H093-11
Towards an Integrated Terrestrial Freshwater Remote Sensing System using the NASA Land Information System, Data Assimilation, and Synthetic Retrievals of Snow, Soil Moisture, and Vegetation over Western Colorado

Thursday, 10 December 2020: 06:00
Virtual
Lizhao Wang1, Barton A Forman1, Sujay V Kumar2, Yonghwan Kwon2, Paul Grogan3, Rhae Sung Kim2,4 and Yeosang Yoon2,5, (1)University of Maryland College Park, College Park, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)Stevens Institute of Technology, Hoboken, NJ, United States, (4)Universities Space Research Association Columbia, Columbia, MD, United States, (5)Science Applications International Corporation, McLean, VA, United States
Abstract:
In terms of the components of terrestrial freshwater storage (TWS), snow, soil moisture, and vegetation are the most temporally-dynamic as well as the most spatially-variable to model. Accurately retrieving snow, soil moisture, or vegetation using space-borne sensors often requires simultaneous knowledge of one or more of the other components. In other words, accurately characterizing terrestrial freshwater requires careful consideration of the coupled snow-soil moisture-vegetation system that is implicit in both TWS and the hydrologic cycle.

To better investigate the coupled snow-soil moisture-vegetation system, we designed an observing system simulation experiment in order to explore the value of coordinated observations of these three separate, yet mutually dependent, state variables. In the experiment, we first generate a “synthetic truth” of snow water equivalent, surface soil moisture, and vegetation biomass using the NoahMP land surface model within the NASA Land Information System (LIS). Afterwards, a series of hypothetical sensors with different orbital configurations are prescribed in order to retrieve snow, soil moisture, and vegetation. The ground track and footprint of each sensor is simulated using the Trade-space Analysis Tool for Constellations (TAT-C). The space-time subsampler provided by TAT-C is then applied to the “synthetic truth” in order to yield a realistic synthetic retrieval for each hypothetical sensor configuration. The synthetic retrievals will then be assimilated in the NoahMP model using different boundary conditions from those used to generate the synthetic truth such that those differences serve as a realistic proxy for real-world boundary condition errors. To evaluate the assimilation of these synthetic retrievals, we build a “baseline” Open Loop simulation where no retrievals are assimilated.

The impact on model estimates of SWE, soil moisture, vegetation, TWS, and river discharge serve as metrics for evaluation in order to assess which sensor(s) yield the most utility in terms of improved model performance. The results from this OSSE will aid mission planners in determining how to get the most observational “bang for the buck” based on the myriad of different sensor types and orbital configurations in the selection of a future terrestrial water mass mission.

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