Benchmarks, datasets & evaluation★ Highlight

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 et al.

Earth System Science Data · 28 June 2023 · Deutscher Wetterdienst (DWD), ECMWF, Karlsruhe Institute of Technology (KIT), Met Office, Météo-France (incl. CNRM), MeteoSwiss

PaperCode

Why it matters. Standardized open benchmark dataset for ML postprocessing of ensemble forecasts from major European centres; valuable community infrastructure.

In one sentence. Releases EUPPBench, a time-aligned forecast-observation dataset with evaluation methods and a first benchmark of 2 m temperature postprocessing.

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 (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.

Labels

Topic

Benchmarks, datasets & evaluation, Post-processing & calibration

Type

Benchmark or dataset

Method

Other

Time scale

3–15 days

Domain

Regional

Data

NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ

Status

Research

Identifiers

DOI:10.5194/essd-15-2635-2023 OpenAlex:W4383056506

Added

2026-10-22

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