Code for Ice-Front and Rampart-Moat Detection and Quantification in ICESat-2 Laser Altimetry

This is the initial release of the code for Ice-Front and Rampart-Moat Detection and Quantification in ICESat-2 Laser Altimetry, described in: Becker, M. K., Howard, S. L., Fricker, H. A., Padman, L., Mosbeux, C., & Siegfried, M. R. (2021). Buoyancy‐driven flexure at the front of Ross Ice Shelf,...

Full description

Bibliographic Details
Main Authors: Susan L. Howard, Maya K. Becker
Format: Software
Language:English
Published: 2021
Subjects:
Online Access:https://zenodo.org/record/4697517
https://doi.org/10.5281/zenodo.4697517
Description
Summary:This is the initial release of the code for Ice-Front and Rampart-Moat Detection and Quantification in ICESat-2 Laser Altimetry, described in: Becker, M. K., Howard, S. L., Fricker, H. A., Padman, L., Mosbeux, C., & Siegfried, M. R. (2021). Buoyancy‐driven flexure at the front of Ross Ice Shelf, Antarctica, observed with ICESat‐2 laser altimetry. Geophysical Research Letters, 48, e2020GL091207. https://doi.org/10.1029/2020GL091207 This repository provides step-by-step tools to download ICESat-2 ATL06 Land Ice Height data (Smith et al., 2019) for a specific region using Python, build a user-friendly MATLAB structure, remove outliers, and search for large along-track jumps in height (that satisfy specified criteria) to identify the ice-shelf front. The code is currently tested on Ross Ice Shelf, where ICESat-2 tracks are usually close to orthogonal to the ice front and height criteria for distinguishing between open water (including where there is sea ice) and the ice-shelf surface are easily established. The method is designed around stepping along track from open water to the ice shelf. Once the ice front is detected, the user can apply the code package to search for rampart-moat (R-M) features at the ice front and quantifies them according to the height of the rampart relative to the moat (dhRM) and the along-track distance from the rampart to the lowest portion of the moat (dxRM). For more information, see subsections 2.2 and 2.3 of Becker et al. (2021). This work was funded by NASA grants 80NSSC20K0977 and NNX17AG63G, and by NSF grants 1443677 and 1443498.