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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

The Effects of an Absorbing Smoke Layer on MODIS Marine Boundary Layer Cloud Optical Property Retrievals and Radiative Forcing

Clouds, aerosols, and their interactions are widely considered to be key uncertainty components in our current understanding of the Earth's atmosphere and radiation budget. The work presented here is focused on the quasi-permanent marine boundary layer . (MBL) clouds off the southern Atlantic coast of Africa and the effects on MODIS cloud optical property retrievals (MOD06) of an overlying absorbing smoke layer. During much of August and September, a persistent smoke layer resides over this region, produced from extensive biomass burning throughout the southern African savanna. The resulting absorption, which increases with decreasing wavelength, potentially introduces biases into the MODIS cloud optical property retrievals of the underlying MBL clouds. This effect is more pronounced in the cloud optical thickness retrievals, which over ocean are derived from the wavelength channel centered near 0.86 micron (effective particle size retrievals are derived from the longer-wavelength near-IR channels at 1.6, 2.1, and 3.7 microns). Here, the spatial distributions of the scalar statistics of both the cloud and aerosol layers are first determined from the CALIOP 5 km layer products. Next, the MOD06 look-up tables (LUTs) are adjusted by inserting an absorbing smoke layer of varying optical thickness over the cloud. Retrievals are subsequently performed for a subset of MODIS pixels collocated with the CALIOP ground track, using smoke optical thickness from the CALIOP 5km aerosol layer product to select the appropriate LUT. The resulting differences in cloud optical property retrievals due to the inclusion of the smoke layer in the LUTs will be examined. In addition, the direct radiative forcing of this smoke layer will be investigated from the perspective of the cloud optical property retrieval differences.

Meyer, Kerry↗
NASA NTRS1981

Contextual classification of multispectral image data: An unbiased estimator for the context distribution

A key input to a statistical classification algorithm, which exploits the tendency of certain ground cover classes to occur more frequently in some spatial context than in others, is a statistical characterization of the context: the context distribution. An unbiased estimator of the context distribution is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real LANDSAT data sets are presented and contrasted with results from non-contextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

Tilton, J. C.↗
NASA NTRS2022

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision-makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods either rely on chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time-series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product – NASA’s Goddard Earth Observing System “Composition Forecasting” (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next five days at a 1-km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next two days had overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following three forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with validation normalized mean bias close to zero, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

PM2.5↗
NASA NTRS1993

A description of rainfall interception over large areas

A new scheme is developed for describing interception at spatial scales comparable to the typical resolution of climate models. The scheme is based on the Rutter model of interception and statistical description of the subgrid-scale spatial variability of canopy storage and rainfall. The interception loss simulated by the new scheme is significantly smaller than those simulated by other schemes that do not include considerations for spatial variability. The explanation of this result is partly in the enhancement of spatially averaged canopy drainage due to the large local drainage from the few buckets of large canopy storage. The relative reduction in interception loss simulated by the new scheme may explain the overestimation of interception loss by climate models that do not include the effects of spatial variability on interception processes.

Eltahir, E. A. B.↗
DOE OSTI2026

Associated Particle Imaging of Neutron Inelastic Scatter: 3-D Reconstruction, Capabilities, and Challenges

Associated particle imaging (API) offers unique advantages for 3-D imaging of neutron inelastic scatter, including single-view tomographic imaging and data acquisition when access is limited to only one side of the interrogated object. However, widespread adoption of neutron inelastic scatter imaging has been impeded by several inherent challenges, most prominently spatial resolution, self-attenuation, and statistical noise. Here, in this work, the capabilities and challenges of neutron inelastic scatter imaging are investigated. Instead of focusing on a single imaging application, we identify imaging principles that hold for various neutron inelastic scatter imaging techniques. The primary challenges for 3-D imaging are characterized. The inherent spatial resolution in the time-of-flight (TOF) dimension is derived based on the known system timing resolution and scan geometry. Three reconstruction algorithms are described and demonstrated, including the introduction of modern iterative reconstruction incorporating a physics-based system model. Simulation is leveraged to demonstrate imaging capability with varying coincidence count levels and system timing resolution. An example of measured data with both back-scatter and forward-scatter detector positioning is presented. System design characteristics and their effects on image quality are identified. The imaging framework presented in this article has the potential to facilitate growth of 3-D neutron inelastic scatter API by identifying applications that are a good match for the technique and by targeting system development resources toward the requirements of a specific imaging task.

