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A classification analysis is conducted concerning the tropical cloud types as remotely sensed in the visual and infrared range by ITOS scanning radiometers. A statistical pattern recognition technique is used to examine the ability of coincident dual-channel and single-channel data to classify four main forms of clouds, including cumulus, stratocumulus, cumulonimbus, and cirrus.
To investigate the effects of physical training on cardiac dimensions and function, eight dogs were exercised for 12 weeks by treadmill running 1 hour/day, 5 days/week. Five dogs were confined in cages as controls for an 8-week period. Heart rates were monitored by telemetry during rest and exercise. Maximum QRS spatial magnitudes were calculated from records of McFee lead electrocardiograms. Left ventricular end-diastolic dimensions were determined radiographically by the bead and clip technique. Training produced statistically significant decreases in heart rate at rest and at a standard work load of 6.1 mph on a level treadmill and statistically significant increases in work load at a standard heart rate of 194 beats/min. Improvements were rapid during the first 4 weeks of training but gradual during the remaining 8 weeks. Training caused small but statistically significant increases in left ventricular end-diastolic wall thickness, estimated left ventricular mass, and maximum QRS spatial magnitude.
Information is provided necessary to use the LOVES Computer Program in its existing state, or to modify the program to include studies not properly handled by the basic model. The Users Guide defines the basic elements assembled together to form the model for servicing satellites in orbit. As the program is a simulation, the method of attack is to disassemble the problem into a sequence of events, each occurring instantaneously and each creating one or more other events in the future. The main driving force of the simulation is the deterministic launch schedule of satellites and the subsequent failure of the various modules which make up the satellites. The LOVES Computer Program uses a random number generator to simulate the failure of module elements and therefore operates over a long span of time typically 10 to 15 years. The sequence of events is varied by making several runs in succession with different random numbers resulting in a Monte Carlo technique to determine statistical parameters of minimum value, average value, and maximum value.
Work towards establishing a vector wind profile gust model for the Space Transportation System flight operations and trade studies is reported. To date, all the statistical and computational techniques required were established and partially implemented. An analysis of wind profile gust at Cape Kennedy within the theoretical framework is presented. The variability of theoretical and observed gust magnitude with filter type, altitude, and season is described. Various examples are presented which illustrate agreement between theoretical and observed gust percentiles. The preliminary analysis of the gust data indicates a strong variability with altitude, season, and wavelength regime. An extension of the analyses to include conditional distributions of gust magnitude given gust length, distributions of gust modulus, and phase differences between gust components has begun.
Modifications made to data bases and to four programs in a series of computer programs (Sun Series) which run on the REEDA HP minicomputer system to aid NASA's solar activity predictions used in orbital life time predictions are described. These programs utilize various mathematical smoothing technique and perform statistical and graphical analysis of various solar activity data bases residing on the REEDA System.
Planetary geological studies are almost entirely based on the analysis of orbital imagery. In the case of Mars, optical power spectra are providing the photogeologist with an additional aid in his task of classification and characterization of diverse terrains. Statistical pattern recognition techniques using optical power spectral data may be especially valuable in subdividing terrain units with characteristics that are only subtly different and in correlation of isolated patches of similar materials that are widely separated on the planet's surface.
Statistical model identification techniques being developed to provide workable solutions to problems in density estimation and function smoothing are examined.
Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperature measurements over the global oceans have been examined with the help of statistical and empirical techniques. Such analyses show that zonal averages of brightness temperature measured by SMMR, over the oceans, on a large scale are primarily influenced by the water vapor in the atmosphere. Liquid water in the clouds and rain, which has a much smaller spatial and temporal scale, contributes substantially to the variability of the SMMR measurements within the latitudinal zones. The surface wind not only increases the surface emissivity but through its interactions with the atmosphere produces correlations, in the SMMR brightness temperature data, that have significant meteorological implications. It is found that a simple meteorological model can explain the general characteristics of the SMMR data. With the help of this model methods to infer over the global oceans, the surface temperature, liquid water content in the atmosphere, and surface wind speed are developed. Monthly mean estimates of the sea surface temperature and surface winds are compared with the ship measurements. Estimates of liquid water content in the atmosphere are consistent with earlier satellite measurements.
Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperature measurements over the global oceans have been examined with the help of statistical and empirical techniques. Such analyses show that zonal averages of brightness temperature measured by SMMR over the oceans on a large scale are primarily influenced by the water vapor in the atmosphere. Liquid water in the clouds and rain, which has a much smaller spatial and temporal scale, contributes substantially to the variability of the SMMR measurements within the latitudinal zones. The surface wind not only increases the surface emissivity, but through its interactions with the atmosphere produces correlations in the SMMR brightness temperature data that have significant meteorological implications. It is found that a simple meteorological model can explain the general characteristics of the SMMR data. With the help of this model, methods to infer over the global oceans, the surface temperature, liquid water content in the atmosphere, and surface wind speed are developed. Monthly mean estimates of the sea surface temperature and surface winds are compared with the ship measurements. Estimates of liquid water content in the atmosphere are consistent with earlier satellite measurements. Previously announced in STAR as N83-19187
Research conducted during the past year in the climate and atmospheric modeling programs was focused on the development of appropriate atmospheric and upper ocean models, and preliminary applications of these models. Prinicpal models are a one-dimensional radiative-convection model, a three-dimensional global climate model, and an upper ocean model. Principal application is the study of the impact of CO2, aerosols and the solar constant on climate. Also the performance of the International Satellite Cloud Climatology Project cloud detection algorithm was evaluated, concentrating initially on its application to geosynchronous data, with an eventual switch of the developed methodologies to data from polar orbiting satellites. In the process, a number of improvements were made, in particular: an improved technique for tracking small scale day to day variability in clear sky continental temperatures; a number of techniques for the statistical assessment of cloud detection uncertainties due to cloud types which are spatially and temporally invariant; and a method used to detect those cloudy regions which have long term spatial and temporal stability.
