Publication Date:
2019
Description:
Abstract
This study tests the utility of convolutional neural networks (CNN) as a postprocessing framework for improving the National Center for Environmental Prediction's Global Forecast System's (GFS) integrated vapor transport (IVT) forecast field in the Eastern Pacific and Western United States. IVT 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 squared error (RMSE) at forecast leads from 3 hours to 7 days (9‐17% reduction), while increasing correlation between observations and predictions (0.5‐12% increase). This represents a ~1‐2‐day lead time improvement in RMSE. Decomposing RMSE shows that random error and conditional biases are predominantly reduced. Systematic error is reduced up to 5‐days forecast lead, but accounts for a smaller portion of RMSE. This work demonstrates CNNs potential to improve forecast skill out to 7 days for precipitation events affecting the western U.S.
Print ISSN:
0094-8276
Electronic ISSN:
1944-8007
Topics:
Geosciences
,
Physics
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