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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Spatial Variability of Barrow-Area Shore-Fast Sea Ice and Its Relationships to Passive Microwave Emissivity

Aircraft-acquired passive microwave data, laser radar height observations, RADARSAT synthetic aperture radar imagery, and in situ measurements obtained during the AMSR-Ice03 experiment are used to investigate relationships between microwave emission and ice characteristics over several space scales. The data fusion allows delineation of the shore-fast ice and pack ice in the Barrow area, AK, into several ice classes. Results show good agreement between observed and Polarimetric Scanning Radiometer (PSR)-derived snow depths over relatively smooth ice, with larger differences over ridged and rubbled ice. The PSR results are consistent with the effects on snow depth of the spatial distribution and nature of ice roughness, ridging, and other factors such as ice age. Apparent relationships exist between ice roughness and the degree of depolarization of emission at 10,19, and 37 GHz. This depolarization .would yield overestimates of total ice concentration using polarization-based algorithms, with indications of this seen when the NT-2 algorithm is applied to the PSR data. Other characteristics of the microwave data, such as effects of grounding of sea ice and large contrast between sea ice and adjacent land, are also apparent in the PSR data. Overall, the results further demonstrate the importance of macroscale ice roughness conditions such as ridging and rubbling on snow depth and microwave emissivity.

Maslanik, J. A.↗
NASA NTRS2008

Arctic Sea Ice Variability and Trends, 1979-2006

Analysis of Arctic sea ice extents derived from satellite passive-microwave data for the 28 years, 1979-2006 yields an overall negative trend of -45,100 +/- 4,600 km2/yr (-3.7 +/- 0.4%/decade) in the yearly averages, with negative ice-extent trends also occurring for each of the four seasons and each of the 12 months. For the yearly averages the largest decreases occur in the Kara and Barents Seas and the Arctic Ocean, with linear least squares slopes of -10,600 +/- 2,800 km2/yr (-7.4 +/- 2.0%/decade) and -10,100 +/- 2,200 km2/yr (-1.5 +/- 0.3%/decade), respectively, followed by Baffin Bay/Labrador Sea, with a slope of -8,000 +/- 2,000 km2/yr) -9.0 +/- 2.3%/decade), the Greenland Sea, with a slope of -7,000 +/- 1,400 km2/yr (-9.3 +/- 1.9%/decade), and Hudson Bay, with a slope of -4,500 +/- 900 km2/yr (-5.3 +/- 1.1%/decade). These are all statistically significant decreases at a 99% confidence level. The Seas of Okhotsk and Japan also have a statistically significant ice decrease, although at a 95% confidence level, and the three remaining regions, the Bering Sea, Canadian Archipelago, and Gulf of St. Lawrence, have negative slopes that are not statistically significant. The 28-year trends in ice areas for the Northern Hemisphere total are also statistically significant and negative in each season, each month, and for the yearly averages.

Parkinson, Claire L.↗
NASA NTRS1987

Evolution of microwave sea ice signatures during early summer and midsummer in the marginal ice zone

Emissivities at frequencies from 5 to 94 GHz and backscatter at frequencies from 1 to 17 GHz were measured from sea ice in Fram Strait during the marginal Ice Zone Experiment in June and July of 1983 and 1984. The ice observed was primarily multiyear; the remainder, first-year ice, was often deformed. Results from this active and passive microwave study include the description of the evolution of the sea ice during early summer and midsummer; the absorption properties of summer snow; the interrelationship between ice thickness and the state and thickness of snow; and the modulation of the microwave signature, especially at the highest frequencies, by the freezing of the upper few centimeters of the ice.

Onstott, R. G.↗
NASA NTRS1989

Sensitivity of passive microwave sea ice concentration algorithms to the selection of locally and seasonally adjusted tie points

The sensitivity of passive microwave sea-ice concentration (SIC) algorithms to the selection of tie points was analyzed. SICs were derived with the NASA Team ice algorithm for global tie points and for locally and seasonally adjusted tie points. The SSM/I SIC was then compared to Landsat-MSS-derived SICs. Preliminary results show a mean difference of SSM/I- and Landsat-derived SICs for 50 x 50 km grid cells of 2.7 percent along the ice edge of the Beaufort Sea during fall with local tie points. The accuracy decreased to 9.7 percent when global tie points were used. During freeze-up in the Beaufort Sea, with grey ice and nilas as dominant ice cover, the mean difference was 4.3 percent for local tie points and 13.9 percent for global tie points. For the spring ice cover in the Bering Sea a mean difference of 4.4 percent for local tie points and 15.7 percent for global tie points was found. This large difference reveals some limitations of the NASA-Team algorithm under freeze-up and spring conditions (thin ice areas).

