期刊
GEOPHYSICAL RESEARCH LETTERS
卷 46, 期 17-18, 页码 10627-10635出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2019GL083662
关键词
atmospheric river; machine learning; convolutional neural network; postprocess; forecasting
资金
- U.S. Army Corps of Engineers (USACE)-Cooperative Ecosystem Studies Unit (CESU) as part of Forecast Informed Reservoir Operations (FIRO) [W912HZ-15-2-0019]
- California Department of Water Resources Atmospheric River Program [4600010378 TO 15 Am 22]
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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