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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Spatial variability of summer Florida precipitation and its impact on microwave radiometer rainfall-measurement systems

Three-dimensional radar data for three summer Florida storms are used as input to a microwave radiative transfer model. The model simulates microwave brightness observations by a 19-GHz, nadir-pointing, satellite-borne microwave radiometer. The statistical distribution of rainfall rates for the storms studied, and therefore the optimal conversion between microwave brightness temperatures and rainfall rates, was found to be highly sensitive to the spatial resolution at which observations were made. The optimum relation between the two quantities was less sensitive to the details of the vertical profile of precipitation. Rainfall retrievals were made for a range of microwave sensor footprint sizes. From these simulations, spatial sampling-error estimates were made for microwave radiometers over a range of field-of-view sizes. The necessity of matching the spatial resolution of ground truth to radiometer footprint size is emphasized. A strategy for the combined use of raingages, ground-based radar, microwave, and visible-infrared (VIS-IR) satellite sensors is discussed.

Turner, B. J.↗
NASA NTRS2013

Future Extreme Heat Scenarios to Enable the Assessment of Climate Impacts on Public Health over the Coterminous U.S.

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km), to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wideranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S

Quattrochi, Dale A.↗
NASA NTRS2014

Linking Excessive Heat with Daily Heat-Related Mortality over the Coterminous United States

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km) much finer than that of GCMs, to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices, and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.

Quattrochi, Dale A.↗
NASA NTRS1987

Comparison of HCMM and GOES satellite temperatures and evaluation of surface statistics

Infrared data from GOES and HCMM satellites for areas in northern central and southern Florida were compared for February 1, 1979 to examine spatial variations of surface temperatures within GOES pixels using nested HCMM pixel surface temperatures. Standard deviations of HCMM pixel temperatures associated with mean temperature of a GOES pixel temperature during this period were found to be smallest for homogeneous water surfaces (about 0.5 K), to be slightly larger for homogeneous land surfaces (about 0.1 K), and to range up as high as 3.5 K for mixed water-land surfaces.

Chen, E.↗
DOE OSTI2025

Kinetic Monte Carlo simulations of aging in δ -Pu

We have developed a first-passage kinetic Monte Carlo approach for materials aging to investigate the sensitivity of void swelling to model parameters, including helium bubble density and size distribution. In addition to explicitly accounting for the spatial distribution of individual point defects, bubbles, and voids, our approach can simulate total doses equivalent to 100 years of natural aging on statistically representative volumes of materials. This technique enables us to study the effects on swelling and radiation damage evolution due to temperature and dose rate (as altered in artificially aged experiments), differences in effective interaction radii between vacancies and interstitials, and varying defect diffusion activation energies, while providing more detailed information than previous rate-equation based approaches. In conclusion, our results indicate that spatial effects that are not modeled in mean-field rate theories could play a significant role in void swelling initiation and growth for certain regimes of model parameters.

Actinides↗
NASA NTRS2014

Impact of Satellite Viewing-Swath Width on Global and Regional Aerosol Optical Thickness Statistics and Trends

We use the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite aerosol optical thickness (AOT) product to assess the impact of reduced swath width on global and regional AOT statistics and trends. Alongtrack and across-track sampling strategies are employed, in which the full MODIS data set is sub-sampled with various narrow-swath (approximately 400-800 km) and single pixel width (approximately 10 km) configurations. Although view-angle artifacts in the MODIS AOT retrieval confound direct comparisons between averages derived from different sub-samples, careful analysis shows that with many portions of the Earth essentially unobserved, spatial sampling introduces uncertainty in the derived seasonal-regional mean AOT. These AOT spatial sampling artifacts comprise up to 60%of the full-swath AOT value under moderate aerosol loading, and can be as large as 0.1 in some regions under high aerosol loading. Compared to full-swath observations, narrower swath and single pixel width sampling exhibits a reduced ability to detect AOT trends with statistical significance. On the other hand, estimates of the global, annual mean AOT do not vary significantly from the full-swath values as spatial sampling is reduced. Aggregation of the MODIS data at coarse grid scales (10 deg) shows consistency in the aerosol trends across sampling strategies, with increased statistical confidence, but quantitative errors in the derived trends are found even for the full-swath data when compared to high spatial resolution (0.5 deg) aggregations. Using results of a model-derived aerosol reanalysis, we find consistency in our conclusions about a seasonal-regional spatial sampling artifact in AOT Furthermore, the model shows that reduced spatial sampling can amount to uncertainty in computed shortwave top-ofatmosphere aerosol radiative forcing of 2-3 W m(sup−2). These artifacts are lower bounds, as possibly other unconsidered sampling strategies would perform less well. These results suggest that future aerosol satellite missions having significantly less than full-swath viewing are unlikely to sample the true AOT distribution well enough to obtain the statistics needed to reduce uncertainty in aerosol direct forcing of climate.

