Time Series Model based Probabilistic Temperature Forecasting

Annette Möller, Bielefeld University

Co-authors: David Jobst, University of Hildesheim; Jürgen Groß, University of Hildesheim

Abstract: Weather prediction is traditionally conducted via numerical weather prediction (NWP) models. The uncertainty in NWP models is quantified via ensemble prediction systems (EPS). Although NWP forecasts continue to be improved, they still suffer from systematic bias and dispersion errors. Statistical postprocessing methods such as ensemble model output statistics (EMOS) have been shown to successfully correct the forecasts. We propose extensions of EMOS in a time series framework and apply them for postprocessing of 2 m surface temperature forecasts at observation stations in Germany.