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 Demaeyer, jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem

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: final response (author comments only)

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
  • RC2: 'Comment on essd-2022-465', Anonymous Referee #2, 05 Feb 2023
  • RC3: 'Comment on essd-2022-465', Anonymous Referee #3, 11 Feb 2023

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.