B0539
Title: Autoregressive extensions of EMOS with application to surface temperature ensemble postprocessing
Authors: David Jobst - University of Hildesheim (Germany)
Juergen Gross - University of Hildesheim (Germany)
Annette Moeller - Bielefeld University (Germany) [presenting]
Abstract: Two extensions of the autoregressive adjusted EMOS (AR-EMOS) which are based on the idea of the smooth EMOS (SEMOS) model are proposed: The deseasonalized SEMOS (DAR-SEMOS) approach models time series behaviour 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 for the incorporation of seasonal and trend effects as well as autoregressive behaviour into the mean and variance parameters of the predictive distribution. Due to this explicit modelling of seasonal and trend behaviour, a rolling training period is not required anymore, and a longer static training period can be utilized. The extended models can post-process 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 behaviour in the mean and variance parameters makes it appealing for practical and possibly operational use.
