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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 NTRS2022

Quantifying Errors in 3D CME Parameters Derived From Synthetic Data Using White-Light Reconstruction Techniques

Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make these predictions, methods have been developed to derive the true CME speed, size, position, and mass, among others. Difficulties in determining the input parameters for CME forecasting models arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters after eruption. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques that assume more or less complex geometries. This is the first study from our International Space Science Institute (ISSI) team “Understanding Our Capabilities in Observing and Modeling Coronal Mass Ejections”, in which we explore how subjectivity affects the 3D CME parameters that are obtained from the Graduated Cylindrical Shell (GCS) reconstruction technique, which is widely used in CME research. To be able to quantify such uncertainties, the “true” values that are being fitted should be known, which are impossible to derive from observational data. We have designed two different synthetic scenarios where the “true” geometric parameters are known in order to quantify such uncertainties for the first time. We explore this by using two sets of synthetic data: 1) Using the ray-tracing option from the GCS model software itself, and 2) Using 3D magnetohydrodynamic (MHD) simulation data from the Magnetohydrodynamic Algorithm outside a Sphere code. Our experiment includes different viewing configurations using single and multiple viewpoints. CME reconstructions using a single viewpoint had the largest errors and error ranges overall for both synthetic GCS and simulated MHD white-light data. As the number of viewpoints increased from one to two, the errors decreased by approximately 4° in latitude, 22° in longitude, 14° in tilt, and 10° in half-angle. Our results quantitatively show the critical need for at least two viewpoints to be able to reduce the uncertainty in deriving CME parameters. We did not find a significant decrease in errors when going from two to three viewpoints for our specific hypothetical three spacecraft scenario using synthetic GCS white-light data. As we expected, considering all configurations and numbers of viewpoints, the mean absolute errors in the measured CME parameters are generally significantly higher in the case of the simulated MHD white-light data compared to those from the synthetic white-light images generated by the GCS model. We found the following CME parameter error bars as a starting point for quantifying the minimum error in CME parameters from white-light reconstructions: Δθ (latitude)=6° +2° -3° , Δϕ (longitude)=11° +18° -6° , Δγ (tilt)=25° +8° -7° , Δx (half-angle)=10° +12° -6° , Δh (height)=0.6 +1.2 -0.4 R ⨀ , and Δκ (ratio)=0.1 +0.03 -0.02 .

Coronal mass ejections↗
NASA NTRS2002

Estimating the Single-Trial Characteristics of Event-Related Responses: Evaluation of the MCERP Algorithm

Single-trial event-related responses collected during the course of an experiment are typically averaged before analysis resulting in a rather crude picture of event-related brain dynamics. It has been quite clear for some time that these responses exhibit trial-to-trial variability: however, the computational techniques necessary to deal with such responses in noisy conditions have not been available. To this end we have developed the multiple-component, event-related potential model (mcERP), which assumes that the each event-related response consists of a sum of multiple evoked components each described by a stereotypical waveshape. These waveshapes are allowed to vary in amplitude and onset latency from trial to trial, which allows us to capture, to first-order, the trial-dependent variations in event-related brain dynamics. We have constructed many sets of synthetic data designed to simulate intracortical recordings from a 15 channel, linear-array multielectrode implanted acutely in V1 of an awake-behaving macaque undergoing visual stimulation with a red light flash. This synthetic data was used to characterize the performance of the mcERP algorithm. First we quantified the degree to which such trial-to-trial variability aids in the identification of multiple components, and we demonstrate that amplitude variability is a more important factor in component separation than latency variability. Second, we quantified the behavior of the algorithm under two distinct signal-to-noise ratio (SNR) conditions: Gaussian noise independently present in each channel, and highly correlated (1/f distributed), far-field noise presented identically in each channel of the array. The mcERP algorithm was found to be robust to noise accurately identifying all component waveshapes and their associated single-trial characteristics down to SNR levels of -20dB for Gaussian noise and -7dB for 1/f far-field noise. Comparisons of the performance of this algorithm with factor analysis (FA) and independent component analysis (ICA) will be described by Knuth et al. (SFN abstracts, 2002). In addition, the advantages of application of mcERP to real data will be described by Shah et al, (these abstracts, 2002: SFN abstracts, 2002).

