Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-737-2026
https://doi.org/10.5194/tc-20-737-2026
Research article
 | 
28 Jan 2026
Research article |  | 28 Jan 2026

Ensemble-based snow depth data assimilation for a multi-layer snow scheme over the European Arctic

Åsmund Bakketun, Jostein Blyverket, and Malte Müller

Viewed

Total article views: 1,519 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,202 265 52 1,519 53 73
  • HTML: 1,202
  • PDF: 265
  • XML: 52
  • Total: 1,519
  • BibTeX: 53
  • EndNote: 73
Views and downloads (calculated since 09 May 2025)
Cumulative views and downloads (calculated since 09 May 2025)

Viewed (geographical distribution)

Total article views: 1,519 (including HTML, PDF, and XML) Thereof 1,488 with geography defined and 31 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Apr 2026
Download
Short summary
Obtaining accurate estimates of seasonal snow conditions requires a combination of observations and numerical models. We use a model accounting for the vertical structure of the snow, and a data assimilation method representing varying uncertainty of the model in time and space. Compared to existing products, neglecting these considerations, our system produced improved estimates of seasonal snow conditions. Snow mass estimates suggest a potential impact on derived hydrological applications.
Share