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Radiance Assimilation Shows Promise for Snowpack Characterization: A 1-D Case StudyWe demonstrate an ensemble-based radiometric data assimilation (DA) methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) observations at 18.7 and 36.5 GHz collected during the NASA CLPX-1, March 2003, Colorado, USA. A land surface model was used to develop a prior estimate of the snowpack states, and a radiative transfer model was used to relate the modeled states to the observations. Snow depth bias was -53.3 cm prior to the assimilation, and -7.3 cm after the assimilation. Snow depth estimated by a non-DA-based retrieval algorithm using the same PM data had a bias of -18.3 cm. The sensitivity of the assimilation scheme to the grain size uncertainty was evaluated; over the range of grain size uncertainty tested, the posterior snow depth estimate bias ranges from -2.99 cm to -9.85 cm, which is uniformly better than both the prior and retrieval estimates. This study demonstrates the potential applicability of radiometric DA at larger scales.
Document ID
Document Type
Conference Paper
Durand, Michael (Ohio State Univ. Cleveland, OH, United States)
Kim, Edward (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Margulis, Steve (California Univ. Los Angeles, CA, United States)
Date Acquired
August 24, 2013
Publication Date
December 15, 2008
Subject Category
Earth Resources and Remote Sensing
Meeting Information
American Geophysical Union (AGU) Fall Meeting(San Francisco, CA)
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