12 Jun 2026

Spatiotemporal Patterns of Accumulation and Surface Roughness in Interior Greenland with a GNSS-IR Network

Abstract: The dry-snow zone is the largest region of the Greenland Ice Sheet, yet temporally and spatially dense observations of surface accumulation and surface roughness in this area are lacking. We use the global navigation satellite system interferometric reflectometry (GNSS-IR) technique with a novel, low-cost GNSS network of 12 stations in the vicinity of the ice sheet summit to reveal temporal and spatial patterns of accumulation of the upper snow layer. We show that individual measurements are highly precise, while the aggregate of hundreds of daily measurements across a large spatial footprint can detect millimeter-level surface changes and is biased by −2.7 ± 3.0 cm com-pared to a unique validation data set that covers a similar spatial extent to the instrument sensing footprint. Using the validation data set, we find that the reflectometry technique is most sensitive to the surrounding 4–20 m of the surface, with the GNSS antenna at a height of 1–2 m above ground level. Along with an exceptionally high accumulation rate at the beginning of the study, we also detect an across-slope dependence in accumulation rates at yearly timescales. For the first time, we also validate GNSS-IR sensitivity to meter-scale surface heterogeneities such as sastrugi, and we construct a time series of surface roughness evolution that suggests a seasonal pattern of heightened wintertime roughness features in this region. These surface accumulation and rough-ness measurements provide a novel data set for these critical variables and show a statistically significant relationship with occurrences of both high winds and precipitation events but only moderate correlations, suggesting that other processes may also contribute to accumulation and enhanced surface roughness in the interior region of Greenland.

15 Mar 2022

Autonomous GPR Surveys using the Polar Rover Yeti

Abstract: The National Science Foundation operates stations on the ice sheets of Antarctica and Greenland to investigate Earth’s climate history, life in extreme environments, and the evolution of the cosmos. Understandably, logistics costs predominate budgets due to the remote locations and harsh environments involved. Currently, manual ground-penetrating radar (GPR) surveys must preceed vehicle travel across polar ice sheets to detect subsurface crevasses or other voids. This exposes the crew to the risks of undetected hazards. We have developed an autonomous rover, Yeti, specifically to conduct GPR surveys across polar ice sheets. It is a simple four-wheel-drive, battery-powered vehicle that executes autonomous surveys via GPS waypoint following. We describe here three recent Yeti deployments, two in Antarctica and one in Greenland. Our key objective was to demonstrate the operational value of a rover to locate subsurface hazards. Yeti operated reliably at −30 ◦C, and it has good oversnow mobility and adequate GPS accuracy for waypoint-following and hazard georeferencing. It has acquired data on hundreds of crevasse encounters to improve our understanding of heavily crevassed traverse routes and to develop automated crevasse-detection algorithms. Importantly, it helped to locate a previously undetected buried building at the South Pole. Yeti can improve safety by decoupling survey personnel from the consequences of undetected hazards. It also enables higher-quality systematic surveys to improve hazard-detection probabilities, increase assessment confidence, and build datasets to understand the evolution of these regions. Yeti has demonstrated that autonomous vehicles have great potential to improve the safety and efficiency of polar logistics.