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An ultra-wideband, microwave radar for measuring snow thickness on sea ice and mapping near-surface internal layers in polar firn
- Ben Panzer, Daniel Gomez-Garcia, Carl Leuschen, John Paden, Fernando Rodriguez-Morales, Aqsa Patel, Thorsten Markus, Benjamin Holt, Prasad Gogineni
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- Journal:
- Journal of Glaciology / Volume 59 / Issue 214 / 2013
- Published online by Cambridge University Press:
- 10 July 2017, pp. 244-254
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Sea ice is generally covered with snow, which can vary in thickness from a few centimeters to >1 m. Snow cover acts as a thermal insulator modulating the heat exchange between the ocean and the atmosphere, and it impacts sea-ice growth rates and overall thickness, a key indicator of climate change in polar regions. Snow depth is required to estimate sea-ice thickness using freeboard measurements made with satellite altimeters. The snow cover also acts as a mechanical load that depresses ice freeboard (snow and ice above sea level). Freeboard depression can result in flooding of the snow/ice interface and the formation of a thick slush layer, particularly in the Antarctic sea-ice cover. The Center for Remote Sensing of Ice Sheets (CReSIS) has developed an ultra-wideband, microwave radar capable of operation on long-endurance aircraft to characterize the thickness of snow over sea ice. The low-power, 100 mW signal is swept from 2 to 8 GHz allowing the air/snow and snow/ ice interfaces to be mapped with 5 cm range resolution in snow; this is an improvement over the original system that worked from 2 to 6.5 GHz. From 2009 to 2012, CReSIS successfully operated the radar on the NASA P-3B and DC-8 aircraft to collect data on snow-covered sea ice in the Arctic and Antarctic for NASA Operation IceBridge. The radar was found capable of snow depth retrievals ranging from 10 cm to >1 m. We also demonstrated that this radar can be used to map near-surface internal layers in polar firn with fine range resolution. Here we describe the instrument design, characteristics and performance of the radar.
The Separation of Sea-Ice Types in Radar Imagery (Abstract)
- Benjamin Holt, F.D. Carsey
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- Journal:
- Annals of Glaciology / Volume 9 / 1987
- Published online by Cambridge University Press:
- 20 January 2017, p. 247
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The ability to distinguish the several major types of sea ice with active radar instruments has been well studied in recent years. The separation of sea-ice types by radar results principally from variations in radar back-scatter due to characteristic differences of these ice types in surface morphology and brine content. When sea ice is viewed with an active radar at angles greater than about 20° from nadir, undeformed ice reflects radar waves and results in a low return, while ridges, hummocks, and small-scale surface features scatter the radar waves and produce a high return. The presence of salt increases the dielectric constant of ice; penetration by radar into the ice is then negligible, and the return is essentially determined by surface morphology. The absence of salt reduces the dielectric properties of ice; radar waves can then penetrate the ice to some depth and are scattered by air bubbles and brine-drainage channels (called volume scattering), thereby enhancing the return even for roughened surfaces. All these properties vary significantly with radar frequency and polarization as well as seasonally. For example, higher radar frequencies respond to smaller-scale surface features, while lower radar frequencies penetrate further into the ice with resulting volume scattering.
The high-resolution imagery from synthetic aperture radars (SAR), mounted on aircraft, shuttle, or satellite platforms, is very effective for many sea-ice studies, including the separation of ice types. An aircraft-mounted X-band (9 GHz) SAR, for example, can discriminate smooth first-year ice, rough first-year ice, multi-year ice, and open water by the intensity (tone) of the radar returns and floe geometry. The preferred SARs to date for satellites and shuttle platforms have been L-band (1–2 GHz) systems. SAR imagery of sea ice was extensively acquired by Seasat in 1978 over the Beaufort Sea, with limited quantities obtained by the Shuttle Imaging Radar (SIR-B) over the Weddell Sea in 1984. While L-band SAR can discriminate rough and smooth ice along with roughened open water based on image intensity and floe geometry, the returns from thick first-year ice and multi-year ice are not clearly distinguishable. The fact that there is volume scattering from multi-year ice suggests that there may be textural or spatial frequency variations that could be used to separate these two major ice types in radar imagery. In order to investigate the separation of sea-ice types in the large amount of L-band SAR imagery available, image-analysis techniques including filtering and classification programs have been utilized, pointing towards an automatic classification algorithm for use in future SAR sea-ice data sets, especially from space.
An important characteristic of all SAR imagery is the presence of image speckle, a coherent form of noise caused by the random variability of scatterers across even a uniform surface. Most SAR processors reduce this effect by averaging multiple independent samples but this is done at the cost of reducing resolution. Speckle reduction can also be accomplished by filtering. Several filters have been tested including median, box, and adaptive edge filters. Each filter has different characteristics in terms of smoothing speckle and in the response to sharp gradients or edges, such as ridge or lead openings, as well as computational requirements. Optimization of each filter’s parameters has been determined by the quality of classification of each ice type.
The classification programs that have been tested are based on tone and texture image characteristics. The programs are supervised; that is, a small training area for each class is pre-selected for statistical analysis. From these statistics, the remainder of the imagery is subjected to the particular classification algorithm. The tone program separates classes based on the mean, standard deviation, and number of standard deviations of each class, and includes a Bayesian maximum-likelihood classifier for ambiguous elements. The texture program determines the statistical homogeneity of each class and the optimal segmentation of each small area into the various classes.
Epidemic Parenteral Exposure to Volatile Sulfur-Containing Compounds at a Hemodialysis Center
- Dejana Selenic, Francisco Alvarado-Ramy, Mathew Arduino, Stacey Holt, Fred Cardinali, Benjamin Blount, Jeff Jarrett, Forrest Smith, Neil Altman, Charlotte Stahl, Adelisa Panlilio, Michele Pearson, Jerome Tokars
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 25 / Issue 3 / March 2004
- Published online by Cambridge University Press:
- 02 January 2015, pp. 256-261
- Print publication:
- March 2004
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Objective:
To determine the cause of acute illness on August 30, 2000, among patients at an outpatient dialysis center (center A).
Design:We performed a cohort study of all patients receiving dialysis on August 30, 2000; reviewed dialysis procedures; and analyzed dialysis water samples using microbiologic and chemical assays.
Setting:Dialysis center (center A).
Patients:A case-patient was defined as a patient who developed chills within 5 hours after starting hemodialysis at center A on August 30, 2000.
Results:Sixteen (36%) of 44 patients at center A met the case definition. All case-patients were hospitalized; 2 died. Besides chills, 15 (94%) of the case-patients experienced nausea; 12 (75%), vomiting; and 4 (25%), fever. Illness was more frequent on the second than the first dialysis shift (16 of 20 vs 0 of 24, P < .001); no other risk factors were identified. The center's water treatment system had received inadequate maintenance and disinfection and a sulfurous odor was noted during sampling of the water from the reverse osmosis (RO) unit. The water had elevated bacterial counts. Volatile sulfur-containing compounds (ie, methanethiol, carbon disulfide, dimethyldisulfide, and sulfur dioxide) were detected by gas chromatography and mass spectrometry in 8 of 12 water samples from the RO unit and in 0 of 28 samples from other areas (P < .001). Results of tests for heavy metals and chloramines were within normal limits.
Conclusions:Parenteral exposure to volatile sulfur-containing compounds, produced under anaerobic conditions in the RO unit, could have caused the outbreak. This investigation demonstrates the importance of appropriate disinfection and maintenance of water treatment systems in hemodialysis centers.