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Results for “Propagation of Variance”

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 NTRS2012

Gravity Wave Variances and Propagation Derived from AIRS Radiances

As the first gravity wave (GW) climatology study using nadir-viewing infrared sounders, 50 Atmospheric Infrared Sounder (AIRS) radiance channels are selected to estimate GW variances at pressure levels between 2-100 hPa. The GW variance for each scan in the cross-track direction is derived from radiance perturbations in the scan, independently of adjacent scans along the orbit. Since the scanning swaths are perpendicular to the satellite orbits, which are inclined meridionally at most latitudes, the zonal component of GW propagation can be inferred by differencing the variances derived between the westmost and the eastmost viewing angles. Consistent with previous GW studies using various satellite instruments, monthly mean AIRS variance shows large enhancements over meridionally oriented mountain ranges as well as some islands at winter hemisphere high latitudes. Enhanced wave activities are also found above tropical deep convective regions. GWs prefer to propagate westward above mountain ranges, and eastward above deep convection. AIRS 90 field-of-views (FOVs), ranging from +48 deg. to −48 deg. off nadir, can detect large-amplitude GWs with a phase velocity propagating preferentially at steep angles (e.g., those from orographic and convective sources). The annual cycle dominates the GW variances and the preferred propagation directions for all latitudes. Indication of a weak two-year variation in the tropics is found, which is presumably related to the Quasi-biennial oscillation (QBO). AIRS geometry makes its out-tracks capable of detecting GWs with vertical wavelengths substantially shorter than the thickness of instrument weighting functions. The novel discovery of AIRS capability of observing shallow inertia GWs will expand the potential of satellite GW remote sensing and provide further constraints on the GW drag parameterization schemes in the general circulation models (GCMs).

AIRS↗
NASA NTRS1995

Equatorial Kelvin wave variability during 1992 and 1993

Temperature and ozone data from the Microwave Limb Sounder (MLS) instrument on Upper Atmosphere Research Satellite (UARS) are used to analyze the variability of Kelvin wave activity during the first two years of the UARS mission. The analysis is carried out using the asynoptic mapping technique. Time frequency plots for zonal wavenumbers 1 and 2, at two heights representing the middle stratosphere and the stratopause, respectively, are used to analyze the temporal variability of the waves, and its possible relationship to the equatorial quasi-biennial oscillation (QBO) and semiannual oscillation (SAO). Kelvin wave activity reaches a maximum during the solstice seasons and almost disappears during the equinoxes, in agreement with previous studies. Eastward propagating variance is estimated for wave periods from 4 to 20 days, at all UARS pressure surfaces currently available for MLS. The semiannual modulation of variance is observed to extend down to the lower limits of the height ranges of the temperature and ozone retrievals. Furthermore, a superposed QBO modulation is detected up to the stratopause. Comparison between the variance in eastward propagating waves and the mean zonal wind shows a possible participation of kelvin waves in the forcing of the QBO. At the stratopause the role of Kelvin waves in forcing the SAO appears to be limited, in agreement with previous results. Between the 21-hPa and 4.6-hPa surfaces there appears to be a transition zone where there is no clear relationship between Kelvin wave activity and mean zonal flow acceleration.

Canziani, Pablo O.↗
DOE OSTI2020

Propagating sample variance uncertainties in redshift calibration: simulations, theory, and application to the COSMOS2015 data

