The EUPPBench postprocessing benchmark dataset v1.0

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

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<p align=justify>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 <span class="uri"></span> (31 December 2022) and on Zenodo (<a href=""></a>, <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx16">Demaeyer</a>, <a href="#bib1.bibx16">2022</a><a href="#bib1.bibx16">b</a></span> and <a href=""></a>, <span class="cit" id="xref_altparen.2"><a href="#bib1.bibx5">Bhend et al.</a>, <a href="#bib1.bibx5">2023</a></span>). 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.</p>
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