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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Measurement of ice thickness (icing) in aeronautics

Pulsed ultrasonic techniques have been used to measure the formation of ice in flight in an icing wind tunnel with a precision of + or - 0.5 mm. Two icing regimes, humid and dry, are identified. Both natural and artificial conditions are considered. On the basis of ice formation rates obtained by the ultrasound technique and the observed surface conditions, it is found that the heat transfer coefficients are larger in the wind tunnel tests than in actual flight, presumably due to the higher level of turbulence in the wind tunnel tests. Profiles obtained during flight under natural conditions are compared with mechanical-type measurements and with the results of stereographic analysis.

Hansman, R. John↗
NASA NTRS2007

Antarctic Sea Ice Thickness and Snow-to-Ice Conversion from Atmospheric Reanalysis and Passive Microwave Snow Depth

Passive microwave snow depth, ice concentration, and ice motion estimates are combined with snowfall from the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis (ERA-40) from 1979-200 1 to estimate the prevalence of snow-to-ice conversion (snow-ice formation) on level sea ice in the Antarctic for April-October. Snow ice is ubiquitous in all regions throughout the growth season. Calculated snow- ice thicknesses fall within the range of estimates from ice core analysis for most regions. However, uncertainties in both this analysis and in situ data limit the usefulness of snow depth and snow-ice production to evaluate the accuracy of ERA-40 snowfall. The East Antarctic is an exception, where calculated snow-ice production exceeds observed ice thickness over wide areas, suggesting that ERA-40 precipitation is too high there. Snow-ice thickness variability is strongly controlled not just by snow accumulation rates, but also by ice divergence. Surprisingly, snow-ice production is largely independent of snow depth, indicating that the latter may be a poor indicator of total snow accumulation. Using the presence of snow-ice formation as a proxy indicator for near-zero freeboard, we examine the possibility of estimating level ice thickness from satellite snow depths. A best estimate for the mean level ice thickness in September is 53 cm, comparing well with 51 cm from ship-based observations. The error is estimated to be 10-20 cm, which is similar to the observed interannual and regional variability. Nevertheless, this is comparable to expected errors for ice thickness determined by satellite altimeters. Improvement in satellite snow depth retrievals would benefit both of these methods.

Markus, Thorsten↗
NASA NTRS2004

Advances in Measuring Antarctic Sea-Ice Thickness and Ice-Sheet Elevations with ICESat Laser Altimetry

NASA's Ice, Cloud and Land Elevation Satellite (ICESat) has been measuring elevations of the Antarctic ice sheet and sea-ice freeboard elevations with unprecedented accuracy. Since February 20,2003, data has been acquired during three periods of laser operation varying from 36 to 54 days, which is less than the continuous operation of 3 to 5 years planned for the mission. The primary purpose of ICESat is to measure time-series of ice-sheet elevation changes for determination of the present-day mass balance of the ice sheets, study of associations between observed ice changes and polar climate, and estimation of the present and future contributions of the ice sheets to global sea level rise. ICESat data will continue to be acquired for approximately 33 days periods at 3 to 6 month intervals with the second of ICESat's three lasers, and eventually with the third laser. The laser footprints are about 70 m on the surface and are spaced at 172 m along-track. The on-board GPS receiver enables radial orbit determinations to an accuracy better than 5 cm. The orbital altitude is around 600 km at an inclination of 94 degrees with a 8-day repeat pattern for the calibration and validation period, followed by a 91 -day repeat period for the rest of the mission. The expected range precision of single footprint measurements was 10 cm, but the actual range precision of the data has been shown to be much better at 2 to 3 cm. The star-tracking attitude-determination system should enable footprints to be located to 6 m horizontally when attitude calibrations are completed. With the present attitude calibration, the elevation accuracy over the ice sheets ranges from about 30 cm over the low-slope areas to about 80 cm over areas with slopes of 1 to 2 degrees, which is much better than radar altimetry. After the first period of data collection, the spacecraft attitude was controlled to point the laser beam to within 50 m of reference surface tracks over the ice sheets. Detection of ice elevation changes over select areas of the ice sheet is demonstrated with using both crossover analysis and precise-repeat track analysis. Sea ice freeboard-height distributions over the Antarctic sea pack are derived over distances of 50 km and converted into maps of average freeboard thickness and sea-ice thickness.

