Articles | Volume 15, issue 3
https://doi.org/10.5194/tc-15-1277-2021
https://doi.org/10.5194/tc-15-1277-2021
Research article
 | 
11 Mar 2021
Research article |  | 11 Mar 2021

Estimating parameters in a sea ice model using an ensemble Kalman filter

Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, and Edward Blanchard-Wrigglesworth

Viewed

Total article views: 2,065 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,308 651 106 2,065 272 104 94
  • HTML: 1,308
  • PDF: 651
  • XML: 106
  • Total: 2,065
  • Supplement: 272
  • BibTeX: 104
  • EndNote: 94
Views and downloads (calculated since 13 May 2020)
Cumulative views and downloads (calculated since 13 May 2020)

Viewed (geographical distribution)

Total article views: 2,065 (including HTML, PDF, and XML) Thereof 1,949 with geography defined and 116 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Mar 2025
Download
Short summary
Sea ice models suffer from large uncertainties arising from multiple sources, among which parametric uncertainty is highly under-investigated. We select a key ice albedo parameter and update it by assimilating either sea ice concentration or thickness observations. We found that the sea ice albedo parameter is improved by data assimilation, especially by assimilating sea ice thickness observations. The improved parameter can further benefit the forecast of sea ice after data assimilation stops.
Share