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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Estimated Accuracy of Three Common Trajectory Statistical Methods

Three well-known trajectory statistical methods (TSMs), namely concentration field (CF), concentration weighted trajectory (CWT), and potential source contribution function (PSCF) methods were tested using known sources and artificially generated data sets to determine the ability of TSMs to reproduce spatial distribution of the sources. In the works by other authors, the accuracy of the trajectory statistical methods was estimated for particular species and at specified receptor locations. We have obtained a more general statistical estimation of the accuracy of source reconstruction and have found optimum conditions to reconstruct source distributions of atmospheric trace substances. Only virtual pollutants of the primary type were considered. In real world experiments, TSMs are intended for application to a priori unknown sources. Therefore, the accuracy of TSMs has to be tested with all possible spatial distributions of sources. An ensemble of geographical distributions of virtual sources was generated. Spearman s rank order correlation coefficient between spatial distributions of the known virtual and the reconstructed sources was taken to be a quantitative measure of the accuracy. Statistical estimates of the mean correlation coefficient and a range of the most probable values of correlation coefficients were obtained. All the TSMs that were considered here showed similar close results. The maximum of the ratio of the mean correlation to the width of the correlation interval containing the most probable correlation values determines the optimum conditions for reconstruction. An optimal geographical domain roughly coincides with the area supplying most of the substance to the receptor. The optimal domain s size is dependent on the substance decay time. Under optimum reconstruction conditions, the mean correlation coefficients can reach 0.70 0.75. The boundaries of the interval with the most probable correlation values are 0.6 0.9 for the decay time of 240 h and 0.5 0.95 for the decay time of 12 h. The best results of source reconstruction can be expected for the trace substances with a decay time on the order of several days. Although the methods considered in this paper do not guarantee high accuracy they are computationally simple and fast. Using the TSMs in optimum conditions and taking into account the range of uncertainties, one can obtain a first hint on potential source areas.

Kabashnikov, Vitaliy P.↗
NASA NTRS1990

Statistical methods and neural network approaches for classification of data from multiple sources

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Benediktsson, Jon Atli↗
NASA NTRS1977

A statistical method for time-continuous assimilation of remote sounding temperatures

A time-continuous statistical method is presented for the four-dimensional assimilation of remote sounding temperatures based on radiometric measurements from polar-orbiting satellites. This method is applied to DST-6 data from the NOAA-4 and Nimbus-6 satellites. Experiments are reported in which the state of the atmosphere throughout the test period was determined using a varying amount of satellite data and in which different methods were used for their assimilation. Data from the NOAA-4 satellite only, from Nimbus-6 only, and from both satellites together were used; the methods tested include different variations of the statistical method as well as more traditional methods. The conclusions are that: (1) satellite-derived temperature data can have a modest, but statistically significant positive impact on numerical weather prediction in the two-to-three day range; (2) this impact is highly sensitive to the quantity of data available; and (3) the assimilation method plays a major role in the magnitude of the impact for the same data.

Ghil, M.↗
NASA NTRS1972

Recent statistical methods for orientation data

The application of statistical methods for determining the areas of animal orientation and navigation are discussed. The method employed is limited to the two-dimensional case. Various tests for determining the validity of the statistical analysis are presented. Mathematical models are included to support the theoretical considerations and tables of data are developed to show the value of information obtained by statistical analysis.

Batschelet, E.↗
NASA NTRS2016

A Statistical Method for Reducing Sidelobe Clutter for the Ku-Band Precipitation Radar on Board the GPM Core Observatory

A statistical method to reduce the sidelobe clutter of the Ku-band precipitation radar (KuPR) of the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory is described and evaluated using DPR observations. The KuPR sidelobe clutter was much more severe than that of the Precipitation Radar on board the Tropical Rainfall Measuring Mission (TRMM), and it has caused the misidentification of precipitation. The statistical method to reduce sidelobe clutter was constructed by subtracting the estimated sidelobe power, based upon a multiple regression model with explanatory variables of the normalized radar cross section (NRCS) of surface, from the received power of the echo. The saturation of the NRCS at near-nadir angles, resulting from strong surface scattering, was considered in the calculation of the regression coefficients.The method was implemented in the KuPR algorithm and applied to KuPR-observed data. It was found that the received power from sidelobe clutter over the ocean was largely reduced by using the developed method, although some of the received power from the sidelobe clutter still remained. From the statistical results of the evaluations, it was shown that the number of KuPR precipitation events in the clutter region, after the method was applied, was comparable to that in the clutter-free region. This confirms the reasonable performance of the method in removing sidelobe clutter. For further improving the effectiveness of the method, it is necessary to improve the consideration of the NRCS saturation, which will be explored in future work.

