Search NASA⌕ Search

SEARCH · Search NASA

Results for “error estimation and analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Quantitative analysis of aircraft multispectral-scanner data and mapping of water-quality parameters in the James River in Virginia

Statistical analysis techniques were applied to develop quantitative relationships between in situ river measurements and the remotely sensed data that were obtained over the James River in Virginia on 28 May 1974. The remotely sensed data were collected with a multispectral scanner and with photographs taken from an aircraft platform. Concentration differences among water quality parameters such as suspended sediment, chlorophyll a, and nutrients indicated significant spectral variations. Calibrated equations from the multiple regression analysis were used to develop maps that indicated the quantitative distributions of water quality parameters and the dispersion characteristics of a pollutant plume entering the turbid river system. Results from further analyses that use only three preselected multispectral scanner bands of data indicated that regression coefficients and standard errors of estimate were not appreciably degraded compared with results from the 10-band analysis.

Johnson, R. 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 NTRS1992

Asteroid orbital error analysis: Theory and application

We present a rigorous Bayesian theory for asteroid orbital error estimation in which the probability density of the orbital elements is derived from the noise statistics of the observations. For Gaussian noise in a linearized approximation the probability density is also Gaussian, and the errors of the orbital elements at a given epoch are fully described by the covariance matrix. The law of error propagation can then be applied to calculate past and future positional uncertainty ellipsoids (Cappellari et al. 1976, Yeomans et al. 1987, Whipple et al. 1991). To our knowledge, this is the first time a Bayesian approach has been formulated for orbital element estimation. In contrast to the classical Fisherian school of statistics, the Bayesian school allows a priori information to be formally present in the final estimation. However, Bayesian estimation does give the same results as Fisherian estimation when no priori information is assumed (Lehtinen 1988, and reference therein).

Muinonen, K.↗
DOE OSTI2025

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Disentangling AGN and Star Formation in Soft X-Rays

We have explored the interplay of star formation and active galactic nucleus (AGN) activity in soft X-rays (0.5-2 keV) in two samples of Seyfert 2 galaxies (Sy2s). Using a combination of low-resolution CCD spectra from Chandra and XMM-Newton, we modeled the soft emission of 34 Sy2s using power-law and thermal models. For the 11 sources with high signal-to-noise Chandra imaging of the diffuse host galaxy emission, we estimate the luminosity due to star formation by removing the AGN, fitting the residual emission. The AGN and star formation contributions to the soft X-ray luminosity (i.e., L(sub x,AGN) and L(sub x,SF)) for the remaining 24 Sy2s were estimated from the power-law and thermal luminosities derived from spectral fitting. These luminosities were scaled based on a template derived from XSINGS analysis of normal star-forming galaxies. To account for errors in the luminosities derived from spectral fitting and the spread in the scaling factor, we estimated L(sub x,AGN) and L(sub x,SF))from Monte Carlo simulations. These simulated luminosities agree with L(sub x,AGN) and L(sub x,SF) derived from Chandra imaging analysis within a 3sigma confidence level. Using the infrared [Ne ii]12.8 micron and [O iv]26 micron lines as a proxy of star formation and AGN activity, respectively, we independently disentangle the contributions of these two processes to the total soft X-ray emission. This decomposition generally agrees with L(sub x,SF) and L(sub x,AGN) at the 3 sigma level. In the absence of resolvable nuclear emission, our decomposition method provides a reasonable estimate of emission due to star formation in galaxies hosting type 2 AGNs.

Disentangling↗
NASA NTRS1995

Accuracy of sea ice temperature derived from the advanced very high resolution radiometer

