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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

An interactive system for compositing digital radar and satellite data

This paper describes an approach for compositing digital radar data and GOES satellite data for meteorological analysis. The processing is performed on a user-oriented image processing system, and is designed to be used in the research mode. It has a capability to construct PPIs and three-dimensional CAPPIs using conventional as well as Doppler data, and to composite other types of data. In the remapping of radar data to satellite coordinates, two steps are necessary. First, PPI or CAPPI images are remapped onto a latitude-longitude projection. Then, the radar data are projected into satellite coordinates. The exact spherical trigonometric equations, and the approximations derived for simplifying the computations are given. The use of these approximations appears justified for most meteorological applications. The largest errors in the remapping procedure result from the satellite viewing angle parallax, which varies according to the cloud top height. The horizontal positional error due to this is of the order of the error in the assumed cloud height in mid-latitudes. Examples of PPI and CAPPI data composited with satellite data are given for Hurricane Frederic on 13 September 1979 and for a squall line on 2 May 1979 in Oklahoma.

Heymsfield, G. M.↗
NASA NTRS1989

Optimized variational analysis scheme of single Doppler radar wind data

A computer scheme for extracting singularities has been developed and applied to single Doppler radar wind data. The scheme is planned for use in real-time wind and singularity analysis and forecasting. The method, known as Doppler Operational Variational Extraction of Singularities is outlined, focusing on the principle of local symmetry. Results are presented from the application of the scheme to a storm-generated gust front in Oklahoma on May 28, 1987.

Sasaki, Yoshi K.↗
NASA NTRS1990

Assessment of tropical forest stand characteristics with multipolarization SAR data acquired over a mountainous region in Costa Rica

A digital terrain elevation data set was coregistered with radar data for assessing tropical forest stand characteristics. Both raw and topographically corrected L-band polarimetric radar data acquired over the tropical forests of Costa Rica were analyzed and correlated with field-collected tree parameter data to study the stand characteristics. The results of analyses using 18 out of 81 plots for sites A and B indicated that per-plot bole volume and tree volume are related to SAR data, particularly at site A. The topographically corrected SAR data appear to produce the same findings as those of uncorrected data.

Wu, Shih-Tseng↗
NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗
NASA NTRS2016

Characterization of Potential Springs in the Lower Colorado Desert of Southern California using Satellite Radar and Landsat Time Series Analysis

Renewable energy development in southern California is receiving increasing attention due to potential impacts on wildlife habitats and water sources. This study was designed to quantify and map, for the first time, sub-surface water sources from springs in the Lower Colorado Desert area of southern California using satellite radar data and 30 years of Landsat satellite image data. Synthetic Aperture Radar (SAR) data was used to identify sub-surface water sources not already documented as springs or seeps in the National Hydrography Database. Landsat imagery starting in 1985 was used to characterize vegetation growth patterns at these suspected spring locations detected from SAR analysis. Results showed a total of 104 potential spring locations across the Lower Colorado Desert study area, 19 of which were detected within Solar Energy Zone (SEZ) development boundaries, roughly evenly split between Riverside East and Imperial East, and 13 of which (both inside and outside SEZs) showed relatively high green vegetation index values over the period of 1985 to 2015 that would depend on non-precipitation water sources associated with active springs.

DRECP↗
DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Mercury's Global Topography from Radar Ranging Data

When Mercury's radius is expanded in Legendre functions to the second degree and order, the systematic error in radar ranging data is reduced substantially. Previously, data spanning an observing interval from 1966 to 1990 were used to infer an equatorial ellipticity (a - b)/a = (540 +/- 54) X 10(exp -6) and a center-of-figure minus center-of-mass offset of (640 +/- 78) m. The magnitude of this equatorial center of figure offset implies an excess crustal thickness of 12 km or less, comparable to the Moon's excess. By comparing the equatorial ellipticity with the Mariner 10 gravity field, and assuming Airy isostatic compensation, bounds on crustal thickness can be derived. Mercury's crustal thickness is in the range from 100 to 300 km. The Mercury radar ranging observing interval has been extended from 1966 to the present. In addition, improvements in data reduction techniques have resulted in a set of Mercury ranging data less affected by systematic error, in particular the biases introduced by local topographic variations. We use this new set of reduced ranging data to improve Mercury's global topography and center-of-figure minus center-of-mass offset. New results on crustal thickness are derived, and prospects for further improvement with Mercury Orbiter data are discussed.

Anderson, J. D.↗
NASA NTRS1992

MAC Europe 1991: Evaluation of AVIRIS, GER imaging spectrometry data for the land application testsite Oberpfaffenhofen

During the MAC Europe 91 Campaign, the area of Oberpfaffenhofen including the land application testsite Oberpfaffenhofen was flown by the AVIRIS imaging spectrometer, the GER 2 imaging spectrometer (63 band scanner), and two SAR systems (NASA/JPL AIRSAR and DLR E-SAR). In parallel to the overflights ground spectrometry (ASD, IRIS M IV) and atmospheric measurements were carried out in order to provide data for optical sensor calibration. Ground spectrometry measurements were carried out in the runway area of the DLR research center Oberpfaffenhofen. This area was used as well during the GER 2 European flight campaign EISAC 89 as a calibration target. The land application testsite Oberpfaffenhofen is located 3 km north of the DLR research center. During the MAC Europe 91 Campaign a ground survey was carried out for documentation in the ground information data base (vegetation type, vegetation geometry, soil type, and soil mixture). Crop stands analyzed were corn, barley and rape. The DLR runway area and the land application testsite Oberpfaffenhofen were flown with the AVIRIS on 29 July and with the GER 2 on 12 and 23 July and 3 Sep. AVIRIS and GER 2 scenes were processed and atmospherically corrected for optical data analysis of optical and radar data. For the AVIRIS and the GER 2 scenes, signal-to-noise ratios (SNR) estimates were calculated. An example of the reflectance of 6 calibration targets inside a GER 2 scene of Oberpfaffenhofen is given. SNR values for the GER 2 for a medium albedo target are given. The integrated analysis for the optical and radar data was carried out in cooperation with the DLR Institute for Microwave Technologies.

