Autoregressive extensions of EMOS with application to surface temperature ensemble postprocessing

Two extensions of the autoregressive EMOS (AR-EMOS) which are based on the idea of smooth EMOS (SEMOS) model are proposed: The de-seasonalized EMOS (DAR-SEMOS) approach models time series behavior in the mean and variance of the predictive distribution separately, the standardized AR-SEMOS (SAR-SEMOS) method attempts to incorporate both effects jointly by fitting a time series model to the standardized forecast errors. The proposed modifications both allow to incorporate seasonal and trend effects as well as autoregressive behavior into the mean and variance parameter of the predictive distribution. Due to this explicit modelling of seasonal and trend behavior a rolling training period is not required anymore, and a longer (static) training period can be utilized for model fitting. The extended models can postprocess ensemble forecasts with arbitrary forecast horizons. In a case study for 2m surface temperature the extensions DAR- and SAR-SEMOS yield substantial improvements over AR-EMOS and SEMOS, for all considered forecast horizons and at the majority of observations stations. Overall, the SAR-SEMOS model yields the most noticeable improvements. At the same time its seamless approach of jointly modelling the time series behavior in the mean and variance parameter makes it appealing for practical and possibly operational use.