Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2024

Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts

Résumé

Accurate precipitation forecasts have a high socio-economic value due to their role in decision-making in various fields such as transport networks and farming. We propose a global statistical postprocessing method for grid-based precipitation ensemble forecasts. This U-Net-based distributional regression method predicts marginal distributions in the form of parametric distributions inferred by scoring rule minimization. Distributional regression U-Nets are compared to state-of-the-art postprocessing methods for daily 21-h forecasts of 3-h accumulated precipitation over the South of France. Training data comes from the Météo-France weather model AROME-EPS and spans 3 years. A practical challenge appears when consistent data or reforecasts are not available. Distributional regression U-Nets compete favorably with the raw ensemble. In terms of continuous ranked probability score, they reach a performance comparable to quantile regression forests (QRF). However, they are unable to provide calibrated forecasts in areas associated with high climatological precipitation. In terms of predictive power for heavy precipitation events, they outperform both QRF and semi-parametric QRF with tail extensions
Fichier principal
Vignette du fichier
Preprint_UNets.pdf (2.66 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04631942 , version 1 (02-07-2024)

Licence

Identifiants

  • HAL Id : hal-04631942 , version 1

Citer

Romain Pic, Philippe Naveau, Clément Dombry, Maxime Taillardat. Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts. 2024. ⟨hal-04631942⟩
0 Consultations
0 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More