The Cryosphere
The Cryosphere
TC
Articles | Volume 20, issue 1
Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-609-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-609-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 1
Research article
 | 
23 Jan 2026
Research article |  | 23 Jan 2026

Advancing snow data assimilation with a dynamic observation uncertainty

Devon Dunmire, Michel Bechtold, Lucas Boeykens, and Gabriëlle J. M. De Lannoy

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Latest update: 17 Jun 2026
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Short summary
Snow is vital for society and the climate, yet estimates of snowpack remain uncertain due to observational and modeling limitations. Data assimilation (DA) helps by integrating observations with models. Here, we integrate snow depth retrievals into a physically-based snow model across the European Alps. This work offers advancements for snow data assimilation, such as incorporating a dynamic observational uncertainty, which is essential for forecasting and water resource management.
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