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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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NASA NTRS2011

Using Ensemble Decisions and Active Selection to Improve Low-Cost Labeling for Multi-View Data

This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines high-confidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly reduces training set reliability, causing an associated drop in prediction accuracy. We propose the use of ensemble labeling to improve reliability in such cases. We also discuss and show promising results on combining low-cost ensemble labeling with active (low-confidence) example selection. We unify these example selection and labeling strategies under collaborative learning, a family of techniques for multi-view learning that we are developing for distributed, sensor-network environments.

machine learning↗

NASA NTRS2001

A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

Shen, Samuel S. P.↗

NASA NTRS2013

Modeling Weather Impact on Airport Arrival Miles-in-Trail Restrictions

When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-intrail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011. Then, machine-learning methods for predicting (1) situations in which MIT restrictions for ATL arrivals are implemented under low demand scenarios, and (2) days in which a large number of MIT restrictions are required to properly manage and control ATL arrivals are presented. More specifically, these predictions were accomplished by using an ensemble of decision trees with Bootstrap aggregation (BDT) and supervised machine learning was used to train the BDT binary classification models. The models were subsequently validated using data cross validation methods. When predicting the occurrence of arrival MIT restrictions under low demand situations, the model was able to achieve over all accuracy rates ranging from 84% to 90%, with false alarm ratios ranging from 10% to 15%. In the second set of studies designed to predict days on which a high number of MIT restrictions were required, overall accuracy rates of 80% were achieved with false alarm ratios of 20%. Overall, the predictions proposed by the model give better MIT usage information than what has been currently provided under current day operations. Traffic flow managers can use these predictions to identify potential MIT restrictions to eliminate (e.g., those occurring during low arrival demand periods), and to determine the days in which a significant number of restrictions may be required

Operation↗

NASA NTRS1985

Spatial variation of cosmic rays near the heliospheric current sheet

A quantitative comparison between theoretical predictions and observations of the intensity of galactic cosmic rays near the interplanetary current sheet is reported. Comparison of model calculations is made with a statistical analysis of observations of galactic cosmic rays at Earth and the simultaneous position of the current sheet. An ensemble of different current sheet inclinations is used, in order to make the analysis of the computations approximate the method used to analyses the data.

Jokipii, J. R.↗

NASA NTRS1984

Computer simulation of surface and film processes

All the investigations which were performed employed in one way or another a computer simulation technique based on atomistic level considerations. In general, three types of simulation methods were used for modeling systems with discrete particles that interact via well defined potential functions: molecular dynamics (a general method for solving the classical equations of motion of a model system); Monte Carlo (the use of Markov chain ensemble averaging technique to model equilibrium properties of a system); and molecular statics (provides properties of a system at T = 0 K). The effects of three-body forces on the vibrational frequencies of triatomic cluster were investigated. The multilayer relaxation phenomena for low index planes of an fcc crystal was analyzed also as a function of the three-body interactions. Various surface properties for Si and SiC system were calculated. Results obtained from static simulation calculations for slip formation were presented. The more elaborate molecular dynamics calculations on the propagation of cracks in two-dimensional systems were outlined.

Tiller, W. A.↗

NASA NTRS2026

Towards an improved understanding of the Antarctic coastal zone and its contribution to future global sea level

Understanding the coastal zone of the Antarctic Ice Sheet, where it interacts with the Southern Ocean and warmer air masses, is crucial for predicting Antarctica's influence on the global climate. This region has multiple tipping mechanisms that could trigger large, rapid, and potentially irreversible changes in the coming centuries. The Antarctic Ice Sheet remains the largest source of uncertainty in future sea-level projections. Insufficient knowledge of bed topography beneath the ice shelves and the coastal ice sheet is not yet well documented, but is a major source of this uncertainty. This review assesses current knowledge of the coastal zone and highlights methods to investigate it, including aerogeophysical surveys, ground- and ship-based measurements, satellite observations, and computer modeling. An ensemble analysis of published bed topography datasets identifies significant data gaps and their regional distribution, framed in the context of current ice-sheet behavior and potential instability. We propose scientific priorities and guidelines for future aerogeophysical surveys, advocating for a comprehensive, coordinated international effort to build a next-generation dataset of Antarctic bed properties. Such an initiative would significantly advance understanding of the role of coastal processes in ice-sheet dynamics, reducing uncertainties in sea-level rise projections and enhancing predictions of future ocean and climate changes.

