Preprints
https://doi.org/10.5194/essd-2022-465
https://doi.org/10.5194/essd-2022-465
 
12 Jan 2023
12 Jan 2023
Status: this preprint is currently under review for the journal ESSD.

The EUPPBench postprocessing benchmark dataset v1.0

Jonathan Demaeyer1,2, jonas Bhend3, Sebastian Lerch4, Cristina Primo5, Bert Van Schaeybroeck1, Aitor Atencia6, Zied Ben Bouallègue7, Jieyu Chen4, Markus Dabernig6, Gavin Evans8, Jana Faganeli Pucer9, Ben Hooper8, Nina Horat4, David Jobst10, Janko Merše11, Peter Mlakar9,11, Annette Möller12, Olivier Mestre13, Maxime Taillardat13, and Stéphane Vannitsem1,2 Jonathan Demaeyer et al.
  • 1Royal Meteorological Institute of Belgium, Brussels, Belgium
  • 2European Meteorological Network (EUMETNET), Brussels, Belgium
  • 3Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
  • 4Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 5Deutscher Wetterdienst, Offenbach, Germany
  • 6GeoSphere Austria, Vienna, Austria
  • 7European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
  • 8Met Office, Exeter, United Kingdom
  • 9University of Ljubljana, Faculty of Computer and Information Science, Slovenia
  • 10University of Hildesheim, Hildesheim, Germany
  • 11Slovenian Environment Agency, Ljubljana, Slovenia
  • 12Bielefeld University, Bielefeld, Germany
  • 13Meteo France, Ecole Nationale de la Meteorologie, Toulouse, France

Abstract. Statistical Postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods have been proposed, complementing an already very diverse field. However, one of the questions that frequently arises when considering different methods in the framework of implementing operational postprocessing is the relative performance of the methods for a given specific task. It is particularly challenging to find or construct a common comprehensive dataset that can be used to perform such comparisons. Here, we introduce the first version of EUPPBench, a dataset of time-aligned forecasts and observations, with the aim to facilitate and standardize this process. This dataset is publicly available at https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark. We provide examples on how to download and use the data, propose a set of evaluation methods, and perform a first benchmark of several methods for the correction of 2-meter temperature forecasts.

Jonathan Demaeyer et al.

Status: open (until 09 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-465', Anonymous Referee #1, 16 Jan 2023 reply

Jonathan Demaeyer et al.

Data sets

EUPPBench postprocessing benchmark dataset - gridded data Demaeyer, Jonathan https://doi.org/10.5281/zenodo.7429236

EUPPBench postprocessing benchmark dataset - station data Bhend, Jonas; Dabernig, Markus; Demaeyer, Jonathan; Mestre, Olivier; Taillardat, Maxime https://doi.org/10.5281/zenodo.7428239

ESSD benchmark output data Chen, Jieyu; Dabernig, Markus; Demaeyer, Jonathan; Evans, Gavin; Faganeli Pucer, Jana; Hooper, Ben; Horat, Nina; Jobst, David; Lerch, Sebastian; Mlakar, Peter; Möller, Annette; Merše, Janko; Zied Ben Bouallègue https://doi.org/10.5281/zenodo.7469465

Model code and software

ESSD benchmark code Chen, Jieyu; Dabernig, Markus; Demaeyer, Jonathan; Evans, Gavin; Hooper, Ben; Horat, Nina; Jobst, David; Lerch, Sebastian; Mlakar, Peter; Möller, Annette; Zied Ben Bouallègue https://github.com/EUPP-benchmark/ESSD-benchmark

Jonathan Demaeyer et al.

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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-meter temperature forecasts is performed.