Articles | Volume 27, issue 2
https://doi.org/10.5194/npg-27-349-2020
© Author(s) 2020. This work is distributed under
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the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/npg-27-349-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulation-based comparison of multivariate ensemble post-processing methods
Institute for Stochastics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Sándor Baran
Department of Applied Mathematics and Probability Theory, University of Debrecen, Debrecen, Hungary
Annette Möller
Institute for Mathematics, Technical University of Clausthal, Clausthal, Germany
Jürgen Groß
Institute for Mathematics and Applied Informatics, University of Hildesheim, Hildesheim, Germany
Roman Schefzik
German Cancer Research Center (DKFZ), Heidelberg, Germany
Stephan Hemri
Federal Office of Meteorology and Climatology MeteoSwiss,
Zurich-Airport, Switzerland
Maximiliane Graeter
Institute for Stochastics, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Weather forecasts 14 days in advance generally have a low skill but not always. We identify reasons thereof depending on the atmospheric flow, shown by Weather Regimes (WRs). If the WRs during the forecasts follow climatological patterns, forecast skill is increased. The forecast of a cold-wave day is better when the European Blocking WR (high pressure around the British Isles) is present a few days before a cold-wave day. These results can be used to assess the reliability of predictions.
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Stephan Hemri, Sebastian Lerch, Maxime Taillardat, Stéphane Vannitsem, and Daniel S. Wilks
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Stephan Hemri, Sebastian Lerch, Maxime Taillardat, Stéphane Vannitsem, and Daniel S. Wilks
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Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
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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 occurring in practical applications and allow ready interpretation of the effects of different misspecifications of the numerical weather prediction ensemble.
Accurate models of spatial, temporal, and inter-variable dependencies are of crucial importance...