Search NASA⌕ Search

SEARCH · Search NASA

Results for “RADAR method”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Geosynchronous Environment for ORDEM2008

The new version of the NASA Orbital Debris Engineering Model (ORDEM2008) requires accurate populations as input template files to be used in the calculation of orbital debris fluxes on chosen spacecraft or within telescope/radar fields-of-view. Populations in ORDEM2008 are derived from a consortium of data and modeling. Geosynchronous (GEO) satellites and debris form a distinct ORDEM2008 population that is applied to the distinct analysis of GEO fluxes. Low Earth orbit (LEO) populations are derived by combining modeling results with ground-based data, primarily from radar systems and in-situ data. In contrast, the GEO region has not been as well observed. The distance between orbiting objects and ground-based instruments precludes the wide usage of radar as a means of observation. Instead, optical instruments dominate in the study of GEO. Of these, the NASA sponsored Michigan Orbital Debris Survey Telescope (MODEST) has provided 4 years of surveys of the region detecting cataloged objects (correlated targets) and non-cataloged objects (uncorrelated targets) to an estimated minimum size of 30 cm. This paper describes the methods of combining NASA launch database and satellite breakup and orbital propagation modeling with MODEST 2004-to-2006 uncorrelated target data to attain a GEO environment to 10 cm. Assuming that MODEST uncorrelated targets are breakup debris allows for the extension of the debris survey data to smaller sizes with the NASA Standard Breakup model. Each orbit within the total resulting GEO population is marked by a random argument of perigee and nearly constant mean motion, eccentricity, inclination, and node over the nearly 3 years of observation. Lack of published references of past breakups in GEO is mitigated by the orbital propagation of MODEST extended data to 1995 (the beginning epoch of ORDEM2008).

Krisko, Paula H.↗
NASA NTRS2009

The Geosynchrous Environment for ORDEM2010

The new version of the NASA Orbital Debris Engineering Model (ORDEM2010) requires accurate populations as input files to be used in the calculation of orbital debris fluxes on chosen spacecraft or within telescope/radar fields-of-view. Populations in ORDEM2010 are derived from an amalgam of data and modeling. Geosynchronous orbit (GEO) satellites and debris form a distinct ORDEM2010 population that is applied to the distinct analysis of GEO fluxes. Low Earth orbit (LEO) populations are derived by combining modeling results with ground-based data, primarily from radar systems, and in-situ data. In contrast, the GEO region has not been as well observed. The distance between orbiting objects and ground-based instruments precludes the wide usage of radar as a means of observation. Instead, optical instruments dominate in the study of GEO. Of these, the NASA sponsored Michigan Orbital Debris Survey Telescope (MODEST) has provided 3 years of surveys of the region detecting cataloged objects (correlated targets) and non-cataloged objects (uncorrelated targets) to an estimated minimum size of 30 cm. This paper describes the methods of combining NASA launch database and satellite breakup and orbital propagation modeling with MODEST 2004-to-2006 uncorrelated target data to attain a GEO environment to 10 cm. Assuming that MODEST uncorrelated targets are breakup debris allows for the extension of the debris survey data to smaller sizes using the NASA Standard Breakup Model. Each orbit within the total resulting GEO population is marked by a random argument of perigee and nearly constant mean motion, eccentricity, inclination, and right ascension of ascending node (RAAN) over the nearly 3 years of observation. Lack of published references of past breakups in GEO is mitigated by the orbital propagation of MODEST extended data to 1995 (the beginning epoch of ORDEM2010).

Krisko, Paula H.↗
DOE OSTI2016

Use of a GPS-Derived Troposphere Model to Improve InSAR Deformation Estimates in the San Gabriel Valley, California

We evaluate the potential of troposphere models derived from ground meteorological data (pressure, temperature, and relative humidity) and Global Positioning System (GPS) data to improve InSAR measurements and models derived from them. We test this approach on an ERS-2/Envisat data set collected during a transient surface deformation episode that occurred from January to July 2005 in the San Gabriel Valley, southern California, USA. We find that the interferometric phase change observed over the corresponding period cannot be solely attributed to hydrological uplift associated with rising groundwater levels but also includes a significant contribution from differential tropospheric delay due to differing quantities of water vapor in the troposphere on the two SAR observation dates. We show that, if the tropospheric phase contribution is mistakenly interpreted as the range change associated with changes in groundwater storage, both the surface displacement and the groundwater storage coefficient may be overestimated by up to 30%. Finally, this method could be applied in real time where meteorological measurements are available near one or more GPS permanent site(s).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

