Civil & Environmental Engineering Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1657

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Author: search.filters.author.Xue, Yuan

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    Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines
    (2018-04-16) Xue, Yuan; Forman, Barton; Reichle, Rolf; Forman, Barton
    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.
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    Soil temperature simulation results in Alaska (1980 - 2014) – Data archive for “Evaluation and enhancement of permafrost modeling with the NASA Catchment Land Surface Model”
    (2017) Tao, Jing; Reichle, Rolf; Koster, Randal; Forman, Barton; Xue, Yuan
    The datasets archived here include simulation results discussed in the paper, “Evaluation and enhancement of permafrost modeling with the NASA Catchment Land Surface Model”, to be published in Journal of Advances in Modeling Earth Systems. Specifically, subsurface soil temperatures for 1980-2014 across Alaska were produced by a baseline simulation with the NASA Catchment Land Surface Model (CLSM). Five sets of point simulations were also conducted at permafrost sites in Alaska, including 1) T1BC - the top layer temperature is prescribed to observations, 2) T1BC_OrgC – repeat of the T1BC simulation but using the updated model version that incorporates soil thermal impacts of organic carbon content, 3) T2BC - the temperatures of both the 1st and 2nd layer are prescribed to observations, 4) T2BC_OrgC – repeat of the T2BC simulation but using the updated model version, and 5) M2_OrgC – simulations with the updated model version driven by MERRA-2 forcing. Details about the model configuration and the changes defining the updated model version can be found in the paper. The major findings in this paper include: a) profile-average RMSE of simulated soil temperature versus in situ observations is reduced by using corrected local forcing and land cover; b) subsurface heat transport is mostly realistic, and when not, it is improved via treatment of soil organic carbon-related thermal properties; and c) mean bias and RMSE of climatological ALT between simulations and observations are significantly reduced with updated model version.