Steffen, Konrad↗
NASA NTRS2017

Intercomparison of Snow Depth Retrievals over Arctic Sea Ice from Radar Data Acquired by Operation IceBridge

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 (ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis) 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.

Operation IceBridge↗
NASA NTRS1986

Imaging radar contributions to a major air-sea-ice interaction study in the Greenland Sea

By virtue of the Synthetic Aperture Radar (SAR's) imaging capabilities, such as all-weather imaging, relatively high resolution, and large dynamic range of backscatter from SAR ice and open ocean, information on the important marginal ice zone (MIZ) parameters can be derived from the SAR data. Information on ice edge location and location of ice-edge eddies, for example, can be obtained directly from examination of the imagery as can detection of ocean fronts and internal waves. With machine-assisted manual image analysis, estimates of ice concentration, floe size distributions, and ice field motion can also be derived. Full digital analysis, however, is required to obtain gravity wave spectral information and backscatter statistics for ice type discrimination and automated ice concentration algorithms.

Shuchman, Robert A.↗
NASA NTRS1984

Sea Ice

Scientific objectives and observational requirements relative to sea ice studies in the 1990's are discussed. The development of coupled ice-ocean-atmosphere models that provide information comparable to real data sets through interactive processes is discussed. A series of particular problems that would provide the basis for such a verification data set and which is amenable to satellite systems available or realizable over the next 20 years is presented.

Ackley, S. F.↗
NASA NTRS1987

Sea ice tracking by nested correlations

Spatial differences in sea ice displacement affect ice stress, ice production, and the mass balance of the ice cover. Concepts about the spatial structure of this field have been undernourished because of a paucity of data with high spatial detail and because of the tedium of extracting such measurements from images manually. A method is described that measures displacements from synthetic aperture radar digital imagery with fine spatial resolution, and does so fully automatically. Many small areas of ice common to two images are identified by correlating the two images. The strategy is to acquire a crude displacement field first from highly averaged images, and to refine this field with images of successively higher resolution. The median discrepancy between automatically and manually measured displacements is three pixels (0.075 km). The algorithm operates successfully on compact ice with large floes and modest rotation rates; it is believed it will prove applicable to most of the arctic ice cover throughout the year.

Fily, Michael↗
NASA NTRS1989

Observations of winter polynyas and fractures using NOAA AVHRR TIR images and Nimbus-7 SMMR sea ice concentration charts

The validity of the daily sea ice concentration charts obtained from Nimbus-7 SMMR data is tested against thermal IR NOAA AVHRR-4 images with a 1.1 x 1.1 km resolution. Polynyas and fractures recorded in both data sets are compared for the same times over the same areas. Although the coarser resolution of SMMR data leads to a loss of detail, it is found that the ice concentration charts provide valuable data on openings in the ice. The SMMR data on the location, orientation, and size of polynyas and fractures are shown to be in good agreement with the AVHRR data. Also, the results suggest that the location and orientation of plynyas and fractures are related to the direction of surface winds.

Dey, B.↗
NASA NTRS1985

Processes and imagery of first-year fast sea ice during the melt season

In June and July 1982, a field program was conducted in the Canadian Arctic on Prince Patrick Island to study sea ice during the melt season with in situ measurements and microwave instrumentation operated near the surface and from aircraft. The objective of the program was to measure physical characteristics together with microwave backscatter and emission coefficients of sea ice during this major period of transition. The present paper is concerned with a study of both surface measurements and imagery of first-year fast ice during the melt season. The melting process observed in first-year fast ice was found to begin with the gradual reduction of the snow cover. For a two- to three-day period in this melt stage, a layer of superimposed ice nodules formed at the snow/ice interface as meltwater froze around ice and snow grains.

