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Open AccessArticle

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

1
Department of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD 20742, USA
2
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
3
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
4
Science Applications International Corporation, McLean, VA 22102, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2265; https://doi.org/10.3390/rs11192265
Received: 16 August 2019 / Revised: 26 September 2019 / Accepted: 26 September 2019 / Published: 28 September 2019
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. View Full-Text
Keywords: support vector machine; passive microwave brightness temperature; data assimilation; snow water equivalent; High Mountain Asia; synthetic twin experiment; NASA Land Information System support vector machine; passive microwave brightness temperature; data assimilation; snow water equivalent; High Mountain Asia; synthetic twin experiment; NASA Land Information System
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Kwon, Y.; Forman, B.A.; Ahmad, J.A.; Kumar, S.V.; Yoon, Y. Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia. Remote Sens. 2019, 11, 2265.

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