Knuth, K. H.↗
NASA NTRS2019

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Machine learning (ML) is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using ML algorithms for atmospheric retrieval by implementing a random forest (RF) to perform retrievals in seconds that are consistent with the traditional, computationally expensive nested-sampling retrieval method. We expand upon their approach by presenting a new ML model, plan-net, based on an ensemble of Bayesian neural networks (BNNs) that yields more accurate inferences than the RF for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use BNNs for atmospheric retrieval, we also introduce a new loss function for BNNs that learns correlations between the model outputs. Importantly, we show that designing ML models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply plan-net to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher resolution spectra and a larger number of atmospheric parameters.

Adam D. Cobb↗
NASA NTRS1989

Methods for local gravity field approximation

The most widely known modern method for estimating gravity field values from observed data is least-squares collocation. Its advantages are that it can make estimates at arbitrary locations based on irregularly spaced observations, and that it makes use of statistical information about errors in the input data while providing corresponding information about the quality of the output estimates. Disadvantages of collocation include the necessity of inverting square matrices of dimension equal to the number of data values and the need to assume covariance models for the gravity field and the data errors. Fourier methods are an important alternative to collocation; having the advantage of greater computational efficiency, but requiring data estimates to be on a regular grid and not using or providing statistical accuracy information. The GEOFAST algorithm is an implementation of collocation that achieves high computational efficiency by transforming the estimation equations into the frequency domain where an accurate approximation may be made to reduce the workload. The forward and inverse Fast Fourier Transforms (FFTs) are utilized. The accuracy and computational efficiency of the GEOFAST algorithm is demonstrated using two sets of synthetic gravity data: marine gravity for an ocean trench region including wavelengths longer than 200 km; and local land gravity containing wavelengths as short as 5 km. These results are discussed along with issues such as the advantages of first removing reference field models before carrying out the estimation algorithm.

Sailor, R. V.↗
NASA NTRS1984

The spatial evolution of t he directional wave spectrum in the Southern Ocean: Its relation to extreme waves in Agulhas Current

An experiment using the Shuttle Imaging Radar-B (SIR-B) to monitor certain properties of the ocean wave directional spectrum and to track the long swell systems as they propagate northward to encounter the Agulhas near the southeastern coast of Africa is discussed. The experiment is designed around the unique capability of SIR-B to overcome key limitations of the Seasat synthetic aperture radar data set, and to extend the existing Seasat results into new areas. Ocean wave systems will be tracked. The variable-incidence-angle capability to examine wave imaging quality will be utilized. Doppler current measurements will be attempted. An effort will be made to verify that the lower range-to-velocity ratio of SIR-B will lead to the improved response of azimuth-traveling wave systems.

Beal, R. C.↗
NASA NTRS1982

Report on the subpanel on remapping procedures

Problems associated with remapping procedures are defined and research tasks are proposed. It is noted that the remapping/rectification process could be significantly aided through engineering systems improvements in attitude control, with subsequent improvement in spacecraft ephemeris modeling accuracy. There is a need for state-of-the-art technology assessments prior to initiation of major programs, and the high potential return from well formulated testing of algorithms on selected data sets of actual and synthetic imagery. In addition, there is a need for tasks that incorporate standard photogrammetric methodology and formulas and that more fully utilize platform and calibration data from current and proposed sensors to reproject digital imagery. The impact of improved platform stability and integration of global positioning system measurements on reduced ground segment processing needs to be critically assessed. Also highlighted is the need for a substantial effort in the development of remapping software and systems that are modular and transportable.

Source record↗
NASA NTRS2003

Fine Resolution Topographic Mapping of the Jovian Moons: A Ka-Band High Resolution Topographic Mapping Interferometric Synthetic Aperture Radar

The topographic data set obtained by MOLA has provided an unprecedented level of information about Mars' geologic features. The proposed flight of JIMO provides an opportunity to accomplish a similar mapping of and comparable scientific discovery for the Jovian moons through use of an interferometric imaging radar analogous to the Shuttle radar that recently generated a new topographic map of Earth. A Ka-band single pass across-track synthetic aperture radar (SAR) interferometer can provide very high resolution surface elevation maps. The concept would use two antennas mounted at the ends of a deployable boom (similar to the Shuttle Radar Topographic Mapper) extended orthogonal to the direction of flight. Assuming an orbit altitude of approximately 100km and a ground velocity of approximately 1.5 km/sec, horizontal resolutions at the 10 meter level and vertical resolutions at the sub-meter level are possible.

