A Hybrid Analog-Ensemble-Convolutional-Neural-Network Method for Postprocessing Precipitation Forecasts

Abstract

An ensemble precipitation forecast postprocessing 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 postprocessing 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 postprocessing and neural networks, and is one of only a few studies pertaining to precipitation ensemble postprocessing in BC.