Zwally, H. Jay↗
NASA NTRS2020

Analyzing the Impact of CryoSat-2 Ice Thickness Initialization on Seasonal Arctic Sea Ice Prediction

Twin 5-month seasonal forecast experiments are performed to predict the September 2018 minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 for the ocean and sea ice but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M sq.km (5.27 M sq.km) versus the ensemble set of forecasts initialized with CS2 ice thickness that had an error of 0.40 M sq.km (4.99 M sq.km), a 56% reduction in error. The September mean Integrated Ice Edge Error (IIEE) shows a 19% improvement for the entire Arctic with the CS2 data versus the control run. Comparison against Upward Looking Sonar (ULS) ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of experiments show ice free conditions as observed at moorings A and D.

time-lagged ensembles↗
NASA NTRS2022

Effects of Increasing the Category Resolution of the Sea Ice Thickness Distribution in a Coupled Climate Model on Arctic and Antarctic Sea Ice Mean State

Many modern sea ice models used in global climate models represent the subgrid-scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea-ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard 5 ice thickness categories and 15 ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year-round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice volume. The spatial resolution of the ICESat-2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat-2; 2018–2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.

Madison M. Smith↗
NASA NTRS1999

Ice Thicknesses and Driving Stresses of Greenland Ice Sheet

Surface elevations from satellite radar altimetry (Geosat, Seasat, and ERS-1) and bedrock topography from airborne radar sounding (Simon Ekholm's Danish compilation) are combined to derive maps of the driving stresses in the Greenland ice sheet. The stress vector, tau = rho g h sin(alpha), is calculated using surface slope vectors, alpha, from surface elevations and ice thicknesses, h, from the difference between surface and basal elevations. Since the 5-km scale of the surface slope is only about 2 times the ice thickness, the stress maps show spatial variations indicative of longitudinal stress gradients associated with topographic undulations. Values of alpha generally vary from near zero at the ice divides to maxima values around 120 kpa, returning to near zero in a narrow band at the edges. The distribution of alpha's peaks at 60 kpa with an approximate sigma of +/- 20 kpa. Areas of very low alpha near the origin of the northeast ice stream may indicate small sub-glacial lakes. The profile of alpha, down the ice stream from near the ice divide, increases to a maximum of about 120 kpa near the margin, which is characteristic of East Antarctic outlet glaciers and in contrast to West Antarctic ice streams where alpha has maximum values 400 to 500 km inland from the grounding lines. Overall distributions of alpha values are compared with those for the Antarctic ice sheet and the Mars Northern ice cap.

Zwally, H. J.↗
NASA NTRS1976

Passive microwave mapping of ice thickness

Basic calculations are presented for evaluating the feasibility of a scanning microwave radiometer system for mapping the thickness of lake ice. An analytical model for the apparent brightness temperature as a function of ice thickness has been developed, and elaborated to include such variables as galactic and atmospheric noise, aspect angle, polarization, temperature gradient in the ice, the presence of transition layers such as snow, slush, and water, increased loss due to air inclusions in the ice layer, and the presence of multiple ice thicknesses within the antenna footprint. It was found that brightness temperature measurements at six or seven frequencies in the range of 0.4 to 0.7 GHz were required to obtain unambiquous thickness estimates. A number of data processing methods were examined. The effects of antenna beamwidth, scanning rate, receiver bandwidth, noise figure, and integration time were studied.

Apinis, J. J.↗
NASA NTRS2005

Remote sensing of sea ice thickness by a combined spatial and frequency domain interferometer : formulations, instrument design & development

The thickness of Arctic sea ice plays a critical role in Earth's climate and ocean circulation. An accurate measurement of this parameter on synoptic scales at regular intervals would enable characterization of this important component for the understanding of ocean circulation and the global heat balance. Presented in this paper is a low frequency VHF interferometer technique and associated radar instrument design to measure sea ice thickness based on the use of backscatter correlation functions. The sea ice medium is represented as a multi-layered medium consisting of snow, seaice and sea water, with the interfaces between layers characterized as rough surfaces. This technique utilizes the correlation of two radar waves of different frequencies and incident and observation angles, scattered from the sea ice medium. The correlation functions relate information about the sea ice thickness. Inversion techniques such as the genetic algorithm, gradient descent, and least square methods, are used to derive sea ice thickness from the phase information related by the correlation functions.

