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

Viewed

Total article views: 4,341 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,192 979 170 4,341 267 195 232
  • HTML: 3,192
  • PDF: 979
  • XML: 170
  • Total: 4,341
  • Supplement: 267
  • BibTeX: 195
  • EndNote: 232
Views and downloads (calculated since 28 May 2025)
Cumulative views and downloads (calculated since 28 May 2025)

Viewed (geographical distribution)

Total article views: 4,341 (including HTML, PDF, and XML) Thereof 4,165 with geography defined and 176 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 26 Jul 2026
Download
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.
Read more
Share