Associated particle imaging (API)↗
NASA NTRS2001

Implications of the Observed Mesoscale Variations of Clouds for Earth's Radiation Budget

The effect of small-spatial-scale cloud variations on radiative transfer in cloudy atmospheres currently receives a lot of research attention, but the available studies are not very clear about which spatial scales are important and report a very large range of estimates of the magnitude of the effects. Also, there have been no systematic investigations of how to measure and represent these cloud variations. We exploit the cloud climatology produced by the International Satellite Cloud Climatology Project (ISCCP) to: (1) define and test different methods of representing cloud variation statistics, (2) investigate the range of spatial scales that should be included, (3) characterize cloud variations over a range of space and time scales covering mesoscale (30 - 300 km, 3-12 hr) into part of the lower part of the synoptic scale (300 - 3000 km, 1-30 days), (4) obtain a climatology of the optical thickness, emissivity and cloud top temperature variability of clouds that can be used in weather and climate GCMS, together with the parameterization proposed by Cairns et al. (1999), to account for the effects of small-scale cloud variations on radiative fluxes, and (5) evaluate the effect of observed cloud variations on Earth's radiation budget. These results lead to the formulation of a revised conceptual model of clouds for use in radiative transfer calculations in GCMS. The complete variability climatology can be obtained from the ISCCP Web site at http://isccp.giss.nasa.gov.

Rossow, William B.↗
DOE OSTI2022

Superior Daily and Sub-Daily Precipitation Statistics for Intense and Long-Lived Storms in Global Storm-Resolving Models

Here, daily and sub-daily precipitation statistics are investigated from three global model ensembles: (a) global storm-resolving models (GSRMs) with typical horizontal resolutions of ~4 km, (b) “high”-resolution global models with typical resolutions of ~50 km and (c) “standard”-resolution global models with typical resolutions of ~100 km. Compared to two satellite rainfall datasets, GSRMs convincingly exhibit superior performance for statistics of heavier rain rate events including their diurnal cycle, spatial propagation and the amount contributed by intense precipitation, but not for statistics of weaker or shorter duration precipitation. Both high- and standard-resolution models fail to simulate the correct phase and amplitude of diurnal cycle of precipitation and the propagating convection in the Central US, but high-resolution models show relative improvement in the distribution of precipitation frequency and amount, especially for intense precipitation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

A unified N-body and statistical treatment of stellar dynamics. I - The hybrid code. II - Applications to globular cluster cores

A unified N-body and statistical treatment of stellar dynamics is developed and applied to the late stages of core collapse and early stages of post collapse evolution in globular clusters. A 'hybrid' computer code is joined to a direct N-body code which is used to calculate exactly the behavior of particles in the inner spatial region, and the combination is used to follow particles statistically in the outer spatial region. A transition zone allows the exchange of particles and energy between the two regions. The main application results include: formation of a hard central binary system, reversal of core collapse and expansion due to the heat input from this binary, ejection of the binary from the core, and recollapse of the core; density profiles that form a one-parameter sequence during the core oscillations; and indications that these oscillations will eventually cease.

Mcmillan, S. L. W.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-12973-DND

CFD methods are employing semi-empirical models used in analysis of hypersonic airbreathing engine flow paths. RANS (or Favre averaged) codes have models for turbulent stresses, mass and energy transport; turbulence chemistry interactions. LES methods have models for subgrid scale turbulence. Models depend upon experimental data for validation. Information on mean flow and statistics of the turbulent fluctuation in flow properties is useful. Data requirements: simple well defined supersonic combustion flows with well-known boundary conditions, time and spatially resolved, good instrument precision, and converged statistics. Approach: dual-pump CARS and planar laser-induced fluorescence imaging of OH radical (PLIF).

