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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 NTRS1975

Background estimation in a (gnu e)-diffusion detector

Background conditions for detecting (gamma e)-diffusion are discussed with reference to the utilization of a hodoscope system consisting of wide area flat Geiger counters connected for coincidence and of a controllable spark chamber. When calculating the background, any organic compounds synthesized from C, O2 and H2 with radioactive admixtures is presumed to be the preferrable material for the construction of the detector. The number of background events simulating the effect sought is estimated by analyzing the spectrum of the organic glass gamma radiation caused by disintegration and fission of long lived isotopes and by nuclear reactions with the participation of alpha particles and neutrons.

Glotov, V. I.↗
NASA NTRS1989

Nucleon interaction data bases for background estimates

Nucleon interaction data bases available in the open literature are examined for potential use in a recently developed nucleon transport code. Particular attention is given to secondary particle penetration and the multiple charged ion products. A brief description of the transport algorithm is given.

Wilson, John W.↗
NASA NTRS1993

Diagnostic and quality-assurance tools for low-contrast images obtained from array detectors

We investigate methods of estimating a background image frame for subtraction from a data frame for use when a more suitable measured background frame is not available. We define background as any signal component that is not attributable to the phenomenon currently under investigation. We describe a technique that is based on pixel-by-pixel least-squares regression of images for computing a background frame from available data. We argue that the same technique can be a useful quality-assurance tool for evaluating instrument performance. For example, it can help to separate image structure resulting from the reading process from structure resulting from the characteristics of the detector itself. We demonstrate that background estimation can be nontrivial by comparing the results of different background estimation procedures by using data obtained from a CCD array detector. We investigate the temperature-dependent contributions of the detector and readout electronics to the total signal as a demonstration of the diagnostic capabilities of least-squares image regression.

Hatfield, D. B.↗
NASA NTRS1991

The estimation of background production by cosmic rays in high-energy gamma ray telescopes

A calculational method of estimating instrumental background in high-energy gamma-ray telescopes, using the hadronic Monte Carlo code FLUKA87, is presented. The method is applied to the SAS-2 and EGRET telescope designs and is also used to explore the level of background to be expected for alternative configurations of the proposed GRITS telescope, which adapts the external fuel tank of a Space Shuttle as a gamma-ray telescope with a very large collecting area. The background produced in proton-beam tests of EGRET is much less than the predicted level. This discrepancy appears to be due to the FLUKA87 inability to transport evaporation nucleons. It is predicted that the background in EGRET will be no more than 4-10 percent of the extragalactic diffuse gamma radiation.

Edwards, H. L.↗
NASA NTRS1986

Formulation and implementation of a practical algorithm for non-stationary adaptive state estimation

Background information on the Kalman filter is given first. A discussion of the filter parameters and their a priori determination follows. The discussion points out the need for adaptive determination of the process noise statistics. The filter innovations are presented as a means for developing the adaptive criteria. The criteria center around the estimation of the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of a locally stationary random process is presented. The definition of a local stationarity is presented. Local stationarity allows for the separation of the process statistics into a stationary component and a time-varying component. The separation method is discussed. A method for estimating the stationary and time-varying components is presented. As an example of its application to real problems, the algorithm is applied to the problem to the problem of reentry trajectory estimation for the Space Shuttle. Both the adaptive algorithm and the steady-state Kalman filter are applied to the problem. The results of the reconstructions are presented. The adaptive algorithm exhibits superior performance.

Whitemore, S. A.↗
NASA NTRS2002

XTE Observations of Intermediate Polars: RXTE TOO Observations of Supernovae

Two intermediate polar cataclysmic variables, PQ Gem and AO Psc, were observed jointly with the Rossi X-ray Timing Explorer (RXTE) and ground-based photometry. The analysis of intermediate polars (IPs) is complex because these objects exhibit light curves that behave differently as a function of energy and that behave differently when phased on the orbital or on the spin periods. The presence of two periods in one system is essentially equivalent to analyzing two different X-ray sources. A preliminary analysis of the PQ Gem data was carried out and presented at the Annapolis Workshop on Magnetic Cataclysmic Variables. The final analysis of the data were held up by problems with the background estimation. The RXTE PCA team has released a new version of the background estimator. The PQ Gem must be reanalyzed using the new background. We have also installed a spectral model that calculates the expected emission from an accretion column. That model is undergoing final testing before we apply it to the data.

