Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-609-2026
https://doi.org/10.5194/tc-20-609-2026
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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Cited articles

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Bachand, C. L., Andrews, L. C., Rouf, T., and Girotto, M.: The Utility of Satellite Snow Depth Observations for Downscaling Hydrologic Variables over the Indus Basin Mountain Ranges, Journal of Hydrometeorology, 26, 555–575, https://doi.org/10.1175/JHM-D-24-0131.1, 2025. a
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Bechtold, M., Modanesi, S., Lievens, H., Baguis, P., Brangers, I., Carrassi, A., Getirana, A., Gruber, A., Heyvaert, Z., Massari, C., Scherrer, S., Vannitsem, S., and De Lannoy, G.: Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments, Journal of Hydrometeorology, 24, 2389–2408, https://doi.org/10.1175/JHM-D-22-0198.1, 2023. a
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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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