Time‐series‐based ensemble model output statistics for temperature forecasts postprocessing

The uncertainty in numerical weather prediction models is nowadays quantified by the use of ensemble forecasts. Although these forecasts are continuously improved, they still suffer from systematic bias and dispersion errors. Statistical postprocessing methods, such as the ensemble model output statistics (EMOS), have been shown to substantially correct the forecasts. This work proposes an extension of EMOS in a time‐series framework. Besides taking account of seasonality and trend in the location and scale parameter of the predictive distribution, the autoregressive process in the mean forecast errors or the standardized forecast errors is considered. The models can be further extended by allowing generalized autoregressive conditional heteroscedasticity. Furthermore, it is outlined how to use these models for arbitrary forecast horizons. To illustrate the performance of the suggested EMOS models in time‐series fashion, we present a case study for the postprocessing of 2 m surface temperature forecasts using five different lead times and a set of observation stations in Germany. The results indicate that the time‐series EMOS extensions are able to significantly outperform the benchmark models EMOS and autoregressive EMOS (AR‐EMOS) in most of the lead time–station cases. The standardized autoregressive SEMOS (SAR‐SEMOS) significantly outperforms the SEMOS (49%), AR‐EMOS (76%), and EMOS (97%) in terms of continuous ranked probability score (CRPS) across all lead times and stations. Furthermore, DAR‐SEMOS and DAR‐GARCH‐SEMOS provide significant improvements over the benchmark methods in terms of CRPS, which are however not so pronounced as for SAR‐SEMOS.