Articles | Volume 19, issue 9
https://doi.org/10.5194/tc-19-3831-2025
https://doi.org/10.5194/tc-19-3831-2025
Research article
 | 
16 Sep 2025
Research article |  | 16 Sep 2025

Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter

Marco Mazzolini, Kristoffer Aalstad, Esteban Alonso-González, Sebastian Westermann, and Désirée Treichler

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Cited articles

Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. a, b
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography, Remote Sens. Environ., 239, 111618, https://doi.org/10.1016/j.rse.2019.111618, 2020. a, b, c
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Alonso-González, E.: Inputs (forcing and observations) ready for use by “MuSA: The Multiscale Snow Data Assimilation System (v1.0)”, Zenodo [data set], https://doi.org/10.5281/zenodo.7248635, 2022. a, b, c
Alonso-González, E., Gutmann, E., Aalstad, K., Fayad, A., Bouchet, M., and Gascoin, S.: Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area, Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, 2021. a
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Short summary
In this work, we showcase the use the satellite laser altimeter ICESat-2, which is able to retrieve snow depth in areas where snow amounts are still poorly estimated despite the importance of these water resources. We can update snow models with these observations through algorithms that spatially propagate the information beyond the satellite profiles. The positive results show the potential of the approach to improve snow simulations, in terms of average snow depth and spatial distribution.
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