Improving Precipitation Forecasts with Convolutional Neural Networks

A machine learning method based on spatial convolution to capture complex spatial precipitation patterns is proposed to identify and reduce biases affecting predictions of a dynamical model. The method is based on a combina-tion of a classification and dual-regression model approach using modified U-Net convolutional neural networks (CNN) to postprocess daily accumulated precipitation over the U.S. West Coast. In this study, we leverage 34 years of high-resolution deterministic Western Weather Research and Forecasting (West-WRF) precipitation reforecasts as training data for the U-Net CNN. The data are split such that the test set contains 4 water years of data that encompass characteristic West Coast precipitation regimes: El Niño, La Niña, and dry and wet El Niño–Southern Oscillation (ENSO neutral) water years. On the unseen 4-yr dataset, the trained CNN yields a 12.9%–15.9% reduction in root-mean-square error (RMSE) and 2.7%–3.4% improvement in Pearson correlation (PC) over West-WRF for lead times of 1–4 days. Compared to an adapted model output statistics correction, the CNN reduces RMSE by 7.4%–8.9% and improves PC by 3.3%–4.2% across 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, which we define as the top 10% of events with the great-est average daily accumulated precipitation. The improvement over West-WRF’s RMSE (PC) for these events is 19.8%–21.0% (4.9%–5.5%) and MOS’s RMSE (PC) is 8.8%–9.7% (4.2%–4.7%). Hence, the proposed U-Net CNN shows significantly improved forecast skill over existing methods, highlighting a promising path forward for improving precipitation forecasts.