The Cryosphere
The Cryosphere
TC
Articles | Volume 13, issue 11
Articles | Volume 13, issue 11
https://doi.org/10.5194/tc-13-2915-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-13-2915-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 11
Research article
 | 
08 Nov 2019
Research article |  | 08 Nov 2019

Estimating early-winter Antarctic sea ice thickness from deformed ice morphology

M. Jeffrey Mei, Ted Maksym, Blake Weissling, and Hanumant Singh

Viewed

Total article views: 3,478 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,287 1,078 113 3,478 142 163
  • HTML: 2,287
  • PDF: 1,078
  • XML: 113
  • Total: 3,478
  • BibTeX: 142
  • EndNote: 163
Views and downloads (calculated since 09 Jul 2019)
Cumulative views and downloads (calculated since 09 Jul 2019)

Viewed (geographical distribution)

Total article views: 3,478 (including HTML, PDF, and XML) Thereof 2,988 with geography defined and 490 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Oct 2025
Download
Short summary
Sea ice thickness is hard to measure directly, and current datasets are very limited to sporadically conducted drill lines. However, surface elevation is much easier to measure. Converting surface elevation to ice thickness requires making assumptions about snow depth and density, which leads to large errors (and may not generalize to new datasets). A deep learning method is presented that uses the surface morphology as a direct predictor of sea ice thickness, with testing errors of < 20 %.
Read more
Share