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TC | Volume 13, issue 11
The Cryosphere, 13, 2915–2934, 2019
https://doi.org/10.5194/tc-13-2915-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 13, 2915–2934, 2019
https://doi.org/10.5194/tc-13-2915-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 08 Nov 2019

Research article | 08 Nov 2019

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

M. Jeffrey Mei et al.
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Cited articles  
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Baldi, P.: Autoencoders, unsupervised learning, and deep architectures, in: Proceedings of ICML workshop on unsupervised and transfer learning, 37–49, 2012. a
Behrendt, A., Dierking, W., Fahrbach, E., and Witte, H.: Sea ice draft in the Weddell Sea, measured by upward looking sonars, Earth Syst. Sci. Data, 5, 209–226, https://doi.org/10.5194/essd-5-209-2013, 2013. a
Brock, J. C., Wright, C. W., Clayton, T. D., and Nayegandhi, A.: LIDAR optical rugosity of coral reefs in Biscayne National Park, Florida, Coral Reefs, 23, 48–59, https://doi.org/10.1007/s00338-003-0365-7, 2004. a
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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 %.
Sea ice thickness is hard to measure directly, and current datasets are very limited to...
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