Civil & Environmental Engineering

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Author: search.filters.author.Xue, YuanStart date: 1990

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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.
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    ASSIMILATION OF PASSIVE MICROWAVE BRIGHTNESS TEMPERATURES FOR SNOW WATER EQUIVALENT ESTIMATION USING THE NASA CATCHMENT LAND SURFACE MODEL AND MACHINE LEARNING ALGORITHMS IN NORTH AMERICA
    (2017) Xue, Yuan; Forman, Barton A.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Snow is a critical component in the global energy and hydrologic cycle. It is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. To accurately estimate the depth of snow and mass of water within a snow pack across regional or continental scales is a challenge, especially in the presence of dense vegetations since direct quantification of SWE is complicated by spatial and temporal variability. To overcome some of the limitations encountered by traditional SWE retrieval algorithms or radiative transfer-based snow emission models, this study explores the use of a well-trained support vector machine to merge an advanced land surface model within a variant of radiance emission (i.e., brightness temperature) assimilation experiments. In general, modest improvements in snow depth, and SWE predictability were witnessed as a result of the assimilation procedure over snow-covered terrain in North America when compared against available snow products as well as ground-based observations. These preliminary findings are encouraging and suggest the potential for global-scale snow estimation via the proposed assimilation procedure.
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    SENSITIVITY ANALYSIS OF MACHINE LEARNING IN BRIGHTNESS TEMPERATURE PREDICTIONS OVER SNOW-COVERD REGIONS USING THE ADVANCED MICROWAVE SCANNING RADIOMETER
    (2014) Xue, Yuan; Forman, Barton; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Snow is a critical component in the global energy and hydrologic cycle. Further, it is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. Since direct quantification of snow water equivalent (SWE) is complicated by spatial and temporal variability, space-borne passive microwave SWE retrieval products have been utilized over regional and continental-scales to better estimate SWE. Previous studies have explored the possibility of employing machine learning, namely an artificial neural network (ANN) or a support vector machine (SVM), to replace the traditional radiative transfer model (RTM) during brightness temperatures (Tb) assimilation. However, we still need to address the following question: What are the most significant parameters in the machine-learning model based on either ANN or SVM? The goal of this study is to compare and contrast sensitivity analysis of Tb with respect to each model input between the ANN- and SVM-based estimates. In general, the results suggest the SVM (relative to the ANN) may be more beneficial during Tb assimilation studies where enhanced SWE estimation is the main objective.