The accuracy of Arctic sea ice surface temperatures T(sub s) dericed from advanced very high resolution radiometer (AVHRR) thermal channels is evaluated in the cold seasons by comparing them with surface air temperatures T(sub air) from drifting buoys and ice stations. We use three different estimates of satellite surface temperatures, a direct estimate from AVHRR channel 4 with only correction for the snow surface emissivity but not for the atmosphere, a single-channel regression of T(sub s) with T(sub air), and Key and Haefliger's (1992) polar multichannel algorithm. We find no measurable bias in any of these estimates and few differences in their statistics. The similar performance of all three methods indicates that an atmospheric water vapor correction is not important for the dry winter atmosphere in the central Arctic, given the other sources of error that remain in both the satellite and the comparison data. A record of drifting station data shows winter air temperature to be 1.4 C warmer than the snow surface temperature. `Correcting' air temperatures to skin temperature by subtracting this amount implies that satellite T(sub s) estimates are biased warm with respect to skin temperature by about this amount. A case study with low-flying aircraft data suggests that ice crystal precipitation can cause satellite estimates of T(sub s) to be several degrees warmer than radiometric measurements taken close to the surface, presumably below the ice crystal precipitation layer. An analysis in which errors are assumed to exist in all measurements, not just the satellite measurements, gives a standard deviation in the satellite estimates of 0.9 C, about half the standard deviation of 1.7 C estimated by assigning all the variation between T(sub s) and T(sub air) to errors in T(sub s).

Yu, Y.↗
NASA NTRS1972

Statistical accuracy of cosmic radiation neutron component recorders

A method is examined for calculating the error of experimental data from a standard neutron monitor with the multiplicity of neutron generation. It is shown that use of the Poisson distribution to estimate the real statistical error is inadequate. The method proposed is for analysis of low-amplitude periodicities in the cosmic-ray variations.

Dorman, L. I.↗
NASA NTRS2018

Real-Time Parameter Estimation for Flexible Aircraft

A method for estimating aeroelastic stability and control derivatives for flexible aircraft is developed and demonstrated using flight test data for the X-56A subscale demonstrator. The method uses the equation-error approach with frequency-domain data, and can be applied post-flight or in real time during flight. The non-dimensional aeroelastic forces and moments and the explanatory variables (including generalized displacement, rate, and acceleration states for the vibration modes) are estimated using a finite element model and onboard sensor measurements in both a least squares and Kalman filtering framework. The data are then transformed into the frequency domain for parameter estimation using equation error. This method can result in a more efficient analysis than with other iterative methods, and can leverage existing statistical tools for model structure determination, data collinearity detection, combining multiple maneuvers or prior information, and others to improve model quality.

Grauer, Jared A.↗
NASA NTRS2019

Characterizing the System Impulse Response Function from Photon-Counting LiDAR Data

NASA's Multiple Altimeter Beam Experimental LiDAR (MABEL) is an aircraft-based photon-counting laser altimeter designed as a simulator to test measurement techniques and algorithms for Advanced Topographic Laser Altimeter System (ATLAS), the sole instrument on NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission. By measuring the time of flight, pointing angle, and absolute position for individual photons, ICESat-2 provides detailed elevation measurements of earth's surface. Calculating accurate and precise elevations requires an understanding of how photons interact with surfaces, and characterization of the photon distribution after returning from surfaces. Neither MABEL nor ATLAS records the transmitted laser pulse shape, relying instead on aggregating several pulses worth of photons, often using histograms, to characterize the pulse shape. In this paper, we assess the limitations of using histograms and propose a more robust method to describe MABEL's system impulse-response function using an exponentially modified Gaussian distribution. We also provide standard error estimates for the arithmetic mean and standard deviation calculations, and for exponentially modified Gaussian parameters using a Monte Carlo sensitivity analysis. We apply this method to photon returns from a sea ice lead and from a dry salt lake bed as case studies for estimating the standard error associated with sample size for the arithmetic mean and standard deviation, and for the exponentially modified Gaussian parameters. We use these standard errors to calculate the minimum number of photons required to find both Gaussian and exponentially modified Gaussian distribution parameters within 3 cm of their parent population values.

