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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

The Special Sensor Microwave Imager Wind Dataset: A Source of Quantitative Information for the Ocean-to-Land Advection

It is well recognized that advection from the North Atlantic has a profound effect on the climatic conditions in central Europe. A new dataset of the ocean-surface winds, derived from the Special Sensor Microwave Imager, SSM/1, is now available. This satellite instrument measures the wind speed, but not the direction. However, variational analysis developed at the Data Assimilation Office, NASA Goddard Space Flight Center, by combining the SSM/I measurements with wind vectors measured from ships, etc., produced global maps of the ocean surface winds suitable for climate analysis. From this SSM/I dataset, a specific index I(sub na) of the North Atlantic surface winds has been developed, which pertinently quantifies the low-level advection into central Europe. For a selected time-period, the index I(sub na) reports the average of the amplitude of the wind, averaging only the speed when the direction is from the southwest (when the wind is from another direction, the contribution counts to the average as zero speed). Strong correlations were found between February I(sub na) and the surface air temperatures in Europe 50-60 deg N. In the present study, we present the correlations between I(sub na) and temperature I(sub s), and also the sensitivity of T(sub s), to an increase in I(sub na), in various seasons and various regions. We specifically analyze the flow of maritime-air from the North Atlantic that produced two extraordinary warm periods: February 1990, and early-winter 2000/2001. The very cold December 2001 was clearly due to a northerly flow. Our conclusion is that the SSM/I dataset is very useful for providing insight to the forcing of climatic fluctuations in Europe.

Otterman, J.↗
NASA NTRS2009

North Atlantic Basin Tropical Cyclone Activity in Relation to Temperature and Decadal- Length Oscillation Patterns

Yearly frequencies of North Atlantic basin tropical cyclones, their locations of origin, peak wind speeds, average peak wind speeds, lowest pressures, and average lowest pressures for the interval 1950-2008 are examined. The effects of El Nino and La Nina on the tropical cyclone parametric values are investigated. Yearly and 10-year moving average (10-yma) values of tropical cyclone parameters are compared against those of temperature and decadal-length oscillation, employing both linear and bi-variate analysis, and first differences in the 10-yma are determined. Discussion of the 2009 North Atlantic basin hurricane season, updating earlier results, is given.

Wilson, Robert M.↗
NASA NTRS2011

Using High-Resolution Forward Model Simulations of Ideal Atmospheric Tracers to Assess the Spatial Information Content of Inverse CO2 Flux Estimates

Attribution of observed atmospheric carbon concentrations to emissions on the country, state or city level is often inferred using "inversion" techniques. Such computations are often performed using advanced mathematical techniques, such as synthesis inversion or four-dimensional variational analysis, that invoke tracing observed atmospheric concentrations backwards through a transport model to a source region. It is, to date, not well understood how well such techniques can represent fine spatial (and temporal) structure in the inverted flux fields. This question is addressed using forward-model computations with idealized tracers emitted at the surface in a large number of grid boxes over selected regions and examining how distinctly these emitted tracers can be detected downstream. Initial results show that tracers emitted in half-degree grid boxes over a large region of the Eastern USA cannot be distinguished from each other, even at short distances over the Atlantic Ocean, when they are emitted in grid boxes separated by less than five degrees of latitude - especially when only total-column observations are available. A large number of forward model simulations, with varying meteorological conditions, are used to assess how distinctly three types observations (total column, upper tropospheric column, and surface mixing ratio) can separate emissions from different sources. Inferences inverse modeling and source attribution will be drawn.

Pawson, Steven↗
NASA NTRS2015

RACORO Continental Boundary Layer Cloud Investigations: 1. Case Study Development and Ensemble Large-Scale Forcings

Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60 h case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in situ measurements from the Routine AAF (Atmospheric Radiation Measurement (ARM) Aerial Facility) CLOWD (Clouds with Low Optical Water Depth) Optical Radiative Observations (RACORO) field campaign and remote sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functions for concise representation in models. Values of the aerosol hygroscopicity parameter, kappa, are derived from observations to be approximately 0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing data sets are derived from the ARM variational analysis, European Centre for Medium-Range Weather Forecasts, and a multiscale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in "trial" large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary clouds.

Boundary layers↗
NASA NTRS2021

Decoding the effects of synonymous variants

Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.

Zishuo Zeng↗
NASA NTRS2023

The Gmao Hybrid 4d-Envar Observing System Simulation Experiment Framework.

