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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 NTRS2013

Error Consistency Analysis Scheme for Infrared Ultraspectral Sounding Retrieval Error Budget Estimation

Great effort has been devoted towards validating geophysical parameters retrieved from ultraspectral infrared radiances obtained from satellite remote sensors. An error consistency analysis scheme (ECAS), utilizing fast radiative transfer model (RTM) forward and inverse calculations, has been developed to estimate the error budget in terms of mean difference and standard deviation of error in both spectral radiance and retrieval domains. The retrieval error is assessed through ECAS without relying on other independent measurements such as radiosonde data. ECAS establishes a link between the accuracies of radiances and retrieved geophysical parameters. ECAS can be applied to measurements from any ultraspectral instrument and any retrieval scheme with its associated RTM. In this manuscript, ECAS is described and demonstrated with measurements from the MetOp-A satellite Infrared Atmospheric Sounding Interferometer (IASI). This scheme can be used together with other validation methodologies to give a more definitive characterization of the error and/or uncertainty of geophysical parameters retrieved from ultraspectral radiances observed from current and future satellite remote sensors such as IASI, the Atmospheric Infrared Sounder (AIRS), and the Cross-track Infrared Sounder (CrIS).

Zhou, Daniel K.↗
NASA NTRS2007

Human Error: A Concept Analysis

Human error is the subject of research in almost every industry and profession of our times. This term is part of our daily language and intuitively understood by most people however, it would be premature to assume that everyone's understanding of human error s the same. For example, human error is used to describe the outcome or consequence of human action, the causal factor of an accident, deliberate violations,a nd the actual action taken by a human being. As a result, researchers rarely agree on the either a specific definition or how to prevent human error. The purpose of this article is to explore the specific concept of human error using Concept Analysis as described by Walker and Avant (1995). The concept of human error is examined as currently used in the literature of a variety of industries and professions. Defining attributes and examples of model, borderline, and contrary cases are described. The antecedents and consequences of human error are also discussed and a definition of human error is offered.

Hansen, Frederick D.↗
NASA NTRS2011

Error Propagation Analysis in the SAE Architecture Analysis and Design Language (AADL) and the EDICT Tool Framework

This report documents the capabilities of the EDICT tools for error modeling and error propagation analysis when operating with models defined in the Architecture Analysis & Design Language (AADL). We discuss our experience using the EDICT error analysis capabilities on a model of the Scalable Processor-Independent Design for Enhanced Reliability (SPIDER) architecture that uses the Reliable Optical Bus (ROBUS). Based on these experiences we draw some initial conclusions about model based design techniques for error modeling and analysis of highly reliable computing architectures.

LaValley, Brian W.↗
NASA NTRS1985

Integrated analysis of error detection and recovery

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms.

Shin, K. G.↗
NASA NTRS2000

Low-dimensional Representation of Error Covariance

Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix, a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady-state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time-dependent systems. If much of the steady-state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady-state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting nonmodal transient growth. Failure to observe growing modes leads to increased steady-state analysis error variances. Leading eigenvectors of the steady-state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest-order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady-state analysis error covariance matrix.

Tippett, Michael K.↗
NASA NTRS1982

GP-B error modeling and analysis

Individual source errors and their effects on the accuracy of the Gravity Probe B (GP-B) experiment were investigated. Emphasis was placed on: (1) the refinement of source error identification and classifications of error according to their physical nature; (2) error analysis for the GP-B data processing; and (3) measurement geometry for the experiment.

Hung, J. C.↗
NASA NTRS1993

In-flight measurement of the National Oceanic and Atmospheric Administration (NOAA)-10 static Earth sensor error

Analysis performed in the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) measures error in the static Earth sensor onboard the National Oceanic and Atmospheric Administration (NOAA)-10 spacecraft using flight data. Errors are computed as the difference between Earth sensor pitch and roll angle telemetry and reference pitch and roll attitude histories propagated by gyros. The flight data error determination illustrates the effect on horizon sensing of systemic variation in the Earth infrared (IR) horizon radiance with latitude and season, as well as the effect of anomalies in the global IR radiance. Results of the analysis provide a comparison between static Earth sensor flight performance and that of scanning Earth sensors studied previously in the GSFC/FDD. The results also provide a baseline for evaluating various models of the static Earth sensor. Representative days from the NOAA-10 mission indicate the extent of uniformity and consistency over time of the global IR horizon. A unique aspect of the NOAA-10 analysis is the correlation of flight data errors with independent radiometric measurements of stratospheric temperature. The determination of the NOAA-10 static Earth sensor error contributes to realistic performance expectations for missions to be equipped with similar sensors.

Harvie, E.↗
NASA NTRS1988

Relative error covariance analysis techniques and application

A technique for computing the error covariance of the difference between two estimators derived from different (possibly overlapping) data arcs is presented. The relative error covariance is useful for predicting the achievable consistency between Kalman-Bucy filtered estimates generated from two (not necessarily disjoint) data sets. The relative error covariance analysis technique is then applied to a Venus Orbiter simulation.

Wolff, Peter, J.↗
NASA NTRS2014

Development, Validation, and Application of OSSEs at NASA-GMAO

The GMAO OSSE framework has two general classes of applications. One is to estimate the potential improvements to weather forecasting and analysis by using new proposed instruments that are not as yet built or deployed. This exploits the simulated nature of the OSSE. The other is to assess various aspects of the GMAO data assimilation system. This exploits the availability of truth provided by the OSSE. Two examples of the first class of application will be offered. One concerns deployment of constellations of passive MW sounders on small CUBESATs placed in very low orbits. The other concern is increasing the frequency of radiosonde observations to 4-times daily at all current stations. Several examples of the second class will also be presented. One is an estimation of analysis error characteristics. Another is a comparison between covariances directly determined from explicitly known background errors and those estimated by computing differences between lagged forecasts using the NMC-method prior to tuning. A third is a comparison of effects of model and observation errors on analysis and forecast skill. The last is an examination of spectra of forecast errors in a study of predictability. This latter is an ongoing study.

OSSE↗