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At least 19 records

Problems and programming for analysis of IUE high resolution data for variability

Observations of variability in stellar winds provide an important probe of their dynamics. It is crucial however to know that any variability seen in a data set can be clearly attributed to the star and not to instrumental or data processing effects. In the course of analysis of IUE high resolution data of alpha Cam and other O, B and Wolf-Rayet stars several effects were found which cause spurious variability or spurious spectral features in our data. Programming was developed to partially compensate for these effects using the Interactive Data language (IDL) on the LASP PDP 11/34. Use of an interactive language such as IDL is particularly suited to analysis of variability data as it permits use of efficient programs coupled with the judgement of the scientist at each stage of processing.

Grady, C. A.↗

Constrained Inversion of a Microwave Snowpack Emission Model Using Dictionary Matching: Applications for GPM Satellite

This article presents a new algorithmic framework for multilayer inversion of the dense media radiative transfer (DMRT) equations of snowpack emission, with particular emphasis on the role of high-frequency microwave channels above 60 GHz. The approach relies on dictionary matching and locally constrained least squares. The results demonstrate that the algorithm can invert the DMRT model and retrieve depth, density, and grain size of a single-layer snowpack when dependencies of density and grain size on depth are properly accounted for. However, as the number of layers increases, the sensitivity of the inversion to observation noise grows markedly. Using observations, over the Great Plains in the United States, from the microwave imager onboard the global precipitation measurement (GPM, 10-166 GHz) core satellite, the initial results demonstrate that under a clear-sky condition and no vegetation canopy, the algorithm is capable to retrieve the snow depth and water equivalent of seasonal snow with a mean absolute error (MAE) of less than 0.15 m--when compared to the high-resolution analysis data from the SNOw Data Assimilation System (SNODAS).

Snow↗

Effective use of principal component analysis with high resolution remote sensing data to delineate hydrothermal alteration and carbonate rocks

Methods of applying principal component (PC) analysis to high resolution remote sensing imagery were examined. Using Airborne Imaging Spectrometer (AIS) data, PC analysis was found to be useful for removing the effects of albedo and noise and for isolating the significant information on argillic alteration, zeolite, and carbonate minerals. An effective technique for using PC analysis using an input the first 16 AIS bands, 7 intermediate bands, and the last 16 AIS bands from the 32 flat field corrected bands between 2048 and 2337 nm. Most of the significant mineralogical information resided in the second PC. PC color composites and density sliced images provided a good mineralogical separation when applied to a AIS data set. Although computer intensive, the advantage of PC analysis is that it employs algorithms which already exist on most image processing systems.

Feldman, Sandra C.↗

Analysis of high resolution satellite data for cosmic gamma ray bursts

Cosmic gamma ray bursts detected a germanium spectrometer on the low altitude satellite 1972-076B were surveyed. Several bursts with durations ranging from approximately 0.032 to 15 seconds were found and are tabulated. The frequency of occurrence/intensity distribution of these events was compared with the S to the -3/2 power curve of confirmed events. The longer duration events fall above the S to the -3/2 power curve of confirmed events, suggesting they are perhaps not all true cosmic gamma-ray bursts. The narrow duration events fall closely on the S to the -3/2 power curve. The survey also revealed several counting rate spikes, with durations comparable to confirmed gamma-ray bursts, which were shown to be of magnetospheric origin. Confirmation that energetic electrons were responsible for these bursts was achieved from analysis of all data from the complete payload of gamma-ray and energetic particle detectors on board the satellite. The analyses also revealed that the narrowness of the spikes was primarily spatial rather than temporal in character.

Imhof, W. L.↗

High spectral resolution lidar to measure optical scattering properties of atmospheric aerosols. II - Calibration and data analysis

The high spectral resolution lidar (HSRL) measures optical properties of atmospheric aerosols by interferometically separating the elastic aerosol backscatter from the Doppler broadened molecular contribution. Calibration and data analysis procedures developed for the HSRL are described. Data obtained during flight evaluation testing of the HSRL system are presented with estimates of uncertainties due to instrument calibration. HSRL measurements of the aerosol scattering cross section are compared with in situ integrating nephelometer measurements.

Sroga, J. T.↗

High Resolution Nature Runs and the Big Data Challenge

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

big data analysis↗

The Lockheed OSO-8 program. Task 2: Analysis of data from the high resolution ultraviolet spectrometer experiment

The complete set of C 4 time sequences generated by the University of Colorado high resolution ultraviolet spectrometer experiment on OSO-8 were examined in a comprehensive and systematic fashion. As a result a new limit is placed on the acoustic flux passing through the transition zone of the Sun's atmosphere. It is found to be three orders of magnitude too small to heat the corona, and is consistent with zero. In collaborative efforts, the properties of transient C 4 brightenings were examined in considerable detail.

