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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 NTRSDate not supplied

Application of Lidar-Radar Combined Data Products during the IMPACTS 2020 Field Campaign

The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a three-year field campaign investigating the formation and lifecycle of banded, precipitation structures within intense wintertime cyclones impacting the highly-populated northeast United States. Unlike previous campaigns in the region, IMPACTS mission scientists have access to and coordinate the NASA ER-2 and P-3 aircraft to collect observations from both a remote sensing and in-situ perspective, respectively. For IMPACTS, the ER-2 aircraft was equipped with lidar (CPL), radar (CRS, HIWRAP, EXRAD), microwave radiometer (CoSMIR, AMPR) instruments, each with their own unique capabilities and limitations. Our on-going work effort aims to develop IMPACTS lidar and radar merged data products, which can be used to supplement the raw data sources, tease out additional aspects of the mechanisms underpinning wintertime cyclones, and enhance our understanding of microphysical properties in sensor overlap regions. Preliminary merged data products combine CPL lidar with any one of the NASA High Altitude Radar (HAR) radar products (CRS, HIWRAP, EXRAD) and represent these data as both normalized values and translated to reflectivity signal (dB). Continuing work will further refine our merged data product algorithm to provide additional information on hydrometeor species identification, ice crystal habit, and ice-water content, which will help IMPACTS achieve its mission goal of improving microphysical properties retrievals from both airborne and spaceborne platforms.

Stephen D. Nicholls↗
NASA NTRS1979

SEASAT-A synthetic aperture radar performance

The SEASAT-A satellite synthetic aperture radar (SAR) is presented, and its performance since it began operation in July 1978, is examined. The imaging radar system, the first to be designed as an orbital scientific instrument, was intended to generate continuous imagery over a 100-km swath at 25-m resolution from an altitude of 800 km. SAR image quality is measured in terms of resolution, the range of detectable radar backscatter, and the contrast ratio, each of which is affected by antenna, sensor, data link, and recording/processor subsystem parameters. Microdensitometer scans of output image film of actual SEASAT-A SAR data reveal a range resolution of less than 25 m, an azimuth resolution of approximately 40 m, a noise equivalent dynamic range of from 9.6 to -21 + or - 5 dB, and an image contrast ratio of 7.5 dB. SEASAT-A SAR performance is concluded to have confirmed the utility of spaceborne radar sensing.

Jordan, R. L.↗
NASA NTRS1981

Training site statistics from Landsat and Seasat satellite imagery registered to a common map base

Landsat and Seasat satellite imagery and training site boundary coordinates were registered to a common Universal Transverse Mercator map base in the Newport Beach area of Orange County, California. The purpose was to establish a spatially-registered, multi-sensor data base which would test the use of Seasat synthetic aperture radar imagery to improve spectral separability of channels used for land use classification of an urban area. Digital image processing techniques originally developed for the digital mosaics of the California Desert and the State of Arizona were adapted to spatially register multispectral and radar data. Techniques included control point selection from imagery and USGS topographic quadrangle maps, control point cataloguing with the Image Based Information System, and spatial and spectral rectifications of the imagery. The radar imagery was pre-processed to reduce its tendency toward uniform data distributions, so that training site statistics for selected Landsat and pre-processed Seasat imagery indicated good spectral separation between channels.

Clark, J.↗
NASA NTRS1982

The Shuttle Imaging Radar (SIR-A) sensor and experiment

The SIR-A acquired images which cover about 10 million square kilometers in different regions around the world. These images are being analyzed to assess the capability of spaceborne radar sensor for geologic mapping and earth observation. An overview of the data acquired and preliminary science results will be presented.

Elachi, C.↗
NASA NTRS1983

Development of land based radar polarimeter processor system

The processing subsystem of a land based radar polarimeter was designed and constructed. This subsystem is labeled the remote data acquisition and distribution system (RDADS). The radar polarimeter, an experimental remote sensor, incorporates the RDADS to control all operations of the sensor. The RDADS uses industrial standard components including an 8-bit microprocessor based single board computer, analog input/output boards, a dynamic random access memory board, and power supplis. A high-speed digital electronics board was specially designed and constructed to control range-gating for the radar. A complete system of software programs was developed to operate the RDADS. The software uses a powerful real time, multi-tasking, executive package as an operating system. The hardware and software used in the RDADS are detailed. Future system improvements are recommended.

Kronke, C. W.↗
NASA NTRS1994

Ice-atmosphere interactions in the central Arctic: Remotely-sensed and simulated ice concentration and motion

The response of the Beaufort Sea ice pack to the passage of strong low pressure systems is studied using SAR (synthetic aperture radar), AVHRR (advanced very high resolution radiometer), and SSM/I (special sensor microwave/imager) data combined with model simulations. The SAR, AVHRR, and modeled concentrations concur generally in showing a 1 to 5 percent decrease in ice fraction during the passage of the lows. SSM/I derived concentrations appear to underestimate overall concentration and overestimate the opening of the pack relative to the other data sets. Ice motion derived from SAR and AVHRR and model simulations using two ice rheologies agree reasonably well in direction and magnitude, although the cavitating fluid approximation tends to overestimate ice motion compared to a viscous plastic rheology. Ice motions from SAR, AVHRR, and buoy observations can be merged to yield uniform grids particularly well suited to synoptic scale studies and model validation.

