Abstract
Traditional post-processing methods have relied on point-based
applications that are unable to capture complex spatial precipitation
error patterns. With novel ML methods using convolution to more
effectively identify and reduce spatial biases, we propose a modified
U-Net convolutional neural network (CNN) to post-process daily
accumulated precipitation over the US west coast. For training, we
leverage 34 years of deterministic Western Weather Research and
Forecasting (West-WRF) reforecasts. On an unseen 4-year data set, the
trained CNN yields a 12.9-15.9% reduction in root mean-square error
(RMSE) over West-WRF for lead times of 1-4 days. Compared to an adapted
Model Output Statistics baseline, the CNN reduced RMSE by 7.4-8.9% for
all events. Effectively, the CNN adds more than a day of predictive
skill when compared to West-WRF. The CNN outperforms the other methods
also for the prediction of extreme events, highlighting a promising path
forward for improving precipitation forecasts.