The Detection of Mesoscale Convective Systems by the GPM Ku-Band Spaceborne Radar

The Global Precipitation Measurement (GPM) core observatory satellite launched in 2014 features more extended latitudinal coverage (65 degree S-65 degree N) than its predecessor Tropical Rainfall Measuring Mission (TRMM, 35 degree S-35 degree N). The Ku-band radar onboard of the GPM is known to be capable of characterizing the 3D structure of deep convection globally. In this study, GPM’s capability for detecting mesoscale convective systems (MCSs) is evaluated. Extreme convective echoes seen by GPM are compared against an MCS database based on tracking convective entities over the contiguous US. The tracking is based on geostationary satellite and ground-based Next Generation Radar (NEXRAD) network data obtained during the 2014-2016 warm seasons. Results show that more than 70% of the GPM-detected Deep-Wide Convective Core (DWC) and Wide Convective Core (WCC) objects are part of NEXRAD identified MCSs, indicating that GPM-classified DWCs and WCCs correlate well with typical MCSs containing large convective features. By applying this method to the rest of the world, a global view of MCS distribution is obtained. This work reveals GPM’s potential in MCS detection at the global scale, particularly over remote regions without dense observation network.

3D reflectivity structure↗
DOE OSTI2020

A Review of Existing Test Methods for Occupancy Sensors

One of the key new features of connected lighting systems (CLS) is their ability to collect data from various types of integral sensors and share that data with other lighting or building systems. Occupancy and vacancy sensors have been widely adopted as an energy-saving strategy in buildings, yet published test methods for reproducibly characterizing their performance remain few and limited in their sophistication. As a result, it has been difficult to predict their performance in a specific application, and in practice, they frequently do not meet energy-savings expectations. Occupants at times remove or otherwise bypass occupancy sensors that hinder their work or otherwise do not perform as expected, thereby compromising the sensors’ potential to reduce energy consumption. Poor performance can result from multiple causes – ranging from fundamental limitations of the sensor technology, to misconfiguration, to poor placement in the room or space. Innovative occupancy sensors, some of them combining multiple sensing technologies, have come on the market over the years, with claims of improved performance compared to their predecessors; yet in practice, their performance has led to neither high deployment nor high user satisfaction in any significant application, nor has it differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them. While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them have typically been loosely described and incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology. The lack of a well-described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones. Motivated by a desire to fairly characterize new technologies that continue to enter the market and claim not only improved occupancy detection but, in some cases, also additional capabilities (e.g., the ability to measure traffic or discern between different object types), this report presents the results of a literature review of recent published, well-described test methods for characterizing occupancy sensor performance, as well as research articles containing ad-hoc test methods. The review also identifies and consolidates the best techniques for characterizing sensor performance in indoor spaces and identifies test method gaps that need to be filled in order to evaluate emerging technologies and products.

47 OTHER INSTRUMENTATION↗
NASA NTRS1981

Some analysis on the diurnal variation of rainfall over the Atlantic Ocean

Data collected from the GARP Atlantic Tropical Experiment (GATE) was examined. The data were collected from 10,000 grid points arranged as a 100 x 100 array; each grid covered a 4 square km area. The amount of rainfall was measured every 15 minutes during the experiment periods using c-band radars. Two types of analyses were performed on the data: analysis of diurnal variation was done on each of grid points based on the rainfall averages at noon and at midnight, and time series analysis on selected grid points based on the hourly averages of rainfall. Since there are no known distribution model which best describes the rainfall amount, nonparametric methods were used to examine the diurnal variation. Kolmogorov-Smirnov test was used to test if the rainfalls at noon and at midnight have the same statistical distribution. Wilcoxon signed-rank test was used to test if the noon rainfall is heavier than, equal to, or lighter than the midnight rainfall. These tests were done on each of the 10,000 grid points at which the data are available.

Gill, T.↗
NASA NTRS1983

A comment on some drawbacks of monostatic radars

All Mesosphere, Stratosphere, Troposphere (MST) radars now in operation or under construction use the monostatic mode, that is to say, the same antenna for both transmitting and receiving. Consequently, it is never possible to determine more than one component of the atmospheric velocity at one point in space. Two important properties of gravity waves which are difficult if not impossible to determine by the monostatic method are discussed. The first of these is the polarization of the gravity waves; that is, the complex ratio of the wave functions of vertical and horizontal motion. A second quantity of importance is the horizontal wavelength of gravity waves. Both of these problems could be overcome by use of a bistatic system. Such a system has the additional advantage of making it possible to measure at very low altitudes, and to look away from the transmitter (for example to make a horizontal section through a thunderstorm). However, bistatic operation, in the practical sense, requires not only an additional highly directive antenna, but also that both the transmitting and receiving antennas should be fully steerable.