Madsen, S. N.↗
NASA NTRS2003

Fine resolution topographic mapping of the Jovian moons: a Ka-band high resolution topographic mapping interferometric synthetic aperture radar

The topographic data set obtained by MOLA has provided an unprecedented level of information about Mars' geologic features. The proposed flight of JIMO provides an opportunity to accomplish a similar mapping of and comparable scientific discovery for the Jovian moons through us of an interferometric imaging radar analogous to the Shuttle radar that recently generated a new topographic map of Earth. A Ka-band single pass across-track synthetic aperture radar (SAR) interferometer can provide very high resolution surface elevation maps. The concept would use two antennas mounted at the ends of a deployable boom (similar to the Shuttle Radar Topographic Mapper) extended orthogonal to the direction of flight. Assuming an orbit altitude of approximately 100 km and a ground velocity of approximately 1.5 km/sec, horizontal resolutions at the 10 meter level and vertical resolutions at the sub-meter level are possible.

Jupiter Icy Moon Orbiter↗
NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental tool in numerous image processing and remote sensing applications. For example, unsupervised clustering is often used to obtain vegetation maps of an area of interest. This approach is useful when reliable training data are either scarce or expensive, and when relatively little a priori information about the data is available. Unsupervised clustering methods play a significant role in the pursuit of unsupervised classification. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points (or samples) in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute a set of cluster centers in d-space. Although there is no specific optimization criterion, the algorithm is similar in spirit to the well known k-means clustering method in which the objective is to minimize the average squared distance of each point to its nearest center, called the average distortion. One significant feature of ISOCLUS over k-means is that clusters may be merged or split, and so the final number of clusters may be different from the number k supplied as part of the input. This algorithm will be described in later in this paper. The ISOCLUS algorithm can run very slowly, particularly on large data sets. Given its wide use in remote sensing, its efficient computation is an important goal. We have developed a fast implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm, the filtering algorithm, by Kanungo et al.. They showed that, by storing the data in a kd-tree, it was possible to significantly reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm. For technical reasons, which are explained later, it is necessary to make a minor modification to the ISOCLUS specification. We provide empirical evidence, on both synthetic and Landsat image data sets, that our algorithm's performance is essentially the same as that of ISOCLUS, but with significantly lower running times. We show that our algorithm runs from 3 to 30 times faster than a straightforward implementation of ISOCLUS. Our adaptation of the filtering algorithm involves the efficient computation of a number of cluster statistics that are needed for ISOCLUS, but not for k-means.

Memarsadeghi, Nargess↗
NASA NTRS1982

The SEASAT Synthetic Aperture Radar - Engineering performance evaluation

Results are presented for a study conducted in order to characterize the quality and consistency of the SEASAT Synthetic Aperture Radar image data set. The study evaluated a large spectrum of parameters, including the resolution, position location accuracy, and the amplitude calibration of the imagery produced from the raw data by processors at several institutions. It is demonstrated that this data set can be used to obtain resolution up to the theoretical limit of 6 m x 21 m, absolute position location to within 200 m, and relative calibration of the image data to within + or - 2 dB.

Held, D. N.↗
NASA NTRS1991

Automatic analysis of stereoscopic satellite image pairs for determination of cloud-top height and structure

Results are presented on an automatic stereo analysis of cloud-top heights from nearly simultaneous satellite image pairs from the GOES and NOAA satellites, using a massively parallel processor computer. Comparisons of computer-derived height fields and manually analyzed fields show that the automatic analysis technique shows promise for performing routine stereo analysis in a real-time environment, providing a useful forecasting tool by augmenting observational data sets of severe thunderstorms and hurricanes. Simulations using synthetic stereo data show that it is possible to automatically resolve small-scale features such as 4000-m-diam clouds to about 1500 m in the vertical.

Hasler, A. F.↗
NASA NTRSDate not supplied

Comparison of Boeing 777 Landing Gear Noise Simulations with Flight Test Data

Acoustic phased microphone array measurements of aircraft flyover noise acquired during the 2005 Quiet Technology Demonstrator II test were used to assess the accuracy of high-fidelity, full-scale simulations of landing gear noise produced by a large civilian aircraft. The simulations, conducted with the lattice Boltzmann solver PowerFLOW®, used a highly accurate digital model of a Boeing 777-300ER aircraft with the nose and main landing gear components replicating the full-scale geometries. The simulations were performed for aircraft parameters that matched those recorded during the flyover test conditions. For benchmarking purposes, several aircraft configurations were simulated: a) nose landing gear deployed with main landing gear and wing high-lift devices stowed, b) nose and main landing gear deployed with wing high-lift devices stowed and c) nose and main landing gear with wing high-lift devices deployed. To facilitate direct comparison with measured data, the simulated data sets were used to generate synthetic pressure records at the same array microphone locations as those used during the flight test. Broadly self-consistent beamforming techniques and procedures were used to process the synthetic pressure records and the measured data. Integration of select regions of the beamform maps containing the nose or main landing gear yielded good agreement between predicted and measured integrated far-field spectra for forward directivity angles where airframe noise is more prominent.

