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In previous papers we've shown how a well known data compression algorithm called Entropy-constrained Vector Quantization ( can be modified to reduce the size and complexity of very large, satellite data sets. In this paper, we descuss how to visualize and understand the content of such reduced data sets.
The main thrust of the program was to obtain UV spectroscopy of a number of massive and hot luminous (OB type) stars in the nearby galaxy called the Small Magellanic Cloud (SMC). The objective was to analyze their atmospheres and winds so as to determine the effect of the lower abundance of the SIVIC on these parameters. Furthermore, the differences in evolution could be investigated. Additionally, the UV spectra themselves would be suitably weighted and systematically combined to provide a template for comparison to very distant galaxies formed in the early history of the Universe which also have a low abundance of elements. The spectra have been obtained and the analysis is proceeding, primarily by the groups in Munich and at STScl who are the leads for this project. Given the important role of the nearby SMC galaxy as a template of low metal abundance, I have begun to investigate the YOUNGEST phases of massive star birth, before the most massive and hottest stars become optically visible. Typically these stars form in clusters, in some cases having tens to hundreds of OB type stars. In this phase, each star is still buried in its natal cloud and visible only in the infrared (IR) from its self-heated dust and/or from radio free-free emission of the surrounding hydrogen (HII) region. Efforts to find and identify these buried clusters were conducted using a large radio telescope. A number of these were found and further analysis of the data is underway. These clusters are not visible optically, but ought to be seen in the IR, and are a likely topic for HST photometry on NICMOS. A proposal to do this will be made next semester. These objects are the precursors of the optically visible clusters that contain massive and hot luminous stars.
Globular clusters are thought to be among the oldest objects in the Galaxy, and provide, in this connection, important clues for determining the age and process of formation of the Galaxy. The present investigation is concerned with puzzles relating to the X-ray emission of globular clusters, taking into account questions regarding the location of X-ray emitting clusters (XEGC) unusually near the galactic plane and/or galactic center. An adopted model is discussed for the nature, formation, and lifetime of X-ray sources in globular clusters. An analysis of the available data is conducted in connection with a search for correlations between binary formation time scales, central relaxation times, galactic locations, and X-ray emission. The positive correlation found between distance from galactic center and two-body binary formation time for globular clusters, explanations for this correlation, and the hypothesis that X-ray sources in globular clusters require binary star systems provide a possible explanation of the considered puzzles.
In systems engineering practices system design and analysis have historically been performed using a document-centric approach where stakeholders produce a number of documents that represent their views on a system under development. Given the ad-hoc, disparate, and informal nature of natural language documents, these views become quickly inconsistent. Rigor in engineering work is also lost in the transition from model based engineering design and analysis to engineering documents. Once the documents are delivered, the engineering portion of the work is disconnected. In the Open Model Based Engineering Environment (OpenMBEE), Cross-References (aka transclusions) synthesize data for engineering when the information is not simply hyperlinked, but referenced in place in a document, upgrading a document-based process with model-based engineering technology. Those Cross-References are nowadays partially created manually, putting a burden on the engineer who is authoring the document.This paper presents an approach which can assist the engineer by providing machine-generated suggestions for Cross-References using language processing, graph analysis, and clustering technologies on model data managed by the OpenMBEE infrastructure.
Systematic changes of clouds and precipitation are notoriously difficult to ascribe to aerosols. This presentation will showcase yet one more attempt to at least credibly detect the signal of aerosol-cloud-precipitation interactions. We surmise that the concept of cloud regimes (CRs) is appropriate to conduct such an investigation. Previous studies focused on what we call here dynamical CRs, and while we continue to adopt those too for our analysis, we have found that a different way of organizing cloud systems, namely via microphysical regimes is also promising. Our analysis relies on MODIS Collection 6 Level-3 data for clouds and aerosols, and TRMM-TMPA data for precipitation. The regimes are derived by applying clustering analysis on MODIS joint histograms, and once each grid cell is assigned a regime, aerosol and precipitation data can be spatiotemporally matched and composited by regime. The composites of various cloud and precipitation variables for high (upper quartile of distribution) and low (lower quartile) aerosol loadings can then be contrasted. We seek evidence of aerosol effects both in regimes with large fractions of deep ice-rich clouds, as well as regimes where low liquid phase clouds dominate. Signals can be seen, especially when the analysis is broken by land-ocean and when additional filters are applied, but there are of course caveats which will be discussed.
