Abstract
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.
Highlights
High Mountain Asia (HMA) is a vast, high elevation mid-latitude region comprising the world’s highest mountains
Since the current study aimed to demonstrate the feasibility of the support vector machine (SVM)-DA framework to improve snow water equivalent (SWE) estimates for the highly uncertain environment of HMA, we concluded that both the MERRA-2 and Tropical Rainfall Measuring Mission (TRMM) precipitation data (for open-loop (OL) and data assimilation (DA)) could be used in this synthetic study as the differences in precipitation between the two different sets are representative of the real-world errors that could be encountered by an operational modeling and assimilation system
The SVM was trained on a 0.25◦ equidistant cylindrical grid, the same spatial resolution and map projection used for Noah-MP, and the optimal nonlinear SVM parameters were determined for each training target, for each fortnight, and for each 0.25◦ grid cell in the study area
Summary
High Mountain Asia (HMA) is a vast, high elevation mid-latitude region comprising the world’s highest mountains. Forman et al [20] first employed an artificial neural network (ANN) to map geophysical (model) states into PMW TB space to explore the feasibility of using machine learning algorithms as an observation operator within a DA framework They showed that an ANN could be used to estimate TB at multiple frequencies and polarizations over snow-covered land using LSM snow outputs as inputs to the ANN. Xue et al [23] conducted TB spectral difference assimilation over snow-covered areas in North America using a well-trained SVM and presented promising initial results Building on these prior efforts, we adopted the use of SVM regression algorithm in this study, but applying it to a different land surface model using a different set of boundary conditions in a different part of the globe where the accurate estimation of snow is very challenging. The updated (posterior) ensemble of snow states was used as the initial conditions for subsequent model forecasts
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