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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
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 NTRS1982

Laser Doppler anemometer measurements using nonorthogonal velocity components - Error estimates

Laser Doppler anemometers (LDAs) that are arranged to measure nonorthogonal velocity components (from which orthogonal components are computed through transformation equations) are more susceptible to calibration and sampling errors than are systems with uncoupled channels. In this paper uncertainty methods and estimation theory are used to evaluate, respectively, the systematic and statistical errors that are present when such devices are applied to the measurement of mean velocities in turbulent flows. Statistical errors are estimated for two-channel LDA data that are either correlated or uncorrelated. For uncorrelated data the directional uncertainty of the measured velocity vector is considered for applications where mean streamline patterns are desired.

Orloff, K. L.↗
NASA NTRS1988

An error estimation procedure for plate bending elements

Procedures for identifying and eliminating errors inherent in individual finite elements and those due to the discretization of the continuum are presented. The elemental errors are identified through the use of an element formulation procedure based on physically interpretable strain gradient interpolation functions. The use of physically interpretable notation allows these errors to be eliminated using rational arguments. The discretization errors are identified by comparing the finite-element solution with a smoothed superconvergent solution. The errors thus identified are used to guide an adaptive mesh refinement procedure which produces improved results.

Dow, John O.↗
NASA NTRS2016

Validation and Expected Error Estimation of Suomi-NNP VIIRS Aerosol Optical Thickness and Angstrom Exponent with AERONET

The new-generation polar-orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET)measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of 0.0008 with root-mean-square error(RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06.The mean bias of VIIRS Ocean Angstrom Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S-NPP to future polar-orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations missions come to an end.

Huang, Jingfeng↗
NASA NTRS1972

Mars gravitational field estimation error

The error covariance matrices associated with a weighted least-squares differential correction process have been analyzed for accuracy in determining the gravitational coefficients through degree and order five in the Mars gravitational potential junction. The results are presented in terms of standard deviations for the assumed estimated parameters. The covariance matrices were calculated by assuming Doppler tracking data from a Mars orbiter, a priori statistics for the estimated parameters, and model error uncertainties for tracking-station locations, the Mars ephemeris, the astronomical unit, the Mars gravitational constant (G sub M), and the gravitational coefficients of degrees six and seven. Model errors were treated by using the concept of consider parameters.

Compton, H. R.↗
NASA NTRS1999

Model Error Estimation for the CPTEC Eta Model

Statistical data assimilation systems require the specification of forecast and observation error statistics. Forecast error is due to model imperfections and differences between the initial condition and the actual state of the atmosphere. Practical four-dimensional variational (4D-Var) methods try to fit the forecast state to the observations and assume that the model error is negligible. Here with a number of simplifying assumption, a framework is developed for isolating the model error given the forecast error at two lead-times. Two definitions are proposed for the Talagrand ratio tau, the fraction of the forecast error due to model error rather than initial condition error. Data from the CPTEC Eta Model running operationally over South America are used to calculate forecast error statistics and lower bounds for tau.

Tippett, Michael K.↗
NASA NTRS1997

Error Estimation and h-Adaptivity for Optimal Finite Element Analysis

The objective of adaptive meshing and automatic error control in finite element analysis is to eliminate the need for the application engineer from re-meshing and re-running design simulations to verify numerical accuracy. The user should only need to enter the component geometry and a coarse finite element mesh. The software will then autonomously and adaptively refine this mesh where needed, reducing the error in the fields to a user prescribed value. The ideal end result of the simulation is a measurable quantity (e.g. scattered field, input impedance), calculated to a prescribed error, in less time and less machine memory than if the user applied typical uniform mesh refinement by hand. It would also allow for the simulation of larger objects since an optimal mesh is created.

Cwik, Tom↗
NASA NTRS1989

Local error estimates for discontinuous solutions of nonlinear hyperbolic equations

Let u(x,t) be the possibly discontinuous entropy solution of a nonlinear scalar conservation law with smooth initial data. Suppose u sub epsilon(x,t) is the solution of an approximate viscosity regularization, where epsilon greater than 0 is the small viscosity amplitude. It is shown that by post-processing the small viscosity approximation u sub epsilon, pointwise values of u and its derivatives can be recovered with an error as close to epsilon as desired. The analysis relies on the adjoint problem of the forward error equation, which in this case amounts to a backward linear transport with discontinuous coefficients. The novelty of this approach is to use a (generalized) E-condition of the forward problem in order to deduce a W(exp 1,infinity) energy estimate for the discontinuous backward transport equation; this, in turn, leads one to an epsilon-uniform estimate on moments of the error u(sub epsilon) - u. This approach does not follow the characteristics and, therefore, applies mutatis mutandis to other approximate solutions such as E-difference schemes.