This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Data assimilation↗
NASA NTRS1988

A multivariate variational objective analysis-assimilation method. Part 1: Development of the basic model

The variational method of undetermined multipliers is used to derive a multivariate model for objective analysis. The model is intended for the assimilation of 3-D fields of rawinsonde height, temperature and wind, and mean level temperature observed by satellite into a dynamically consistent data set. Relative measurement errors are taken into account. The dynamic equations are the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation. The model Euler-Lagrange equations are eleven linear and/or nonlinear partial differential and/or algebraic equations. A cyclical solution sequence is described. Other model features include a nonlinear terrain-following vertical coordinate that eliminates truncation error in the pressure gradient terms of the horizontal momentum equations and easily accommodates satellite observed mean layer temperatures in the middle and upper troposphere. A projection of the pressure gradient onto equivalent pressure surfaces removes most of the adverse impacts of the lower coordinate surface on the variational adjustment.

Achtemeier, Gary L.↗
NASA NTRS1996

Variational Methods in Sensitivity Analysis and Optimization for Aerodynamic Applications

Variational methods (VM) sensitivity analysis, which is the continuous alternative to the discrete sensitivity analysis, is employed to derive the costate (adjoint) equations, the transversality conditions, and the functional sensitivity derivatives. In the derivation of the sensitivity equations, the variational methods use the generalized calculus of variations, in which the variable boundary is considered as the design function. The converged solution of the state equations together with the converged solution of the costate equations are integrated along the domain boundary to uniquely determine the functional sensitivity derivatives with respect to the design function. The determination of the sensitivity derivatives of the performance index or functional entails the coupled solutions of the state and costate equations. As the stable and converged numerical solution of the costate equations with their boundary conditions are a priori unknown, numerical stability analysis is performed on both the state and costate equations. Thereafter, based on the amplification factors obtained by solving the generalized eigenvalue equations, the stability behavior of the costate equations is discussed and compared with the state (Euler) equations. The stability analysis of the costate equations suggests that the converged and stable solution of the costate equation is possible only if the computational domain of the costate equations is transformed to take into account the reverse flow nature of the costate equations. The application of the variational methods to aerodynamic shape optimization problems is demonstrated for internal flow problems at supersonic Mach number range. The study shows, that while maintaining the accuracy of the functional sensitivity derivatives within the reasonable range for engineering prediction purposes, the variational methods show a substantial gain in computational efficiency, i.e., computer time and memory, when compared with the finite difference sensitivity analysis.

Ibrahim, A. H.↗
NASA NTRS1988

A multivariate variational objective analysis-assimilation method. Part 2: Case study results with and without satellite data

The variational multivariate assimilation method described in a companion paper by Achtemeier and Ochs is applied to conventional and conventional plus satellite data. Ground-based and space-based meteorological data are weighted according to the respective measurement errors and blended into a data set that is a solution of numerical forms of the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation for a dry atmosphere. The analyses serve first, to evaluate the accuracy of the model, and second to contrast the analyses with and without satellite data. Evaluation criteria measure the extent to which: (1) the assimilated fields satisfy the dynamical constraints, (2) the assimilated fields depart from the observations, and (3) the assimilated fields are judged to be realistic through pattern analysis. The last criterion requires that the signs, magnitudes, and patterns of the hypersensitive vertical velocity and local tendencies of the horizontal velocity components be physically consistent with respect to the larger scale weather systems.

Achtemeier, Gary L.↗
DOE OSTI2024

Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine learning methods for discovery of physical and chemical phenomena from the data, such as manifestations of symmetry breaking phenomena in electron and scanning tunneling microscopy images, or variability of the nanoparticles. Variational autoencoders (VAEs) are emerging as a powerful paradigm for the unsupervised data analysis, allowing to disentangle the factors of variability and discover optimal parsimonious representation. Here, we summarize recent developments in VAEs, covering the basic principles and intuition behind the VAEs. The invariant VAEs are introduced as an approach to accommodate scale and translation invariances present in imaging data and separate known factors of variations from the ones to be discovered. We further describe the opportunities enabled by the control over VAE architecture, including conditional, semi-supervised, and joint VAEs. Several case studies of VAE applications for toy models and experimental datasets in Scanning Transmission Electron Microscopy are discussed, emphasizing the deep connection between VAE and basic physical principles. Python codes and datasets discussed in this article are available at https://github.com/saimani5/VAE-tutorials and can be used by researchers as an application guide when applying these to their own datasets.

36 MATERIALS SCIENCE↗
NASA NTRS2024

Land Cover Change Analysis and Spatial Variations in Southeast Asian Nations: Insights on Spatial Scale Dynamics