remote sensing↗
NASA NTRS1983

Spatial inventory integrating raster databases and point sample data

A timber inventory of the Eldorado National Forest, located in east-central California, provides an example of the use of a Geographic Information System (GIS) to stratify large areas of land for sampling and the collection of statistical data. The raster-based GIS format of the VICAR/IBIS software system allows simple and rapid tabulation of areas, and facilitates the selection of random locations for ground sampling. Algorithms that simplify the complex spatial pattern of raster-based information, and convert raster format data to strings of coordinate vectors, provide a link to conventional vector-based geographic information systems.

Strahler, A. H.↗
DOE OSTI2024

Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions

Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Aquaplanets as a Framework for Examination of Aerosol Effects

Although fundamental to the planetary radiative balance, aerosol impacts are highly uncertain in climate simulations because of the uneven distribution of aerosol sources and the complex interactions with radiation and clouds that are difficult to represent in climate models. This study proposes that aquaplanet configurations represent an idealized framework to investigate aerosol effects. As a simple demonstration, a series of aquaplanet simulations with the Community Atmosphere Model version 6 shows that the spatial distribution of aerosol emissions changes the aerosol effective radiative forcing even with unchanged total emissions. Some statistical properties of the simulations are presented to show that relatively short model integrations yield robust results. Much of the aerosol effect is shown to arise from aerosol–cloud interactions, especially through rapid adjustments associated with the aerosol lifetime effect that alter the cloud optical thickness.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Correlative piezoresponse and micro-Raman imaging of CuInP 2 S 6 –In 4/3 P 2 S 6 flakes unravels phase-specific phononic fingerprint via unsupervised learning

Characterizing the novel properties of layered van der Waals materials is key for their application in functional devices. A better understanding of this type of material requires correlative imaging of diverse nanoscale material properties. Within this class of materials, CuInP 2 S 6 (CIPS) has received a significant degree of interest due to its ionically mediated room temperature ferroelectricity. Moreover, it is possible to form stable self-assembled heterostructures of ferroelectric CuInP 2 S 6 (CIPS) and non-ferroelectric (i.e., lacking Cu) In 4/3 P 2 S 6 (IPS) phases, by controlling the targeted composition and kinetics of synthesis. In this work, we present a correlative nanometric imaging study of the phononic modes and piezoelectricity of the phase-separated thin heteroepitaxial CIPS/IPS flakes. Here, we show that it is possible to isolate the different phononic modes of the two phases by spatially correlating them with their distinct ferroelectric behavior. The coupling of our experimental data with unsupervised learning statistical methods enables unraveling specific Raman peaks that are characteristic of each chemical phase (CIPS and IPS) present in the composite sample, discarding the less significant ones.

correlative microscopy↗
DOE OSTI2025

First observations of solar halo gamma rays over a full solar cycle

We analyze 15 years of Fermi-LAT data and produce a detailed model of the Sun’s inverse-Compton scattering emission (solar halo), which is powered by interactions between ambient cosmic-ray electrons and positrons with sunlight. By developing a novel analysis method to analyze moving sources, we robustly detect the solar halo at energies between 31.6 MeV and 100 GeV, and angular extensions up to 45° from the Sun, providing new insight into spatial regions where there are no direct measurements of the Galactic cosmic-ray flux. The large statistical significance of our signal allows us to subdivide the data and provide the first 𝛾-ray probes into the time variation and azimuthal asymmetry of the solar modulation potential, finding time-dependent changes in solar modulation both parallel and perpendicular to the ecliptic plane. Our results are consistent with (but with independent uncertainties from) local cosmic-ray measurements, unlocking new probes into astrophysical processes near the solar surface.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. Here, the novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial- temporal correlations simultaneously. Second, two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad- GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model

Abstract Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting) and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective “average event.” We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT’s capacities on a wave–mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass, which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also backward in time to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the reactive current . The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions.

