Articles | Volume 27, issue 2
https://doi.org/10.5194/npg-27-349-2020
https://doi.org/10.5194/npg-27-349-2020
Research article
 | 
12 Jun 2020
Research article |  | 12 Jun 2020

Simulation-based comparison of multivariate ensemble post-processing methods

Sebastian Lerch, Sándor Baran, Annette Möller, Jürgen Groß, Roman Schefzik, Stephan Hemri, and Maximiliane Graeter

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Cited articles

Allen, S., Ferro, C. A. T., and Kwasniok, F.: Regime-dependent statistical post-processing of ensemble forecasts, Q. J. Roy. Meteor. Soc., 145, 3535–3552, https://doi.org/10.1002/qj.3638, 2019. a
Baran, S. and Lerch, S.: Mixture EMOS model for calibrating ensemble forecasts of wind speed, Environmetrics, 27, 116–130, https://doi.org/10.1002/env.2380, 2016. a
Baran, S. and Möller, A.: Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging, Environmetrics, 26, 120–132, https://doi.org/10.1002/env.2316, 2015. a, b
Baran, S. and Möller, A.: Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature, Meteorol. Atmos. Phys., 129, 99–112, https://doi.org/10.1007/s00703-016-0467-8, 2017. a
Ben Bouallègue, Z., Heppelmann, T., Theis, S. E., and Pinson, P.: Generation of Scenarios from Calibrated Ensemble Forecasts with a Dual-Ensemble Copula-Coupling Approach, Mon. Weather Rev., 144, 4737–4750, https://doi.org/10.1175/MWR-D-15-0403.1, 2016. a, b
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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.