The EUPPBench postprocessing benchmark dataset v1.0

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

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 (EUMETNET postprocessing benchmark), 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 (31 December 2022) and on Zenodo (https://doi.org/10.5281/zenodo.7429236, Demaeyer, 2022b and https://doi.org/10.5281/zenodo.7708362, Bhend et al., 2023). We provide examples showing how to download and use the data, we propose a set of evaluation methods, and we perform a first benchmark of several methods for the correction of 2 m temperature forecasts.

• Language: English
• Keywords: benchmark dataset, ensemble weather forecasts, post-processing, temperature
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: Str. 2635-2653
• Numbering: Vol. 15, iss. 6
• PID: 20.500.12556/RUL-152342
• UDC: 004:551.509
• ISSN on article: 1866-3508
• DOI: 10.5194/essd-15-2635-2023
• COBISS.SI-ID: 157415427
• Publication date in RUL: 21.11.2023

DEMAEYER, Jonathan, BHEND, Jonas, LERCH, Sebastian, PRIMO, Cristina, VAN SCHAEYBROECK, Bert, ATENCIA, Aitor, BOUALLÈGUE, Zied Ben, CHEN, Jieyu, DABERNIG, Markus, EVANS, Gavin, FAGANELI PUCER, Jana, HOOPER, Ben, HORAT, Nina, JOBST, David, MERŠE, Janko, MLAKAR, Peter, MÖLLER, Annette, MESTRE, Olivier, TAILLARDAT, Maxime and VANNITSEM, Stéphane, 2023, The EUPPBench postprocessing benchmark dataset v1.0. Earth system science data [online]. 2023. Vol. 15, no. 6, p. 2635–2653. [Accessed 13 March 2025]. DOI 10.5194/essd-15-2635-2023. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=152342

Record is a part of a journal

• Title: Earth system science data
• Shortened title: Earth syst. sci. data
• Publisher: Copernicus Publications
• ISSN: 1866-3508
• COBISS.SI-ID: 517920537

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: podatkovna zbirka, ansambelske vremenske napovedi, statistično poprocesiranje, temperatura