A Particle Batch Smoother Approach to Snow Water Equivalent Estimation

Steven A. Margulis Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California

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Manuela Girotto NASA Goddard Space Flight Center, Greenbelt, and GESTAR, Universities Space Research Association, Columbia, Maryland

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Gonzalo Cortés Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California

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Michael Durand School of Earth Sciences and Byrd Polar Research Center, The Ohio State University, Columbus, Ohio

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Abstract

This paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%–82% and 60%–68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBS RMSE was ~54% of that seen in the EnBS, while for snow courses the PBS RMSE was ~79% of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist.

Corresponding author address: Steve Margulis, Department of Civil and Environmental Engineering, University of California, Los Angeles, 5732D Boelter Hall, Los Angeles, CA 90095. E-mail: margulis@seas.ucla.edu

Abstract

This paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%–82% and 60%–68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBS RMSE was ~54% of that seen in the EnBS, while for snow courses the PBS RMSE was ~79% of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist.

Corresponding author address: Steve Margulis, Department of Civil and Environmental Engineering, University of California, Los Angeles, 5732D Boelter Hall, Los Angeles, CA 90095. E-mail: margulis@seas.ucla.edu
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