Maslanik, J. A.↗
NASA NTRS1998

Comparisons Between Total Lightning Data, Mesocyclone Strength, and Storm Damage Associated with the Florida Tornado Outbreak of February 23, 1998

During the early morning hours of February 23 1998, the worst tornado outbreak ever recorded occurred over the central Florida peninsula. At least 7 confirmed tornadoes, associated with 4 supercells, developed, with 3 of the tornadoes reaching F3 intensity. Many of the tornadoes where on the ground for tens of miles, uncommon for the state of Florida. A total of 42 people were killed, with over 250 people injured. During the outbreak, National Weather Service Melbourne, in collaboration with the National Aeronautics and Space Administration and the Massachusetts Institute of Technology was collecting data from a unique lightning observing system called Lightning Imaging Sensor Data Applications Display (LISDAD, Boldi et.al., this conference). This system marries radar data collected from the KMLB WSR-88D, cloud to ground data collected from the National Lightning Detection Network, and total lightning data collected from NASKs Lightning Detection And Ranging system. This poster will display, concurrently, total lightning data (displayed in 1 minute increments), time/height storm relative velocity products from the KMLB WSR-88D, and damage information (tornado/hail/wind) from each of the supercell thunderstorms. The primary objective of this poster presentation is to observe how total lightning activity changes as the convective storm intensifies, and how the lightning activity changes with respect to mesocyclone strength (vortex stretching) and damaging weather on the ground.

Hodanish, S↗
NASA NTRS1974

Investigation of new radar-data-reduction techniques used to determine drag characteristics of a free-flight vehicle

An investigation was conducted of new techniques used to determine the complete transonic drag characteristics of a series of free-flight drop-test models using principally radar tracking data. The full capabilities of the radar tracking and meteorological measurement systems were utilized. In addition, preflight trajectory design, exact kinematic equations, and visual-analytical filtering procedures were employed. The results of this study were compared with the results obtained from analysis of the onboard, accelerometer and pressure sensor data of the only drop-test model that was instrumented. The accelerometer-pressure drag curve was approximated by the radar-data drag curve. However, a small amplitude oscillation on the latter curve precluded a precise definition of its drag rise.

Woodbury, G. E.↗
NASA NTRS1972

Intensive probing of clear air convective fields by radar and instrumented drone aircraft.

Clear air convective fields were probed in three summer experiments (1969, 1970, and 1971) on an S-band monopulse tracking radar at Wallops Island, Virginia, and a drone aircraft with a takeoff weight of 5.2 kg, wingspan of 2.5 m, and cruising glide speed of 10.3 m/sec. The drone was flown 23.2 km north of the radar and carried temperature, pressure/altitude, humidity, and vertical and airspeed velocity sensors. Extensive time-space convective field data were obtained by taking a large number of RHI and PPI pictures at short intervals of time. The rapidly changing overall convective field data obtained from the radar could be related to the meteorological information telemetered from the drone at a reasonably low cost by this combined technique.

Rowland, J. R.↗
NASA NTRS1982

Other remote sensing systems: Retrospect and outlook

The history of remote sensing is reviewed and the scope and versatility of the several remote sensing systems already in orbit are discussed, especially those with sensors operating in other EM spectral modes. The multisensor approach is examined by interrelating LANDSAT observations with data from other satellite systems. The basic principles and practices underlying the use of thermal infrared and radar sensors are explored and the types of observations and interpretations emanating from the Nimbus, Heat Capacity Mapping Mission, and SEASAT programs are examined. Approved or proposed Earth resources oriented missions for the 1980's previewed include LANDSAT D, Stereosat, Gravsat, the French satellite SPOT-1, and multimission modular spacecraft launched from space shuttle. The pushbroom imager, the linear array pushbroom radiometer, the multispectral linear array, and the operational LANDSAT observing system, to be designed the LANDSAT-E series are also envisioned for this decade.

Source record↗
NASA NTRS1988

A study of rain effects on radar scattering from water waves

Results are presented from a laboratory investigation of microwave power return due to rain-generated short waves on a wind wave surface. The wind wave tank, sensor, and data processing methods used in the study are described. The study focuses on the response of a 36-GHz radar system, orientated 30 deg from nadir and pointing upwind, to surface waves generated by various combinations of rain and wind. The results show stronger radar signal levels due to short surface waves generated by rain impacting the wind wave surface, supporting the results of Moore et al. (1979) for a 14-GHz radar.

Bliven, Larry F.↗
NASA NTRSDate not supplied

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as the optical and SAR data proves high resolution (5-10 m) imagery, and SAR data is unaffected by cloud cover and light availability (day vs. night), which are common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a MODIS snow mask product to mask global snow coverage, which would affect land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd watershed located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management..

