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

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

doi:https://doi.org/10.5194/tc-19-3831-2025

Articles | Volume 19, issue 9

https://doi.org/10.5194/tc-19-3831-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-19-3831-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 9

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

Supplement

https://doi.org/10.5194/tc-19-3831-2025-supplement

Data sets

Inputs (forcing, observations and config file) for the experiments included in "Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter" Mazzolini et al. https://doi.org/10.5281/zenodo.13860511

Inputs (forcing and observations) ready for use by "MuSA: The Multiscale Snow Data Assimilation System (v1.0)" Alonso-González https://doi.org/10.5281/zenodo.7248635

SlideRule: Enabling rapid, scalable, open science for the NASA ICESat-2 mission and beyond Shean et al. https://doi.org/10.21105/joss.04982

Google Earth Engine: Planetary-scale geospatial analysis for everyone Gorelick et al. https://doi.org/10.1016/j.rse.2017.06.031

xarray: N-D labeled Arrays and Datasets in Python Hoyer and Hamman https://doi.org/10.5334/jors.148

Model code and software

ealonsogzl/MuSA: v2.1 TC submission (v2.1) Alonso-González et al. (2024) https://doi.org/10.5281/zenodo.11147258

xdem, version v0.0.2 xDEM contributors https://doi.org/10.5281/zenodo.4809698

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.