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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Recent Changes in the Greenland Ice Sheet as Seen from Space

Many changes in the Greenland Ice Sheet have been reported in the recent scientific literature and have been attributed to various responses of the ice sheet due to regional (and global) warming. Because melting of the ice sheet would contribute approximately 7 m to sea-level rise, the lives and habitat of hundreds of millions of people worldwide would be directly and indirectly affected if continued ice-sheet melting occurs. As mean-annual global temperatures have increased, there has been an increasing focus on studying the Greenland Ice Sheet using available satellite data, and numerous expeditions have been undertaken. Regional "clear-sky" surface temperature increases since the early 1980s in the Arctic, measured using Advanced Very High Resolution Radiometer (AVHRR) infrared data, range from 0.57+/-0.02 C to 0.72+/-0.10 C per decade. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near O C during the melt season, and is thus vulnerable to more extensive melting if temperatures continue to increase. An increase in melting of the ice sheet would accelerate sea-level rise, an issue of increasing concern to billions of people worldwide. The surface temperature of the ice sheet has been studied in even greater detail using Moderate-Resolution Imaging Spectroradiometer (MODIS) data in the six individual drainage basins as well as for the ice sheet as a whole. Surface temperature trends in the decade of the 2000s have not been strong, according to the MODIS measurements. In addition to surface-temperature increases over the last few decades as measured by AVHRR, other changes have been observed such as accelerated movement of many of Greenland's outlet glaciers and sudden draining of supraglacial lakes. Decreasing mass of the ice sheet since (at least) 2002 has been measured using Gravity Recovery and Climate Experiment (GRACE) data, along with an build-up of ice at the higher elevations and a decrease of ice at the lower elevations as measured using airborne Lidar and Ice, Cloud and Land Elevation Satellite (ICESat) data. The seminar will address the above issues using a variety of NASA satellite data and ground observations.

Hall, Dorothy K.↗
NASA NTRS2004

Gravitational Effects on Flow Instability and Transition in Low Density Jets

Experiments were conducted in Earth gravity and microgravity to acquire quantitative data on near field flow structure of helium jets injected into air. Microgravity conditions were simulated in the 2.2-second drop tower at NASA Glenn Research Center. The jet flow was observed by quantitative rainbow schlieren deflectometry, a non-intrusive line of sight measurement technique suited for the microgravity environment. The flow structure was characterized by distributions of helium mole fraction obtained from color schlieren images taken at 60 Hz. Results show that the jet in microgravity was up to 70 percent wider than that in Earth gravity. Experiments reveal that the global flow oscillations observed in Earth gravity are absent in microgravity. The report provides quantitative details of flow evolution as the experiment undergoes change in gravity in the drop tower.

Agrawal, Ajay K.↗
NASA NTRS1992

Paradigm lost: Venus crater depths and the role of gravity in crater modification

Previous to Magellan, a convincing case had been assembled that predicted that complex impact craters on Venus were considerably shallower than their counterparts on Mars, Mercury, the Moon, and perhaps even Earth. This was fueled primarily by the morphometric observation that, for a given diameter (D), crater depth (d) seems to scale inversely with surface gravity for the other planets in the inner solar system. The unpredicted depth of fresh impact craters on Venus argues against a simple inverse relationship between surface gravity and crater depth. Factors that could contribute to deep craters on Venus include (1) more efficient excavation on Venus, possibly reflecting rheological effects of the hot venusian environment; (2) more melting and efficient removal of melt from the crater cavity; and (3) enhanced ejection of material out of the crater, possibly as a result of entrainment in an atmosphere set in motion by the passage of the projectile. The broader issue raised by the venusian crater depths is whether surface gravity is the predominant influence on crater depths on any planet. While inverse gravity scaling of crater depths has been a useful paradigm in planetary cratering, the venusian data do not support this model and the terrestrial data are equivocal at best. The hypothesis that planetary gravity is the primary influence over crater depths and the paradigm that terrestrial craters are shallow should be reevaluated.

Sharpton, Virgil L.↗
NASA NTRS2005

Gravitational Effects on Near Field Flow Structure of Low Density Gas Jets

Experiments were conducted in Earth gravity and microgravity to acquire quantitative data on near field flow structure of helium jets injected into air. Microgravity conditions were simulated in the 2.2- second drop tower at NASA Glenn Research Center. The jet flow was observed by quantitative rainbow schlieren deflectometry, a non-intrusive line of site measurement technique for the whole field. The flow structure was characterized by distributions of angular deflection and helium mole percentage obtained from color schlieren images taken at 60 Hz. Results show that the jet in microgravity was up to 70 percent wider than that in Earth gravity. The global jet flow oscillations observed in Earth gravity were absent in microgravity, providing direct experimental evidence that the flow instability in the low density jet was buoyancy induced. The paper provides quantitative details of temporal flow evolution as the experiment undergoes change in gravity in the drop tower.

Griffin, D. W.↗
NASA NTRS1997

Lunar Prospector: Gravity Mapping

Lunar Prospector, the third mission of the NASA Discovery series, is scheduled to launch Oct. 24, 1997. One of the objectives of the mission is to map the gravity field of the Moon using radiometric Doppler tracking data from JPL's Deep Space Network.

Gravity mapping Lunar Prospector Moon↗
NASA NTRS2015

Orbit Determination for the Lunar Reconnaissance Orbiter Using an Extended Kalman Filter

Since launch, the FDF has performed daily OD for LRO using the Goddard Trajectory Determination System (GTDS). GTDS is a batch least-squares (BLS) estimator. The tracking data arc for OD is 36 hours. Current operational OD uses 200 x 200 lunar gravity, solid lunar tides, solar radiation pressure (SRP) using a spherical spacecraft area model, and point mass gravity for the Earth, Sun, and Jupiter. LRO tracking data consists of range and range-rate measurements from: Universal Space Network (USN) stations in Sweden, Germany, Australia, and Hawaii. A NASA antenna at White Sands, New Mexico (WS1S). NASA Deep Space Network (DSN) stations. DSN data was sparse and not included in this study. Tracking is predominantly (50) from WS1S. The OD accuracy requirements are: Definitive ephemeris accuracy of 500 meters total position root-mean-squared (RMS) and18 meters radial RMS. Predicted orbit accuracy less than 800 meters root sum squared (RSS) over an 84-hour prediction span.

LRO↗
NASA NTRS1943

Determination of the Mass Moments and Radii of Inertia of the Sections of a Tapered Wing and the Center-of-Gravity Line along the Wing Span

For computing the critical flutter velocity of a wing among the data required are the position of the line of centers of gravity of the wing sections along the span and the mass moments and radii of inertia of any section of the wing about the axis passing through the center of gravity of the section. A sufficiently detailed computation of these magnitudes even if the weights of all the wing elements are known, requires a great deal of time expenditure. Thus a rapid competent worker would require from 70 to 100 hours for the preceding computations for one wing only, while hundreds of hours would be required if all the weights were included. With the aid of the formulas derived in the present paper, the preceding work can be performed with a degree of accuracy sufficient for practical purposes in from one to two hours, the only required data being the geometric dimensions of the outer wing (tapered part), the position of its longerons, the total weight of the outer wing, and the approximate weight of the longerons, The entire material presented in this paper is applicable mainly to wings of longeron construction of the CAHI type and investigations are therefore being conducted by CAHI for the derivation of formulas for the determination of the preceding data for wings of other types.

Savelyev, V. V.↗
DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
NASA NTRSDate not supplied

An Update on the Hydrological Land Surface Data and Services at NASA GES DISC

"The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelve NASA Earth Observing System data centers that document, process, archive and distribute data from Earth science missions and related projects. The GES DISC hosts many hydrological land surface data products including North American Land Data Assimilation System (NLDAS), Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and assimilated-Gravity Recovery and Climate Experiment (GRACE). The Land Surface Model (LSM) products contain model output, including heat fluxes, rain, snow, soil temperature, soil moisture and runoff; and observational forcing data, including surface pressure, temperature, precipitation, downward shortwave and longwave radiation, humidity, and wind. The temporal resolution of the hydrology data at the GES DISC ranges from hourly to monthly and the spatial resolution 0.1 degree to 1.0 degree. The variability of Earth’s water and energy cycles can be characterized with these spatially and temporally consistent, and quality controlled hydrological land surface data. These data could support ongoing modeling activities, and help improve our understanding of land-surface-atmosphere interactions and their impact on climate. Over the years, new data is added, and older data is reprocessed and updated. This presentation provides a summary table of the hydrological data and discusses recent updates to the data and data services at the GES DISC. New data products include (1) GRACE Global Version 3.0 drought indicators and (2) FLDAS monthly global and Central Asia daily data. Reprocessed and updated products include (1) NLDAS Version 2.0 and (2) GRACE United States Version 4.0 drought indicators. Lastly, this presentation will discuss the opportunities for accessing some of our hydrology data from the cloud. As the hydrology land surface data is moved to the cloud, users can expect faster service for longer time series, notably the hydrology data rods time series service."