photoncounting↗
NASA NTRS2007

Advanced Systems for Monitoring Underwater Sounds

The term "Passive Acoustic Monitoring System" (PAMS) describes a developmental sensing-and-data-acquisition system for recording underwater sounds. The sounds (more precisely, digitized and preprocessed versions from acoustic transducers) are subsequently analyzed by a combination of data processing and interpretation to identify and/or, in some cases, to locate the sources of those sounds. PAMS was originally designed to locate the sources such as fish of species that one knows or seeks to identify. The PAMS unit could also be used to locate other sources, for example, marine life, human divers, and/or vessels. The underlying principles of passive acoustic sensing and analyzing acoustic-signal data in conjunction with temperature and salinity data are not new and not unique to PAMS. Part of the uniqueness of the PAMS design is that it is the first deep-sea instrumentation design to provide a capability for studying soniferous marine animals (especially fish) over the wide depth range described below. The uniqueness of PAMS also lies partly in a synergistic combination of advanced sensing, packaging, and data-processing design features with features adapted from proven marine instrumentation systems. This combination affords a versatility that enables adaptation to a variety of undersea missions using a variety of sensors. The interpretation of acoustic data can include visual inspection of power-spectrum plots for identification of spectral signatures of known biological species or artificial sources. Alternatively or in addition, data analysis could include determination of relative times of arrival of signals at different acoustic sensors arrayed at known locations. From these times of arrival, locations of acoustic sources (and errors in those locations) can be estimated. Estimates of relative locations of sources and sensors can be refined through analysis of the attenuation of sound in the intervening water in combination with water-temperature and salinity data acquired by instrumentation systems other than PAMS. A PAMS is packaged as a battery-powered unit, mated with external sensors, that can operate in the ocean at any depth from 2 m to 1 km. A PAMS includes a pressure housing, a deep-sea battery, a hydrophone (which is one of the mating external sensors), and an external monitor and keyboard box. In addition to acoustic transducers, external sensors can include temperature probes and, potentially, underwater cameras. The pressure housing contains a computer that includes a hard drive, DC-to- DC power converters, a post-amplifier board, a sound card, and a universal serial bus (USB) 4-port hub.

Lane, Michael↗
NASA NTRS1980

On minimal surfaces, membranes and compressible flows

The theory, variational formulation and finite element analysis of a class of nonlinear problems arising in optimization, thin membrane deflection and compressible flows are developed. Error estimates are summarized, and representative numerical results are presented. Particular theoretical questions pertaining to the existence and uniqueness of solutions are addressed, and their implications for such applications of finite element analysis as the study of compressible flows indicated.

Carey, G. F.↗
NASA NTRS2000

Global Warming Estimation from MSU: Correction for Drift and Calibration Errors

Microwave Sounding Unit (MSU) radiometer observations in Ch 2 (53.74 GHz), made in the nadir direction from sequential, sun-synchronous, polar-orbiting NOAA morning satellites (NOAA 6, 10 and 12 that have about 7am/7pm orbital geometry) and afternoon satellites (NOAA 7, 9, 11 and 14 that have about 2am/2pm orbital geometry) are analyzed in this study to derive global temperature trend from 1980 to 1998. In order to remove the discontinuities between the data of the successive satellites and to get a continuous time series, first we have used shortest possible time record of each satellite. In this way we get a preliminary estimate of the global temperature trend of 0.21 K/decade. However, this estimate is affected by systematic time-dependent errors. One such error is the instrument calibration error. This error can be inferred whenever there are overlapping measurements made by two satellites over an extended period of time. From the available successive satellite data we have taken the longest possible time record of each satellite to form the time series during the period 1980 to 1998 to this error. We find we can decrease the global temperature trend by about 0.07 K/decade. In addition there are systematic time dependent errors present in the data that are introduced by the drift in the satellite orbital geometry arises from the diurnal cycle in temperature which is the drift related change in the calibration of the MSU. In order to analyze the nature of these drift related errors the multi-satellite Ch 2 data set is partitioned into am and pm subsets to create two independent time series. The error can be assessed in the am and pm data of Ch 2 on land and can be eliminated. Observations made in the MSU Ch 1 (50.3 GHz) support this approach. The error is obvious only in the difference between the pm and am observations of Ch 2 over the ocean. We have followed two different paths to assess the impact of the errors on the global temperature trend. In one path the entire error is placed in the am data while in the other it is placed in the pm data. Global temperature trend is increased or decreased by about 0.03 K/decade depending upon this placement. Taking into account all random errors and systematic errors our analysis of MSU observations leads us to conclude that a conservative estimate of the global warming is 0. 11 (+-) 0.04 K/decade during 1980 to 1998.

