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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Comparison of active and passive microwave signatures of Arctic sea ice

In March 1988, overlapping active and passive microwave instrument data were acquired over Arctic sea ice using the NASA DC-8 aircraft equipped with multifrequency, variable polarization SAR and radiometer. Flights were conducted as a series of coordinated underflights of the DMSP SSM/I satellite radiometer in order to validate ice products derived from the SSM/I radiances. Subsequent flights by an NRL P-3 aircraft enabled overlapping high-resolution, single frequency image data to be acquired over the same regions using a Ka-band scanning microwave radiometer. In this paper, techniques are discussed for the accurate coregistration of the three aircraft datasets. Precise coregistration to an accuracy of 100 m plus or minus 25 m has, for the first time, enabled the detailed comparison of temporally and spatially coincident active and passive airborne microwave datasets. Preliminary results from the intercomparisons indicate that the SAR has highly frequency- and polarization-dependent signatures, which at 5.3 GHz (C-band) show an extremely high correlation with the 37 GHz radiometric temperatures.

Drinkwater, M. R.↗
NASA NTRS1990

Extraction of lead and ridge characteristics from SAR images of sea ice

Image-processing techniques for extracting the characteristics of lead and pressure ridge features in SAR images of sea ice are reported. The methods are applied to a SAR image of the Beaufort Sea collected from the Seasat satellite on October 3, 1978. Estimates of lead and ridge statistics are made, e.g., lead and ridge density (number of lead or ridge pixels per unit area of image) and the distribution of lead area and orientation as well as ridge length and orientation. The information derived is useful in both ice science and polar operations for such applications as albedo and heat and momentum transfer estimates, as well as ship routing and offshore engineering.

Vesecky, John F.↗
NASA NTRS1988

Active microwave measurements of artificial sea ice

A 5 m x 15 m outdoor tank of 1.2 m depth was filled with sea water, and polarimetric radar backscatter data were collected at 1.8, 5, and 10 GHz for incidence angles 0 to 60 deg. Observations commenced with open water and continued until 30 cm of sea ice formed. The roughness of the ice surface is important in determining the general backscatter level for first-year ice. Experiments were performed to study the change in backscatter with various roughness scales. Effects of freeze and thaw conditions were also examined. Absolute backscatter levels for new to gray ice are small, except for the large, coherent returns at vertical. The backscatter responses of new, gray, rough grey, and desalinated first-year ice at linear polarization are dissimilar. This is confirmed by examining their polarization signatures.

Onstott, R. G.↗
NASA NTRS1992

Identification of sea ice types in spaceborne synthetic aperture radar data

This study presents an approach for identification of sea ice types in spaceborne SAR image data. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by a land-based scatterometer. Extensive scatterometer observations and experience accumulated in field campaigns during the last 10 yr were used to construct these look-up tables. The classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics are discussed. Results using both aircraft and simulated ERS-1 SAR data are presented and compared to limited field ice property measurements and coincident passive microwave imagery. The importance of an integrated postlaunch program for the validation and improvement of this approach is discussed.

Kwok, Ronald↗
NASA NTRS1994

The Effects of Sensor Field-of-View on the Geometrical Characteristics of Sea Ice Leads and Implications for Large-Area Heat Flux Estimates

The release of heat from sea ice fractures ('leads') is an important component of the heat budget in the Arctic, but their impact on regional scale climate is difficult to assess without more information on their distribution in both space and time. Remote sensing of leads using satellite data, specifically AVHRR thermal and Landsat visible-band imagery, is examined empirically with respect to lead width, orientation, and area fraction. The geometrical aspects of the sensor are simulated so that the effect of sensor field-of-view on retrieved lead width statistics can be assessed. This is done using Landsat data and simulated lead networks degraded to AVHRR pixel sizes. The analyses illustrate how leads of sufficiently high contrast tend to 'grow' with increasing pixel size and how small or low contrast leads disappear. The relationship between lead contrast and the width/field-of-view ratio is also examined in order to determine the limits of lead detectability, and illustrates the multivalued nature of the problem of lead width retrieval. To help quantify the importance of changes in lead statistics, turbulent heat flux is calculated as a function of lead width and lead fraction. It is shown that pixel size has a substantial effect on estimates of turbulent heat transfer from leads to the atmosphere.

Key, J.↗
NASA NTRS2020

Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination

The snow layer atop Arctic sea ice is an important component of the climate system. In winter, the insulating effects of snow slow the rate of ice growth. In spring, the onset of ice surface melt is delayed until the highly reflective snow layer disappears. During summer, meltwater from snow collects in depressions to form melt ponds, which enhances the absorption of solar radiation leading to more rapid surface warming. Presently, there are no routine measurements of snow depth suitable for assessing the impact of climate changes on the precipitation and accumulation of snow on sea ice.Our current understanding of snow depth is limited and has been based on field measurements conducted in the middle of last century and from airborne surveys conducted over the last decade. In this paper, we combine the measurements from two altimetry missions (ICESat‐2 and CryoSat‐2) to calculate snow depth over the entire Arctic Ocean. ICESat‐2 (a lidar) and CryoSat‐2 (a radar) measure the heights to the top and bottom of the snow layer, respectively. Differencing the two heights provides an estimate of the thickness of the snow layer. This paper describes this novel approach and an assessment of the snow depth estimates.

R. Kwok↗
NASA NTRS2011

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km ? 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.

