1075 Remote Snow Strength Detection Using Multifrequency/Multipolarization Radar

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Elias J. Deeb, Cold Regions Research and Engineering Laboratory, Hanover, NH; and H. P. Marshall, Z. Courville, J. Lever, R. Forster, and S. A. Shoop

Estimates of the effects of snow on vehicle movement rely on dated empirical parametrizations relating snow depth to speed. In this case, snow depth inaccurately represents a surrogate for surface load bearing capacity. Snow density and the degree of inter-grain sintering controls this load bearing capacity commonly referred to as snow strength. Direct observations of snow strength are difficult to collect and require labor-intensive (expensive) in-situ measurements and testing. The spatial/temporal variability of seasonal snow (with differing elevations/aspects/climates) also complicates our understanding of snow strength estimates and how they evolve over time. Other work for remote snow property estimation typically concentrate on bulk properties such as snow depth or snow water equivalent. These properties are incomplete toward estimating snow strength. To date, no remote methods exist examining the use of remote sensing to estimate snow strength for mobility and maneuver.

This work attempts to address the feasibility of using non-destructive radar observations of snow at multiple frequencies and polarizations to predict snow strength. Through several field campaigns supporting the NASA SnowEx program and other activities, coordinated field samples, snow strength measurements, and radar observations have been acquired. For example, with micro computerized tomography (MicroCT) scanning, a size distribution of snow grains and bonds, as well as a correlation length of snow grains, is derived. With a snow mircopenetrometer (SMP), the penetration force needed to rupture these snow bonds at microstructural scale is captured. The correlation length has been empirically shown to describe the penetration force as well as the observed radar backscatter. However, this correlation length alone does not fully describe snow strength. The question of how these snow grains are “electrically” connected (or represented by radar backscatter) remains under debate. To this goal, a community radiative transfer model (RTM) is used to predict the microwave backscatter response of snow. The preliminary efforts of parameterizing snow microstructure in the RTM (using microCT and SMP results) will be presented toward the effort of predicting snow strength with remotely sensed, non-destructive, broadband radar.

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