Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-209-2026
https://doi.org/10.5194/tc-20-209-2026
Research article
 | 
14 Jan 2026
Research article |  | 14 Jan 2026

Ensemble-based data assimilation improves hyperresolution snowpack simulations in forests

Esteban Alonso-González, Adrian Harpold, Jessica D. Lundquist, Cara Piske, Laura Sourp, Kristoffer Aalstad, and Simon Gascoin

Data sets

Forest-Snow Interactions in a High Elevation Critical Zone, CA 2022 C. Piske https://doi.org/10.5069/G9ZG6QF7

Model code and software

MuSA (v2.2) Esteban Alonso-González et al. https://doi.org/10.5281/zenodo.14065646

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Short summary
Simulating the snowpack is challenging, as there are several sources of uncertainty due to e.g. the meteorological forcing. Using data assimilation techniques, it is possible to improve the simulations by fusing models and snow observations. However in forests, observations are difficult to obtain, because they cannot be retrieved through the canopy. Here, we explore the possibility of propagating the information obtained in forest clearings to areas covered by the canopy.
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