Cavalieri, D. J.↗
NASA NTRS2001

Sea Ice and Ice Temperature Variability as Observed by Microwave and Infrared Satellite Data

Recent reports of a retreating and thinning sea ice cover in the Arctic have pointed to a strong suggestion of significant warming in the polar regions. It is especially important to understand what these reports mean in light of the observed global warning and because the polar regions are expected to be most sensitive to changes in climate. To gain insight into this phenomenon, co-registered ice concentrations and surface temperatures derived from two decades of satellite microwave and infrared data have been processed and analyzed. While observations from meteorological stations indicate consistent surface warming in both regions during the last fifty years, the last 20 years of the same data set show warming in the Arctic but a slight cooling in the Antarctic. These results are consistent with the retreat in the Arctic ice cover and the advance in the Antarctic ice cover as revealed by historical satellite passive microwave data. Surface temperatures derived from satellite infrared data are shown to be consistent within 3 K with surface temperature data from the limited number of stations. While not as accurate, the former provides spatially detailed changes over the twenty year period. In the Arctic, for example, much of the warming occurred in the Beaufort Sea and the North American region in 1998 while slight cooling actually happened in parts of the Laptev Sea and Northern Siberia during the same time period. Big warming anomalies are also observed during the last five years but a periodic cycle of about ten years is apparent suggesting a possible influence of the North Atlantic Oscillation. In the Antarctic, large interannual and seasonal changes are also observed in the circumpolar ice cover with regional changes showing good coherence with surface temperature anomalies. However, a mode 3 is observed to be more dominant than the mode 2 wave reported in the literature. Some of these spatial and temporal changes appear to be influenced by the Antarctic Circumpolar Wave (ACW) and changes in coastal polynya activities.

Comiso, Josefino C.↗
NASA NTRS1982

Analysis of atmosphere-sea ice interactions in the Arctic basin using ESMR microwave data

Data from the Electrically Scanning Microwave Radiometer (ESMR) on Nimbus-5 are used to determine the spatial and temporal patterns of change in microwave brightness signatures of Arctic sea ice during a full annual cycle (1973/74). Interactions of ice conditions with the atmosphere are examined using grid point data for surface air temperature and atmospheric pressure. Principal components analysis is used to examine the major elements present in the microwave and atmospheric data. Component scores from these analyses are then used in a canonical correlation analysis to determine interassociations present between the ice and atmosphere in the Beaufort Sea and the European sectors on a synoptic time scale. The synoptic weather conditions associated with the pattern of snow melt on the ice in spring 1974 are described, and a clarification of possible alternative interpretations of features identified as polynyi occurring at 80-85 deg N during the late summer 1974 is presented.

Crane, R. G.↗
NASA NTRS1992

Sea ice classification using fast learning neural networks

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Dawson, M. S.↗
NASA NTRS1997

Passive Microwave Algorithms for Sea Ice Concentration: A Comparison of Two Techniques

The most comprehensive large-scale characterization of the global sea ice cover so far has been provided by satellite passive microwave data. Accurate retrieval of ice concentrations from these data is important because of the sensitivity of surface flux(e.g. heat, salt, and water) calculations to small change in the amount of open water (leads and polynyas) within the polar ice packs. Two algorithms that have been used for deriving ice concentrations from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in but larger disagreements in the seasonal regions and in summer. In some ares in the Antarctic, the Bootstrap technique show ice concentrations higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows ice concentrations lower by as much as 30%. The The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher concentrations than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.

Comiso, Josefino C.↗
NASA NTRS2017

Sea Ice Outlook for September 2017: June Report - NASA Global Modeling and Assimilation Office

The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging.

Sea Ice↗
NASA NTRS2017

Sea Ice Outlook for September 2017 July Report - NASA Global Modeling and Assimilation Office

The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging.

Arctic↗
NASA NTRS1973

Remote sensing of sea ice motion

Remote sensing by ERTS-1 provides overlapping coverage on sequential days of dynamic changes in Arctic Sea ice and allows for route planning of shipping in the polar region.

Rango, A.↗
NASA NTRS1982

SEASAT views oceans and sea ice with synthetic aperture radar

Fifty-one SEASAT synthetic aperture radar (SAR) images of the oceans and sea ice are presented. Surface and internal waves, the Gulf Stream system and its rings and eddies, the eastern North Pacific, coastal phenomena, bathymetric features, atmospheric phenomena, and ship wakes are represented. Images of arctic pack and shore-fast ice are presented. The characteristics of the SEASAT SAR system and its image are described. Maps showing the area covered, and tables of key orbital information, and listing digitally processed images are provided.

Fu, L. L.↗
NASA NTRS1984

Observations of sea ice and icebergs from satellite radar altimeters

Satellite radar altimeters can make useful contributions to the study of sea ice both by enhancing observations from other instruments and by providing a unique probe of ocean-ice interaction in the Marginal Ice Zone (MIZ). The problems, results and future potential of such observations are discussed.

Rapley, C. G.↗
NASA NTRS1984

Active microwave measurements of sea ice under fall conditions: The RADARSAT/FIREX fall experiment

A series of measurements of the active microwave properties of sea ice under fall growing conditions was conducted. Ice in the inland waters of Mould Bay, Crozier Channel, and intrepid inlet and ice in the Arctic Ocean near Hardinge Bay was investigated. Active microwave data were acquired using a helicopter borne scatterometer. Results show that multiyear ice frozen in grey or first year ice is easily detected under cold fall conditions. Multiyear ice returns were dynamic due to response to two of its scene constituents. Floe boundaries between thick and thin ice are well defined. Multiyear pressure ridge returns are similar in level to background ice returns. Backscatter from homogeneous first year ice is seen to be primarily due to surface scattering. Operation at 9.6 GHz is more sensitive to the detailed changes in scene roughness, while operation at 5.6 GHz seems to track roughness changes less ably.

Onstott, R. G.↗