Schlegel, E.↗
NASA NTRS2010

The Revised Space Environment Models in CREME-MC: A Replacement for CREME96

The CREME96 model has been available on the WWW for more than 10 years now. While principally for the estimation of radiation effects on spacecraft electronics, it contains space radiation environment models that have been used for instrument design calculations, estimation of instrumental background, estimation of radiation hazards and many other purposes. Because of the evolution of electronic part design we have found it necessary to revise CREME96, creating CREME-MC. As part of this revision, we are revising and extending the environmental models in CREME96. This talk will describe the revised radiation environment models that are being made available in CREME-MC

Adams, James H., Jr.↗
NASA NTRS2002

XTE Observations of Intermediate Polars: RXTE TOO Observations of Supernovae

Two Intermediate Mars were observed with XTE (X-Ray Timing Explorer): PQ Gem and AO Psc. A very preliminary analysis for PQ Gem was presented at the Annapolis Workshop on Magnetic Cataclysmic Variables. At about the same time, the XTE team created a new background estimator for faint sources. The PQ Gem data must be reanalyzed using the new background estimator. The data for AO Psc have been analyzed and await the analysis of PQ Gem.

Schlegel, E.↗
NASA NTRS1989

High-energy radiation background in space

The radiation environment of near-earth space and its effects on biological and hardware systems are examined in reviews and reports. Sections are devoted to particle interactions and propagation, data bases, instrument background and dosimetry, detectors and experimental progress, biological effects, and future needs and strategies. Particular attention is given to angular distributions and spectra of geomagnetically trapped protons in LEO, bremsstrahlung production by electrons, nucleon-interaction data bases for background estimates, instrumental and atmospheric background lines observed by the SMM gamma-ray spectrometer, the GRAD high-altitude balloon flight over Antarctica, space protons and brain tumors, a new radioprotective antioxidative agent, LEO radiation measurements on the Space Station, and particle-background effects on the Hubble Space Telescope and the Lyman FUV Spectroscopic Explorer.

Rester, A. C., Jr.↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
NASA NTRS2010

Aerosol Observability and Predictability: From Research to Operations for Chemical Weather Forecasting. Lagrangian Displacement Ensembles for Aerosol Data Assimilation

A challenge common to many constituent data assimilation applications is the fact that one observes a much smaller fraction of the phase space that one wishes to estimate. For example, remotely sensed estimates of the column average concentrations are available, while one is faced with the problem of estimating 3D concentrations for initializing a prognostic model. This problem is exacerbated in the case of aerosols because the observable Aerosol Optical Depth (AOD) is not only a column integrated quantity, but it also sums over a large number of species (dust, sea-salt, carbonaceous and sulfate aerosols. An aerosol transport model when driven by high-resolution, state-of-the-art analysis of meteorological fields and realistic emissions can produce skillful forecasts even when no aerosol data is assimilated. The main task of aerosol data assimilation is to address the bias arising from inaccurate emissions, and Lagrangian misplacement of plumes induced by errors in the driving meteorological fields. As long as one decouples the meteorological and aerosol assimilation as we do here, the classic baroclinic growth of error is no longer the main order of business. We will describe an aerosol data assimilation scheme in which the analysis update step is conducted in observation space, using an adaptive maximum-likelihood scheme for estimating background errors in AOD space. This scheme includes e explicit sequential bias estimation as in Dee and da Silva. Unlikely existing aerosol data assimilation schemes we do not obtain analysis increments of the 3D concentrations by scaling the background profiles. Instead we explore the Lagrangian characteristics of the problem for generating local displacement ensembles. These high-resolution state-dependent ensembles are then used to parameterize the background errors and generate 3D aerosol increments. The algorithm has computational complexity running at a resolution of 1/4 degree, globally. We will present the result of assimilating AOD retrievals from MODIS (on both Aqua and TERRA satellites) from AERONET for validation. The impact on the GEOS-5 Aerosol Forecasting will be fully documented.

da Silva, Arlindo↗
NASA NTRS2014

Use of an OSSE to Evaluate Background Error Covariances Estimated by the 'NMC Method'

The NMC method has proven utility for prescribing approximate background-error covariances required by variational data assimilation systems. Here, untunedNMCmethod estimates are compared with explicitly determined error covariances produced within an OSSE context by exploiting availability of the true simulated states. Such a comparison provides insights into what kind of rescaling is required to render the NMC method estimates usable. It is shown that rescaling of variances and directional correlation lengths depends greatly on both pressure and latitude. In particular, some scaling coefficients appropriate in the Tropics are the reciprocal of those in the Extratropics. Also, the degree of dynamic balance is grossly overestimated by the NMC method. These results agree with previous examinations of the NMC method which used ensembles as an alternative for estimating background-error statistics.

Data Assimilation↗
NASA NTRS1977

Neutral particle background in cosmic ray telescopes composed of silicon solid state detectors

The energy loss-spectrum of secondary charged particles produced by the interaction of gamma-rays and energetic neutrons in silicon solid state detectors has been measured with a satellite-borne cosmic ray telescope. In the satellite measurements presented here two distinct neutral background effects are identified: secondary protons and alpha particles with energies of about 2 to 100 MeV produced by neutron interactions, and secondary electrons with energies of about 0.2 to 10 MeV produced by X-ray interactions. The implications of this neutral background for satellite measurements of low energy cosmic rays are discussed, and suggestions are given for applying these results to other detector systems in order to estimate background contamination and optimize detector system design.

