• temperature;
  • gridding;
  • ensemble prediction;
  • forecast calibration

We propose a further refinement of the the non-homogeneous Gaussian regression approach for temperature, which transforms the output of an ensemble prediction system into predictive Gaussian distributions at each location of interest. Model fitting is partly done within a regression framework using a penalized version of the least-squares loss function. This is conceptually simpler than the original approach and at the same time is able to prevent overfitting. While calibration is initially performed at observation locations only, geostatistical methods are used to provide predictive distributions on the entire grid. The incorporation of land-use information in this interpolation scheme further improves predictive performance, even though a simpler statistical model than in the original approach is used. The assessment of predictive performance and calibration is carried out with dynamical forecasts of 2 m temperatures by the COSMO-DE-EPS, an application of the COSMO (Consortium for Small-scale Modeling) model system which covers Germany and neighbouring countries.