Tadmor, Eitan↗
NASA NTRS1976

Control by model error estimation

Modern control theory relies upon the fidelity of the mathematical model of the system. Truncated modes, external disturbances, and parameter errors in linear system models are corrected by augmenting to the original system of equations an 'error system' which is designed to approximate the effects of such model errors. A Chebyshev error system is developed for application to the Large Space Telescope (LST).

Likins, P. W.↗
NASA NTRS1987

Error estimates for cell-vertex solutions of the compressible Euler equations

The cell-vertex schemes due to Ni and Jameson, et al. have been subjected to a theoretical analysis of their truncation error. The analysis confirms the authors' claims for second-order accuracy on smooth grids, but shows that the same accuracy cannot be obtained on arbitrary grids. It is shown that the schemes have a unique generalization to axisymmetric flow that preserves the second-order accuracy.

Roe, P. L.↗
NASA NTRS2020

Improved LOLA Elevation Maps for South Pole Landing Sites: Error Estimates and Their Impact on Illumination Conditions

We present new high-resolution topographic models of 4 high-priority lunar south pole landing sites based exclusively on the laser altimetry data acquired by the Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar Reconnaissance Orbiter. By iteratively adjusting the LOLA tracks to the LOLA-based digital elevation model (LDEM) in a self-consistent fashion, we reduce the orbital geolocation errors by over a factor of 10 such that the new ground track geolocation uncertainty is ~10–20 ​cm horizontally and ~2–4 ​cm vertically over each 16 ​× ​16 km region. These new and improved 5 ​m/pix LDEMs will be useful to constrain higher-resolution topographic models derived from imagery, which are not as well controlled geodetically and which can be hindered by shadows. We developed a method to estimate surface height uncertainty in the new LDEMs, which accounts for the reduced orbital errors and interpolation errors by assuming a fractal behavior for the short-scale topography. The LDEM surface height and slope uncertainties have typical RMS values of ~0.30–0.50 ​m and ~1.5–2.5°, respectively. Finally, we examine how height uncertainties propagate to variations in horizon elevation and thus the predicted illumination conditions at these polar latitudes, and we show how this error characterization can inform landing site studies.

Michael K Barker↗
NASA NTRS1979

Multiclass Bayes error estimation by a feature space sampling technique

A general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.

Mobasseri, B. G.↗
NASA NTRS1982

Error estimation for delta VLBI angle and angle rate measurements over baselines between a ground station and a geosynchronous orbiter

Baselines between a ground station and a geosynchronous orbiter provide high resolution Delta VLBI data which is beyond the capability of ground-based interferometry. The effects of possible error sources on such Delta VLBI data for the determination of spacecraft angle and angle rate are investigated. For comparison, the effects on spacecraft-only VLBI are also studied.

Wu, S. C.↗
NASA NTRS2012

CO2 Flux Estimation Errors Associated with Moist Atmospheric Processes

Vertical transport by moist sub-grid scale processes such as deep convection is a well-known source of uncertainty in CO2 source/sink inversion. However, a dynamical link between vertical transport, satellite based retrievals of column mole fractions of CO2, and source/sink inversion has not yet been established. By using the same offline transport model with meteorological fields from slightly different data assimilation systems, we examine sensitivity of frontal CO2 transport and retrieved fluxes to different parameterizations of sub-grid vertical transport. We find that frontal transport feeds off background vertical CO2 gradients, which are modulated by sub-grid vertical transport. The implication for source/sink estimation is two-fold. First, CO2 variations contained in moist poleward moving air masses are systematically different from variations in dry equatorward moving air. Moist poleward transport is hidden from orbital sensors on satellites, causing a sampling bias, which leads directly to small but systematic flux retrieval errors in northern mid-latitudes. Second, differences in the representation of moist sub-grid vertical transport in GEOS-4 and GEOS-5 meteorological fields cause differences in vertical gradients of CO2, which leads to systematic differences in moist poleward and dry equatorward CO2 transport and therefore the fraction of CO2 variations hidden in moist air from satellites. As a result, sampling biases are amplified and regional scale flux errors enhanced, most notably in Europe (0.43+/-0.35 PgC /yr). These results, cast from the perspective of moist frontal transport processes, support previous arguments that the vertical gradient of CO2 is a major source of uncertainty in source/sink inversion.

moist atmospheric processes↗
NASA NTRS2006

Genetic Algorithm for Optimization: Preprocessing with n Dimensional Bisection and Error Estimation

A knowledge of the appropriate values of the parameters of a genetic algorithm (GA) such as the population size, the shrunk search space containing the solution, crossover and mutation probabilities is not available a priori for a general optimization problem. Recommended here is a polynomial-time preprocessing scheme that includes an n-dimensional bisection and that determines the foregoing parameters before deciding upon an appropriate GA for all problems of similar nature and type. Such a preprocessing is not only fast but also enables us to get the global optimal solution and its reasonably narrow error bounds with a high degree of confidence.

Sen, S. K.↗