remote sensing↗
NASA NTRS2009

Estimation of Sea Ice Thickness Distributions through the Combination of Snow Depth and Satellite Laser Altimetry Data

Combinations of sea ice freeboard and snow depth measurements from satellite data have the potential to provide a means to derive global sea ice thickness values. However, large differences in spatial coverage and resolution between the measurements lead to uncertainties when combining the data. High resolution airborne laser altimeter retrievals of snow-ice freeboard and passive microwave retrievals of snow depth taken in March 2006 provide insight into the spatial variability of these quantities as well as optimal methods for combining high resolution satellite altimeter measurements with low resolution snow depth data. The aircraft measurements show a relationship between freeboard and snow depth for thin ice allowing the development of a method for estimating sea ice thickness from satellite laser altimetry data at their full spatial resolution. This method is used to estimate snow and ice thicknesses for the Arctic basin through the combination of freeboard data from ICESat, snow depth data over first-year ice from AMSR-E, and snow depth over multiyear ice from climatological data. Due to the non-linear dependence of heat flux on ice thickness, the impact on heat flux calculations when maintaining the full resolution of the ICESat data for ice thickness estimates is explored for typical winter conditions. Calculations of the basin-wide mean heat flux and ice growth rate using snow and ice thickness values at the 70 m spatial resolution of ICESat are found to be approximately one-third higher than those calculated from 25 km mean ice thickness values.

Kurtz, Nathan T.↗
NASA NTRS2014

Aquarius Retrieval of Sea Ice Thickness: Initial Results

Aquarius brightness temperature data are used to calculate sea ice thickness in the Arctic region. The method is based on the inversion of a radiative transfer model for icecovered sea. Using this technique, the initial sea ice thickness values retrieved from Aquarius data are compared to the SMOSIce Data as well as to estimates from NASA's Operation IceBridge. The results show similar trends between the SMOS- and Aquarius-derived sea ice thickness, however the Aquarius estimates tend to be higher and noisier than the corresponding SMOS values. The accuracy of retrieved Aquarius ice thickness is possibly influenced by uncertainties in the ancillary input parameters and by the coarser resolutions of Aquarius.

Sea Ice Thickness↗
NASA NTRS1976

Measurement of lake ice thickness with a short-pulse radar system

Measurements of lake ice thickness were made during March 1975 at the Straits of Mackinac by using a short-pulse radar system aboard an all-terrain vehicle. These measurements were compared with ice thicknesses determined with an auger. Over 25 sites were explored which had ice thicknesses in the range 29 to 60 cm. The maximum difference between radar and auger measurements was less than 9.8 percent. The magnitude of the error was less than + or - 3.5 cm. The NASA operating short-pulse radar system used in monitoring lake ice thickness from an aircraft is also described.

Cooper, D. W.↗
NASA NTRS1981

Freshwater ice thickness observations using passive microwave sensors

Walden Reservoir, a freshwater lake in north-central Colorado, was overflown six times by a NASA C-130 aircraft between January 1977 and April 1980. The aircraft was equipped with four microwave radiometers operating between 0.81 and 6.0 cm in wavelength (37.0 to 5.0 GHz). The 6.0-cm radiometer data showed a good relationship with ice thickness based on a sample of four ice thickness values. The 1.67- and 1.35-cm radiometer data showed weaker relationships with ice thickness. The 0.81-cm sensor data showed no positive relationship with ice thickness. None of the relationships was statistically significant because of the small sample size. The 6.0-cm sensor data in the nadir-viewing mode was found to have the most potential of all the wavelengths studied, for use in remotely determining ice thickness. The 6.0-cm radiometer probably sensed the entire thickness of the ice on the reservoir (ranging from 25.4 to 67.3 cm in thickness) and was apparently not significantly affected by the snow overlying the ice. The shorter wavelengths are scattered by the snow overlying the ice and are more suitable for snow studies than for ice thickness studies.

