Articles | Volume 15, issue 12
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-102', Anonymous Referee #1, 30 Jun 2021
  • RC2: 'Comment on tc-2021-102', Anonymous Referee #2, 20 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (21 Oct 2021) by Thomas Mölg
AR by Zhongyang Hu on behalf of the Authors (29 Oct 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (03 Nov 2021) by Thomas Mölg
AR by Zhongyang Hu on behalf of the Authors (09 Nov 2021)  Author's response   Manuscript 
Short summary
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.