Meteorology & Atmospheric Sciences↗
NASA NTRS1985

Simulation and modeling of homogeneous, compressed turbulence

Low Reynolds number homogeneous turbulence undergoing low Mach number isotropic and one-dimensional compression was simulated by numerically solving the Navier-Stokes equations. The numerical simulations were performed on a CYBER 205 computer using a 64 x 64 x 64 mesh. A spectral method was used for spatial differencing and the second-order Runge-Kutta method for time advancement. A variety of statistical information was extracted from the computed flow fields. These include three-dimensional energy and dissipation spectra, two-point velocity correlations, one-dimensional energy spectra, turbulent kinetic energy and its dissipation rate, integral length scales, Taylor microscales, and Kolmogorov length scale. Results from the simulated flow fields were used to test one-point closure, two-equation models. A new one-point-closure, three-equation turbulence model which accounts for the effect of compression is proposed. The new model accurately calculates four types of flows (isotropic decay, isotropic compression, one-dimensional compression, and axisymmetric expansion flows) for a wide range of strain rates.

Wu, C. T.↗
NASA NTRS1990

Probing the tides in interacting galaxy pairs

Detailed spectroscopic and imaging observations of colliding elliptical galaxies revealed unmistakable diagnostic signatures of the tidal interactions. It is possible to compare both the distorted luminosity distributions and the disturbed internal rotation profiles with numerical simulations in order to model the strength of the tidal gravitational field acting within a given pair of galaxies. Using the best-fit numerical model, one can then measure directly the mass of a specific interacting binary system. This technique applies to individual pairs and therefore complements the classical methods of measuring the masses of galaxy pairs in well-defined statistical samples. The 'personalized' modeling of galaxy pairs also permits the derivation of each binary's orbit, spatial orientation, and interaction timescale. Similarly, one can probe the tides in less-detailed observations of disturbed galaxies in order to estimate some of the physical parameters for larger samples of interacting galaxy pairs. These parameters are useful inputs to the more universal problems of (1) the galaxy merger rate, (2) the strength and duration of the driving forces behind tidally stimulated phenomena (e.g., starbursts and maybe quasi steller objects), and (3) the identification of long-lived signatures of interaction/merger events.

Borne, Kirk D.↗
NASA NTRS1992

Distribution of Reynolds stress carried by mesoscale variability in the Antarctic Circumpolar Current

Satellite altimeter data collected by the Geosat Exact Repeat Mission were used to investigate turbulent stress resulting from the variability of surface geostrophic currents in the Antarctic Circumpolar Current. The altimeter measured sea level along the subsatellite track. The variability of the along-track slope of sea level is directly proportional to the variability of surface geostrophic currents in the cross-track direction. Because the grid of crossover points is dense at high latitudes, the satellite data could be used for mapping the temporal and spatial variability of the current. Two and a half years of data were used to compute the statistical structure of the variability. The statistics included the probability distribution functions for each component of the current, the time-lagged autocorrelation functions of the variability, and the Reynolds stress produced by the variability. The results demonstrate that stress is correlated with bathymetry. In some areas the distribution of negative stress indicate that eddies contribute to an acceleration of the mean flow, strengthening the hypothesis that baroclinic instability makes important contributions to strong oceanic currents.

Johnson, Thomas J.↗
NASA NTRS1993

Statistical Approach To Determination Of Texture In SAR

Paper presents statistical approach to analysis of texture in synthetic-aperture-radar (SAR) images. Objective: to extract intrinsic spatial variability of distributed target from overall spatial variability of SAR image.

Rignot, Eric J.↗