Bowhill, S. A.↗
NASA NTRS1990

Estimation of convective rain volumes utilizing the are-time-integral technique

Interest in the possibility of developing useful estimates of convective rainfall with Area-Time Integral (ATI) methods is increasing. The basis of the ATI technique is the observed strong correlation between rainfall volumes and ATI values. This means that rainfall can be estimated by just determining the ATI values, if previous knowledge of the relationship to rain volume is available to calibrate the technique. Examples are provided of the application of the ATI approach to gage, radar, and satellite measurements. For radar data, the degree of transferability in time and among geographical areas is examined. Recent results on transferability of the satellite ATI calculations are presented.

Johnson, L. Ronald↗
NASA NTRS1994

Applications of interferometrically derived terrain slopes: Normalization of SAR backscatter and the interferometric correlation coefficient

Terrain slopes, which can be measured with Synthetic Aperture Radar (SAR) interferometry either from a height map or from the interferometric phase gradient, were used to calculate the local incidence angle and the correct pixel area. Both are required for correct thematic interpretation of SAR data. The interferometric correlation depends on the pixel area projected on a plane perpendicular to the look vector and requires correction for slope effects. Methods for normalization of the backscatter and interferometric correlation for ERS-1 SAR are presented.

Werner, Charles L.↗
NASA NTRS2001

Three Dimensional Atmospheric Radiative Transfer-Applications and Methods Comparison

We review applications of 3D radiative transfer in the atmosphere, emphasizing the wide spectrum of scales important to remote sensing and modeling of cloud fields, and the characteristic scales introduced into observed radiances and fluxes by the distribution of photon pathlengths at conservative and absorbing wavelengths. We define the "plane-parallel bias", which is a measure of the importance of 3D cloud structure in large-scale models, and the "independent pixel errors" that quantify the significance of 3D effects in remote sensing, and emphasize their relative magnitude and scale dependence. A variety of approaches in current use in 3D radiative transfer, and issues of speed, accuracy, and flexibility are summarized. We also describe a recently initiated "International Intercomparison of 3-Dimensional Radiation Codes", or I3RC. I3RC is a 3-phase effort that has as its goals to: (1) understand the errors and limits of 3D methods; (2) provide "baseline" cases for future 3D code development; (3) promote sharing of 3D tools; (4) derive guidelines for 3D tool selection; and (5) improve atmospheric science education in 3D radiative transfer. Selected results from Phases 1 and 2 of I3RC are discussed. These are taken from five cloud fields: a 1D field of bar clouds, a 2D radar-derived field, a 3D Landsat-derived field, a stratiform cloud from the model of C. Moeng, and a convective cloud from the model of B. Stevens. Computations have been carried out for three monochromatic wavelengths (one conservative, one absorptive, and one thermal) and two solar zenith angles (0, 60 degrees).

Cahalan, Robert F.↗
NASA NTRS2022

Assessing Several Non-Traditional Data Sources for Value in Aviation Safety

The NASA System-Wide Safety (SWS) project and its predecessor projects have been developing Machine Learning (ML) algorithms for commercial aviation safety for many years. These algorithms have been applied to Flight Operations Quality Assurance (FOQA); radar track data (e.g., Threaded Track); and safety reports, including Aviation Safety Reporting System (ASRS) and Aviation Safety Action Plan (ASAP). SWS is working with partners to get access to other data that air carriers provide, such as maintenance data, and has been assisting carriers in working with other data, such as Line Operations Safety Audit (LOSA) data, using manual methods. However, the project has discussed whether there are other data that are not traditionally used in aviation safety analysis that may be useful. This paper discusses four sets of data and models that are not traditionally used in aviation safety but that have shown promise for such use. In the future, we plan to incorporate such data into ML algorithms to use with data that we have used before and determine the additional benefit that is actually achieved under different contexts from the inclusion of these non-traditional data sources.

