Skewed logistic distribution for statistical temperature post-processing in mountainous areas

A Skewed Working Paper!

This study shows the use and benefits of having a skewed response distribution rather than a symmetric one for ensemble post-processing of near surface temperature in Central Europe. A small adjustment which can strongly improve the predictive performance in alpine areas where the current ensemble forecast systems miss some of the wintery cold pools and overadiabatic heating of the valley atmosphere.

Short overview

The manuscript by Manuel Gebetsberger can be found on the EconPaper Series of the Faculty of Economics and Statistics of the University of Innsbruck (download pdf).


Non-homogeneous post-processing is often used to improve the predictive performance of probabilistic ensemble forecasts. A common quantity to develop, test, and demonstrate new methods is the near-surface air temperature frequently assumed to follow a Gaussian response distribution. However, Gaussian regression models with only few covariates are often not able to account for site-specific local features leading to strongly skewed residuals. This residual skewness remains even if many covariates are incorporated. Therefore, a simple refinement of the classical non-homogeneous Gaussian regression model is proposed to overcome this problem by assuming a skewed response distribution to account for possible skewness. This study shows a comprehensive analysis of the performance of non-homogeneous post-processing for 2m temperature for three different site types comparing Gaussian, logistic, and skewed logistic response distributions. Satisfying results for the skewed logistic distribution are found, especially for sites located in mountainous areas. Moreover, both alternative model assumptions but in particular the skewed response distribution, can improve on the classical Gaussian assumption with respect to overall performance, sharpness, and calibration of the probabilistic predictions.

techreport workingpaper