ABSTRACT Cosmological analyses of galaxy surveys rely on knowledge of the redshift distribution of their galaxy sample. This is usually derived from a spectroscopic and/or many-band photometric calibrator survey of a small patch of sky. The uncertainties in the redshift distribution of the calibrator sample include a contribution from shot noise, or Poisson sampling errors, but, given the small volume they probe, they are dominated by sample variance introduced by large-scale structures. Redshift uncertainties have been shown to constitute one of the leading contributions to systematic uncertainties in cosmological inferences from weak lensing and galaxy clustering, and hence they must be propagated through the analyses. In this work, we study the effects of sample variance on small-area redshift surveys, from theory to simulations to the COSMOS2015 data set. We present a three-step Dirichlet method of resampling a given survey-based redshift calibration distribution to enable the propagation of both shot noise and sample variance uncertainties. The method can accommodate different levels of prior confidence on different redshift sources. This method can be applied to any calibration sample with known redshifts and phenotypes (i.e. cells in a self-organizing map, or some other way of discretizing photometric space), and provides a simple way of propagating prior redshift uncertainties into cosmological analyses. As a worked example, we apply the full scheme to the COSMOS2015 data set, for which we also present a new, principled SOM algorithm designed to handle noisy photometric data. We make available a catalogue of the resulting resamplings of the COSMOS2015 galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Observational aspects of the low-frequency intraseasonal variability of the atmosphere in middle latitudes

An integrated description of the planetary-scale structures that emerge as organized entities on intraseasonal temporal scales in the middle latitudes is developed. Spatial scales of atmospheric motions involved in the generation of variability in the LF range are specified. An overview of the 3D structure of the seasonally averaged eddies in which the intraseasonal fluctuations are superposed is presented. The generation and maintenance mechanisms possibly responsible for the existence of the seasonal, quasi-stationary disturbances are discussed. It is shown that in the LF, low-zonal wavenumber part of the spectrum, the power displayed by standing variance exceeds that of propagating variance and vice versa for HFs and wavenumbers.

Pandolfo, Lionel↗
NASA NTRS2022

Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems

Motivated by the spurious variance loss encountered during covariance propagation in atmospheric and other large-scale data assimilation systems, we consider the problem for state dynamics governed by the continuity and related hyperbolic partial differential equations. This loss of variance has been attributed to reduced-rank representations of the covariance matrix, as in ensemble methods for example, or else to the use of dissipative numerical methods. Through a combination of analytical work and numerical experiments, we demonstrate that significant variance loss, as well as gain, typically occurs during covariance propagation, even at full rank. The cause of this unusual behavior is a discontinuous change in the continuum covariance dynamics as correlation lengths become small, for instance in the vicinity of sharp gradients in the velocity field. This discontinuity in the covariance dynamics arises from hyperbolicity: the diagonal of the kernel of the covariance operator is a characteristic surface for advective dynamics. Our numerical experiments demonstrate that standard numerical methods for evolving the state are not adequate for propagating the covariance, because16they do not capture the discontinuity in the continuum covariance dynamics as correlations lengths tend to zero. Our analytical and numerical results show that this leads to significant, spurious variance loss in certain regions, and gain in others. The results suggest that developing local covariance propagation methods designed specifically to capture covariance evolution near the diagonal may prove a useful alternative to current methods of covariance propagation.

covariance propagation↗
NASA NTRSDate not supplied

Continuum covariance propagation for understanding variance loss in advective systems

At the heart of modern data assimilation schemes is covariance propagation.Loss of variance experienced in large-scale applications such as numerical weather prediction is problematic, and the development of auxiliary methods to mitigate this issue is an active research area. The focus of this work is to understand the root causes of variance loss and show that for advective dynamics, the covariance propagation by itself typically causes significant, spurious loss of variance, even at full rank. To demonstrate this, we first study continuum covariance propagation by analyzing the covariance evolution equation for advective dynamics. The behavior of this evolution equation changes abruptly as the correlation length tends to zero, for example in the vicinity of sharp gradients in the advection field. This happens because the diagonal of the kernel of the covariance operator is a characteristic surface for advective dynamics. Our numerical experiments then confirm that the variance lost during numerical propagation greatly exceeds that due to numerical dissipation alone. The variance loss is driven primarily by inaccurate variance propagation resulting from standard, full-rank covariance propagation schemes, which have difficulty capturing the abrupt change in dynamics as the correlation length tends to zero. These results suggest that developing local covariance propagation methods may prove useful in ameliorating the variance loss observed in data assimilation schemes

Covariance Propagation↗
NASA NTRSDate not supplied

Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems

We demonstrate for state dynamics governed by the continuity equation and related hyperbolic partial differential equations that significant, spurious variance loss occurs during covariance propagation by traditional methods used in data assimilation, even at full rank. This inaccurate variance evolution is caused not by numerical dissipation, but rather by a discontinuous change in the continuum covariance dynamics as correlation lengths tend to zero.

