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Water & Energy Cycles

NASA's Strategic Science Outcomes include: "Quantify the key reservoirs and fluxes in the global water cycle and improve models of water cycle change and fresh water availability." Snow cover is a crucial water resource and component of the hydrologic cycle, but large-scale snow water equivalent (SWE) patterns cannot be characterized using in situ networks. Given recent evidence for climate-related snow cover change, monitoring global SWE is essential, as recognized by NRC Decadal Survey support for the Snow and Cold Land Processes mission. However, remote sensing SWE characterization has been stymied by the discrepancy between the spatial scale of microwave measurements compared with the point scale in situ measurements. We propose to answer the following questions: To what accuracy can geostatistical probability distribution functions derived from in situ snow property measurements be used to predict satellite microwave measurements? To what accuracy can the microwave measurements be inverted or assimilated to characterize the subgrid probability distribution functions? The CLPX dataset is ideal for addressing these questions. We will utilize CLPX in situ measurements and airborne remote sensing measurements to bridge the gap between in situ and spaceborne measurements. We will use models to describe snowpack dynamics, geostatistical spatial relationships, and dependence of microwave radiance on winter landscape endmembers. Nonetheless, our focus will be on the relationships between in situ and satellite measurements. Our analysis will provide an interpretative framework for multiple decades of microwave measurements from space, including a framework for relating in situ SNOTEL measurements to satellite measurements. Our analysis will also provide a means for mapping microwave signals to different subgrid distributions of snowpack parameters. If successful, this project will pave the way for important future work by characterizing model requirements for constructing a radiance assimilation scheme which would utilize characterize SWE in real time.

Project PI: Michael Durand/The Ohio State University

Room 240 Scott Hall 1090 Carmack Road

Email: durand.8@osu.edu

Phone: (614)247-6048

http://bprc.osu.edu/events/?action=display&id;=61

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Started: Aug 10, 2010

Last Activity: Dec 16, 2010

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