Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 23
Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8799-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8799-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 23
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: 1,190 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
868 261 61 1,190 109 80 62
  • HTML: 868
  • PDF: 261
  • XML: 61
  • Total: 1,190
  • Supplement: 109
  • BibTeX: 80
  • EndNote: 62
Views and downloads (calculated since 05 Dec 2023)
Cumulative views and downloads (calculated since 05 Dec 2023)

Viewed (geographical distribution)

Total article views: 1,190 (including HTML, PDF, and XML) Thereof 1,128 with geography defined and 62 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Jun 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.
Read more
Share