Leveraging spatial patterns in precipitation forecasts using deep learning to support risk-averse flood management

Short-term forecasts of heavy precipitation are critical to regional flood control operations, particularly in the Western U.S. where atmospheric rivers can be predicted reliably days in advance. However, spatial error in these forecasts may reduce their utility for risk-averse system operations, where false negatives could be especially costly. Here we investigate whether deep learning methods can leverage spatial patterns in precipitation forecasts to (1) improve the skill of predicting the occurrence of heavy precipitation events in a target region at lead times from 1-14 days, and (2) balance the tradeoff between the rate of false negatives and false positives (misses and false alarms) by modifying the discrimination threshold of the classifiers. This approach is demonstrated for the Sacramento River Basin, California, using the Global Ensemble Forecast System (GEFS) v2 precipitation fields as input to convolutional neural network (CNN) models. Results show that the deep learning mo...