Leveraging spatial patterns in precipitation forecasts using deep learning to support risk-averse flood management
Zhang, Chen; Brodeur, Zachary; Steinschneider, Scott; Herman, Jonathan (2022), Leveraging spatial patterns in precipitation forecasts using deep learning to support risk-averse flood management, Dryad, Dataset, https://doi.org/10.25338/B8CH1F
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 models do not significantly improve the overall skill (F1 score) relative to the bias-corrected ensemble mean GEFS forecast. However, the models often correct missed predictions from GEFS by compensating for spatial error at longer lead times. Additionally, the deep learning models provide the ability to adjust the rate of false negatives based on a desired level of risk aversion, with the tradeoff of increasing the false positive rate. Finally, analysis of the network activations (saliency) indicates spatial patterns consistent with physical understanding of atmospheric river events in this region, lending additional confidence in the ability of the method to support flood management.
National Science Foundation, Award: 1803563
National Science Foundation, Award: 1803589