IVT postprocessing model V 1.0

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. https://doi.org/10.6075/J0D798R5

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.

The Contents are divided into respective forecast hour as indicated by the filename (e.g., Forecast_GFS_003hr_Merra2grid.nc 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_***hr_Merra2grid.nc files:
Contains the GFS forecasted IVT.

Merra2_ROI_IVT_forecast_***hr.nc 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.

• 2018-01-11 to 2018-04-13

• 2019

• Chapman, William E.

• Kawzenuk, Brian K.

Software Citations:

In order to regrid the GFS model to the MERRA2 grid, climate data operators (CDO) package, remapcon function was used:

CDO (V1.9.3):
Schulzweida, U. (2019). CDO User Guide (Version 1.9.6). https://doi.org/10.5281/zenodo.2558193

The following packages were used to create the *.h5 models used to post-process the GFS forecasts towards the MERRA2 reanalysis.

Tensorflow (V.1.13.1):
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., … Zheng, X. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Retrieved from http://arxiv.org/abs/1603.04467

Keras (V.2.2.4):
Chollet, F. (2015). Keras. GitHub Repository. Retrieved from https://github.com/fchollet/keras

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.

• Atmospheric river
• Data postprocessing
• Machine learning

• data

• English

Doi: https://doi.org/10.6075/J0D798R5

Primary associated publication

• 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. https://doi.org/10.1029/2019GL083662

Source data

• GFS dataset: Moorthi, Ss., Pan, H.-L., & Caplan, P. (2001). Changes to the 2001 NCEP Operational MRF/AVN Global Analysis/ Forecast System. NWS Technical Procedures Bulletin, 484, 1–14. https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID4
• MERRA-2 dataset: https://disc.gsfc.nasa.gov/datasets?keywords=%22MERRA-2%22&page=1&source=Models%2FAnalyses%20MERRA-2

Software

• Chollet, F. (2015). Keras. GitHub Repository. Retrieved from: https://github.com/fchollet/keras

Reference

• Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., … Zheng, X. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. https://arxiv.org/abs/1603.04467
• 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. https://doi.org/10.1175/JCLI-D-16-0758.1
• Schulzweida, U. (2019). CDO User Guide (Version 1.9.6). https://doi.org/10.5281/zenodo.2558193

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2023-07-31