Statistics – Methodology
Scientific paper
Jan 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009georl..3602503d&link_type=abstract
Geophysical Research Letters, Volume 36, Issue 2, CiteID L02503
Statistics
Methodology
4
Cryosphere: Snow (1827, 1863), Atmospheric Processes: Data Assimilation, Cryosphere: Remote Sensing, Hydrology: Remote Sensing (1640), Cryosphere: Modeling
Scientific paper
We demonstrate an ensemble-based radiance assimilation methodology for estimating snow depth and snow grain size using ground-based passive microwave (PM) radiance observations at 18.7 and 36.5 GHz. A land surface model (LSM) 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 within a data assimilation scheme. Snow depth bias was -53.3 cm prior to the assimilation, and -7.3 cm after the assimilation. Snow depth estimated by a non-assimilation-based retrieval algorithm using the same PM observations had a bias of -18.3 cm. Our results suggest that assimilation of PM radiance observations into LSMs shows promise for snowpack characterization, with the potential for improved results over products from instantaneous (``snapshot'') retrieval algorithms or the assimilation of those retrievals into LSMs.
Durand Michael
Kim Edward J.
Margulis Steven A.
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