Articles | Volume 15, issue 6
https://doi.org/10.5194/essd-15-2635-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-2635-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 Jun 2023
Data description paper |
| 28 Jun 2023
| 28 Jun 2023
The EUPPBench postprocessing benchmark dataset v1.0
Jonathan Demaeyer
CORRESPONDING AUTHOR
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
European Meteorological Network (EUMETNET), Brussels, Belgium
Jonas Bhend
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
Sebastian Lerch
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Cristina Primo
Deutscher Wetterdienst, Offenbach, Germany
Bert Van Schaeybroeck
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
Aitor Atencia
GeoSphere Austria, Vienna, Austria
Zied Ben Bouallègue
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Jieyu Chen
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Markus Dabernig
GeoSphere Austria, Vienna, Austria
Gavin Evans
Met Office, Exeter, United Kingdom
Jana Faganeli Pucer
Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Ben Hooper
Met Office, Exeter, United Kingdom
Nina Horat
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
David Jobst
Institute of Mathematics and Applied Informatics, University of Hildesheim, Hildesheim, Germany
Janko Merše
Slovenian Environment Agency, Ljubljana, Slovenia
Peter Mlakar
Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Slovenian Environment Agency, Ljubljana, Slovenia
Annette Möller
Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
Olivier Mestre
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Météo-France, Toulouse, France
Maxime Taillardat
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Météo-France, Toulouse, France
Stéphane Vannitsem
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
European Meteorological Network (EUMETNET), Brussels, Belgium
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Cited
8 citations as recorded by crossref.
- Statistical post‐processing of visibility ensemble forecasts S. Baran & M. Lakatos 10.1002/met.2157
- Improving the blend of multiple weather forecast sources by Reliability Calibration F. Rust et al. 10.1002/met.2142
- Unveiling the backbone of the renewable energy forecasting process: Exploring direct and indirect methods and their applications A. Van Poecke et al. 10.1016/j.egyr.2023.12.031
- Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts M. Song et al. 10.1007/s00376-023-3184-5
- A two‐step machine‐learning approach to statistical post‐processing of weather forecasts for power generation Á. Baran & S. Baran 10.1002/qj.4635
- Parametric model for post-processing visibility ensemble forecasts Á. Baran & S. Baran 10.5194/ascmo-10-105-2024
- D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts D. Jobst et al. 10.1002/qj.4521
- Time‐series‐based ensemble model output statistics for temperature forecasts postprocessing D. Jobst et al. 10.1002/qj.4844
8 citations as recorded by crossref.
- Statistical post‐processing of visibility ensemble forecasts S. Baran & M. Lakatos 10.1002/met.2157
- Improving the blend of multiple weather forecast sources by Reliability Calibration F. Rust et al. 10.1002/met.2142
- Unveiling the backbone of the renewable energy forecasting process: Exploring direct and indirect methods and their applications A. Van Poecke et al. 10.1016/j.egyr.2023.12.031
- Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts M. Song et al. 10.1007/s00376-023-3184-5
- A two‐step machine‐learning approach to statistical post‐processing of weather forecasts for power generation Á. Baran & S. Baran 10.1002/qj.4635
- Parametric model for post-processing visibility ensemble forecasts Á. Baran & S. Baran 10.5194/ascmo-10-105-2024
- D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts D. Jobst et al. 10.1002/qj.4521
- Time‐series‐based ensemble model output statistics for temperature forecasts postprocessing D. Jobst et al. 10.1002/qj.4844
Latest update: 23 Nov 2024
Short summary
A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
A benchmark dataset is proposed to compare different statistical postprocessing methods used in...
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