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The 4 April 1977 severe storms outbreak over Georgia and Alabama provided an excellent opportunity to examine the complementary characteristics between satellite and radar data.
Radar backscatter return interpretation in identifying terrain phenomena and composition
Optimal filter design for data from system consisting of inertial navigator and radar
A study is performed under NASA contract to evaluate data from an AN/FPS-16 radar installed for support of flight programs at Dryden Flight Research Facility of NASA Ames Research Center. The purpose of this study is to provide information necessary for improving post-flight data reduction and knowledge of accuracy of derived radar quantities. Tracking data from six flights are analyzed. Noise and bias errors in raw tracking data are determined for each of the flights. A discussion of an altiude bias error during all of the tracking missions is included. This bias error is defined by utilizing pressure altitude measurements made during survey flights. Four separate filtering methods, representative of the most widely used optimal estimation techniques for enhancement of radar tracking data, are analyzed for suitability in processing both real-time and post-mission data. Additional information regarding the radar and its measurements, including typical noise and bias errors in the range and angle measurements, is also presented. This is in two parts. This is part 1, an analysis of radar data.
A comparison between rain gauge data and radar data from the Tropical Oceans Global Atmosphere/Coupled Ocean Atmosphere Response Experiment (TOGA/COARE) was studied. The rain gauge data from an echo that passed over the Xiangyanghong #5 on December 24, 1992 was compared to what the MIT radar saw at this location from the R/V Vickers, 103.4 km to the east. The precipitation measured by the rain gauge peaked at 108 mm/hr 92 seconds into the period before tapering off 11 1/2 minutes later. This sharp gradient was evident in a PPI plot of the radar reflectivities and the percentage area-rainfall for the radar data statistics. The percentage area curve was converted to rain rates using a GATE Z-R and compared to a percentage time curve of rain rates according to the rain gauge. A four minute running average applied to the rain gauge rates improved the comparison of peak rates between the rain gauge and radar. Differences in peaks between rain rates observed by the rain gauge and reflectivities observed by the radar could be due to variations in rainfall rates within a single radar data bin. For example, two measurements of reflectivity such as 37 and 47 dBZ within the same bin would result in a 44 dBZ average. This range in rates from 12 mm/hr to 74 mm/hr is observed in 30 seconds by the rain gauges within the first two minutes of the radar echo passage.
The primary function of the TRMM Ground Validation (GV) Program is to create GV rainfall products that provide basic validation of satellite-derived precipitation measurements for select primary sites. A fundamental and extremely important step in creating high-quality GV products is radar data quality control. Quality control (QC) processing of TRMM GV radar data is based on some automated procedures, but the current QC algorithm is not fully operational and requires significant human interaction to assure satisfactory results. Moreover, the TRMM GV QC algorithm, even with continuous manual tuning, still can not completely remove all types of spurious echoes. In an attempt to improve the current operational radar data QC procedures of the TRMM GV effort, an intercomparison of several QC algorithms has been conducted. This presentation will demonstrate how various radar data QC algorithms affect accumulated radar rainfall products. In all, six different QC algorithms will be applied to two months of WSR-88D radar data from Melbourne, Florida. Daily, five-day, and monthly accumulated radar rainfall maps will be produced for each quality-controlled data set. The QC algorithms will be evaluated and compared based on their ability to remove spurious echoes without removing significant precipitation. Strengths and weaknesses of each algorithm will be assessed based on, their abilit to mitigate both erroneous additions and reductions in rainfall accumulation from spurious echo contamination and true precipitation removal, respectively. Contamination from individual spurious echo categories will be quantified to further diagnose the abilities of each radar QC algorithm. Finally, a cost-benefit analysis will be conducted to determine if a more automated QC algorithm is a viable alternative to the current, labor-intensive QC algorithm employed by TRMM GV.
Earth based radar observations of the Moon have been taken at many wavelengths during the last ten years -- at 3.8, 70 cm, and most recently, 7.5 cm. Radar returns have been collected in both polarized and depolarized form so that is possible to derive both topographic and local surface roughness from the data. Until recently, work with 3.8 cm radar data had consisted of qualitative correlation of photographic and thermal IR data with individual depolarized radar data frames (local surface roughness) at different wavelengths. These studies provided results which demonstrated that the relationships between surface roughness (measured by either thermal emission or radar reflectivity) at different wavelengths can be used as an index of a crater's state of degradation (age). However, systematic studies of craters, or other local terrain features, as well as regional or global studies of major terrains (involving a number of data frames), cannot be done until individual frames are calibrated, geometric distortion is removed, and corrected frames are mosaicked.