Prabhakara, C.↗
NASA NTRS2024

Summarizing Multiple Aspects of Triple Collocation Analysis in a Single Diagram

With the ongoing expansion of global observation networks, it is expected that we shall routinely analyze records of geophysical variables such as temperature from multiple collocated instruments. Validating datasets in this situation is not a trivial task because every observing system has its own bias and noise. Triple collocation is a general statistical framework to estimate the error characteristics in three or more observational-based datasets. In a triple colocation analysis, several metrics are routinely reported but traditional multiple-panel plots are not the most effective way to display information. A new formula of error variance is derived for connecting the key terms in the triple collocation theory. A diagram based on this formula is devised to facilitate triple collocation analysis of any data from observations, as illustrated using three aerosol optical depth datasets from the recent Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). An observational-based skill score is also derived to evaluate the quality of three datasets by taking into account both error variance and correlation coefficient. Several applications are discussed and sample plotting routines are provided.

triple collocation↗
NASA NTRS2000

Global Warming Estimation from MSU: Correction for Drift and Calibration Errors

Microwave Sounding Unit (MSU) radiometer observations in Ch 2 (53.74 GHz), made in the nadir direction from sequential, sun-synchronous, polar-orbiting NOAA morning satellites (NOAA 6, 10 and 12 that have approximately 7am/7pm orbital geometry) and. afternoon satellites (NOAA 7, 9, 11 and 14 that have approximately 2am/2pm orbital geometry) are analyzed in this study to derive global temperature trend from 1980 to 1998. In order to remove the discontinuities between the data of the successive satellites and to get a continuous time series, first we have used shortest possible time record of each satellite. In this way we get a preliminary estimate of the global temperature trend of 0.21 K/decade. However, this estimate is affected by systematic time-dependent errors. One such error is the instrument calibration error eo. This error can be inferred whenever there are overlapping measurements made by two satellites over an extended period of time. From the available successive satellite data we have taken the longest possible time record of each satellite to form the time series during the period 1980 to 1998 to this error eo. We find eo can decrease the global temperature trend by approximately 0.07 K/decade. In addition there are systematic time dependent errors ed and ec present in the data that are introduced by the drift in the satellite orbital geometry. ed arises from the diurnal cycle in temperature and ec is the drift related change in the calibration of the MSU. In order to analyze the nature of these drift related errors the multi-satellite Ch 2 data set is partitioned into am and pm subsets to create two independent time series. The error ed can be assessed in the am and pm data of Ch 2 on land and can be eliminated. Observation made in the MSU Ch 1 (50.3 GHz) support this approach. The error ec is obvious only in the difference between the pm and am observations of Ch 2 over the ocean. We have followed two different paths to assess the impact of the error ec on the global temperature trend. In one path the entire error ec is placed in the am data while in the other it is placed in the pm data. Global temperature trend is increased or decreased by approximately 0.03 K/decade depending upon this placement. Taking into account all random errors and systematic errors our analysis of MSU observations leads us to conclude that a conservative estimate of the global warming is 0. 11 (+/-) 0.04 K/decade during 1980 to 1998.

Prabhakara, C.↗
NASA NTRS1980

Magnetic field directional discontinuities. I - Minimum variance errors

The paper deals with a statistical analysis of the errors associated with a minimum variance analysis of directional discontinuities by use of an idealized model of these discontinuities and various simulations, and also by an examination of actual Mariner 10 interplanetary magnetic field data. An empirical expression is derived for the magnitude of the error in an estimated discontinuity normal component, relative to the total field across the directional discontinuity. The analysis was performed primarily to aid in differentiating between interplanetary tangential and rotational discontinuities observed by Mariner 10.

Lepping, R. P.↗
NASA NTRS2008

The TRMM Multi-Satellite Precipitation Analysis (TMPA)

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is intended to provide a "best" estimate of quasi-global precipitation from the wide variety of modern satellite-borne precipitation-related sensors. Estimates are provided at relatively fine scales (0.25degx0.25deg, 3-hourly) in both real and post-real time to accommodate a wide range of researchers. However, the errors inherent in the finest scale estimates are large. The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale data to create time/space averages appropriate to the user s application. We review the conceptual basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a recent upgrade to the real-time version incorporates several additional satellite data sources and employs monthly climatological adjustments to approximate the bias characteristics of the research quality post-real-time product. Second, an upgrade of the research quality post-real-time TMPA from Version 6 to Version 7 (in beta test at press time) is designed to provide a variety of improvements that increase the list of input data sets and correct several issues. Future enhancements for the TMPA will include improved error estimation, extension to higher latitudes, and a shift to a Lagrangian time interpolation scheme.

Huffman, George J.↗