Bruner, E. C., Jr.↗

Atmospheric methane at Cape Meares - Analysis of a high-resolution data base and its environmental implications

Between 1979 and 1992 we took some 120,000 measurements of atmospheric methane at Cape Meares on the Oregon coast. The site is representative of methane concentrations in the northern latitudes (from 30 deg N to 90 deg N). The average concentration during the experiment was 1698 parts per billion by volume (ppbv). Methane concentration increased by 190 ppbv (or 11.9 percent) during the 13-year span of the experiment. The rate of increase was about 20 +/- 4 ppbv/yr in the first 2 yr and 10 +/- 2 ppbv/yr in the last 2 yr of the experiment, suggesting a substantial decline in the trend at northern middle and high latitudes. Prominent seasonal cycles were observed. During the year, the concentration stays more or less constant until May and then starts falling, reaching lowest levels in July and August, then rises rapidly to nearly maximum concentrations in October. Interannual variations with small amplitudes of 2-3 ppbv occur with periods of 1.4 and 6.5 yr.

Khalil, M. A. K.↗

High resolution marine meteorological analysis utilizing Seasat data

Seasat scatterometer (SASS) wind data was combined with surface meteorological readings to generate maps of lows, highs, ridges, and fronts. The SASS microwave radar permits all-weather surface wind observations with a 50 km resolution to within 2 m/sec or 10%. The scan area comprised the northern Pacific off the west coast of North America and southward from Alaska. Emphasis was placed on surface pressure analyses alone and in combination with the SASS data. Standard modeling of the existing weather systems was found to be unaltered by addition of the SASS data, although improvements were evident in the accuracy of delineating significant meteorological features and quantifying pressure gradients. Scale eddies and circulations were defined and cols were identified (by low wind speeds).

Peteherych, S.↗

Goddard high resolution spectrograph science verification and data analysis

The data analysis performed was to support the Orbital Verification (OV) and Science Verification (SV) of the GHRS was in the areas of the Digicon detector's performance and stability, wavelength calibration, and geomagnetic induced image motion. The results of the analyses are briefly described. Detailed results are given in the form of attachments. Specialized software was developed for the analyses. Calibration files were formatted according to the specifications in a Space Telescope Science report. IRAS images were restored of the Large Magellanic Cloud using a blocked iterative algorithm. The algorithm works with the raw data scans without regridding or interpolating the data on an equally spaced image grid.

Source record↗

HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere

HEALPix the Hierarchical Equal Area isoLatitude Pixelization is a versatile structure for the pixelization of data on the sphere. An associated library of computational algorithms and visualization software supports fast scientific applications executable directly on discretized spherical maps generated from very large volumes of astronomical data. Originally developed to address the data processing and analysis needs of the present generation of cosmic microwave background experiments (e.g., BOOMERANG, WMAP), HEALPix can be expanded to meet many of the profound challenges that will arise in confrontation with the observational output of future missions and experiments, including, e.g., Planck, Herschel, SAFIR, and the Beyond Einstein inflation probe. In this paper we consider the requirements and implementation constraints on a framework that simultaneously enables an efficient discretization with associated hierarchical indexation and fast analysis/synthesis of functions defined on the sphere. We demonstrate how these are explicitly satisfied by HEALPix.

cosmic microwave background↗

Comparing soil boundaries delineated by digital analysis of multispectral scanner data from high and low spatial resolution systems

The author has identified the following significant results. Computer-aided analysis techniques used with aircraft MSS data showed that the spatial resolution was sufficient to recognize each soil mapping unit of the test site. Some difficulties occurred where different soil series were intricately mixed, and this mixture showed as a separate spectral mapping unit, or where the difference between two soils depended on the depth of silty surface material. Analysis of LANDSAT data with computer-aided techniques showed that it was not possible to find spectrally homogeneous soil features of the seven soil series on the 40 ha test site on the digital display or on a picture print map. Cluster techniques could be used on an extended test area to group spectrally similar data points into cluster classes.

