Advanced Multifrequency Radar Instrumentation for Polar Research

Rodriguez‐Morales, Fernando; Gogineni, S.; Leuschen, C.; Paden, John; Li, Jilu; Lewis, Christopher L.; Panzer, B.; Alvestegui, Daniel Gomez-Garcia; Patel, Ashaben; Byers, Kyle J.; Crowe, R.; Player, Kevin; Hale, Richard; Arnold, Emily; Smith, L.; Gifford, Chris; Braaten, D.; Panton, Christian

doi:10.1109/tgrs.2013.2266415

Cited by 189 publications

(146 citation statements)

References 39 publications

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“…The data are used to evaluate the horizontal extent of firn aquifers and ice layers simulated by the two numerical models. The Accumulation Radar was built and is operated by the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas to image near-surface (up to approximately 400 m depth) internal structure of the ice sheet with a 65 cm vertical resolution (Rodriguez-Morales et al, 2014). The Accumulation Radar is well suited to image the presence of a water table in the firn characterized by a bright reflector in the radar images Miège et al, 2016).…”

Section: Observational Datamentioning

confidence: 99%

Firn Meltwater Retention on the Greenland Ice Sheet: A Model Comparison

et al. 2017

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Runoff has recently become the main source of mass loss from the Greenland Ice Sheet and is an important contributor to global sea level rise. Linking runoff to surface meltwater production is complex, as meltwater can be retained within the firn by refreezing or perennial liquid water storage. To constrain these uncertainties, the outputs of two offline snow/firn models of different complexity (IMAU-FDM and SNOWPACK) are compared to assess the sensitivity of meltwater retention to the model formulation (e.g., densification, irreducible water content, vertical resolution). Results indicate that model differences are largest in areas where firn aquifers form, i.e., particularly along the south-eastern margin of the ice sheet. The IMAU-FDM simulates higher densification rates for such climatic conditions and prescribes a lower irreducible water content than SNOWPACK. As a result, the model predicts substantially lower amounts of refreezing and liquid water storage. SNOWPACK performs better for this area, confirmed both by density profiles from firn cores and radar-inferred observations. Refreezing integrated over the entire ice sheet and averaged for the period 1960-2014 amounts to 216 Gt a −1 (IMAU-FDM) and 242 Gt a −1 (SNOWPACK), which is 41 and 46% of the total liquid water input (snowmelt and rainfall). The mean areal extents of perennial firn aquifers for 2010-2014 simulated by the models are 55,700 km 2 (IMAU-FDM) and 90,200 km 2 (SNOWPACK). Discrepancies between modeled firn profiles and observations emphasize the importance of processes currently not accounted for in most snow/firn models, such as vertical heterogeneous percolation, ponding of water on impermeable layers, lateral (sub-)surface water flow, and the issue of ill-constrained refreezing conditions at the base of firn aquifers.

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Section: Observational Datamentioning

confidence: 99%

Firn Meltwater Retention on the Greenland Ice Sheet: A Model Comparison

et al. 2017

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“…The sounder has a frequency range from 180 to 210 MHz, corresponding to a centre wavelength ∼ 0.87 m in ice. After accounting for pulse shaping and windowing, this results in a depth-range resolution in ice of ∼ 4.3 m (Rodriguez- Morales et al, 2014;Paden, 2015). For the flight lines considered, the alongtrack resolution after synthetic aperture radar (SAR) processing and multi-looking is ∼ 30 m with an along-track-sample spacing of ∼ 15 m .…”

Section: Calculation Of the Hurst Exponent Using The Variogram And Dementioning

confidence: 99%

Self-affine subglacial roughness: consequences for radar scattering and basal water discrimination in northern Greenland

et al. 2017

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Abstract. Subglacial roughness can be determined at a variety of length scales from radio-echo sounding (RES) data either via statistical analysis of topography or inferred from basal radar scattering. Past studies have demonstrated that subglacial terrain exhibits self-affine (power law) roughness scaling behaviour, but existing radar scattering models do not take this into account. Here, using RES data from northern Greenland, we introduce a self-affine statistical framework that enables a consistent integration of topographicscale roughness with the electromagnetic theory of radar scattering. We demonstrate that the degree of radar scattering, quantified using the waveform abruptness (pulse peakiness), is topographically controlled by the Hurst (roughness power law) exponent. Notably, specular bed reflections are associated with a lower Hurst exponent, with diffuse scattering associated with a higher Hurst exponent. Abrupt waveforms (specular reflections) have previously been used as a RES diagnostic for basal water, and to test this assumption we compare our radar scattering map with a recent prediction for the basal thermal state. We demonstrate that the majority of thawed regions (above pressure melting point) exhibit a diffuse scattering signature, which is in contradiction to the prior approach. Self-affine statistics provide a generalised model for subglacial terrain and can improve our understanding of the relationship between basal properties and ice-sheet dynamics.

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“…The snow radar operates over the frequency range from ∼ 2 to 6.5 GHz (Panzer et al, 2013;Rodriguez-Morales et al, 2014). The snow radar uses an FMCW design to provide a vertical-range resolution of ∼ 4 cm in snow/firn, capable of resolving annual layering, where preserved, to tens of meters in depth (Medley et al, 2013).…”

Section: Snow Radar and Datamentioning

confidence: 99%

“…In the past 2 decades, climate warming over the Greenland Ice Sheet (GrIS) has accelerated its mass loss, nearly quadrupling from ∼ 55 Gt a −1 between 1993 and 1999 (Krabill et al, 2004) to ∼ 210 Gt a −1 , equivalent to ∼ 0.6 mm a −1 of sea-level rise, between 2003(Shepherd et al 2012. As GrIS mass loss has accelerated, a fundamental change in the dominant mass loss process has occurred (e.g.…”

Section: Introductionmentioning

confidence: 99%

Annual Greenland accumulation rates (2009–2012) from airborne snow radar

et al. 2016

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Abstract. Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2-6.5 GHz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semiautomated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009-2012. The uncertainty in these radar-derived accumulation rates is on average 14 %. A comparison of the radarderived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and longterm mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.

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Advanced Multifrequency Radar Instrumentation for Polar Research

Cited by 189 publications

References 39 publications

Firn Meltwater Retention on the Greenland Ice Sheet: A Model Comparison

Firn Meltwater Retention on the Greenland Ice Sheet: A Model Comparison

Self-affine subglacial roughness: consequences for radar scattering and basal water discrimination in northern Greenland

Annual Greenland accumulation rates (2009–2012) from airborne snow radar

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