Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines
To 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)...
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ftunivmaryland:oai:drum.lib.umd.edu:1903/20570 2023-05-15T18:40:30+02:00 Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines Xue, Yuan Forman, Barton Reichle, Rolf Forman, Barton 2018-04-16 application/vnd.openxmlformats-officedocument.wordprocessingml.document application/octet-stream http://hdl.handle.net/1903/20570 https://doi.org/10.13016/M2HH6C789 unknown A. James Clark School of Engineering Civil & Environmental Engineering Digital Repository at the University of Maryland University of Maryland (College Park, MD) https://doi.org/10.13016/M2HH6C789 http://hdl.handle.net/1903/20570 snow model data assimilation passive microwave brightness temperature support vector machine Dataset 2018 ftunivmaryland https://doi.org/10.13016/M2HH6C789 2023-02-26T17:51:50Z To 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. NASA https://doi.org/10.1029/2017WR022219 Dataset Tundra Alaska University of Maryland: Digital Repository (DRUM) |
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Open Polar |
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University of Maryland: Digital Repository (DRUM) |
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ftunivmaryland |
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topic |
snow model data assimilation passive microwave brightness temperature support vector machine |
spellingShingle |
snow model data assimilation passive microwave brightness temperature support vector machine Xue, Yuan Forman, Barton Reichle, Rolf Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines |
topic_facet |
snow model data assimilation passive microwave brightness temperature support vector machine |
description |
To 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. NASA https://doi.org/10.1029/2017WR022219 |
author2 |
Forman, Barton |
format |
Dataset |
author |
Xue, Yuan Forman, Barton Reichle, Rolf |
author_facet |
Xue, Yuan Forman, Barton Reichle, Rolf |
author_sort |
Xue, Yuan |
title |
Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines |
title_short |
Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines |
title_full |
Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines |
title_fullStr |
Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines |
title_full_unstemmed |
Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines |
title_sort |
estimating snow mass in north america through assimilation of amsr-e brightness temperature observations using the catchment land surface model and support vector machines |
publishDate |
2018 |
url |
http://hdl.handle.net/1903/20570 https://doi.org/10.13016/M2HH6C789 |
genre |
Tundra Alaska |
genre_facet |
Tundra Alaska |
op_relation |
A. James Clark School of Engineering Civil & Environmental Engineering Digital Repository at the University of Maryland University of Maryland (College Park, MD) https://doi.org/10.13016/M2HH6C789 http://hdl.handle.net/1903/20570 |
op_doi |
https://doi.org/10.13016/M2HH6C789 |
_version_ |
1766229864157806592 |