Mewaldt, R. A.↗
NASA NTRS2015

Formaldehyde Production from Isoprene Oxidation Across NOx Regimes

The chemical link between isoprene and formaldehyde (HCHO) is a strong, non-linear function of NOx (= 27 NO + NO2). This relationship is a linchpin for top-down isoprene emission inventory verification from orbital HCHO column observations. It is also a benchmark for overall mechanism performance with regard to VOC oxidation. Using a comprehensive suite of airborne in situ observations over the Southeast U.S., we quantify HCHO production across the urban-rural spectrum. Analysis of isoprene and its major first-generation oxidation products allows us to define both a "prompt" yield of HCHO (molecules of HCHO produced per molecule of freshly-emitted isoprene) and the background HCHO mixing ratio (from oxidation of longer-lived hydrocarbons). Over the range of observed NOx values (roughly 0.1 - 2 ppbv), the prompt yield increases by a factor of 3 (from 0.3 to 0.9), while background HCHO increases by more than a factor of 2 (from 1.5 to 3.3 ppbv). We apply the same method to evaluate the performance of both a global chemical transport model (AM3) and a measurement-constrained 0-D chemical box model. Both models reproduce the NOx dependence of the prompt HCHO yield, illustrating that models with updated isoprene oxidation mechanisms can adequately capture the link between HCHO and recent isoprene emissions. On the other hand, both models under-estimate background HCHO mixing ratios, suggesting missing HCHO precursors, inadequate representation of later-generation isoprene degradation and/or under-estimated hydroxyl radical concentrations. Moreover, we find that the total organic peroxy radical production rate is essentially independent of NOx, as the increase in oxidizing capacity with NOx is largely balanced by a decrease in VOC reactivity. Thus, the observed NOx dependence of HCHO mainly reflects the changing fate of organic peroxy radicals.

VOC↗
NASA NTRS2008

Auditory alert systems with enhanced detectability

Methods and systems for distinguishing an auditory alert signal from a background of one or more non-alert signals. In a first embodiment, a prefix signal, associated with an existing alert signal, is provided that has a signal component in each of three or more selected frequency ranges, with each signal component in each of three or more selected level at least 3-10 dB above an estimated background (non-alert) level in that frequency range. The alert signal may be chirped within one or more frequency bands. In another embodiment, an alert signal moves, continuously or discontinuously, from one location to another over a short time interval, introducing a perceived spatial modulation or jitter. In another embodiment, a weighted sum of background signals adjacent to each ear is formed, and the weighted sum is delivered to each ear as a uniform background; a distinguishable alert signal is presented on top of this weighted sum signal at one ear, or distinguishable first and second alert signals are presented at two ears of a subject.

Begault, Durand R.↗
NASA NTRS2013

Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.

Data Assimilation↗
NASA NTRS2002

On the Choice of Variable for Atmospheric Moisture Analysis

The implications of using different control variables for the analysis of moisture observations in a global atmospheric data assimilation system are investigated. A moisture analysis based on either mixing ratio or specific humidity is prone to large extrapolation errors, due to the high variability in space and time of these parameters and to the difficulties in modeling their error covariances. Using the logarithm of specific humidity does not alleviate these problems, and has the further disadvantage that very dry background estimates cannot be effectively corrected by observations. Relative humidity is a better choice from a statistical point of view, because this field is spatially and temporally more coherent and error statistics are therefore easier to obtain. If, however, the analysis is designed to preserve relative humidity in the absence of moisture observations, then the analyzed specific humidity field depends entirely on analyzed temperature changes. If the model has a cool bias in the stratosphere this will lead to an unstable accumulation of excess moisture there. A pseudo-relative humidity can be defined by scaling the mixing ratio by the background saturation mixing ratio. A univariate pseudo-relative humidity analysis will preserve the specific humidity field in the absence of moisture observations. A pseudorelative humidity analysis is shown to be equivalent to a mixing ratio analysis with flow-dependent covariances. In the presence of multivariate (temperature-moisture) observations it produces analyzed relative humidity values that are nearly identical to those produced by a relative humidity analysis. Based on a time series analysis of radiosonde observed-minus-background differences it appears to be more justifiable to neglect specific humidity-temperature correlations (in a univariate pseudo-relative humidity analysis) than to neglect relative humidity-temperature correlations (in a univariate relative humidity analysis). A pseudo-relative humidity analysis is easily implemented in an existing moisture analysis system, by simply scaling observed-minus background moisture residuals prior to solving the analysis equation, and rescaling the analyzed increments afterward.

Dee, Dick P.↗