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Statistical postprocessing techniques are commonly used to improve the skillof ensembles of numerical weather forecasts. This paper considers spatialextensions of the well-established nonhomogeneous Gaussian regression (NGR)postprocessing technique for surface temperature and a recent modificationthereof in which the local climatology is included in the regression model fora locally adaptive postprocessing. In a comparative study employing 21 hforecasts from the COSMO-DE ensemble predictive system over Germany, twoapproaches for modeling spatial forecast error correlations are considered: Aparametric Gaussian random field model and the ensemble copula couplingapproach which utilizes the spatial rank correlation structure of the rawensemble. Additionally, the NGR methods are compared to both univariate andspatial versions of the ensemble Bayesian model averaging (BMA) postprocessingtechnique.

Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression