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At least 685 records · Page 38

Paraboloidal Antenna Radiates Fan Or Pencil Beams

Shape of beam determined by type of feed. Theory and experiments show same paraboloidal antenna reflector used to radiate pencil beam or fan beam, depending on configuration of feed. Although pencil-shaped beam desirable in many applications, fan-shaped beam preferred in some scanning-radar and mapping-radar systems. Experiment performed with paraboloidal reflector having focal length of 48 in. (1.22 m) and diameter of 3.65 m. Shows fan beams generated in this way and not seriously distorted by feed offsets tested. Also shows because reflector in near field of feed, fan beam not necessarily focused by placing feed at focal plane.

Huang, John↗

Multiple GISS AGCM Hindcasts and MSU Versions of 1979-1998

Multiple realizations of the 1979-1998 time period have been simulated by the Goddard Institute for Space Studies Atmospheric General Circulation Model (GISS AGCM) to explore its responsiveness to accumulated forcings, particularly over sensitive agricultural regions. A microwave radiative transfer postprocessor has produced the AGCM's lower tropospheric, tropospheric and lower stratospheric brightness temperature (Tb) time series for correlations with the various Microwave Sounding Unit (MSU) time series available. MSU maps of monthly means and anomalies were also used to assess the AGCM's mean annual cycle and regional variability. Seven realizations by the AGCM were forced by observed sea surface temperatures (sst) through 1992 to gather rough standard deviations associated with internal model variability. Subsequent runs hindcast January 1979 through April 1998 with an accumulation of forcings: observed ssts, greenhouse gases, stratospheric volcanic aerosols. stratospheric and tropospheric ozone and tropospheric sulfate and black carbon aerosols. The goal of narrowing gaps between AGCM and MSU time series was complicated by MSU time series, by Tb simulation concerns and by unforced climatic variability in the AGCM and in the real world. Lower stratospheric Tb correlations between the AGCM and MSU for 1979-1998 reached as high as 0.91 +/-0.16 globally with sst, greenhouse gases, volcanic aerosol, stratospheric ozone forcings and tropospheric aerosols. Mid-tropospheric Tb correlations reached as high as 0.66 +/-.04 globally and 0.84 +/-.02 in the tropics. Oceanic lower tropospheric Tb correlations similarly reached 0.61 +/-.06 globally and 0.79 +/-.02 in the tropics. Of the sensitive agricultural areas considered, Nordeste in northeastern Brazil was simulated best with mid-tropospheric Tb correlations up to 0.75 +/- .03. The two other agricultural regions, in Africa and in the northern mid-latitudes, suffered from higher levels of non-sst variability. Zimbabwe had a maximum mid-tropospheric correlation of 0.54 +/- 0.11 while the U.S. Cornbelt had only 0.25 +/- .10. Precipitation and surface temperature performance are also examined over these regions. Correlations of MSU and AGCM time series mostly improved with addition of explicit atmospheric forcings in zonal bands but not in agricultural regional bins each encompassing only six AGCM gridcells.

Shah, Kathryn Pierce↗

Near-IR Spectroscopy of the Atmosphere of Jupiter

The Galileo Near Infrared Mapping Spectrometer obtains spectral images in the wavelength range 0.7 to 5.2 um with a special resolving power of approximately 200. This spectral range allows NIMS to sense cloud-reflected solar radiation, thermal emission from the deep atmosphere, and auroral bands from the thermosphere of Jupiter.

Jupiter Galileo Spectrometer↗

The Kinematic Navigation and Cartography Knapsack (KNaCK): Demonstrating SlLAM (Simultaneous Localization and Mapping) LiDAR as a Tool for Exploration and Mapping of Lunar Pits and Caves

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile LiDAR (Light Detection and Ranging) system. It can map its surroundings in 3 dimensions and localize itself in space. The project is exploring how LiDAR can advance terrain mapping and navigation at the lunar south pole. KNaCK is lead by Dr. Michael Zanetti of NASA MSFC’s Heliophysics and Planetary Science Branch. - KNaCK serves as - A test article for GPS denied mapping and navigation. - A test bed for SLAM (Simultaneous Localization and Mapping) algorithms. - A test bed for commercial LiDAR units. - A tool for terrestrial science.

LiDAR↗

Caves as Planetary Analogs for GPS Denied, Low-Light Mapping and Navigation in Rugged Environments

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile mapping system. It can map its surroundings in 3 dimensions and localize itself in space using a LiDAR (Light Detection and Ranging) sensor and SLAM (Simultaneous Localization and Mapping) algorithm. The KNaCK team is leveraging caves as a proving ground to refine technology for mapping and navigation on other worlds while simultaneously advancing the State of the Art for terrestrial cave exploration and study.

