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- Title
Improving Precipitation Forecasts with Convolutional Neural Networks.
- Authors
Badrinath, Anirudhan; Delle Monache, Luca; Hayatbini, Negin; Chapman, Will; Cannon, Forest; Ralph, Marty
- Abstract
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 combination 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 greatest 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. Significance Statement: Extreme precipitation events and atmospheric rivers, which contain narrow bands of water vapor transport, can cause millions of dollars in damages. We demonstrate the utility of a computer vision-based machine learning technique for improving precipitation forecasts. We show that there is a significant increase in predictive accuracy for daily accumulated precipitation using these machine learning methods, over a 4-yr period of unseen cases, including those corresponding to the extreme precipitation associated with atmospheric rivers.
- Subjects
CONVOLUTIONAL neural networks; PRECIPITATION forecasting; EL Nino; METEOROLOGICAL research; WATER vapor transport; WEATHER forecasting; FORECASTING
- Publication
Weather & Forecasting, 2023, Vol 38, Issue 2, p291
- ISSN
0882-8156
- Publication type
Article
- DOI
10.1175/WAF-D-22-0002.1