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Remote Sens. 2018, 10(2), 316; https://doi.org/10.3390/rs10020316

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model

1
Department of Geography and Environmental Studies, Wilfrid Laurier University, 75 University Ave W, Waterloo, ON N2L 3C5, Canada
2
Global Modeling and Assimilation Office, Code 610.1, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
3
Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA
4
Earth System Science Interdisciplinary Center, College Park, MD 20740, USA
5
Hydrologic Sciences Laboratory, Code 617, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
6
KU Leuven, Department of Earth and Environmental Sciences, Celestijnenlaan 200 E-box 2411, B-3001 Heverlee, Belgium
*
Author to whom correspondence should be addressed.
Received: 26 December 2017 / Revised: 9 February 2018 / Accepted: 16 February 2018 / Published: 19 February 2018
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Abstract

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from −0.017 m for OL to −0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from −0.111 m for OL to −0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA. View Full-Text
Keywords: snow; land surface model; snow cover fraction; snow depth; snow water equivalent; data assimilation snow; land surface model; snow cover fraction; snow depth; snow water equivalent; data assimilation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Toure, A.M.; Reichle, R.H.; Forman, B.A.; Getirana, A.; De Lannoy, G.J.M. Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model. Remote Sens. 2018, 10, 316.

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