A First Assessment of IceBridge Snow and Ice Thickness Data Over Arctic Sea Ice

Farrell, S. L.; Kurtz, N. T.; Connor, L. N.; Elder, Bruce; Leuschen, Carl; Markus, T.; McAdoo, D. C.; Panzer, B.; Richter-Menge, J.; Sonntag, J. G.

doi:10.1109/tgrs.2011.2170843

Cited by 99 publications

(140 citation statements)

References 26 publications

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“…An analysis of histograms of ATM elevations over known flat surfaces, including separate cases with open water and flat snow-covered sea ice showed that the elevation distributions are Gaussian in shape with a standard deviation of ∼ 10 cm or less. This has also been observed in separate studies by Farrell et al (2012) who reported a standard deviation of 5 cm over a flat, snowcovered refrozen sea ice lead, while Connor et al (2012) reported a standard deviation of 10 cm over lead areas. We thus ideally expect the distribution of all h tp points within the length scale less than the Rossby radius to be similar in shape and width.…”

Section: Sea Ice Freeboard Retrievalssupporting

confidence: 73%

“…Comparison of the radar signal with the in situ observations of Farrell et al (2012) have shown that the strongest peak in the return is expected to correspond to the snow-ice interface, but that the snow-air interface does not often correspond to a distinct peak as may be expected from theoretical arguments. Factors such as instrument noise and response, volume scattering, snow density variations, and surface roughness features all combine to create a complex signal that deviates from the ideal theoretical signal where two distinct peaks are expected to correspond to the snow-air and snow-ice interfaces.…”

Section: Snow Depth Retrievalsmentioning

confidence: 88%

“…Retrieval methods for the IceBridge snow radar have been described by Farrell et al (2012). The method of is used to retrieve snow depth for the 2009 IceBridge campaign in this study.…”

Section: Snow Depth Retrievalsmentioning

confidence: 99%

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Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

et al. 2013

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Abstract. The study of sea ice using airborne remote sensing platforms provides unique capabilities to measure a wide variety of sea ice properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in sea ice properties. In this paper we describe methods for the retrieval of sea ice thickness, freeboard, and snow depth using data from a multisensor suite of instruments on NASA's Operation IceBridge airborne campaign. We assess the consistency of the results through comparison with independent data sets that demonstrate that the IceBridge products are capable of providing a reliable record of snow depth and sea ice thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing IceBridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved sea ice thickness. Lastly, we present results for the 2009 and 2010 IceBridge campaigns, which are currently available in product form via the National Snow and Ice Data Center.

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Section: Sea Ice Freeboard Retrievalssupporting

confidence: 73%

Section: Snow Depth Retrievalsmentioning

confidence: 88%

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Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

et al. 2013

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“…The DMS images were orthorectified and geolocated, and at the nominal flight altitude of 460 m, have a horizontal resolution of 10 cm [Dominguez, 2010]. Surface roughness in relation to ice type was determined using a combination of DMS images, in situ ice type identifications, and surface elevation data from the OIB Airborne Topographic Mapper (ATM), a spiral scanning laser altimeter which has a single-shot vertical accuracy of 5-7 cm depending on the surface roughness within the laser footprint [Krabill, 2010;Farrell et al, 2012;Martin et al, 2012].…”

Section: Airborne and In Situ Observationsmentioning

confidence: 99%

Seasonal evolution of melt ponds on Arctic sea ice

et al. 2015

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The seasonal evolution of melt ponds has been well documented on multiyear and landfast first‐year sea ice, but is critically lacking on drifting, first‐year sea ice, which is becoming increasingly prevalent in the Arctic. Using 1 m resolution panchromatic satellite imagery paired with airborne and in situ data, we evaluated melt pond evolution for an entire melt season on drifting first‐year and multiyear sea ice near the 2011 Applied Physics Laboratory Ice Station (APLIS) site in the Beaufort and Chukchi seas. A new algorithm was developed to classify the imagery into sea ice, thin ice, melt pond, and open water classes on two contrasting ice types: first‐year and multiyear sea ice. Surprisingly, melt ponds formed ∼3 weeks earlier on multiyear ice. Both ice types had comparable mean snow depths, but multiyear ice had 0–5 cm deep snow covering ∼37% of its surveyed area, which may have facilitated earlier melt due to its low surface albedo compared to thicker snow. Maximum pond fractions were 53 ± 3% and 38 ± 3% on first‐year and multiyear ice, respectively. APLIS pond fractions were compared with those from the Surface Heat Budget of the Arctic Ocean (SHEBA) field campaign. APLIS exhibited earlier melt and double the maximum pond fraction, which was in part due to the greater presence of thin snow and first‐year ice at APLIS. These results reveal considerable differences in pond formation between ice types, and underscore the importance of snow depth distributions in the timing and progression of melt pond formation.

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“…The sensor suite of Operation IceBridge (OIB) (Koenig et al, 2010) includes an ultra-wideband snow radar that allows estimates of snow depth by resolving the range location of the air-snow (a-s) and snow-ice (s-i) interfaces. Early examination shows that snow depth can be estimated to an uncertainty of about several centimeters and that the mean snow depth is broadly consistent with the W99 climatology except over seasonal sea ice (e.g., Kurtz and Farrell, 2011;Farrell et al, 2012). To date, OIB has acquired 8 years (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) of radar data, including repeat surveys of the early spring snow and ice conditions in different parts of the western Arctic.…”

mentioning

confidence: 91%

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

et al. 2017

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Abstract. Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two groundbased campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

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A First Assessment of IceBridge Snow and Ice Thickness Data Over Arctic Sea Ice

Cited by 99 publications

References 26 publications

Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

Seasonal evolution of melt ponds on Arctic sea ice

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

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