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IVT postprocessing model V 1.0
Contributing Institution
UC San Diego, Library, Research Data Curation Program
Integrated Vapor Transport Forecast Models, Reanalysis, and Postprocessing Machine Learning Models for the North American West Coast [2008-2017]
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Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.
Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
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UC Regents
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 river (AR) events, which provide over 65% of yearly precipitation at some U.S. west-coast locations. When compared to GFS, 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). An RMSE decomposition shows that random error is predominantly reduced. Systematic error is also reduced up to 5-day forecast lead, but accounts for a smaller portion of RMSE. This work demonstrates that CNNs have the potential to improve forecast skill out to 7 days for precipitation events affecting the western U.S. This dataset includes: the calculated IVT from the National Center for Environmental Prediction’s GFS forecasts; the IVT from the National Aeronautics and Space Administration’s Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis; the Machine Learning Models used to Postprocess the GFS IVT. All data sets are for the North American West Coast and the Eastern Pacific.
This study is supported by the U.S. Army Corps of Engineers (USACE)-Cooperative Ecosystem Studies Unit (CESU) as part of Forecast Informed Reservoir Operations (FIRO) under grant W912HZ-15-2-0019 and the California Department of Water Resources Atmospheric River Program under grant number 4600010378 TO#15 Am 22.
Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (
Chapman, William E.; Kawzenuk, Brian (2019). IVT postprocessing model V 1.0. In Integrated Vapor Transport Forecast Models, Reanalysis, and Postprocessing Machine Learning Models for the North American West Coast [2008-2017]. UC San Diego Library Digital Collections.
Is Referenced By: Chapman, W. E., Subramanian, A. C., Delle Monache, L., Xie, S. P., & Ralph, F. M. ( 2019). Improving Atmospheric River Forecasts with Machine Learning. Geophysical Research Letters, 46. References: MERRA-2 publication: Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., … Zhao, B. (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454.
The Contents are divided into respective forecast hour as indicated by the filename (e.g., contains the 003-hour forecast). GFS and MERRA-2 files contain the forecasted IVT magnitude and the reanalysis MERRA-2 IVT magnitude realized on the same day. Time documentation is contained in the Forecast_*hr_times.mat files and contains valid dates for both MERRA-2 and GFS. Forecast_GFS_*** files: Contains the GFS forecasted IVT. Merra2_ROI_IVT_forecast_*** files: Contains the MERRA-2 reanalysis IVT valid on the forecast day. GFSnn_***hr_mse.h5 Contains the Post-Processing Model to correct the ***-hour forecast field. Forecast_***hr_times.mat Contains valid time files for GFS and MERRA-2 models.
Data postprocessing
Machine learning
Atmospheric rivers

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