Kristof, S. J.↗

Cloud cover analysis with Arctic Advanced Very High Resolution Radiometer data. II - Classification with spectral and textural measures

The spectral and textural characteristics of polar clouds and surfaces for a 7-day summer series of AVHRR data in two Arctic locations are examined, and the results used in the development of a cloud classification procedure for polar satellite data. Since spatial coherence and texture sensitivity tests indicate that a joint spectral-textural analysis based on the same cell size is inappropriate, cloud detection with AVHRR data and surface identification with passive microwave data are first done on the pixel level as described by Key and Barry (1989). Next, cloud patterns within 250-sq-km regions are described, then the spectral and local textural characteristics of cloud patterns in the image are determined and each cloud pixel is classified by statistical methods. Results indicate that both spectral and textural features can be utilized in the classification of cloudy pixels, although spectral features are most useful for the discrimination between cloud classes.

Key, J.↗

High Spatial Resolution Airborne Multispectral Thermal Infrared Remote Sensing Data for Analysis of Urban Landscape Characteristics

We have used airborne multispectral thermal infrared (TIR) remote sensing data collected at a high spatial resolution (i.e., 10m) over several cities in the United States to study thermal energy characteristics of the urban landscape. These TIR data provide a unique opportunity to quantify thermal responses from discrete surfaces typical of the urban landscape and to identify both the spatial arrangement and patterns of thermal processes across the city. The information obtained from these data is critical to understanding how urban surfaces drive or force development of the Urban Heat Island (UHI) effect, which exists as a dome of elevated air temperatures that presides over cities in contrast to surrounding non-urbanized areas. The UHI is most pronounced in the summertime where urban surfaces, such as rooftops and pavement, store solar radiation throughout the day, and release this stored energy slowly after sunset creating air temperatures over the city that are in excess of 2-4'C warmer in contrast with non-urban or rural air temperatures. The UHI can also exist as a daytime phenomenon with surface temperatures in downtown areas of cities exceeding 38'C. The implications of the UHI are significant, particularly as an additive source of thermal energy input that exacerbates the overall production of ground level ozone over cities. We have used the Airborne Thermal and Land Applications Sensor (ATLAS), flown onboard a Lear 23 jet aircraft from the NASA Stennis Space Center, to acquire high spatial resolution multispectral TIR data (i.e., 6 bandwidths between 8.2-12.2 (um) over Huntsville, Alabama, Atlanta, Georgia, Baton Rouge, Louisiana, Salt Lake City, Utah, and Sacramento, California. These TIR data have been used to produce maps and other products, showing the spatial distribution of heating and cooling patterns over these cities to better understand how the morphology of the urban landscape affects development of the UHI. In turn, these data have been used by government officials, urban planners, and other decision-makers, to make more informed decisions on how to mitigate the UHI and its subsequent impacts.

Quattrochi, Dale A.↗

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis Along the Karnali Highway, Nepal

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 sq.m were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

Pukar Amatya↗

Scattered light in the echelle modes of the Goddard high-resolution spectrograph aboard the Hubble Space Telescope. I - Analysis of prelaunch calibration data

A study of scattered light in the echelle mode of the Goddard high-resolution spectrograph (GHRS), one of the first-generation science instruments aboard the Hubble Space Telescope, is reported. The data analyzed are from the GHRS ground-based test program and include observations with the 500 channel Digicon detectors through the small science aperture of UV emission-line sources, continuum sources, and continuum sources viewed through molecular absorption cells. The two largest contributors to scattered light are the echelle and cross-disperser, the effects of which have been quantified. A computer simulation using the derived scattering functions demonstrates that the combined effects of these sources of scattered light adequately explain the observed level of interorder light when viewing UV continuum sources. Observing strategies and data reduction algorithms for measuring and removing the effects of the scattered light are discussed.

Cardelli, Jason A.↗

Atmospheric-water absorption features near 2.2 micrometers and their importance in high spectral resolution remote sensing

Selective absorption of electromagnetic radiation by atmospheric gases and water vapor is an accepted fact in terrestrial remote sensing. Until recently, only a general knowledge of atmospheric effects was required for analysis of remote sensing data; however, with the advent of high spectral resolution imaging devices, detailed knowledge of atmospheric absorption bands has become increasingly important for accurate analysis. Detailed study of high spectral resolution aircraft data at the U.S. Geological Survey has disclosed narrow absorption features centered at approximately 2.17 and 2.20 micrometers not caused by surface mineralogy. Published atmospheric transmission spectra and atmospheric spectra derived using the LOWTRAN-5 computer model indicate that these absorption features are probably water vapor. Spectral modeling indicates that the effects of atmospheric absorption in this region are most pronounced in spectrally flat materials with only weak absorption bands. Without correction and detailed knowledge of the atmospheric effects, accurate mapping of surface mineralogy (particularly at low mineral concentrations) is not possible.

Kruse, F. A.↗