Lehmann, F.↗
NASA NTRS1980

Detection of heavy convective precipitation using rapid interval digital radar and satellite data

A study was carried out to investigate the evolution of thunderstorms with short-interval (5 min) geosynchronous digital satellite data and with digital radar data (12 min interval) in order to determine the potential and limitations of using the satellite data to detect regions of heavy convective precipitation. Initial results indicate that satellite-based estimates of thunderstorm updraft intensity are related to storm precipitation rate as indicated in the digital radar data. The conclusions give support to the effort to use satellite data for detection of heavy convective precipitation, but emphasize the need for high time resolution (5 min) data.

Negri, A. J.↗
NASA NTRS1996

New results from mapping Antarctica at high resolution from radar altimeter data

The limits of the altimeter data evaluation were analyzed using geostatistical methods. It is often argued that satellite radar altimeter data over ice can not be used to map ice surfaces with a slope exceeding 0.5 deg. The maps presented, obtained from the Geosat satellite geodetic mission, concern the Antarctica north of 72.1 deg South. The grids constructed from other satellite observations facilitate the analysis of the changes in the Antarctic ice stream/ice shelf systems. The evaluation was applied to the Lambert Glacier/Amery Ice Shelf system.

Matassa, Michael↗
NASA NTRS1980

High resolution vertical profiles of wind, temperature and humidity obtained by computer processing and digital filtering of radiosonde and radar tracking data from the ITCZ experiment of 1977

Results are presented from computer processing and digital filtering of radiosonde and radar tracking data obtained during the ITCZ experiment when coordinated measurements were taken daily over a 16 day period across the Panama Canal Zone. The temperature relative humidity and wind velocity profiles are discussed.

Danielson, E. F.↗
NASA NTRS1981

Application of SEASAT-1 Synthetic Aperture Radar (SAR) data to enhance and detect geological lineaments and to assist LANDSAT landcover classification mapping

Digital SEASAT-1 synthetic aperture radar (SAR) data were used to enhance linear features to extract geologically significant lineaments in the Appalachian region. Comparison of Lineaments thus mapped with an existing lineament map based on LANDSAT MSS images shows that appropriately processed SEASAT-1 SAR data can significantly improve the detection of lineaments. Merge MSS and SAR data sets were more useful fo lineament detection and landcover classification than LANDSAT or SEASAT data alone. About 20 percent of the lineaments plotted from the SEASAT SAR image did not appear on the LANDSAT image. About 6 percent of minor lineaments or parts of lineaments present in the LANDSAT map were missing from the SEASAT map. Improvement in the landcover classification (acreage and spatial estimation accuracy) was attained by using MSS-SAR merged data. The aerial estimation of residential/built-up and forest categories was improved. Accuracy in estimating the agricultural and water categories was slightly reduced.

Sekhon, R.↗
DOE OSTI2020

Columnar Vertical Profile (CVP) Methodology for Validating Polarimetric Radar Retrievals in Ice Using In Situ Aircraft Measurements

Abstract A novel way to process polarimetric radar data collected via plan position indicator (PPI) scans and display those data in a time–height format is introduced. The columnar vertical profile (CVP) methodology uses radar data collected via multiple elevation scans, limited to data within a set region in range and azimuth relative to the radar, to create vertical profiles of polarimetric radar data representative of that limited region in space. This technique is compared to others existing in the literature, and various applications are discussed. Polarimetric ice microphysical retrievals are performed on CVPs created within the stratiform rain region of two mesoscale convective systems sampled during two field campaigns, where CVPs follow the track of research aircraft. Aircraft in situ data are collocated to microphysical retrieval data, and the accuracy of these retrievals is tested against other retrieval techniques in the literature.

Engineering↗
NASA NTRS1993

Synthesis of passive microwave and radar altimeter data for estimating accumulation rates of polar snow

In this paper, we compare dry-snow extinction coefficients derived from radar altimeter data with brightness temperature data from passive microwave measurements over a portion of the East Antarctic plateau. The comparison between the extinction coefficients and the brightness temperatures shows a strong negative correlation, where the correlation coefficients ranged from -0.87 to -0.95. The extinction coefficient of the dry polar snow decreases with increasing surface elevation, while the average brightness temperature increases with surface elevation. Our analysis shows that the observed trends are related to geographic variations in scattering coefficient of snow, which in turn are controlled by variations in surface temperature and snow accumulation rate. By combining information present in the extinction coefficient and brightness temperature data sets, we develop a model that can be used to obtain quantitative estimates of the accumulation rate of dry polar snow.

Davis, Curt H.↗
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 NTRS1976

Description and catalog of ionospheric F-region data, Jicamarca Radar Observatory, November 1966 - April 1969

Equatorial ionospheric F-region data reduced from the Jicamarca Radar Observatory (JRO) incoherent scatter observations for particular periods is described. It lists in catalog form the times of the observations made during those periods. These F-region data include the electron concentration and the electron and ion temperatures. The data were inferred from the incoherent scatter observations of JRO.

Clark, W. L.↗