EGU23-2592, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

U-Net based Methods for the Postprocessing of Precipitation Ensemble Forecasting

Romain Pic1, Clément Dombry1, Maxime Taillardat2, and Philippe Naveau3
Romain Pic et al.
  • 1Laboratoire de Mathématiques de Besançon, Université de Franche Comté, UMR CNRS 6623, Besançon, France
  • 2CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 3Laboratoire des Sciences du Climat et de l’Environnement, UMR 8212, CEA-CNRS-UVSQ, IPSL & U Paris-Saclay, Gif-sur-Yvette, France

Most Numerical Weather Prediction (NWP) systems use statistical postprocessing methods to correct for bias and underdispersion errors made by ensemble forecasting. This underdispersion leads to an underestimation of extreme events. Thus, many statistical postprocessing methods have been used to take into consideration the extremal behavior of meteorological phenomena such as precipitation. State-of-the-art techniques are based on Machine Learning combined with knowledge from Extreme Value Theory in order to improve forecasts. However, some of the best techniques do not consider the spatial dependency between locations. For example, Taillardat et al. (2019) trains a different Quantile Regression Forest at each location of interest and Rasp & Lerch (2018) uses neural networks with an embedding for the station's information in order to train a global model.
The dataset used corresponds to 3-h precipitation amounts produced by the radar-based observations of ANTILOPE and the 17-members ensemble forecast system called PEAROME. The dataset spans over the south of France with a grid resolution of 0.025 degrees. Our method uses a U-Net-like neural network in order to take into account the spatial structure of the data and the output of our model is a parameterized law at each grid point. Among the choices available in the literature, we focused on the Extended Generalized Pareto Distribution  and the truncated logistic with a point mass in 0. The model is trained by minimizing the scoring rules such as the Continuous Ranked Probability Score, the Log-Score or weighted versions of the aforementioned scoring rules. The method developed here is then compared to the raw ensemble as well as state-of-the-art techniques through scoring rules, skill scores and ROC curves.

References :

  • L. Pacchiardi, R. Adewoyin, P. Dueben, and R. Dutta. Probabilistic forecasting with generative networks via scoring rule minimization. Dec. 2021. arXiv:2112.08217
  • M. Taillardat, A.-L. Fougères, P. Naveau, and O. Mestre. Forest-based and semiparametric methods for the postprocessing of rainfall ensemble forecasting. Weather and Forecasting, 34(3):617–634, jun 2019. doi: 10.1175/waf-d-18-0149.1.

How to cite: Pic, R., Dombry, C., Taillardat, M., and Naveau, P.: U-Net based Methods for the Postprocessing of Precipitation Ensemble Forecasting, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2592,, 2023.