Hall, D. K.↗
NASA NTRS1973

Airborne profiling of ice thickness using a short pulse radar

The acquisition and interpretation of ice thickness data from a mobile platform has for some time been a goal of the remote sensing community. Such data, once obtainable, is of value in monitoring the changes in ice thickness over large areas, and in mapping the potential hazards to traffic in shipping lanes. Measurements made from a helicopter-borne ice thickness profiler of ice in Lake Superior, Lake St. Clair and the St. Clair river as part of NASA's program to develop an ice information system are described. The profiler described is a high resolution, non-imaging, short pulse radar, operating at a carrier frequency of 2.7 GHz. The system can resolve reflective surfaces separated by as little as 10 cm. and permits measurement of the distance between resolvable surfaces with an accuracy of about 1 cm. Data samples are given for measurements both in a static (helicopter hovering), and a traverse mode. Ground truth measurements taken by an ice auger team traveling with the helicopter are compared with the remotely sensed data and the accuracy of the profiler is discussed based on these measurements.

Vickers, R. S.↗
NASA NTRS1993

Retrieval of ice thickness from polarimetric SAR data

We describe a potential procedure for retrieving ice thickness from multi-frequency polarimetric SAR data for thin ice. This procedure includes first masking out the thicker ice types with a simple classifier and then deriving the thickness of the remaining pixels using a model-inversion technique. The technique used to derive ice thickness from polarimetric observations is provided by a numerical estimator or neural network. A three-layer perceptron implemented with the backpropagation algorithm is used in this investigation with several improved aspects for a faster convergence rate and a better accuracy of the neural network. These improvements include weight initialization, normalization of the output range, the selection of offset constant, and a heuristic learning algorithm. The performance of the neural network is demonstrated by using training data generated by a theoretical scattering model for sea ice matched to the database of interest. The training data are comprised of the polarimetric backscattering coefficients of thin ice and the corresponding input ice parameters to the scattering model. The retrieved ice thickness from the theoretical backscattering coefficients is compare with the input ice thickness to the scattering model to illustrate the accuracy of the inversion method. Results indicate that the network convergence rate and accuracy are higher when multi-frequency training sets are presented. In addition, the dominant backscattering coefficients in retrieving ice thickness are found by comparing the behavior of the network trained backscattering data at various incidence angels. After the neural network is trained with the theoretical backscattering data at various incidence anges, the interconnection weights between nodes are saved and applied to the experimental data to be investigated. In this paper, we illustrate the effectiveness of this technique using polarimetric SAR data collected by the JPL DC-8 radar over a sea ice scene.

Kwok, R.↗
NASA NTRS2013

Sea Ice Thickness, Freeboard, and Snow Depth products from Operation IceBridge Airborne Data

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

sea ice thickness↗
NASA NTRS2020

Winter Arctic Sea Ice Thickness from ICESat-2 Freeboards

NASA’s ICESat-2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System (ATLAS), is now providing routine, very high-resolution, surface elevation data across the globe, including the Arctic and Southern Oceans. In this study we demonstrate our new processing chain for converting the along-track ICESat-2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model (NESOSIM). The coarse resolution (~100 km) snow data are redistributed onto the high resolution (~30 - 100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA’s Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter-season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from ESA’s CryoSat-2 satellite mission, with ICE 33 Sat-2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19th February – 21st March) 2008 ice thickness estimates using the same input assumptions, which show a ~0.40 m or ~21% thinning across an inner Arctic Ocean domain in this 11-year time period.

Alek A Petty↗
NASA NTRS2012

Satellite Observations of Antarctic Sea Ice Thickness and Volume

We utilize satellite laser altimetry data from ICESat combined with passive microwave measurements to analyze basin-wide changes in Antarctic sea ice thickness and volume over a 5 year period from 2003-2008. Sea ice thickness exhibits a small negative trend while area increases in the summer and fall balanced losses in thickness leading to small overall volume changes. Using a five year time-series, we show that only small ice thickness changes of less than -0.03 m/yr and volume changes of -266 cu km/yr and 160 cu km/yr occurred for the spring and summer periods, respectively. The calculated thickness and volume trends are small compared to the observational time period and interannual variability which masks the determination of long-term trend or cyclical variability in the sea ice cover. These results are in stark contrast to the much greater observed losses in Arctic sea ice volume and illustrate the different hemispheric changes of the polar sea ice covers in recent years.

Kurtz, Nathan↗