Civil & Environmental Engineering Research Works

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

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Start date: 2010Subject: search.filters.subject.data assimilation

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    Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia
    (MDPI, 2019-09-28) Kwon, Yonghwan; Forman, Barton A.; Ahmad, Jawairia A.; Kumar, Sujay V.; Yoon, Yeosang
    This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.
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    SMAP soil moisture assimilated Noah-MP model output
    (2021) Ahmad, Jawairia; Forman, Bart; Kumar, Sujay
    The data archived here includes the NASA Noah-MP (version 4.0.1) land surface model output used in the investigation of the impact of passive microwave-based soil moisture retrieval assimilation on soil moisture estimation in South Asia (Ahmad et al., 2021). SMAP soil moisture retrievals are assimilated into the Noah-MP land surface model to improve the estimation of soil moisture and other related states. The open loop (OL) represents Noah-MP’s modeling capabilities using MERRA2 and IMERG precipitation. Two different types of data assimilation runs were executed using the MERRA2 and IMERG precipitation boundary conditions, i.e., with CDF-matching (DA-CDF) and without CDF matching (DA-NoCDF). The key findings in this paper include: 1) assimilation results without any CDF-matching yielded the lowest error in estimated soil moisture, 2) the best goodness-of-fit statistics were achieved for the IMERG-forced DA-NoCDF soil moisture experiment, 3) biases associated with unmodeled hydrologic processes such as irrigation were corrected via assimilation, and 4) the highest influence of assimilation was observed across croplands.
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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.