We demonstrate a clustering analysis algorithm, KSC, that a) uses all observed values and b) does not discard the partially observed objects. KSC uses soft constraints defined by the fully observed objects to assist in the grouping of objects with missing values. We present an analysis of objects taken from the Sloan Digital Sky Survey to demonstrate how imputing the values can be misleading and why the KSC approach can produce more appropriate results.
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We investigate how multifrequency and polarimetric synthetic aperture radar (SAR) imagery enhances present capability to discriminate different ice conditions in single-frequency, single-polarization satellite SAR data. Frequencies considered are C- (lambda = 5.6cm), L- (lambda = 24cm) and P- (lambda = 68cm) band. Radar backscatter characteristics of six radiometrically and polarimetrically distinct ice types are selected from a cluster analysis of the multifrequency polarimetric SAR data and used to classify SAR images. Validation of these ice conditions is based on information provided by aerial photos, weather and ice surface measurements acquired at an ice camp, together with airborne passive microwave imagery, and visual analysis of the SAR data. The six identified sea-ice types are: (1) multiyear sea-ice; (2) compressed first year ice; (3) first year rubble and ridges; (4) first year rough ice; (5) first year smooth ice; and (6) thin ice. Open water is absent in all analyzed data. Classification of the SAR imagery into those six ice types is performed using a Bayesian Maximum A Posteriori classifier. Two complete scenes acquired at different dates in different locations are classified. The scenes were chosen such that they are representative of typical ice conditions in the Beaufort Sea in March 1988 and because ancillary information is available for validating the segmentation of various ice surface conditions.
A requirements analysis for the Space Station's polar platform data system has been performed. Based upon this analysis, a cluster, layered cluster, and layered-modular implementation of one specific module within the Eos Data and Information System (EosDIS), an active data base for satellite remote sensing research has been developed. It is found that a distributed system based on a layered-modular architecture and employing current generation work station technologies has the requisite attributes ascribed by the remote sensing research community. Although, based on benchmark testing, probabilistic analysis, failure analysis and user-survey technique analysis, it is found that this architecture presents some operational shortcomings that will not be alleviated with new hardware or software developments. Consequently, the potential of a fully-modular layered architectural design for meeting the needs of Eos researchers has also been evaluated, concluding that it would be well suited to the evolving requirements of this multidisciplinary research community.
There are no author-identified significant results in this report.
Three approaches for analyzing Landsat-1 data from Ludwig Mountain in the San Juan Mountain range in Colorado are considered. In the 'supervised' approach the analyst selects areas of known spectral cover types and specifies these to the computer as training fields. Statistics are obtained for each cover type category and the data are classified. Such classifications are called 'supervised' because the analyst has defined specific areas of known cover types. The second approach uses a clustering algorithm which divides the entire training area into a number of spectrally distinct classes. Because the analyst need not define particular portions of the data for use but has only to specify the number of spectral classes into which the data is to be divided, this classification is called 'nonsupervised'. A hybrid method which selects training areas of known cover type but then uses the clustering algorithm to refine the data into a number of unimodal spectral classes is called the 'modified-supervised' approach.
We present multi-wavelength observations of the Abell 1995 galaxy cluster. From analysis of x-ray spectroscopy and imaging data we derive the electron temperature, cluster core radius, and central electron number density. Using optical spectroscopy of 15 cluster members, we derive an accurate cluster redshift and velocity dispersion. Finally, the interferometric imaging of the SZE toward Abell 1995 at 28.5 GHz provides a measure of the integrated pressure through the cluster.
A method of testing the performance of a cluster of wind turbine units an data analysis equations are presented which together form a simple and direct procedure for determining the reduction in energy output caused by the wake of an upwind turbine. This method appears to solve the problems presented by data scatter and wind variability. Test data from the three-unit Mod-2 wind turbine cluster at Goldendale, Washington, are analyzed to illustrate the application of the proposed method. In this sample case the reduction in energy was found to be about 10 percent when the Mod-2 units were separated a distance equal to seven diameters and winds were below rated.
