Articles | Volume 27, issue 2
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.
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.
Research article
| 
12 Jun 2020
Research article |
| 12 Jun 2020
| 12 Jun 2020
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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Cited
16 citations as recorded by crossref.
- A two‐step machine‐learning approach to statistical post‐processing of weather forecasts for power generation Á. Baran & S. Baran 10.1002/qj.4635
- Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts M. Lakatos et al. 10.1002/qj.4436
- Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution L. Li et al. 10.1016/j.atmosres.2023.107133
- Truncated generalized extreme value distribution‐based ensemble model output statistics model for calibration of wind speed ensemble forecasts S. Baran et al. 10.1002/env.2678
- Probabilistic solar forecasting: Benchmarks, post-processing, verification T. Gneiting et al. 10.1016/j.solener.2022.12.054
- The EUPPBench postprocessing benchmark dataset v1.0 J. Demaeyer et al. 10.5194/essd-15-2635-2023
- Ensemble forecasting for intraday electricity prices: Simulating trajectories M. Narajewski & F. Ziel 10.1016/j.apenergy.2020.115801
- Statistical post‐processing of visibility ensemble forecasts S. Baran & M. Lakatos 10.1002/met.2157
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. 10.5194/npg-27-519-2020
- Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions P. Schaumann et al. 10.1002/qj.4751
- Parametric model for post-processing visibility ensemble forecasts Á. Baran & S. Baran 10.5194/ascmo-10-105-2024
- Forecasting: theory and practice F. Petropoulos et al. 10.1016/j.ijforecast.2021.11.001
- Multivariate post‐processing of probabilistic sub‐seasonal weather regime forecasts F. Mockert et al. 10.1002/qj.4840
- Generative machine learning methods for multivariate ensemble postprocessing J. Chen et al. 10.1214/23-AOAS1784
- Spatially Coherent Postprocessing of Cloud Cover Ensemble Forecasts 10.1175/MWR-D-21-0046.1
- Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting B. Schulz et al. 10.1016/j.solener.2021.03.023
16 citations as recorded by crossref.
- A two‐step machine‐learning approach to statistical post‐processing of weather forecasts for power generation Á. Baran & S. Baran 10.1002/qj.4635
- Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts M. Lakatos et al. 10.1002/qj.4436
- Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution L. Li et al. 10.1016/j.atmosres.2023.107133
- Truncated generalized extreme value distribution‐based ensemble model output statistics model for calibration of wind speed ensemble forecasts S. Baran et al. 10.1002/env.2678
- Probabilistic solar forecasting: Benchmarks, post-processing, verification T. Gneiting et al. 10.1016/j.solener.2022.12.054
- The EUPPBench postprocessing benchmark dataset v1.0 J. Demaeyer et al. 10.5194/essd-15-2635-2023
- Ensemble forecasting for intraday electricity prices: Simulating trajectories M. Narajewski & F. Ziel 10.1016/j.apenergy.2020.115801
- Statistical post‐processing of visibility ensemble forecasts S. Baran & M. Lakatos 10.1002/met.2157
- Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. 10.5194/npg-27-519-2020
- Generating synthetic rainfall fields by R‐vine copulas applied to seamless probabilistic predictions P. Schaumann et al. 10.1002/qj.4751
- Parametric model for post-processing visibility ensemble forecasts Á. Baran & S. Baran 10.5194/ascmo-10-105-2024
- Forecasting: theory and practice F. Petropoulos et al. 10.1016/j.ijforecast.2021.11.001
- Multivariate post‐processing of probabilistic sub‐seasonal weather regime forecasts F. Mockert et al. 10.1002/qj.4840
- Generative machine learning methods for multivariate ensemble postprocessing J. Chen et al. 10.1214/23-AOAS1784
- Spatially Coherent Postprocessing of Cloud Cover Ensemble Forecasts 10.1175/MWR-D-21-0046.1
- Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting B. Schulz et al. 10.1016/j.solener.2021.03.023
Latest update: 20 Nov 2024
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...
