Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8799-2024
https://doi.org/10.5194/gmd-17-8799-2024
Methods for assessment of models
 | 
11 Dec 2024
Methods for assessment of models |  | 11 Dec 2024

Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter

Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee

Viewed

Total article views: 1,725 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,216 423 86 1,725 155 111 136
  • HTML: 1,216
  • PDF: 423
  • XML: 86
  • Total: 1,725
  • Supplement: 155
  • BibTeX: 111
  • EndNote: 136
Views and downloads (calculated since 05 Dec 2023)
Cumulative views and downloads (calculated since 05 Dec 2023)

Viewed (geographical distribution)

Total article views: 1,725 (including HTML, PDF, and XML) Thereof 1,653 with geography defined and 72 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Dec 2025
Download
Short summary
We developed an advanced snow water equivalent (SWE) data assimilation framework using satellite data based on a land surface model. The results of this study highlight the beneficial impact of data assimilation by effectively combining land surface model and satellite-derived data according to their relative uncertainty, thereby controlling not only transitional regions but also the regions with heavy snow accumulation that are difficult to detect by satellite.
Share