Kenichi Matsuoka↗

NASA NTRS2026

Towards an Improved Understanding of the Antarctic Coastal Zone and Its Contribution to Future Global Sea Level

Understanding the coastal zone of the Antarctic Ice Sheet (AIS), where it interacts with the Southern Ocean and warmer air masses, is crucial for predicting Antarctica's influence on the global climate and sea level. This region has multiple tipping mechanisms that could trigger large, rapid, and potentially irreversible changes in the AIS, the Southern Ocean and their global connections in the coming centuries. The AIS remains the largest source of uncertainty in future sea-level projections. Bed topography beneath the ice shelves and the coastal ice sheet is not yet well documented, and is a major source of this uncertainty. This review assesses current knowledge of the coastal zone and highlights methods to investigate it, including aerogeophysical surveys, ground- and ship-based measurements, satellite observations, and computer modeling. An ensemble analysis of published bed topography data sets identifies significant data gaps and their regional distribution, framed in the context of current ice-sheet behavior and potential instability. We propose scientific priorities and guidelines for future aerogeophysical surveys, advocating for a comprehensive, coordinated international effort to build a next-generation data set of Antarctic bed properties. Such an initiative would significantly advance understanding of the role of coastal processes in ice-sheet dynamics, reducing uncertainties in sea-level rise projections and improving predictions of future ocean and climate changes.

Kenichi Matsuoka↗

NASA NTRS2001

A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

Kim, Kyu-Myong↗

NASA NTRS1975

Satellite disintegration dynamics

The subject of satellite disintegration is examined in detail. Elements of the orbits of individual fragments, determined by DOD space surveillance systems, are used to accurately predict the time and place of fragmentation. Dual time independent and time dependent analyses are performed for simulated and real breakups. Methods of statistical mechanics are used to study the evolution of the fragment clouds. The fragments are treated as an ensemble of non-interacting particles. A solution of Liouville's equation is obtained which enables the spatial density to be calculated as a function of position, time and initial velocity distribution.

Dasenbrock, R. R.↗

NASA NTRS1982

Static calibration for measurement of loads in wind tunnel tests on Shuttle surface tiles

A description is given of the procedures used for the static calibrations of the Coe (1981) sensor array to determine normal force, pitching moment, and rolling moment from the ensemble of sensor outputs. The use of independent data to confirm the validity of the calibration is also described. Two methods are used in calibrating the Coe sensors. In the first, calibration loads and readings are arranged in matrix form and are assumed to be related in a linear fashion. In the second, each Coe sensor reading is regarded as a sum of contributions from each of the calibration loads. It is shown that the Coe load sensors, in conjunction with either of two data analysis methods, can be used to determine the forces and moments experienced by a Shuttle TPS tile.