As a first step in interchange of data from the Urbana mesosphere-stratosphere-troposphere (MST) radar, a sample tape has been prepared in 9-track 1600-bpi IBM format. It includes all Urbana data for April 1978 (the first month of operation of the radar). The 300-ft tape contains 260 h of typical mesospheric power and line-of-sight velocity data.
Synthetic aperture radars (SAR) image from a non-nadir position. Thus the orientation of the target and sensor to one another is of paramount importance. This has posed problems for data interpretation and with the potentials of radar data for change detection studies. It is possible to use Seasat radar data for change detection even though the look directions are fixed for each location. Especially in areas with repeated coverage on descending or ascending orbits or where the terrain is flat and the targets nonoriented, coverage may be sufficient to provide data for change detection. Examples of Los Angeles and the Everglades of Florida help develop and support the argument.
Report describes radar data system at NASA Western Aeronautical Test Range. System provides real-time and recorded data about flightpaths of research aircraft and Space Shuttle. Called RADATS, processes data from three radars simultaneously; interacts with system operator; enhances data by introducing corrections and smoothing; controls range, azimuth, and elevation of radars; and automatically calibrates itself before and after missions. Software classified into three kinds of programs: utility, real-time, and calibration. Equipment exhibited exceptional reliability. Software matured to become virtually trouble-free.
One means of making MST (mesosphere stratosphere troposphere) radar data more easily accessible for scientific research by the general scientific community is through a centralized data base. Such a data base can be designed to readily provide information on data availability and quality, and to provide copies of data from any radar in a common format to the user. The ionospheric incoherent scatter community has established a centralized data base at NCAR that may serve not only as a model for a possible MST data base, but also as a catalyst for getting an MST data base started. (Some key elements of the NCAR data base are given.) The NCAR data base can include MST data in the same framework with relatively little extra effort. They are willing to handle MST data on a limited basis in order to permit assessment of community interest and in order to provide some experience with a centralized data base for MST data.
Although many radar profiles and images of the area within 20 deg of Mercury's equator had been obtained from 1971 to 1981, at both Goldstone and Arecibo radar facilities, surprisingly little geological analysis had been done with these data until recently. Topographic profiles and radar roughness reflectivity images which can be derived from these data will be crucial in completing the geological mapping of Mercury now underway at the U.S. Geological Survey. Processing of available radar data must be completed to establish any systematic relationship between radar reflectivities and roughness, density, dielectric constant, and other related geological parameters. Specific tasks accomplished for these purposes include the following. Documentation was located and searched to establish the type and quantity of Goldstone 12.5 cm radar observations which were available for Mercury. Data has been collected during approximately 50 observation periods from 1971 to 1981. About half of the data, collected during 1972 and 1973, have been processed, but without adequate documentation. A standardized, well-documented procedure for processing and analysis for all Goldstone Earth-based observations of Mercury was established.
The elevation gradients affecting tropical forest stand characteristics are presently studied in light of multipolarization airborne SAR data. A 'rubber sheeting' computer code was used to georeference the SAR data sets to the digital elevation data. The TOPO code from NASA's NSTL generated the terrain slope and aspect angle data from the terrain elevation data set; computed local incidence angles were used to delete those data areas that were shadowed, and to produce local incidence angle data that were not shadowed. The results obtained demonstrate that the SAR data are related to the elevation gradient.
Airborne C- and L-band radar data acquired over a test site in western kansas were analyzed to determine corn-field identification accuracies obtainable using single-channel, multichannel, and multidate radar data. An automated pattern-recognition procedure was used to classify 144 fields into three categories: corn, pasture land, and bare soil (including wheat stubble and fallow). Corn fields were identified with accuracies ranging from 85 percent for single channel, single-date data to 100 percent for single-channel, multidate data. The effects of radar parameters such as frequency, polarization, and look angle as well as the effects of soil moisture on the classification accuracy are also presented.
