Ellianna Abrahams

All Publications

• Next-generation radar bed measurements should be optimized for assimilation or repeat-pass profiling. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Schroeder, D. M., Abrahams, E., Broome, A. L., Chu, W., Culberg, R., Dawson, E. J., MacKie, E. J., May, D. F., Siegfried, M. R., Teisberg, T. O., Zhao, S. 2026; 384 (2319)

  Abstract

  Ice-penetrating radar observation of bed echoes is a core geophysical technique in glaciology. In early and ongoing exploration and mapping surveys, areas with little or no data were often prioritized, leading to few repeated radar sounding profiles. In parallel, advances in radar sounding instruments, platforms and analysis approaches have dramatically opened possibilities for future survey design. Here, we consider the opportunities these advances present for next-generation bed measurements, including both assimilation-optimized mapping and repeat sounding. Based on this analysis, we argue that repeat-pass profiles of bed echo power and englacial layer echo phase should be key priorities for future observations. To that end, we evaluate the detectability of subglacial water bodies, including ocean intrusion in the grounding zone as a target for repeat-pass surveys. We also discuss the distinct implications of our suggested approaches for instrument, platform and survey choices to combine mapping and repeat-pass surveys across the time scales of ice-sheet change. This article is part of the Theo Murphy meeting issue 'Next generation ice-sheet bed measurements'.

  View details for DOI 10.1098/rsta.2024.0548

  View details for PubMedID 42021654

• Feasibility of ice-penetrating radar sounding from stratospheric UAS platforms Teisberg, T. O., Mascarenhas, C., Abrahams, E., Schroeder, D. M., Anderson, J. G., IEEE IEEE. 2025: 769-774

  View details for DOI 10.1109/IGARSS55030.2025.11243139

  View details for Web of Science ID 001697407700164

• Automatic identification of streamlined subglacial bedforms using machine learning: an open-source Python approach BOREAS Abrahams, E., Mckenzie, M., Perez, F., Venturelli, R. 2024

  View details for DOI 10.1111/bor.12682

  View details for Web of Science ID 001363779900001