Land cover type is a fundamental aspect of studies using remote sensing for environmental analysis such as monitoring deforestation, quantifying wildland fire emissions, and more. There are many land cover products available for varying time periods and spatial resolutions, each with different land cover class definitions and number of land cover classes. These differences yield inherent variation in land cover estimates. In this study, we explore the spatial and areal variation between two major datasets for their commonly available time of 2018 to 2022 (MODIS 500m Land Cover and Esri Sentinel-2 10m land cover) in the continental Southeast Asia countries of Cambodia, Laos, Myanmar, Thailand, and Vietnam. To enable comparison, we resampled the datasets and reclassified the land cover classes to a common scheme. Major disagreement was detected between Esri and MODIS, especially for the wetlands/flooded vegetation class in which only 3.6% of pixels were in agreement. In addition to quantifying variation between the datasets, we quantify land cover change at the decadal scale of 2001, 2011, and 2021 for each country and identified province-level hotspots of land cover change. Cambodia experienced the highest rate of land cover change with 67.8% of total land area changed, followed by Vietnam (58.6%), Laos (50.2%), Myanmar (46.9%), and Thailand (46.3%). The highest rate of land cover change was detected in Odtar Mean Chey, Cambodia, which had large swaths of forest area cleared for agricultural production, river damming, and more. Ultimately, many areas in the region experienced forest clearing for crop production (i.e. rice) which appeared to be classified as wetland in the MODIS product, and crop in the Esri product. Myanmar was the only country which did not experience net decline in forest area over the 2001 to 2021 time period.

Land Cover Change↗
NASA NTRS1987

Variational four-dimensional analysis using quasi-geostrophic constraints

A variational four-dimensional analysis technique using quasi-geostrophic models as constraints is examined using gridded fields as data. The analysis method uses a standard iterative nonlinear minimization technique to find the solution to the constraining forecast model which best fits the data as measured by a predefined functional. The minimization algorithm uses the derivative of the functional with respect to each of the initial condition values. This derivative vector is found by inserting the weighted differences between the model solution and the inserted data into a backwards integrating adjoint model. The four-dimensional analysis system was examined by applying it to fields created from a primitive equations model forecast and to fields created from satellite retrievals. The results show that the technique has several interesting characteristics not found in more traditional four-dimensional assimilation techniques. These features include a close fit of the model solution to the observations throughout the analysis interval and an insensitivity to the frequency of data insertion or the amount of data. The four-dimensional analysis technique is very versatile and can be extended to more complex problems with little theoretical difficulty.

Derber, John C.↗
NASA NTRS1986

An analysis of morphologic variation in simple lunar craters

Previous photogeologic classification of lunar craters have differentiated them into various morphologic types. In an attempt to identify the physical factors responsible for these morphologic variations, this study examined 44 fresh, simple lunar craters between 1.1 and 14 km in diameter on the maria. Photogeologic analysis was performed using Apollo stereo panoramic photography, while a quantitative analysis of cross-sectional shape was done using Fourier analysis. These analyses showed that the sample could not be split into two distinct types (flat- and round-floored) as had been done previously. The two crater types at best represent end-members of a distribution of morphologies. Some correlation was found between the degree of relative degradation and floor roundness, implying that flat floors are primary and round floors are degradational features. No difference was found between the average depth-to-diameter ratio of flat- and round-floored craters, indicating that the factor of two variation in depth-to-diameter ratio in this sample of fresh craters is a primary morphologic feature and that minor degradation of fresh craters has no significant effect on average crater depth.

Ravine, M. A.↗
NASA NTRS2004

Analysis of the Variation of Energetic Electron Flux with Respect to Longitude and Distance Normal to the Magnetic Equatorial Plane for Galileo Energetic Particle Detector Data

In this study we examine ten-minute omni-directional averages of energetic electron data measured by the Galileo spacecraft Energetic Particle Detector (EPD). Count rates from electron channels B1, DC2, and DC3 are evaluated using a power law model to yield estimates of the differential electron fluxes from 1 MeV to 11 MeV at distances between 8 and 51 Jupiter radii. Whereas the orbit of the Galileo spacecraft remained close to the rotational equatorial plane of Jupiter, the approximately 11 degree tilt of the magnetic axis of Jupiter relative to its rotational axis allowed the EPD instrument to sample high energy electrons at limited distances normal to the magnetic equatorial plane. We present a Fourier analysis of the semi-diurnal variation of electron fluxes with longitude.

synchrotron radiation↗
NASA NTRS1978

The analysis of temporal variations in regional models of the Sargasso Sea from GEOS-3 altimetry

The dense coverage of short pulse mode GEOS-3 altimeter data in the western North Atlantic provides a basis for studying time variations in the sea surface heights in the Sargasso Sea. Two techniques are utilized: the method of regional models, and the analysis of overlapping passes. An 88 percent correlation is obtained between the location of cyclonic eddies obtained from infrared imagery and sea surface height minima in the altimeter models. This figure drops to 59 percent in the case of correlations with maxima and minima of surface temperature fields. The analysis of overlapping passes provides a better picture of instantaneous sea state through wavelengths greater than 30 km. The variability of the Sargasso Sea through wavelengths between 150 km and 5000 km is estimated at + or - 28 cm. This value is in reasonable agreement with oceanographic estimates and is compatible with the eddy kinetic energy of a wind driven circulation.

Mather, R. S.↗