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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 373
Proc. IAHS, 373, 109–114, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 373, 109–114, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  12 May 2016

12 May 2016

Regionalization of post-processed ensemble runoff forecasts

Jon Olav Skøien1, Konrad Bogner2, Peter Salamon1, Paul Smith3, and Florian Pappenberger3 Jon Olav Skøien et al.
  • 1European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability, Ispra, 21027 (VA), Italy
  • 2Swiss Federal Institute WSL, Mountain Hydrology and Mass Movements, Birmensdorf, 8903, Switzerland
  • 3European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK

Abstract. For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (, where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

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