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Improving Precipitation Forecasts with Convolutional Neural Networks
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  • Anirudhan Badrinath,
  • Luca Delle Monache,
  • Negin Hayatbini,
  • William Eric Chapman,
  • Forest Cannon,
  • F. Martin Ralph
Anirudhan Badrinath
University of California, Berkeley

Corresponding Author:abadrinath@berkeley.edu

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Luca Delle Monache
University of California San Diego
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Negin Hayatbini
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography
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William Eric Chapman
University of California, San Diego
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Forest Cannon
University of California, San Diego
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F. Martin Ralph
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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.