airframe noise↗
NASA NTRS1987

Studies of multi-baseline spaceborne interferometric synthetic aperture radars

A set of Seasat SAR data that were obtained in nearly repeat ground track orbits is utilized to simulate the performance of spaceborne interferometric synthetic aperture radar (ISAR) systems. A qualitative assessment of the topography measurement capability is presented. A phase measurement error model is described and compared with the data obtained at various baseline separations and signal-to-noise ratios. Finally, the implications of these results on the future spaceborne ISAR design are discussed.

Li, F.↗
NASA NTRS1988

Color graphics workstations used for simultaneous display of satellite and model data sets

Model-measurement display methods for studying actual or synthetic satellite measurements of cloudiness are developed. Visualization and animation techniques are employed to analyze the data. Two types of animation sequences, one based on Insat IR geosynchronous satellite imagery combined with upper level velocity potentials calculations and isoline depictions of the high amplitude centers of low frequency cloudiness activity, and the other combines a mesoscale cloud model simulation of a severe hail storm and synthetic microwave radiation signatures, are described.

Cooper, Harry J.↗
NASA NTRS1993

Geographic information system for fusion and analysis of high-resolution remote sensing and ground data

We seek to combine high-resolution remotely sensed data with models and ground truth measurements, in the context of a Geographical Information System (GIS), integrated with specialized image processing software. We will use this integrated system to analyze the data from two Case Studies, one at a boreal forest site, the other a tropical forest site. We will assess the information content of the different components of the data, determine the optimum data combinations to study biogeophysical changes in the forest, assess the best way to visualize the results, and validate the models for the forest response to different radar wavelengths/polarizations. During the 1990's, unprecedented amounts of high-resolution images from space of the Earth's surface will become available to the applications scientist from the LANDSAT/TM series, European and Japanese ERS-1 satellites, RADARSAT and SIR-C missions. When the Earth Observation Systems (EOS) program is operational, the amount of data available for a particular site can only increase. The interdisciplinary scientist, seeking to use data from various sensors to study his site of interest, may be faced with massive difficulties in manipulating such large data sets, assessing their information content, determining the optimum combinations of data to study a particular parameter, visualizing his results and validating his model of the surface. The techniques to deal with these problems are also needed to support the analysis of data from NASA's current program of Multi-sensor Airborne Campaigns, which will also generate large volumes of data. In the Case Studies outlined in this proposal, we will have somewhat unique data sets. For the Bonanza Creek Experimental Forest (Case 1) calibrated DC-8 SAR (Synthetic Aperture Radar) data and extensive ground truth measurement are already at our disposal. The data set shows documented evidence to temporal change. The Belize Forest Experiment (Case 2) will produce calibrated DC-8 SAR and AVIRIS data, together with extensive measurements on the tropical rain forest itself. The extreme range of these sites, one an Arctic forest, the other a tropical rain forest, has been deliberately chosen to find common problems which can lead to generalized observations and unique problems with data which raise issues for the EOS System.

Freeman, Anthony↗
NASA NTRS1987

Data volume reduction for imaging radar polarimetry

Two data reduction algorithms developed using the scattering and phase matrix approaches are described. In the scattering matrix approach, the scattering matrices of four consecutive along-track pixels are averaged and in the phase matrix approach, the phase matrices of four consecutive along-track pixels are averaged. The basic procedures necessary to generate a synthetic polarization image from original data sets are discussed. The two algorithms are evaluated in terms of data volume reduction and the number of errors introduced in the synthesized images. It is observed that the reduced data set produced by the scattering matrix algorithm is smaller than that generated by the phase matrix algorithm; however, greater errors are introduced into the data set by the scattering matrix algorithm than the phase algorithm. Flowcharts for the scattering and phase matrix approaches and for synthesis of uncompressible data are presented.

Dubois, Pascale C.↗