A hybrid analog-ensemble, convolutional-neural-network method for post-processing precipitation forecasts

Abstract

Abstract An ensemble precipitation forecast post-processing method is proposed by hybridizing the Analog Ensemble (AnEn), Minimum Divergence Schaake Shuffle (MDSS), and Convolutional Neural Network (CNN) methods. This AnEn-CNN hybrid takes the ensemble mean of Global Ensemble Forecast System (GEFS) 3-hourly precipitation forecasts as input and produces bias-corrected, probabilistically calibrated, and physically realistic gridded precipitation forecast sequences out to 7-days. The AnEn-CNN hybrid post-processing is trained on the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and verified against station observations across British Columbia (BC), Canada, from 2017 to 2019. The AnEn-CNN hybrid produces more skillful forecasts than a quantile-mapped GEFS baseline and other conventional AnEn methods, with a roughly 10% increase in Continuous Ranked Probability Skill Score. Further, it outperforms other AnEn methods by 0-60% in terms of Brier Skill Score (BSS) for heavy precipitation periods across disparate hydrological regions. Longer forecast lead times exhibit larger performance gains. Verification against 7-day accumulated precipitation totals for heavy precipitation periods also demonstrates that precipitation sequences are realistically reconstructed. Case studies further show that the AnEn-CNN hybrid scheme produces more realistic spatial precipitation patterns and precipitation intensity spectra. This work pioneers the combination of conventional statistical post-processing and neural networks, and is one of only a few studies pertaining to precipitation ensemble post-processing in BC.