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

TL;DR

This paper introduces a U-Net-based distributional regression method for postprocessing precipitation ensemble forecasts, improving heavy precipitation predictions and competing with existing methods in probabilistic accuracy.

Contribution

The paper presents a novel distributional regression U-Net approach for global precipitation forecast postprocessing, addressing challenges with data availability and enhancing heavy precipitation event prediction.

Findings

Comparable to quantile regression forests in CRPS

Outperforms QRF in heavy precipitation prediction

Struggles with calibration in high climatological areas

Abstract

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 M\'et\'eo-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.