Articles | Volume 15, issue 12
https://doi.org/10.5194/tc-15-5639-2021
https://doi.org/10.5194/tc-15-5639-2021
Research article
 | 
13 Dec 2021
Research article |  | 13 Dec 2021

Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf

Zhongyang Hu, Peter Kuipers Munneke, Stef Lhermitte, Maaike Izeboud, and Michiel van den Broeke

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Cited articles

Arthur, J. F., Stokes, C., Jamieson, S. S., Carr, J. R., and Leeson, A. A.: Recent understanding of Antarctic supraglacial lakes using satellite remote sensing, Prog. Phys. Geogr., 44, 837–869, 2020. a
Bindschadler, R., Choi, H., Wichlacz, A., Bingham, R., Bohlander, J., Brunt, K., Corr, H., Drews, R., Fricker, H., Hall, M., Hindmarsh, R., Kohler, J., Padman, L., Rack, W., Rotschky, G., Urbini, S., Vornberger, P., and Young, N.: Getting around Antarctica: new high-resolution mappings of the grounded and freely-floating boundaries of the Antarctic ice sheet created for the International Polar Year, The Cryosphere, 5, 569–588, https://doi.org/10.5194/tc-5-569-2011, 2011. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Cape, M., Vernet, M., Skvarca, P., Marinsek, S., Scambos, T., and Domack, E.: Foehn winds link climate-driven warming to ice shelf evolution in Antarctica, J. Geophys. Res.-Atmos., 120, 11–037, 2015. a
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 13–17 August 2016, San Francisco, California, USA, 785–794, 2016. a
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Antarctica is shrinking, and part of the mass loss is caused by higher temperatures leading to more snowmelt. We use computer models to estimate the amount of melt, but this can be inaccurate – specifically in the areas with the most melt. This is because the model cannot account for small, darker areas like rocks or darker ice. Thus, we trained a computer using artificial intelligence and satellite images that showed these darker areas. The model computed an improved estimate of melt.