing. In a comparative study employing 21-h forecasts from the Consortium for Small Scale Modelling ensemble predictive system over Germany (COSMO-DE), two approaches for modeling spatial forecast error correlations are considered: a parametric Gaussian random field model and the ensemble copula coupling (ECC) approach, which utilizes the spatial rank correlation structure of the raw ensemble. Additionally, the NGR methods are compared to both univariate and spatial versions of the ensemble Bayesian model averaging (BMA) postprocessing technique.