Lori Berberian↗
NASA NTRS2010

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

Joiner, J.↗
NASA NTRS2011

3D Vegetation Mapping Using UAVSAR, LVIS, and LIDAR Data Acquisition Methods

The overarching objective of this ongoing project is to assess the role of vegetation within climate change. Forests capture carbon, a green house gas, from the atmosphere. Thus, any change, whether, natural (e.g. growth, fire, death) or due to anthropogenic activity (e.g. logging, burning, urbanization) may have a significant impact on the Earth's carbon cycle. Through the use of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and NASA's Laser Vegetation Imaging Sensor (LVIS), which are airborne Light Detection and Ranging (LIDAR) remote sensing technologies, we gather data to estimate the amount of carbon contained in forests and how the content changes over time. UAVSAR and LVIS sensors were sent all over the world with the objective of mapping out terrain to gather tree canopy height and biomass data; This data is in turn used to correlate vegetation with the global carbon cycle around the world.

Uninhabited Aerial Vehicle Synthetic Aperture Rada↗
NASA NTRSDate not supplied

Radar Measurements of Orbital Debris from the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR): 2020 to 2022

For over three decades, the NASA Orbital Debris Program Office (ODPO) has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology’s Lincoln Laboratory (MIT/LL) to collect radar data on the orbital debris (OD) environment. These radar measurements are used to statistically measure cm and sub-cm debris in low Earth orbit (LEO) that is used to define the current debris environment, monitor how the environment evolves, and support predictions of the future debris environment. Currently, the principal ground-based radar sensor used by the ODPO is the MIT/LL Haystack Ultrawideband Satellite Imaging Radar (HUSIR). HUSIR provides data on OD down to approximately 5 mm in size at altitudes below 1000 km. This paper will deliver an overview of recent HUSIR measurements of the LEO debris environment for calendar years 2020 to 2022. Process improvements will be highlighted along with results of interest, including the effects of the 15 November 2021 Russian direct ascent anti-satellite (ASAT) test on the defunct COSMOS 1408 spacecraft.

Jessica A. Arnold↗
NASA NTRS2023

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as SAR data provides high resolution (5-10 m) imagery, unaffected by cloud cover and light availability (day vs. night), common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band (once operational and available on the GEE repository) synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow product to determine regional snow coverage, which affects land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd wetland located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management.

Inundation↗
NASA NTRSDate not supplied

Radar Measurements of Orbital Debris from the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR): 2020-2021

For over three decades, the NASA Orbital Debris Program Office (ODPO) has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology’s Lincoln Laboratory (MIT/LL) to collect radar data on the orbital debris (OD) environment. These radar measurements are used to statistically measure centimeter (cm) and sub-cm debris in low Earth orbit (LEO) that is used to define the current debris environment, monitor how the environment evolves, and support predictions of the future debris environment. Currently, the principal ground-based radar sensor used by the ODPO is the MIT/LL Haystack Ultrawideband Satellite Imaging Radar (HUSIR). HUSIR provides data on OD down to approximately 5 mm in size at altitudes below 1000 km. This paper will deliver an overview of recent HUSIR measurements of the LEO debris environment for calendar years 2020 to 2022. Process improvements will be highlighted along with results of interest, including the effects of the 15 November 2021 Russian direct ascent anti-satellite (ASAT) test on the defunct Cosmos 1408 spacecraft.

Jessica A Arnold↗
NASA NTRS2010

Satellite Data Simulator Unit: A Multisensor, Multispectral Satellite Simulator Package

Several multisensor simulator packages are being developed by different research groups across the world. Such simulator packages [e.g., COSP , CRTM, ECSIM, RTTO, ISSARS (under development), and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in a table. That shows satellite programs particularly suitable for multisensor data analysis: some are single satellite missions carrying two or more instruments, while others are constellations of satellites flying in formation. The TRMM and A-Train are ongoing satellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued or augmented within the decade to come by future multisensor missions such as the GPM and Earth-CARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds and precipitation but are to better understand their interactions with atmospheric dynamics/chemistry and feedback to climate. The SDSU's applicability is not technically limited to hydrometeor measurements either, but may be extended to air temperature and humidity observations by tuning the SDSU to sounding channels. As such, the SDSU and other multisensor simulators would potentially contribute to a broad area of climate and atmospheric sciences. The SDSU is not optimized to any particular orbital geometry of satellites. The SDSU is applicable not only to low-Earth orbiting platforms as listed in Table 1, but also to geostationary meteorological satellites. Although no geosynchronous satellite carries microwave instruments at present or in the near future, the SDSU would be useful for future geostationary satellites with a microwave radiometer and/or a radar aboard, which could become more feasible as engineering challenges are met. In this short article, the SDSU algorithm architecture and potential applications are reviewed in brief.

Masunaga, Hirohiko↗