Rummler, D. R.↗

NASA NTRS2003

Bayes Error Rate Estimation Using Classifier Ensembles

The Bayes error rate gives a statistical lower bound on the error achievable for a given classification problem and the associated choice of features. By reliably estimating th is rate, one can assess the usefulness of the feature set that is being used for classification. Moreover, by comparing the accuracy achieved by a given classifier with the Bayes rate, one can quantify how effective that classifier is. Classical approaches for estimating or finding bounds for the Bayes error, in general, yield rather weak results for small sample sizes; unless the problem has some simple characteristics, such as Gaussian class-conditional likelihoods. This article shows how the outputs of a classifier ensemble can be used to provide reliable and easily obtainable estimates of the Bayes error with negligible extra computation. Three methods of varying sophistication are described. First, we present a framework that estimates the Bayes error when multiple classifiers, each providing an estimate of the a posteriori class probabilities, a recombined through averaging. Second, we bolster this approach by adding an information theoretic measure of output correlation to the estimate. Finally, we discuss a more general method that just looks at the class labels indicated by ensem ble members and provides error estimates based on the disagreements among classifiers. The methods are illustrated for artificial data, a difficult four-class problem involving underwater acoustic data, and two problems from the Problem benchmarks. For data sets with known Bayes error, the combiner-based methods introduced in this article outperform existing methods. The estimates obtained by the proposed methods also seem quite reliable for the real-life data sets for which the true Bayes rates are unknown.

Tumer, Kagan↗

NASA NTRS1994

11 micrometer emissivities and droplet radii for marine stratocumulus

The results of a new multispectral infrared retrieval scheme for obtaining fractional cloud cover and 11 micrometer emissivity with those of the spatial coherence method which obtains fractional cloud cover assuming that the clouds are opaque at infrared wavelengths. Both methods are applied to 4-km NOAA advanced very high resolution radiometer global area coverage data for 250-km-scale regions containing single-layered marine stratocumulus off the coast of South America. The average 11 micrometer emissivity for low level clouds is found to be between 0.70 and 0.85. The low emissivity is evidently due to the thinning of clouds at their edges. Semitransparent cloud edges evidently make up a substantial portion of the area covered by such clouds. This result indicates that cloud cover obtained using the spatial coherence method is underestimated by 0.1 to 0.2, as has been claimed in a previous study. The fractional cloud cover for the ensemble of 250-km-scale regions studied here increased slightly from 0.60 for daytime observations to 0.63 for nighttime observations. The 11 micrometer emissivity also increased slightly, but about half of the increase was related to the increase in cloud cover and a decrease in the relative area covered by cloud edge material. Presumably, the other half was due to an increase in cloud liquid water. Cloud height showed no significant change. The average effective droplet radius increased from 9.3 micrometers for daytime observations to 10.2 micrometers at night.

Luo, Gang↗

NASA NTRS2000

Comparison of Fatigue Life Estimation Using Equivalent Linearization and Time Domain Simulation Methods

The Monte Carlo simulation method in conjunction with the finite element large deflection modal formulation are used to estimate fatigue life of aircraft panels subjected to stationary Gaussian band-limited white-noise excitations. Ten loading cases varying from 106 dB to 160 dB OASPL with bandwidth 1024 Hz are considered. For each load case, response statistics are obtained from an ensemble of 10 response time histories. The finite element nonlinear modal procedure yields time histories, probability density functions (PDF), power spectral densities and higher statistical moments of the maximum deflection and stress/strain. The method of moments of PSD with Dirlik's approach is employed to estimate the panel fatigue life.

Mei, Chuh↗

NASA NTRS1975

Pioneer 9 and OGO 5 observations of an interplanetary multiple shock ensemble on February 2, 1969

A multiple shock system was observed upstream (0.13 AU) of the earth by Pioneer 9 on February 2, 1969. The same system was observed at earth by Ogo 5 and was reported separately in the literature. This paper compares the two sets of observations in still further detail. Both magnetic-field and plasma data are used in a least-squares best-fit method to compute the characteristics of the fast forward shock wave (Pioneer 9 only) and two fast reverse shock waves. Nearly all major features (shock, piston, and tangential discontinuity) retained their characteristics during the transit of the shock ensemble from Pioneer 9 to Ogo 5. The genesis of the ensemble is believed to be due to a complex stream-stream interaction. A substantial density increase (including a large rise of alpha/proton abundance) at Ogo 5, but unobserved at Pioneer 9, is explained by a sudden meridional shift to a flow from below the ecliptic plane while the streams were en route to earth. This study demonstrates a spatial and temporal plasma inhomogeneity which is superimposed on the persistent major features.

