11B.3 Snow Hydrologic Data Assimilation for Water Supply Forecasting with the National Water Model: Observation Error Characterization and Particle Filtering Results

Thursday, 11 January 2018: 12:00 AM
Room 18B (ACC) (Austin, Texas)
James McCreight, NCAR, Boulder, CO; and L. Karsten, P. Romanov, D. Gochis, A. Dugger, J. Poterjoy, T. H. Painter, and J. Deems

The recent study of Liu et al. (2015) suggests that the assimilation of appropriately bias-corrected passive microwave (PM) satellite SWE products into a hydrologic model can improve seasonal streamflow predictions. We explore a similar method in several snow-dominated, mountainous basins in the Western US. These basins have the luxury of repeat airborne LiDAR snow depth retrievals and snow water equivalent estimates, provided by the NASA/JPL Airborne Snow Observatory (Painter et al., 2016). These observations are used for an in-depth exploration of the error characteristics of the satellite snow products. Such valuation of PM retrievals may expand with the ASO program.

This study focuses on both an operational hydrologic model (the National Water Model) and on operational satellite products (NOAA JPSS), though over a retrospective analysis period using ensemble historical (NLDAS2) forcing data to produce seasonal forecasts. The operational SNODAS (Barrett, 2003)product is also compared to the bias-corrected PM satellite products throughout the study.

The bias correction procedure is applied to PM snow depth estimates/observations from ATMS and AMSR2. Bias correction procedures use operationally available snow depth and SWE point observations in the vicinity, methods based on terrain aspect, and snow covered area data from the VIIRS satellite. Raw and bias corrected products and the SNODAS product are compared to the ASO LiDAR snow depth observations.

Bias-corrected PM products and SNODAS are assimilated into the NWM using a localized particle filter (Poterjoy, 2016) implemented in NCAR’s Data Assimilation Research Testbed (Anderson et al., 2009). In addition to forcing error, both model structural and parameter errors are considered in the ensemble assimilation.

Results will provide insight into the limits of current bias correction methods. Assimilation results will highlight the value of various operational observations for diagnosing model errors and for seasonal streamflow forecasting.


Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., & Avellano, A. (2009). The Data Assimilation Research Testbed: A Community Facility. Bulletin of the American Meteorological Society, 90(9), 1283–1296.

Barrett, A. P. (2003). National operational hydrologic remote sensing center snow data assimilation system (SNODAS) products at NSIDC. Retrieved from

Liu, Y., Peters-Lidard, C. D., Kumar, S. V., Arsenault, K. R., & Mocko, D. M. (2015). Blending satellite-based snow depth products with in situ observations for streamflow predictions in the Upper Colorado River Basin. Water Resources Research. http://onlinelibrary.wiley.com/doi/10.1002/2014WR016606/abstract

Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., … Winstral, A. (2016). The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sensing of Environment, 184, 139–152.

Poterjoy, J. (2016). A Localized Particle Filter for High-Dimensional Nonlinear Systems. Monthly Weather Review, 144(1), 59–76.

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