The EUPPBench postprocessing benchmark
- 1Royal Meteorological Institute of Belgium, Brussels, Belgium
- 2European Meteorological Network (EUMETNET), Brussels, Belgium
- 3Federal Office of Meteorology and Climatology MeteoSwiss, Zürich-Flughafen, Switzerland (jonas.bhend@meteoswiss.ch)
- 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
- 13Meteo France, Ecole Nationale de la Météorologie, Toulouse, France
Statistical postprocessing of forecasts from numerical weather prediction systems is an important component of modern weather forecasting systems. A growing variety of postprocessing methods has been proposed, but a comprehensive, community-driven comparison of their relative performance is yet to be established. Important reasons for this lack include the absence of a fair intercomparison protocol, and, 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, ensemble forecasts for training (20 years) and validation (2 years) based on the ECMWF system. The initial dataset is the basis of an ongoing activity 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 activity.
[1] https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark
How to cite: Bhend, J., Demaeyer, 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, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9328, https://doi.org/10.5194/egusphere-egu23-9328, 2023.