Synthetic aperture radar (SAR) instruments on spacecraft are capable of producing huge quantities of data. Onboard lossy data compression is commonly used to reduce the burden on the communication link. In this paper an overview is given of various SAR data compression techniques, along with an assessment of how much improvement is possible (and practical) and how to approach the problem of obtaining it. Synthetic aperture radar (SAR) instruments on spacecraft are capable of acquiring huge quantities of data. As a result, the available downlink rate and onboard storage capacity can be limiting factors in mission design for spacecraft with SAR instruments. This is true both for Earth-orbiting missions and missions to more distant targets such as Venus, Titan, and Europa. (Of course for missions beyond Earth orbit downlink rates are much lower and thus potentially much more limiting.) Typically spacecraft with SAR instruments use some form of data compression in order to reduce the storage size and/or downlink rate necessary to accommodate the SAR data. Our aim here is to give an overview of SAR data compression strategies that have been considered, and to assess the prospects for additional improvements.
Matlab software was written to provide smoothing of radar tracking data to simulate ADS-B (Automatic Dependent Surveillance-Broadcast) data in order to test a tactical conflict probe. The probe, called TSAFE (Tactical Separation-Assured Flight Environment), is designed to handle air-traffic conflicts left undetected or unresolved when loss-of-separation is predicted to occur within approximately two minutes. The data stream that is down-linked from an aircraft equipped with an ADS-B system would include accurate GPS-derived position and velocity information at sample rates of 1 Hz. Nation-wide ADS-B equipage (mandated by 2020) should improve surveillance accuracy and TSAFE performance. Currently, position data are provided by Center radar (nominal 12-sec samples) and Terminal radar (nominal 4.8-sec samples). Aircraft ground speed and ground track are estimated using real-time filtering, causing lags up to 60 sec, compromising performance of a tactical resolution tool. Offline smoothing of radar data reduces wild-point errors, provides a sample rate as high as 1 Hz, and yields more accurate and lag-free estimates of ground speed, ground track, and climb rate. Until full ADS-B implementation is available, smoothed radar data should provide reasonable track estimates for testing TSAFE in an ADS-B-like environment. An example illustrates the smoothing of radar data and shows a comparison of smoothed-radar and ADS-B tracking. This document is intended to serve as a guide for using the smoothing software.
Synthetic aperture radar (SAR) has high data rate because it collects and processes the data coherently. The data rate limitation of the system has to be satisfied while maintaining good image quality. Thus, a quantizer with minimum data rate and high SNR should be employed. An adaptive quantization method is proposed for the burst mode SAR. This adaptive quantizer uses uniformly quantized data to select a subset of bits which is equivalent to changing the step size of the uniform quantizer. A simple implementation which uses the previous burst data to compute the local statistics for the bit selection is presented. The use of previous burst simplifies the implementation because it does not require storage or delay; however, an abrupt change in the terrain could result in incorrect bit selection. An error analysis of this implementation and comparison of two burst mode SAR images formed using the uniformly quantized and adaptively quantized data is presented.
The Tropical Rainfall Measuring Mission (TRMM) satellite was successfully launched in November, 1997. The main purpose of TRMM is to sample tropical rainfall using the first active spaceborne precipitation radar. To validate TRMM satellite observations, a comprehensive Ground Validation (GV) Program has been implemented. The primary goal of TRMM GV is to provide basic validation of satellite-derived precipitation measurements over monthly climatologies for the following primary sites: Melbourne, FL; Houston, TX; Darwin, Australia; and Kwajalein Atoll, RMI. As part of the TRMM GV effort, research analysts at NASA Goddard Space Flight Center (GSFC) generate standardized TRMM GV products using quality-controlled ground-based radar data from the four primary GV sites as input. This presentation will provide an overview of the TRMM GV climatological processing system. A description of the data flow between the primary GV sites, NASA GSFC, and the TRMM Science and Data Information System (TSDIS) will be presented. The radar quality control algorithm, which features eight adjustable height and reflectivity parameters, and its effect on monthly rainfall maps will be described. The methodology used to create monthly, gauge-adjusted rainfall products for each primary site will also be summarized. The standardized monthly rainfall products are developed in discrete, modular steps with distinct intermediate products. These developmental steps include: (1) extracting radar data over the locations of rain gauges, (2) merging rain gauge and radar data in time and space with user-defined options, (3) automated quality control of radar and gauge merged data by tracking accumulations from each instrument, and (4) deriving Z-R relationships from the quality-controlled merged data over monthly time scales. A summary of recently reprocessed official GV rainfall products available for TRMM science users will be presented. Updated basic standardized product results and trends involving monthly accumulation, Z-R relationship, and gauge statistics for each primary GV site will be also displayed.