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dc.contributor.advisorForman, Barton
dc.contributor.authorXue, Yuan
dc.contributor.authorForman, Barton
dc.contributor.authorReichle, Rolf
dc.date.accessioned2018-04-16T12:14:46Z
dc.date.available2018-04-16T12:14:46Z
dc.date.issued2018-04-16
dc.identifierhttps://doi.org/10.13016/M2HH6C789
dc.identifier.urihttp://hdl.handle.net/1903/20570
dc.description.abstractTo estimate snow mass across North America, multi-frequency brightness temperature (Tb) observations collected by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) from 2002 to 2011 were assimilated into the Catchment land surface model using a support vector machine (SVM) as the observation operator as part of a one-dimensional ensemble Kalman filter. The performance of the assimilation system is evaluated through comparisons against ground-based measurements and publicly-available reference SWE and snow depth products. Assimilation estimates agree better with ground-based snow depth measurements than model-only (“open loop”, or OL) estimates in approximately 82% (56 out of 62) of pixels that are colocated with at least two ground-based stations. In addition, assimilation estimates tend to agree better with all snow products over tundra snow, alpine snow, maritime snow, as well as sparsely-vegetated snow-covered pixels. Improvements in snow mass via assimilation translate into improvements in cumulative runoff estimates when compared against discharge measurements in 11 out of 13 major snow-dominated basins in Alaska. These results prove that a SVM can serve as an efficient and effective observation operator for snow mass estimation within a radiance assimilation system.en_US
dc.description.sponsorshipNASAen_US
dc.subjectsnowen_US
dc.subjectmodelen_US
dc.subjectdata assimilationen_US
dc.subjectpassive microwaveen_US
dc.subjectbrightness temperatureen_US
dc.subjectsupport vector machineen_US
dc.titleEstimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machinesen_US
dc.typeDataseten_US
dc.relation.isAvailableAtA. James Clark School of Engineeringen_us
dc.relation.isAvailableAtCivil & Environmental Engineeringen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us


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