Observing water infiltration in firn and it’s impact on the structure is difficult because many of these processes take place at the centimeter to meter scale, well below the intrinsic resolution of most airborne radar systems. Coherent change detection could detect these processes, but has never been applied to airborne ice-penetrating radar data. We develop a workflow for interferometric processing of repeated flight lines and show that significant coherence is maintained, despite the large temporal and spatial baselines.

Perennial firn aquifers may play an important role in modulating the surface mass balance and ice dynamics of the Greenland Ice Sheet, but constraining their physical properties at large scales remains challenging. We develop a Markov Chain Monte Carlo joint inversion of radar reflectivity and attenuation to estimate firn structure and aquifer properties and use it to show that only additional field measurement required to robustly constrain total water storage is the conductivity of the aquifer water.

Englacial layering in ice-penetrating radar data reflects the past atmospheric conditions and flow history of a region. As a result, both the physical cause of this layering and its geometry are of scientific interest. We develop a method for simulating englacial radiostratigraphy using measurements from deep ice cores that can be used to create synthetic radargrams for training machine learning layer tracking algorithms or test physical hypotheses for the nature of englacial radiostratigraphy.

Radar sounding from a satellite platform could vastly improve our spatial and temporal coverage of the continental ice sheets. However, tight constraints on the available frequency bands may limit the feasibility. We show that clutter from small-scale roughness in the firn layer places significant constraints on the link budget for a terrestrial orbital sounder that require either low VHF frequencies or highly directive antennas to overcome.