The long term objectives are: (1) to understand the physics of the multispectral microwave radiative characteristics of sea ice as it goes through different phases; (2) to improve characterization of sea ice cover using satellite microwave sensors; and (3) to study ice/ocean physical and biological processes associated with polynya formations and variability of the marginal sea ice region. Two field experiments were conducted to pursue these objectives. One involved measurements of radiative and physical characteristics of sea ice from a ship during a 3-month long cruise through the Weddell Sea ice pack during the Austral winter of 1986. The other involved similar measurements from two aircrafts and a submarine over the Central Arctic and Greenland Sea region. Preliminary results have already led to an enhanced understanding of the microwave signatures of pancake ice, nilas, first year ice, multiyear ice and effects of snow cover. Coastal and deep ocean polynyas and their role in bottom water formation and ocean circulation were studied using a time series of ice images from SMMR. An unsupervised cluster analysis of Arctic sea ice using SMMR and THIR emissivity and brightness temperature data was implemented. The analysis indicates the existence of several unique and persistent clusters in the Central Arctic region during winter and that the sum of the area of these clusters excluding those of first year ice is about 20 percent less than minimum ice cover area inferred from a previous summer data. This result is consistent with saline surface for some multiyear ice floes as observed during MIZEZ and suggests that a significant fraction of multiyear ice floes in the Arctic have first year ice signatures.
Spectral relationships were investigated for several bare soil fields which were in summer fallow rotation on the date of the imagery. Printouts of each band were examined and compared to aerial photography. Bands with dissimilar reflectance patterns for known areas were then combined using ratio techniques which were proven useful in other studies (Williams, 1983). Selected ratios were Thematic Mapper (TM) 1/TM4, TM3/TM4, and TM5/TM4. Cluster analyses and Baysian and Fastclass classifier images were produced using the three ratio images. Plots of cluster analysis outputs revealed distinct groupings of reflectance data representing green crops, ripened crops, soil and green plants, and bare soil. Bare soil was represented by a line of clusters on plots of the ratios TM5/TM4 and TM3/TM4. The soil line was investigated further to determine factors involved in the distributin of clusters alone the line. The clusters representing the bare soil line were also studied by plotting the Tm5/TM4, TM1/TM4 dimension. A total of 76 soil samples were gathered and analyzed for organic carbon.
Scientific data services are a critical aspect of the NASA Center for Climate Simulations mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyber infrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model inter-comparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.
MapReduce is an approach to high-performance analytics that may be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. We are particularly interested in the potential of MapReduce to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we are prototyping a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. Our initial focus has been on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. Preliminary results suggest this approach can improve efficiencies within data intensive analytic workflows.
We present multiwavelength observations of the Abell 1995 galaxy cluster. From an analysis of X-ray spectroscopy and imaging data, we derive the electron temperature, cluster core radius, and central electron number density. Using optical spectroscopy of 15 cluster members, we derive an accurate cluster redshift and velocity dispersion. Finally, the interferometric imaging of the Sunyaev-Zeldovich effect toward Abell 1995 at 28.5 GHz provides a measure of the integrated pressure through the cluster. The X-ray and Sunyaev-Zeldovich effect observations are combined to determine the angular diameter distance to the cluster of D(sub A) = 1294(sup +294 +438, sub -283 -458) Mpc (Statistical followed by systematic uncertainty), implying a Hubble constant of H(sub 0) = 52.2(sup +11.4 +18.5, sub -11.9 -17.7) km/s.Mpc for Omega(sub M) = 0.3 and Omega(sub lambda) = 0.7. We find a best-fit H(sub 0) of 46 km/s.Mpc for the Omega(sub M) = 1 and Omega(sub lambda) = 0 cosmology, and 48 km/s.Mpc for Omega(sub M) = 0.3 and Omega(sub lambda) = 0.0. The X-ray data are also used to derive a total cluster mass of M(sup HSE, sub tot)(r(sub 500)) = 5.18(sup +0.62, sub -0.48) x 10(exp 14)/h solar mass; the optical velocity dispersion yields an independent and consistent estimate of M(sup virial, sub tot)(r(sub 500)) = 6.35(sup +1.51, sub -1.19) X 10(exp 14) /h solar mass. Both of the total mass estimates are evaluated at a fiducial radius, r(sub 500) = 830 /h kpc, where the overdensity is 500 times the critical density. The total cluster mass is then combined with gas mass measurements to determine a cluster gas mass fraction of F(sub g) = 0.056(sup +0.010, sub -0.013) /h(sup 3/2) in combination with recent baryon density constraints, the measured gas mass fraction yields an upper limit on the mass density parameter of Omega(sub M) h(sup 1/2) <= 0.34(sup +/0.06, sub 0.05.