Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

Yonghwan Kwon Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas

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Zong-Liang Yang Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas

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Long Zhao Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas

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Timothy J. Hoar National Center for Atmospheric Research, Boulder, Colorado

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Ally M. Toure Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, and Universities Space Research Association, Columbia, Maryland

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Matthew Rodell Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting TB based on their correlations with the prior TB (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171 m RMSE), the overall snow depth estimates are improved by 1.6% (0.168 m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177 m RMSE). Significant improvement of the snow depth estimates in the rule-based RA is observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

Corresponding author address: Zong-Liang Yang, Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, 1 University Station, C1100, Austin, TX 78712. E-mail: liang@jsg.utexas.edu

Abstract

This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting TB based on their correlations with the prior TB (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171 m RMSE), the overall snow depth estimates are improved by 1.6% (0.168 m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177 m RMSE). Significant improvement of the snow depth estimates in the rule-based RA is observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

Corresponding author address: Zong-Liang Yang, Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, 1 University Station, C1100, Austin, TX 78712. E-mail: liang@jsg.utexas.edu
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