This paper presents extensive exploratory research which had as its primary objective, the discovery and determination of major areas of concern exhibited by U.S. corporate executives in the preparation and submittal of proposals and bids to the Federal government. The existence of numerous unique concerns inherent in corporate strategies within the government market environment was established. A determination of the relationship of these concerns to each other was accomplished utilizing statistical factor analysis techniques resulting in the identification of major groupings of management concerns. Finally, using analysis of variance, an analysis and discovery of the interrelationship of the factors to corporate demographics was accomplished. The existence of separate and distinct concerns exhibited by corporate executives when contemplating sales and operations in the government marketplace was established. It was also demonstrated that quantifiable relationships exist between such variables and that the decision behavior exhibited by the responsible executives has an interrelationship to their company's demographics.
The calculation of shape and scale parameters of the two-parameter Weibull distribution is described using the least-squares analysis and maximum likelihood methods for volume- and surface-flaw-induced fracture in ceramics with complete and censored samples. Detailed procedures are given for evaluating 90 percent confidence intervals for maximum likelihood estimates of shape and scale parameters, the unbiased estimates of the shape parameters, and the Weibull mean values and corresponding standard deviations. Furthermore, the necessary steps are described for detecting outliers and for calculating the Kolmogorov-Smirnov and the Anderson-Darling goodness-of-fit statistics and 90 percent confidence bands about the Weibull distribution. It also shows how to calculate the Batdorf flaw-density constants by uing the Weibull distribution statistical parameters. The techniques described were verified with several example problems, from the open literature, and were coded. The techniques described were verified with several example problems from the open literature, and were coded in the Structural Ceramics Analysis and Reliability Evaluation (SCARE) design program.
An improved method for fitting asymmetric soft X-ray line profiles from solar flares is presented. A two-component model is used where one component represents the total emission from directed upflow plasma and the other the emission from the plasma at rest. Unlike previous methods, the width of the moving component is independent from that of the stationary component. Time variations of flare plasma characteristics (i.e., temperature, emission measure of moving and stationary plasma, upflow and turbulent velocities) are derived from the Ca XIX and Fe XXV spectra recorded by the Bent Crystal Spectrometer on the Solar Maximum Mission. The fitting technique provides a statistical estimation for the uncertainties in the fitting parameters. The relationship between the directed and turbulent motions has been studied, and a correlation of the random and directed motions has been found in some flares with intensive plasma upflows. Mean temperatures of the upflowing and stationary plasmas are compared for the first time from ratios of calcium to iron X-ray line intensities. Finally, evidence for turbulent motions and the possibility of plasma upflow late into the decay phase is presented and discussed.
The type of interconnection network employed has a profound effect on the performance of a multicomputer and multiprocessor design. Adequate models are needed to aid in the design and development of interconnection networks. A novel modeling approach using statistical and optimization techniques is described. This method represents an attempt to compare diverse interconnection network designs in a way that allows not only the best of existing designs to be identified but to suggest other, perhaps hybrid, networks that may offer better performance. Stepwise linear regression is used to develop a polynomial surface representation of performance in a (k+1) space with a total of k quantitative and qualitative independent variables describing graph-theoretic characteristics such as size, average degree, diameter, radius, girth, node-connectivity, edge-connectivity, minimum dominating set size, and maximum number of prime node and edge cutsets. Dependent variables used to measure performance are average message delay and the ratio of message completion rate to network connection cost. Response Surface Methodology (RSM) optimizes a response variable from a polynomial function of several independent variables. Steepest ascent path may also be used to approach optimum points.
During FIRE, three major stratocumulus clearing events took place over the project region. These clearing events are analyzed using synoptic variables to determine if these clearing events can be predicted by current modeling techniques. A preliminary statistical evaluation of the correlation between satellite cloud brightness parameters and NMC global model parameters is available in Wylie, et al., 1989.
The calculation of shape and scale parametes of the two-parameter Weibull distribution is described using the least-squares analysis and maximum likelihood methods for volume- and surface-flaw-induced fracture in ceramics with complete and censored samples. Detailed procedures are given for evaluating 90 percent confidence intervals for maximum likelihood estimates of shape and scale parameters, the unbiased estimates of the shape parameters, and the Weibull mean values and corresponding standard deviations. Furthermore, the necessary steps are described for detecting outliers and for calculating the Kolmogorov-Smirnov and the Anderson-Darling goodness-of-fit statistics and 90 percent confidence bands about the Weibull distribution. It also shows how to calculate the Batdorf flaw-density constants by using the Weibull distribution statistical parameters. The techniques described were verified with several example problems, from the open literature, and were coded in the Structural Ceramics Analysis and Reliability Evaluation (SCARE) design program.
Combination of established statistical and filtering techniques yields improved estimates of signal-to-noise ratio (SNR) of multispectral imagery produced by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Method yields more accurate estimate of random variability in AVIRIS imagery. Combination of techniques called "geostatistical" method. In first step of method, periodic noise removed by notch filtering in frequency domain. In subsequent steps, sensor noise and variability within picture elements isolated by use of semi-variance of picture elements.
Methods of simulating large scale random atmospheric variations are examined for use in Monte Carlo simulations of hypervelocity vehicles. Random density, temperature, and pressure variations have a significant impact on the performance of such vehicles. Methods for generating random atmosphere profiles are described and compared in the context of a Monte Carlo simulation of the Aeroassist Flight Experiment (AFE). It is seen that these techniques result in statistically significant differences in key simulation point statistics.