The EUPPBench postprocessing benchmark dataset
- 1Royal Meteorological Institute, Climatological Research, Brussels, Belgium (jodemaey@meteo.be)
- 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
- 13CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
- 14Météo-France, Toulouse, France
Statistical postprocessing of forecasts produced by numerical weather prediction systems is an important part of modern weather forecasting systems.
Since the beginning of modern data science, numerous postprocessing methods have been proposed, and one of the questions that frequently arises is the relative performance of the methods for a given specific task.
However, a comprehensive, community-driven comparison of their relative performance is yet to be established. One of the main reasons for this lack is the difficulty of constructing a common comprehensive dataset that can be used to perform such intercomparison.
Here we introduce the first version of the EUPPBench, a dataset of time-aligned medium-range forecasts and observations over Central Europe, with the aim to facilitate and standardize the intercomparison of postprocessing methods. This dataset is publicly available [1], includes station and gridded data based on the ECMWF model forecasts [2]. The initial dataset is the basis of an ongoing project to establish a benchmarking platform for postprocessing of medium-range weather forecasts. We showcase a first benchmark of several methods for the adjustment of near-surface temperature forecasts and outline the future plans for the benchmark project activities.
[1] https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark
[2] Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-465, in review, 2023.
How to cite: Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-229, https://doi.org/10.5194/ems2023-229, 2023.