Shay Gilpin↗
DOE OSTI2019

Measurement Error Variance Estimation in Gamma-Spectroscopy Data using FRAM

The FRAM gamma-ray isotopic analysis code has recently been applied to four Pu data sets and four U data sets, each with relatively well-characterized standard items, in order to estimate the variances of random errors and of item-specific systematic errors. Assay results are available for Pu238, Pu239, Pu240, Pu241, Pu242 (operator entered, not independently measured), Am241, Specific Power, and Pu240eff. For the four U data sets, results are available for the measurands U234, U235, U236 (operator entered, not independently measured), and U238. For example, in Pu data set 1, there are 33 standard reference Pu items, which have well-known nominal/true values (except for a few items). FRAM was used to make approximately 15 repeat measurements for each of the 33 items, so empirical standard deviations can be calculated. In addition, FRAM internally estimates its random error variance using propagation of variance. This paper applies analysis of variance (ANOVA) to separately estimate the repeatability standard deviation and the standard deviation of the item-specific biases δ s in each of the 8 data sets.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Estimating Travel Energy Consumption Uncertainty Based on Inferred Travel Mode and Sensed Travel Length

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 8%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2016

Non-Destructive Assay (NDA) Uncertainties Impact on Physical Inventory Difference (ID) and Material Balance Determination: Sources of Error, Precision/Accuracy, and ID/Propagation of Error (POV)

These are slides from a presentation made by a researcher from Los Alamos National Laboratory. The following topics are covered: sources of error for NDA gamma measurements, precision and accuracy are two important characteristics of measurements, four items processed in a material balance area during the inventory time period, inventory difference and propagation of variance, sum in quadrature, and overview of the ID/POV process.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Estimating the mass variance in neutron multiplicity counting $-$ A comparison of approaches

In the standard practice of neutron multiplicity counting, the first three sampled factorial moments of the event triggered neutron count distribution are used to quantify the three main neutron source terms: the spontaneous fissile material effective mass, the relative (α,n) production and the induced fission source responsible for multiplication. This study compares three methods to quantify the statistical uncertainty of the estimated mass: the bootstrap method, propagation of variance through moments, and statistical analysis of cycle data method. Each of the three methods was implemented on a set of four different NMC measurements, held at the JRC-laboratory in Ispra, Italy, sampling four different Pu samples in a standard Plutonium Scrap Multiplicity Counter (PSMC) well counter.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Data-optimized source modeling with the Backwards Liouville Test–Kinetic method

In the standard practice of neutron multiplicity counting , the first three sampled factorial moments of the event triggered neutron count distribution were used to quantify the three main neutron source terms: the spontaneous fissile material effective mass, the relative (α,n) production and the induced fission source responsible for multiplication. Our study compares three methods to quantify the statistical uncertainty of the estimated mass: the bootstrap method, propagation of variance through moments, and statistical analysis of cycle data method. Each of the three methods was implemented on a set of four different NMC measurements, held at the JRC-laboratory in Ispra, Italy, sampling four different Pu samples in a standard Plutonium Scrap Multiplicity Counter (PSMC) well counter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Bayesian Inference applied to the Solubility of Phase-3 and Phase-5

This Section covers an introduction to the objectives and techniques used in this analysis. The objectives of the report are given in Subsection 1.1. An introduction to aqueous thermodynamics and how variance might propagate through the relevant thermodynamic equations is given in Subsection 1.2. An introduction to Bayesian inference and its application to thermodynamic modeling is given in Subsection 1.3.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