Dryer, M.↗

NASA NTRS2001

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

Shen, Samuel S. P.↗

NASA NTRS1992

Dynamically stratified Monte Carlo forecasting

A new method for performing Monte Carlo forecasts is introduced. The method, called dynamic stratification, selects initial perturbations based on a stratification of the error distribution. A simple implementation is presented in which the error distribution used for the stratification is estimated from a linear model derived from a large ensemble of 12-h forecasts with the full dynamic model. The stratification thus obtained is used to choose a small subsample of initial states with which to perform the dynamical Monte Carlo forecasts. Several test cases are studied using a simple two-level general circulation model with uncertain initial conditions. It is found that the method provides substantial reductions in the sampling error of the forecast mean and variance when compared to the more traditional approach of choosing the initial perturbations at random. The degree of improvement, however, is sensitive to the nature of the initial error distribution and to the base state. In practice the method may be viable only if the computational burden involved in obtaining an adequate estimate of the error distribution is shared with the data-assimilation procedure.

Schubert, Siegfried↗

NASA NTRS1994

Groups of galaxies in the ROSAT north ecliptic pole survey

The X-ray properties of groups of galaxies are presented. Their distribution of luminosity and temperature appears to be associated with the extrapolation of these distributions from rich clusters of galaxies. The properties of the ensemble of groups of galaxies are almost totally unknown. Only a few X-ray observations of groups that were selected by optical methods were published so far. A sample of eight groups with 'z' inferior to 0.04, of which three have 'z' inferior to 0.03 was investigated. The temperature and the luminosity functions at one point were determined.

Henry, J. Patrick↗

NASA NTRS2001

Retrieval of Polar Stratospheric Cloud Microphysical Properties From Lidar Measurements: Dependence on Particle Shape Assumptions

Knowledge of particle sizes and number densities of polar stratospheric clouds (PSCs) is highly important, because they are critical parameters for the modeling of the ozone chemistry of the stratosphere. In situ measurements of PSC particles are rare. the main instrument for the accumulation of PSC data are lidar systems. Therefore the derivation of some microphysical properties of PSCS from the optical parameters measured by lidars would be highly beneficial for ozone research. Inversion of lidar data obtained in the presence of PSCs formed from crystalline particles type 11 and the various nitric acid tri Ydrrate (NAT) types cannot be easily accomplished, because a suitable scattering theory for small faceted crystals has not been readily available tip to now. As a consequence, the T-matrix method is commonly used for the interpretation of these PSC lidar data. Here the assumption is made that the optical properties of an ensemble of spheroids resemble those of crystalline PSCs, and microphysical properties of the PSC are inferred from the optical signatures of the PSC at two or more wavelengths. The problem with the T-matrix approach is that the assumption of spheroidal instead of faceted particles can lead to dramatically wrong results: Usually cloud particle properties are deduced from analysis of lidar profiles of backscatter ratio and depolarization ratio. The particle contribution to the backscatter ratio is given by the product of the particle number density and the backscattering cross section. The latter is proportional to the value of the particle's scattering phase function at 180 degrees scattering angle. At 180 degrees however, the phase functions of rough, faceted crystals and of spheroids with same maximum dimension differ by a factor of 6. From this it follows that for a PSC consisting of faceted crystals, the particle number density is underestimated by roughly the same factor if spheroidal particles are unrealistically assumed. We are currently developing a retrieval technique for determining the microphysical parameters of crystalline PSCs that takes into account the faceted shape of the PSC particles. This approach utilizes finite-difference time-domain (FDTD) calculations of particle optical properties. The accuracy and the free choice of the shape of the scattering particle make the FDTD technique a promising tool for the inversion of PSC lidar data. A first comparison of FDTD and T-matrix calculations will be presented.

Reichardt, Susanne↗