Holt, B.↗
NASA NTRS1996

Sea Ice Identification Using Dual-Polarized Ku-Band Scatterometer Data

Maps of sea ice have been based on a combination of observations from aircraft and ships, and infrared and passive microwave data from various spaceborne sensors. Described here is the potential of using dual-polarized scatterometer returns from the polar oceans to identify the edge of the ice cover. This ice edge could be used to compute the ice extent, which may be a useful indicator of climatic changes.

Ocean↗
NASA NTRS2019

Retrieval of Snow Freeboard of Antarctic Sea Ice Using Waveform Fitting of CryoSat-2 Returns

In this paper we develop a CryoSat-2 algorithm to retrieve the surface elevation of the air–snow interface over Antarctic sea ice. This algorithm utilizes a two-layer physical model that accounts for scattering from a snow layer atop sea ice as well as scattering from below the snow surface. The model produces waveforms that are fit to CryoSat-2 level 1B data through a bounded trust region least-squares fitting process. These fit waveforms are then used to track the air–snow interface and retrieve the surface elevation at each point along the CryoSat-2 ground track, from which the snow freeboard is computed. To validate this algorithm, we compare retrieved surface elevation measurements and snow surface radar return power levels with those from Operation IceBridge, which flew along a contemporaneous CryoSat-2 orbit in October 2011 and November 2012. Average elevation differences (standard deviations) along the flight lines (IceBridge Airborne Topographic Mapper, ATM – CryoSat-2) are found to be 0.016 cm (29.24 cm) in 2011 and 2.58 cm (26.65 cm) in 2012. The spatial distribution of monthly average pan-Antarctic snow freeboard found using this method is similar to what was observed from NASA's Ice, Cloud, and land Elevation Satellite (ICESat), where the difference (standard deviation) between October 2011–2017 CryoSat-2 mean snow freeboard and spring 2003–2007 mean freeboard from ICESat is 1.92 cm (9.23 cm). While our results suggest that this physical model and waveform fitting method can be used to retrieve snow freeboard from CryoSat-2, allowing for the potential to join laser and radar altimetry data records in the Antarctic, larger (∼30 cm) regional differences from ICESat and along-track differences from ATM do exist, suggesting the need for future improvements to the method. Snow–ice interface elevation retrieval is also explored as a potential to obtain snow depth measurements. However, it is found that this retrieval method often tracks a strong scattering layer within the snow layer instead of the actual snow–ice interface, leading to an overestimation of ice freeboard and an underestimation of snow depth in much of the Southern Ocean but with promising results in areas such as the East Antarctic sector.

Fons, Steven W.↗
NASA NTRS1982

Sensitivity of a climatologically-driven sea ice model to the ocean heat flux

Ocean heat flux sensitivity was studied on a numerical model of sea ice covering the Weddell Sea region of the southern ocean. The model is driven by mean monthly climatological atmospheric variables. For each model run, the ocean heat flux is uniform in both space and time. Ocean heat fluxes below 20 W m to the minus 2 power do not provide sufficient energy to allow the ice to melt to its summertime thicknesses and concentrations by the end of the 14 month simulation, whereas ocean heat fluxes of 30 W m to the minus 2 power and above result in too much ice melt, producing the almost total disappearance of ice in the Weddell Sea by the end of the 14 months. These results are dependent on the atmospheric forcing fields.

Parkinson, C. L.↗
NASA NTRS2001

Monitoring Fram Strait sea ice outflow and thin ice thickness

We propose to: 1) use sequential SAR maps to monitor the profile of sea ice motion through Fram Strait over the period 2003 throught 2005; and 2) explore the potential of using L-band polarimetric data to determine the thickness of thin ice over the same region.

Fram Strait sea ice thickness↗
NASA NTRS2019

Multiyear sea ice thickness estimation using wideband P/L-band radiometric measurements

A new wideband radiometer covering P/L-band was developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The instrument was deployed on the US Coast Guard Cutter Healy for an Arctic Ocean research cruise from September 13, 2018 to October 20, 2018. This work shows the first results relating sea ice thickness obtained from the measurements taken with the wideband P/L-band radiometer during the campaign. Results from the Artic cruise campaign were also used to study wideband spectral properties of salinity. In addition to this paper, Salinity and wide-band calibration challenges are presented in two other companion papers.

Brown, Shannon↗