Nikunj C. Oza↗
DOE OSTI2024

WFIP3 High Frequency Radar / Processed Data

These data were collected by Kirincich as part of the DOE Wind Forecast Improvement Project, based on support from NOAA-IOOS, WHOI, and DOE. The data consist of estimates of the near-surface horizontal (East and North) ocean currents made via High Frequency (HF) radar-based remote sensing of the Ocean backscatter spectrum. The effective measurement depth of the WHOI HF radars is 0.5 m below the ocean surface. The WHOI HF radar system, as operated during the experiment, consisted of 4 land-based sites spaced at 30-40km intervals along the area spanning from Nantucket,MA to Narragansett,RI that operate at 16 MHz and 25 MHz systems (see Kirincich et al, 2019 and Kirincich et al 2022 for details). Received doppler spectra from each were processed using the advanced methods of Kirincich et al. 2012, and Kirincich et al 2019, and unpublished work Kirincich, 2024) into radial or elliptical velocity estimates every 30 min based on a 30 min averaging window.

17 WIND ENERGY↗
NASA NTRS1995

Accurate estimation of sigma(exp 0) using AIRSAR data

During recent years signature analysis, classification, and modeling of Synthetic Aperture Radar (SAR) data as well as estimation of geophysical parameters from SAR data have received a great deal of interest. An important requirement for the quantitative use of SAR data is the accurate estimation of the backscattering coefficient sigma(exp 0). In terrain with relief variations radar signals are distorted due to the projection of the scene topography into the slant range-Doppler plane. The effect of these variations is to change the physical size of the scattering area, leading to errors in the radar backscatter values and incidence angle. For this reason the local incidence angle, derived from sensor position and Digital Elevation Model (DEM) data must always be considered. Especially in the airborne case, the antenna gain pattern can be an additional source of radiometric error, because the radar look angle is not known precisely as a result of the the aircraft motions and the local surface topography. Consequently, radiometric distortions due to the antenna gain pattern must also be corrected for each resolution cell, by taking into account aircraft displacements (position and attitude) and position of the backscatter element, defined by the DEM data. In this paper, a method to derive an accurate estimation of the backscattering coefficient using NASA/JPL AIRSAR data is presented. The results are evaluated in terms of geometric accuracy, radiometric variations of sigma(exp 0), and precision of the estimated forest biomass.

Holecz, Francesco↗
NASA NTRS1995

Evaluation of passive microwave precipitation algorithms in wintertime midlatitude situations

The second intercomparison project of the Global Precipitation Climatology Project (GPCP) examined the estimation of midlatitude, cool-season precipitation. As part of that effort, the authors report here on the results of two microwave techniques, the Goddard scattering algorithm and the physical retrieval algorithm of Kummerow. Results from the estimation of instantaneous rain rate for five overpasses of the Special Sensor Microwave/Imager (SSM/I) are presented in a case study mode to illustrate both the strong and weak points of each technique. These five cases represent a sampling of the various types of precipitating systems observed. Results for the complete set of 20 swaths chosen by the United Kingdom Meteorological Office (UKMO) are then categorized by scatterplots and statistics of instantaneous radar versus microwave-estimated rain rate, rain no-rain contingency tables, and scatterplots of areal coverage of rainfall. Neither algorithm produced a good statistical correlation with the radar data, yet in general, both did well at determining rainy areas. Two reasons are suggested for the low correlation coefficients between both algorithms and the radar data. Time differences between the SSM/I overpass and the radar observations can occasionally account for some of the differences. The primary reason for the low correlations, however, appears to be the predominance of very light rain in the area of interest during the winter. Both algorithms are in good spatial agreement with the radar when the radar data are restricted to rates above 1 mm/h. When all radar rain rates are included, the radar areal coverage increases by as much as a factor of 10 in some cases. Because the Kummerow algorithm does not handle such low rain rates over land very well, and because the Goddard scattering algorithm uses 1 mm/h as the minimum reliably detectable rain rate, regimes that contain large areas of very light rain present inherent difficulties for these retrieval methods. Therefore, the proliferation of low rain rates observed during the experiment is the main contributor to low correlation coefficients and high root-mean-square differences. Mididentification of cold surface (e.g., snow cover) as precipitation was also a problem in several instances.