Ashley Heath↗
NASA NTRS2024

Surface Gravimetry Using Rover Navigation Systems

This prototype seeks to demonstrate the utility of repurposing a Micro-ElectroMechancical (MEMS) Inertial measurement Unit (IMU) to perform surface gravimetry on a rover. Gravimetry is a common analytical tool used for probing density distributions in the subsurface of a planetary body. Historically, extraterrestrial gravimetry has been confined to orbital platforms. While orbital surveys allow for the construction of global gravity models, the spatial resolution of the data is constrained by the platform’s orbital altitude and high inherent speed. Data collected at or near the surface would increase spatial resolution and allow finer-scale crustal structure to be resolved. To date, there have been only two extraterrestrial surface gravity surveys: the Apollo 17 Traverse Gravimeter Experiment and a survey using the MEMS accelerometers contained within the Curiosity rover’s IMUs. The Curiosity survey highlighted the potential of using MEMS technology to perform planetary gravimetry, albeit with lower sensitivities than traditional surface gravimeters. MEMS accelerometers are included on every rover platform as part of the IMU navigation systems. MEMS accelerometers have low mass, cost, and power requirements while being robust across a range of environments, whereas traditional gravimeters are fragile, costly, and relatively massive (≥8kg versus ≈50g for MEMS IMUs). Thus, the emergence of MEMS gravimeters provides a low-risk and cost-effective method for performing planetary surface gravimetry [3,4]. Here, we present a method to recalibrate the MEMS accelerometers in rover IMUs to collect gravimetric measurements. Such measurements could assist current and future rover missions and support wider efforts to mature MEMS gravimeters.

C S Lawson↗
NASA NTRS1978

Mars gravity - Additional resolution from Viking Orbiter I

Doppler radio tracking data taken from Viking Orbiter I at a 300 km periapsis altitude are now capable of resolving shorter wavelength features such as Olympus Mons and Alba Patera. The number of data is limited as is the area of high resolution which forms a narrow band near 35 deg N latitude. The masses of 71 disks, placed in a geometric pattern on the surface, were estimated. Location of each disk, the mass estimate, and the corresponding uncertainty are given for each disk mass included in the estimator. The new gravity results are compared with previous gravity reductions. The corresponding acceleration surface at 350 km altitude is displayed. It is concluded that systematic postfit residuals imply that further information can be extracted with more detailed modeling.

Sjogren, W. L.↗
NASA NTRS1988

Marine gravity of the Southern Ocean and Antarctic margin from Geosat

Geosat altimeter data, collected from an orbit with a ground rack that repeated every 17 days and overlayed one of the 17-day Seasat ground tracks, were used to map the gravity field of the Southern Ocean and the continental margin of Antarctica. The combination of ascending an descending profiles produced a typical Geosat ground track spacing of 70 km at the equator, with the best coverage occurring between the latitudes of 60 and 72 deg in both the Northern and Southern hemispheres. The new data reveal many previously uncharted seamounts and fracture zones in the extreme Southern Ocean areas adjacent to Antarctica, showing the detailed gravity signatures of the passive and active continental margins of Antarctica. Seven large age-offset fracture zones apparent in the Geosat data record the early breakup of Gondwana.

Sandwell, David T.↗
NASA NTRS1985

Gravity wave motions and momentum fluxes in the middle atmosphere at Adelaide, Australia

A study was made of gravity wave momentum fluxes in the middle atmosphere using data collected during June 1984 at Adelaide, Australia (35 deg S). The primary objectives were to identify that portion of the gravity wave spectrum that contributes most of the momentum transport and flux divergence and to examine the temporal variability of wave energies and momentum fluxes. The data were obtained with an HF (2 MHz) radar operated in a Doppler configuration with two coplanar off-vertical beams. This technique provides a direct measure of the vertical flux of horizontal momentum due to an arbitrary spectrum of gravity wave and other motions in the plane of the radar beams.

Vincent, R. A.↗
DOE OSTI2023

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ~28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity ($V_s$) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high $V_s$ and low $V_p$/$V_s$ ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Vp/Vs imaging↗
NASA NTRS1974

An evaluation of the ATM man/machine interface. Phase 3: Analysis of SL-3 and SL-4 data

The functional adequacy of human factored crew operated systems under operational zero-gravity conditions is considered. Skylab ATM experiment operations generated sufficient telemetry and voice transcript data to support such an assessment effort. Discussions are presented pertaining to the methodology and procedures used to evaluate the hardware, training and directive aspects of Skylab 3 and Skylab 4 manned ATM experiment operations.

Bathurst, J. R., Jr.↗
NASA NTRS1977

Global correlation of topographic heights and gravity anomalies

The short wavelength features were obtained by subtracting a calculated 24th-degree-and-order field from observed data written in 1 deg x 1 deg squares. The correlation between the two residual fields was examined by a program of linear regression. When run on a worldwide scale over oceans and continents separately, the program did not exhibit any correlation; this can be explained by the fact that the worldwide autocorrelation function for residual gravity anomalies falls off much faster as a function of distance than does that for residual topographic heights. The situation was different when the program was used in restricted areas, of the order of 5 deg x 5 deg square. For 30% of the world,fair-to-good correlations were observed, mostly over continents. The slopes of the regression lines are proportional to apparent densities, which offer a large spectrum of values that are being interpreted in terms of features in the upper mantle consistent with available heat-flow, gravity, and seismic data.

Roufosse, M. C.↗