Glaciological Monitoring Using the Sun as a Radio Source for Echo Detection

S T Peters,W Chu,D Castelletti,M S Haynes,A Romero‐Wolf,D M Schroeder,P Christoffersen

doi:10.1029/2021gl092450

Abstract

AbstractIce‐penetrating radar observations are critical for projecting ice‐sheet contribution to sea‐level rise; however, these prognostic models have significant uncertainties due to an incomplete understanding of glacial subsurface processes. Existing radars that can characterize subsurface conditions are too resource‐intensive to simultaneously monitor ice sheets at both the necessary temporal—daily to multiannual—and spatial—tributary to continental—scales. Here, we investigate using an ambient radio source, instead of transmitting a signal, for glaciological monitoring. We demonstrate, for the first time, passive radio sounding using the Sun to accurately measure ice thickness on Store Glacier, Greenland. Passive radar sounding could provide low‐resource time‐series measurements of the cryosphere, enabling us to observe and understand evolving englacial and subglacial conditions across Greenland and Antarctica with unprecedented coverage and resolution.

Highlights

Active radar sounders provide critical measurements of ice sheets (Dowdeswell & Evans, 2004), such as basal melt rates (Khazendar et al, 2016), reflectivity time series (Chu et al, 2016), vertical velocities (Kingslake et al, 2014), ice thickness (Gogineni et al, 2014), englacial water storage (Kendrick et al, 2018), and subglacial conditions
While active radar sounding is the principle remote sensing technique used to observe the subsurface of Greenland and Antarctica, existing radar systems are resource-intensive in terms of cost, power, and logistics when simultaneously monitoring ice sheets at both their evolving temporal and spatial scales. These observations are critical as ice sheet contribution to sea-level rise presents one of the greatest challenges our society faces in the century. We address this challenge by developing a novel, low-resource, passive radar sounding technique that uses ambient radio signals from the Sun to observe the subsurface of ice sheets at these spatiotemporal scales, instead of transmitting its own powerful radio signal for echo detection
The power level of the Sun in the 200–400 MHz radio frequency band is an order of magnitude below that of the galactic background noise (Figure S13), requiring low noise amplifiers and a sufficient number of samples for the correlation; this is obtained by either increasing the acquisition time or signal bandwidth. Anthropogenic radio sources, such as FM stations, TV stations, and electronic equipment, can exceed the Sun's power level; we have addressed this issue by using front-end notch filtering to prevent receiver saturation, and digital signal processing to remove radio frequency interference (RFI) from the acquired signal

Summary

Introduction

Active radar sounders provide critical measurements of ice sheets (Dowdeswell & Evans, 2004), such as basal melt rates (Khazendar et al, 2016), reflectivity time series (Chu et al, 2016), vertical velocities (Kingslake et al, 2014), ice thickness (Gogineni et al, 2014), englacial water storage (Kendrick et al, 2018), and subglacial conditions While active radio sounding is widely used for both airborne and ground-based surveys (Bell et al, 2011; Jenkins et al, 2006), these measurements are expensive and resource intensive when performed for multiple years, with fine temporal resolution, and on spatial scales greater than several kilometers. If radar sounders could perform these measurements without transmitting an electromagnetic pulse for echo detection, this would greatly reduce the power consumption, size, design complexity, and cost of the system. Active systems could be miniaturized, their low-power transmitted signal would experience geometric power fall-off, whereas a passive sounding technique exploiting a distant source would experience almost no geometric spreading loss and enable lower-resource systems in challenging environments (Schroeder et al, 2016).

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Results

Discussion

Conclusion