  Nowadays, weather prediction is based on numerical weather prediction (NWP)
models to produce an ensemble of forecasts. Despite of large improvements over
the last few decades, they still tend to exhibit systematic bias and dispersion
errors. Consequently, these forecasts may be improved by statistical
postprocessing. This work proposes an extension of the ensemble model output
statistics (EMOS) method in a time series framework. Besides of 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 (GARCH).
Last but not least, 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 EMOS and
autoregressive adjusted EMOS (AR-EMOS) in most of the lead time-station cases.
To complement this article, our method is accompanied by an R-package called
tsEMOS.