Negri, Andrew J.↗
NASA NTRS2012

Creating a Realistic Weather Environment for Motion-Based Piloted Flight Simulation

A flight simulation environment is being enhanced to facilitate experiments that evaluate research prototypes of advanced onboard weather radar, hazard/integrity monitoring (HIM), and integrated alerting and notification (IAN) concepts in adverse weather conditions. The simulation environment uses weather data based on real weather events to support operational scenarios in a terminal area. A simulated atmospheric environment was realized by using numerical weather data sets. These were produced from the High-Resolution Rapid Refresh (HRRR) model hosted and run by the National Oceanic and Atmospheric Administration (NOAA). To align with the planned flight simulation experiment requirements, several HRRR data sets were acquired courtesy of NOAA. These data sets coincided with severe weather events at the Memphis International Airport (MEM) in Memphis, TN. In addition, representative flight tracks for approaches and departures at MEM were generated and used to develop and test simulations of (1) what onboard sensors such as the weather radar would observe; (2) what datalinks of weather information would provide; and (3) what atmospheric conditions the aircraft would experience (e.g. turbulence, winds, and icing). The simulation includes a weather radar display that provides weather and turbulence modes, derived from the modeled weather along the flight track. The radar capabilities and the pilots controls simulate current-generation commercial weather radar systems. Appropriate data-linked weather advisories (e.g., SIGMET) were derived from the HRRR weather models and provided to the pilot consistent with NextGen concepts of use for Aeronautical Information Service (AIS) and Meteorological (MET) data link products. The net result of this simulation development was the creation of an environment that supports investigations of new flight deck information systems, methods for incorporation of better weather information, and pilot interface and operational improvements for better aviation safety. This research is part of a larger effort at NASA to study the impact of the growing complexity of operations, information, and systems on crew decision-making and response effectiveness; and then to recommend methods for improving future designs.

Daniels, Taumi S.↗
NASA NTRS2014

High Resolution Mapping of Soils and Landforms for the Desert Renewable Energy Conservation Plan (DRECP)

The Desert Renewable Energy Conservation Plan (DRECP), a major component of California's renewable energy planning efforts, is intended to provide effective protection and conservation of desert ecosystems, while allowing for the sensible development of renewable energy projects. This NASA mapping report was developed to support the DRECP and the Bureau of Land Management (BLM). We outline in this document remote sensing image processing methods to deliver new maps of biological soils crusts, sand dune movements, desert pavements, and sub-surface water sources across the DRECP area. We focused data processing first on the largely unmapped areas most likely to be used for energy developments, such as those within Renewable Energy Study Areas (RESA) and Solar Energy Zones (SEZs). We used imagery (multispectral and radar) mainly from the years 2009-2011.

Mapping↗
NASA NTRS2021

Maya Forest Water Resources I: Using NASA Earth Observations to Map Forested Inundation in the Maya Forest

As climate change increases the severity and frequency of extreme weather events in the tropics, it is vital for the safety of local communities and the health of ecosystems to monitor seasonal inundation. Forested inundation affects the ability of forested wetlands to provide ecosystem services, such as flood mitigation, water filtration, carbon storage, and erosion mitigation. While ground-based monitoring has traditionally been used to map inundation extent, those methods are costly and time-intensive. The NASA DEVELOP team focused on seasonal inundation throughout 2008 in the Maya Forest, when changes in inundation were drastic. To monitor seasonal inundation, our team used in situ field data and Earth observations from Landsat 7 Enhanced Thematic Mapper (ETM+), Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 1, Shuttle Radar Topography Mission (SRTM), and products from the Ice, Cloud, and Land Elevation Satellite (ICESat). The team applied a Random Forest algorithm to Landsat 7 imagery, generating an object-level land cover classification with an overall accuracy of 72.1% and forest class with 100% recall and 78% precision. The team applied L-band backscatter thresholds from existing literature to forest-masked ALOS imagery and refined the thresholds in an iterative process using field data and hydrology models to delineate seasonal inundation extent. These publicly available data products help end users from Belize’s Land Information Center (LIC) and Forest Department, Guatemala’s Center for Monitoring and Evaluation (CEMEC), and Mexico’s El Colegio de la Frontera Sur (ECOSUR) to inform land management and protect community infrastructure.

Madelyn Savan↗
NASA NTRS2000

Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Sample Model

Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote-sensing error and, in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that Root-mean-square (RMS) random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain-gauge and radar data. This relationship was examined using satellite SSM/I data obtained over the western equatorial Pacific during TOGA COARE. RMS error inferred directly from SSM/I rainfall estimates was found to be larger than predicted from surface data, and to depend less on local rain rate than was predicted. Preliminary examination of TRMM microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be directly computed from the satellite data.

Bell, Thomas L.↗