Improving Atmospheric River Forecasts With Machine Learning

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Abstract
This study tests the utility of convolutional neural networks as a postprocessing framework for improving the National Center for Environmental Prediction's Global Forecast System's integrated vapor transport forecast field in the Eastern Pacific and western United States. Integrated vapor transport is the characteristic field of atmospheric rivers, which provide over 65% of yearly precipitation at some western U.S. locations. The method reduces full‐field root‐mean‐square error (RMSE) at forecast leads from 3 hr to seven days (9–17% reduction), while increasing correlation between observations and predictions (0.5–12% increase). This represents an approximately one‐ to two‐day lead time improvement in RMSE. Decomposing RMSE shows that random error and conditional biases are predominantly reduced. Systematic error is reduced up to five‐day forecast lead, but accounts for a smaller portion of RMSE. This work demonstrates convolutional neural networks potential to improve forecast skill out to seven days for precipitation events affecting the western United States.

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