LiDAR↗

Caves as Planetary Analogs for GPS Denied, Low-Light Mapping and Navigation in Rugged Environments

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile mapping system. It can map its surroundings in 3 dimensions and localize itself in space using a LiDAR (Light Detection and Ranging) sensor and SLAM (Simultaneous Localization and Mapping) algorithm. The KNaCK team is leveraging caves as a proving ground to refine technology for mapping and navigation on other worlds while simultaneously advancing the State of the Art for terrestrial cave exploration and study.

Relevant environment testing↗

Mobile LiDAR as a Tool for Terrestrial and Planetary Cave Exploration and Mapping

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile mapping system. It can map its surroundings in 3 dimensions and localize itself in space using a LiDAR (Light Detection and Ranging) sensor and SLAM (Simultaneous Localization and Mapping) algorithm. The KNaCK team is leveraging caves as a proving ground to refine technology for mapping and navigation on other worlds while simultaneously advancing the State of the Art for terrestrial cave exploration and study.

LiDAR↗

Caves as Planetary Analogs for GPS Denied, Low-Light Mapping and Navigation in Rugged Environments. Lightning Talk. LSIC 2023 Fall Meeting.

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile mapping system. It can map its surroundings in 3 dimensions and localize itself in space using a LiDAR (Light Detection and Ranging) sensor and SLAM (Simultaneous Localization and Mapping) algorithm. The KNaCK team is leveraging caves as a proving ground to refine technology for mapping and navigation on other worlds while simultaneously advancing the State of the Art for terrestrial cave exploration and study.

Relevant environment testing↗

Caves as Planetary Analogs for GPS Denied, Low-Light Mapping and Navigation in Rugged Environments

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile mapping system. It can map its surroundings in 3 dimensions and localize itself in space using a LiDAR (Light Detection and Ranging) sensor and SLAM (Simultaneous Localization and Mapping) algorithm. The KNaCK team is leveraging caves as a proving ground to refine technology for mapping and navigation on other worlds while simultaneously advancing the State of the Art for terrestrial cave exploration and study.

LiDAR↗

Quantification of Global Cloud Properties with Use of Spherical Harmonic Functions

Spherical harmonic (SH) expansion is a useful tool to study any variable that has valid values at all latitudes and longitudes. The variable can be quantified as a sum of different spherical harmonic components, which are the spherical harmonic functions multiplied by their expansion coefficients. We find that the SH components of cloud radiative effect (CRE) have correlations with El Niño-Southern Oscillation (ENSO) and the Hadley Circulation (HC). In particular, the expansion degree 2 (l = 2) SH power spectrum component anomaly of CRE is strongly correlated with ENSO. The two dipole patterns appearing in the l = 2 SH component anomaly map can be reasonably explained by a known mechanism of ENSO’s impact on cloud properties. The l = 3 and l = 5 SH power spectrum components are correlated with HC intensity, whereas the l = 6 and l = 8 components are correlated with HC latitudinal widths. In ENSO warm and cold phases, the HC-correlated SH components have opposite anomalies, which suggests the impact of ENSO on HC. This study illustrates that the SH expansion technique provides a different perspective to study the impacts of large-scale atmospheric circulation on global cloud properties and radiative effects.

clouds↗

Azimuthal Patterns of the Radiated Sound Field from a Turbofan Model

The azimuthal directivity of a scale fan model was measured extensively. The model is a 12 inch diameter fan with 16 rotors and 40 stator vanes and tests were done at a tip speed of 905 ft/sec. Tests were conducted in an anechoic chamber with an inflow control device installed on the stationary fan model. The acoustic far field of the fan was surveyed with a circular hoop, with a diameter of six fan diameters, centered on the fan axis and was moved along the fan axis at polar angles from 20 to 110 degrees in increments of 10 degrees. The hoop, with 16 microphones evenly spaced at intervals of 22.5 degrees was rotated in 24 increments in the azimuthal direction for a total 384 points. From this extensive mapping of the directivity it is shown that the azimuthal directivity of the fundamental and first two harmonics is significant and can vary up to 15 dB. The broadband can also have an azimuthal directivity with as much as a 4 dB variation. A theory is proposed with relates the radiated modes with the generation of the far field patterns which produce the azimuthal directivity.

Thomas, R. H.↗

Recent Progress in Entry Radiation Measurements in the NASA Ames Electric ARC Shock Tube Facility

The Electric Arc Shock Tube (EAST) at NASA Ames Research Center is NASA's only working shock tube capable of obtaining conditions representative of entry in a multitude of planetary atmospheres. The facility is capable of mapping spectroscopic signatures of a wide range of planetary entries from the Vacuum Ultraviolet through Mid-Wave Infrared (120-5500 nm). This paper summarizes the tests performed in EAST for Earth, Mars and Venus entries since 2008, then focuses on a specific test case for CO2/N2 mixtures. In particular, the paper will focus on providing information for the proper interpretation of the EAST data.

