| description abstract | Although the quality of numerical ensemble prediction systems (EPS) has greatly improved during the last few years, these systems still show systematic deficiencies. Specifically, they are underdispersive and lack both reliability and sharpness. A variety of statistical postprocessing methods allows for improving direct model output. Since 2007, Aire Limitée Adaptation Dynamique Développement International Limited Area Ensemble Forecasting (ALADIN-LAEF) has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG), and its 2-m temperature model output subject to calibration. This work follows the approach of nonhomogeneous Gaussian regression (NGR) that addresses a statistical correction of the first and second moment (mean bias and dispersion) for Gaussian-distributed continuous variables. It is based on the multiple linear regression technique and provides a predictive probability density function (PDF) in terms of a normal distribution. Fitting the regression coefficients, a minimum continuous ranked probability score (CRPS) estimation has been chosen instead of the more traditional maximum likelihood technique. The use of high-resolution analysis data on a 1 km ? 1 km grid as training data improves the forecast skill in terms of CRPS by about 35%, especially on the local scale. The percentage of outliers decreases significantly without loss of sharpness. Sensitivity studies confirm that about half of the total improvement can be attributed to the effect of a bias correction. The training length plays a minor role, at least for the chosen verification period. A rescaling of the predictive PDF is important in order to obtain sharp forecasts, especially in the short range. Applying the same method to the global ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF) gives improvements of similar magnitude. However, the calibrated 2-m temperature of ALADIN-LAEF still remains slightly better than the 2-m temperature from calibrated ECMWF-EPS, which leads to the conclusion that statistical downscaling of EPS cannot replace dynamical downscaling. Finally, an advanced version of NGR, the so-called NGR-TD, which uses time-weighted averaging within minimum CRPS estimation, is able to yield a further improvement of about 5% in terms of the CRPS. | |
