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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/npg-2019-62
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-62
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Jan 2020

Submitted as: research article | 16 Jan 2020

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This preprint is currently under review for the journal NPG.

Simulation-based comparison of multivariate ensemble post-processing methods

Sebastian Lerch1, Sándor Baran2, Annette Möller3, Jürgen Groß4, Roman Schefzik5, Stephan Hemri6, and Maximiliane Graeter1 Sebastian Lerch et al.
  • 1Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 2University of Debrecen, Debrecen, Hungary
  • 3Technical University of Clausthal, Clausthal, Germany
  • 4University of Hildesheim, Hildesheim, Germany
  • 5German Cancer Research Center (DKFZ), Heidelberg, Germany
  • 6Federal Office of Meteorology and Climatology MeteoSwiss, Zurich-Airport, Switzerland

Abstract. Many practical applications of statistical post-processing methods for ensemble weather forecasts require to accurately model spatial, temporal and inter-variable dependencies. Over the past years, a variety of approaches has been proposed to address this need. We provide a comprehensive review and comparison of state of the art methods for multivariate ensemble post-processing. We focus on generally applicable two-step approaches where ensemble predictions are first post-processed separately in each margin, and multivariate dependencies are restored via copula functions in a second step. The comparisons are based on simulation studies tailored to mimic challenges occurring in practical applications and allow to readily interpret the effects of different types of misspecifications in the mean, variance and covariance structure of the ensemble forecasts on the performance of the post-processing methods. Overall, we find that the Schaake shuffle provides a compelling benchmark that is difficult to outperform, whereas the forecast quality of parametric copula approaches and variants of ensemble copula coupling strongly depend on the misspecifications at hand.

Sebastian Lerch et al.

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Sebastian Lerch et al.

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Latest update: 23 Feb 2020
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Short summary
Accurate models of spatial, temporal and inter-variable dependencies are of crucial importance for many practical applications. We review and compare several methods for multivariate ensemble post-processing, where such dependencies are imposed via copula functions. Our investigations utilize simulation studies that mimic challenges occuring in practical applications and allow to readily interpret the effects of different types of misspecifications of the numerical weather prediction ensemble.
Accurate models of spatial, temporal and inter-variable dependencies are of crucial importance...
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