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Articles | Volume 19, issue 8
Articles | Volume 19, issue 8
https://doi.org/10.5194/tc-19-2895-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-2895-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 8
Research article
 | 
06 Aug 2025
Research article |  | 06 Aug 2025

Assimilation of L-band interferometric synthetic aperture radar (InSAR) snow depth retrievals for improved snowpack quantification

Prabhakar Shrestha and Ana P. Barros

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Cited articles

Abolafia-Rosenzweig, R., He, C., Chen, F., and Barlage, M.: Evaluating and enhancing snow compaction process in the Noah-MP land surface model, J. Adv. Model. Earth Sy., 16, e2023MS003869, https://doi.org/10.1029/2023MS003869, 2024. 
Anderson, J.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, 2009. 
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The data assimilation research testbed: A community facility, B. Am Meteorol. Soc., 90, 1283–1296, 2009. 
Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, 2003. 
APBarrosResearchGroup-open: APBarrosResearchGroup-open/mpdaf: MPDAF (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.16580886, 2025. 
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The study presents the first assimilation of snow depth obtained from repeat pass airborne L-band synthetic aperture radar with a snow hydrology model. The assimilation of snow depth was found to be equivalent to the downscaling of precipitation forcing with a bias correction, which improved the snowpack simulation compared to ground-based observations.
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