Cruden, Brett A.↗

Study of second breakdown in power transistors using infrared techniques

Infrared thermal maps pinpoint exact location where second breakdown will occur before phenomenon happens and before physical damage develops at hot spot. Crystal structure analysis at that point determines cause of fault. Absolute power of radiation emitted from hot spot is direct indication of voltage level at which second breakdown occurs

Source record↗

Design and test of the Near Infrared Mapping Spectrometer /NIMS/ focal plane for the Galileo Jupiter Orbiter mission

A Near Infrared Mapping Spectrometer (NIMS) will fly past Jupiter on the Galileo spacecraft in 1982. The infrared detector assembly (focal plane) used in the NIMS instrument is to be passively cooled to 80 K by radiation into space. The focal plane contains 15 InSb and two silicon detectors, each with an associated dual J-FET preamp and feedback resistor. Preliminary fabrication and test experience will be described for an experimental five-channel hybrid detector assembly. Measurements of spectral noise density along with a plasma cleaning technique will also be described. The final NIMS focal plane will contain much of the design experience gained from the experimental hybrid detector assembly.

Bailey, G.↗

Assimilation of global cloud imagery from multiple satellites

Synoptic images of the global cloud field are created from measurements of IR radiation taken aboard four geostationary and two polar-orbiting platforms simultaneously observing the earth's cloud field. A series of spatial and temporal interpolations, together with data reliability criteria, are used to map synoptic measurements from the geostationary satellites and asynoptic measurements from the polar-orbiting satellites. The synoptic global cloud imagery (GCI) that results has a horizontal resolution of 0.7 deg in longitude by 0.35 deg in latitude and a temporal resolution of 3 h, providing an unprecedented view of the global cloud pattern. Each composite image represents a nearly instantaneous snapshot of the entire earth's cloud field. Collectively, the composite imagery resolve, on a global basis, most of the variability inherent to organized convection, including several harmonics of the diurnal cycle. Because of its homogeneous properties, the GCI lends itself easily to a variety of space-time analyses useful for studying global cloud behavior.

Tanaka, Ken↗

G301: The Flying Falcon geological remote sensing experiment

Get-Away Special (GAS) G-301, named the Flying Falcon and scheduled for launch on the STS-77 Space Shuttle in April, 1996, is being prepared to perform an experiment designed by the Department of Geology, Bowling Green State University (BGSU). The experiment will employ a new type of infrared imager designed and built by a consortium of Teltron Technologies Inc., Hudson Research Inc., and BGSU that is an uncooled, quantrum ferro-electric, infrared return beam vidicon (IRBV) camera capable of detecting thermal infrared radiation throughout the 2.0-50.0 micron wavelength region, and to which an integral, unable Fabry-Perot filter and a telescopic lens have been added. The primary objectives in the experiment include the mapping of methane plumes from solid waste landfills and wetlands in the midwestern U.S., the mapping of methane plumes offshore in the Gulf of Mexico and in the Middle East, brief monitoring for precursors of volcanoes or earthquakes in the South China sea and the East Pacific Rise (about 300 km west of Easter Island), and the mapping of silica content in exposed outcrops and residual soils of the southwestern U.S. and Middle East.

Vincent, Robert K.↗

The Correlation Between Arctic Sea Ice, Cloud Phase and Radiation Using A-Train Satellites

Climate warming has a stronger impact on Arctic climate and sea ice cover (SIC) decline than previously thought. Better understanding and characterization of the relationship between sea ice and clouds and the implications for surface radiation is key to improving our confidence in Arctic climate projections. Here we analyze the relationship between sea ice, cloud phase and surface radiation over the Arctic, defined as north of 60° N, using active- and passive-sensor satellite observations from three different datasets. We find that all datasets agree on the climatology of and seasonal variability in total and liquid-bearing (liquid and mixed-phase) cloud covers. Similarly, our results show a robust relationship between decreased SIC and increased liquid-bearing clouds in the lowest levels (below 3 km) for all seasons (strongest in winter) but summer, while increased SIC and ice clouds are positively correlated in two of the three datasets. A refined map correlation analysis indicates that the relationship between SIC and liquid-bearing clouds can change sign over the Bering, Barents and Laptev seas, likely because of intrusions of warm air from low latitudes during winter and spring. Finally, the increase in liquid clouds resulting from decreasing SIC is associated with enhanced radiative cooling at the surface. Our findings indicate that the newly formed liquid clouds reflect more shortwave (SW) radiation back to space compared to the surface, generating a cooling effect of the surface, while their downward longwave (LW) radiation is similar to the upward LW surface emission, which has a negligible radiative impact on the surface. This overall cooling effect should contribute to dampening future Arctic surface warming as SIC continues to decline.

Arctic climate↗

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

Snow is an important component of the terrestrial freshwater budget in high mountainAsia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despitethe importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (1Tb) as a function of geophysical variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASALand Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical variables that are then used for SVM training. The SVMsserve as a nonlinear map between the geophysical space (modeled in Noah-MP) andthe observation space (1Tb as measured by the radiometer). Advanced MicrowaveScanning Radiometer-Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.

high mountain Asia↗