Atm/Ocean Structure/ Phenomena; Precipitation; Obs↗
NASA NTRS2000

Application of Statistical Methods of Rain Rate Estimation to Data From The TRMM Precipitation Radar

The TRMM Precipitation Radar is well suited to statistical methods in that the measurements over any given region are sparsely sampled in time. Moreover, the instantaneous rain rate estimates are often of limited accuracy at high rain rates because of attenuation effects and at light rain rates because of receiver sensitivity. For the estimation of the time-averaged rain characteristics over an area both errors are relevant. By enlarging the space-time region over which the data are collected, the sampling error can be reduced. However. the bias and distortion of the estimated rain distribution generally will remain if estimates at the high and low rain rates are not corrected. In this paper we use the TRMM PR data to investigate the behavior of 2 statistical methods the purpose of which is to estimate the rain rate over large space-time domains. Examination of large-scale rain characteristics provides a useful starting point. The high correlation between the mean and standard deviation of rain rate implies that the conditional distribution of this quantity can be approximated by a one-parameter distribution. This property is used to explore the behavior of the area-time-integral (ATI) methods where fractional area above a threshold is related to the mean rain rate. In the usual application of the ATI method a correlation is established between these quantities. However, if a particular form of the rain rate distribution is assumed and if the ratio of the mean to standard deviation is known, then not only the mean but the full distribution can be extracted from a measurement of fractional area above a threshold. The second method is an extension of this idea where the distribution is estimated from data over a range of rain rates chosen in an intermediate range where the effects of attenuation and poor sensitivity can be neglected. The advantage of estimating the distribution itself rather than the mean value is that it yields the fraction of rain contributed by the light and heavy rain rates. This is useful in estimating the fraction of rainfall contributed by the rain rates that go undetected by the radar. The results at high rain rates provide a cross-check on the usual attenuation correction methods that are applied at the highest resolution of the instrument.

Meneghini, R.↗
NASA NTRS2004

Application of a Semiparametric Statistical Method to Spacecraft Sensor and Calibration Data

A semiparametric statistical method is applied to spacecraft sensor data and spacecraft sensor residual data. The method stipulates a reference distribution and deviations from it. The problem is to estimate the reference distribution and the distorted distributions from all available data from all the sources under consideration. The problem can be solved by assuming a distortion form and independent data.

Kedem, Benjamin↗
NASA NTRSDate not supplied

Launch Vehicle Design and Requirements Verification Using Statistical Methods

- This presentation applies statistics to launch vehicle design, but the methods may be used for other engineering applications - Vehicle Models: What do we know when? How to separate parameters for making Monte Carlo runs - Known when assembling the vehicle but not during design - Known prior to committing to flight - Unknown at lift off - How do we correctly model the vehicle during the design phases? - Number of Monte Carlo samples - Requirements success - Design parameter values

Monte Carlo↗
NASA NTRS2015

North American Extreme Temperature Events and Related Large Scale Meteorological Patterns: A Review of Statistical Methods, Dynamics, Modeling, and Trends

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and landatmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

Cold Spells↗
NASA NTRS1991

Knowledge acquisition for expert systems using statistical methods

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. A statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors is presented. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data is described. The development method invokes two statistical techniques: Analysis-of-Variance (ANOVA) and the ID3 algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set.

Belkin, Brenda L.↗
NASA NTRS1987

Robust statistical methods for automated outlier detection

The computational challenge of automating outlier, or blunder point, detection in radio metric data requires the use of nonstandard statistical methods because the outliers have a deleterious effect on standard least squares methods. The particular nonstandard methods most applicable to the task are the robust statistical techniques that have undergone intense development since the 1960s. These new methods are by design more resistant to the effects of outliers than standard methods. Because the topic may be unfamiliar, a brief introduction to the philosophy and methods of robust statistics is presented. Then the application of these methods to the automated outlier detection problem is detailed for some specific examples encountered in practice.

Jee, J. R.↗
NASA NTRS1986

Statistical methods for astronomical data with upper limits. II - Correlation and regression

Statistical methods for calculating correlations and regressions in bivariate censored data where the dependent variable can have upper or lower limits are presented. Cox's regression and the generalization of Kendall's rank correlation coefficient provide significant levels of correlations, and the EM algorithm, under the assumption of normally distributed errors, and its nonparametric analog using the Kaplan-Meier estimator, give estimates for the slope of a regression line. Monte Carlo simulations demonstrate that survival analysis is reliable in determining correlations between luminosities at different bands. Survival analysis is applied to CO emission in infrared galaxies, X-ray emission in radio galaxies, H-alpha emission in cooling cluster cores, and radio emission in Seyfert galaxies.

Isobe, T.↗
NASA NTRS1985

Composite statistical method for modeling wind gusts for aircraft simulation

This paper discusses the application of three statistical methods in combination to model wind gusts for use in aircraft flight simulation. The approach combines principal components analysis, time series analysis and probability distribution model to analyze and simulate wind gust components. Comparisons are given between wind gust components generated by the model and components measured onboard an aircraft.

Schiess, J. R.↗
NASA NTRS1996

System Synthesis in Preliminary Aircraft Design using Statistical Methods

This paper documents an approach to conceptual and preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically design of experiments (DOE) and response surface methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an overall evaluation criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting a solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a high speed civil transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabalistic designs (and eventually robust ones).

DeLaurentis, Daniel↗