Andrew Cutler↗
DOE OSTI2021

Good and proper: self-similarity of N -body simulations with proper force softening

ABSTRACT Analysis of self-similarity in scale-free N-body simulations reveals the spatial and temporal scales for which statistics measured in cosmological simulations are converged to the physical continuum limit. We examine how the range of scales in which the two-point correlation function is converged depends on the force softening length and whether it is held constant in comoving or proper coordinates. We find that a proper softening that reaches roughly 1/30th of the inter-particle spacing by the end of the simulation resolves the same spatial and temporal scales as a comoving softening of the same length while using a third fewer time-steps, for a range of scale factors typical to Lambda cold dark matter (ΛCDM) simulations. We additionally infer an inherent resolution limit, set by the particle mass and scaling as a−1/2, beyond which reducing the softening does not improve the resolution. We postulate a mapping of these results with spectral index n = −2 to ΛCDM simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Evidence of galaxy assembly bias in SDSS DR7 galaxy samples from count statistics

We present observational constraints on the galaxy–halo connection, focusing particularly on galaxy assembly bias from a novel combination of counts-in-cylinders statistics, P(N CIC ), with the standard measurements of the projected two-point correlation function w p (r p ), and number density n gal of galaxies. We measure n gal , w p (r p ), and P(N CIC ) for volume-limited, luminosity-threshold samples of galaxies selected from SDSS DR7, and use them to constrain halo occupation distribution (HOD) models, including a model in which galaxy occupation depends upon a secondary halo property, namely halo concentration. We detect significant positive central assembly bias for the M r < -20.0 and M r <-19.5 samples. Central galaxies preferentially reside within haloes of high concentration at fixed mass. Positive central assembly bias is also favoured in the M r < -20.5 and M r < -19.0 samples. We find no evidence of central assembly bias in the M r < -21.0 sample. We observe only a marginal preference for negative satellite assembly bias in the M r < -20.0 and M r < -19.0 samples, and non-zero satellite assembly bias is not indicated in other samples. Our findings underscore the necessity of accounting for galaxy assembly bias when interpreting galaxy survey data, and demonstrate the potential of count statistics in extracting information from the spatial distribution of galaxies, which could be applied to both galaxy–halo connection studies and cosmological analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Statistical upscaling of ecosystem CO 2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties

Abstract The regional variability in tundra and boreal carbon dioxide (CO 2 ) fluxes can be high, complicating efforts to quantify sink‐source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO 2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO 2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 from 148 terrestrial high‐latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO 2 fluxes and test the accuracy and uncertainty of different statistical models. CO 2 fluxes were upscaled at relatively high spatial resolution (1 km 2 ) across the high‐latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE‐focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO 2 sink strength was larger in the boreal biome (observed and predicted average annual NEE −46 and −29 g C m −2 yr −1 , respectively) compared to tundra (average annual NEE +10 and −2 g C m −2 yr −1 ). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO 2 budget, estimated using the annual NEE ensemble prediction, suggests the high‐latitude region was on average an annual CO 2 sink during 1990–2015, although uncertainty remains high.

Virkkala, Anna‐Maria↗
DOE OSTI2024

Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials

Using knowledge from statistical thermodynamics and crystallography, we develop an image–image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate–adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. In conclusion, employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R 2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

Sun, Yangzesheng↗
NASA NTRS2016

Uncertainties in Coastal Ocean Color Products: Impacts of Spatial Sampling

With increasing demands for ocean color (OC) products with improved accuracy and well characterized, per-retrieval uncertainty budgets, it is vital to decompose overall estimated errors into their primary components. Amongst various contributing elements (e.g., instrument calibration, atmospheric correction, inversion algorithms) in the uncertainty of an OC observation, less attention has been paid to uncertainties associated with spatial sampling. In this paper, we simulate MODIS (aboard both Aqua and Terra) and VIIRS OC products using 30 m resolution OC products derived from the Operational Land Imager (OLI) aboard Landsat-8, to examine impacts of spatial sampling on both cross-sensor product intercomparisons and in-situ validations of R(sub rs) products in coastal waters. Various OLI OC products representing different productivity levels and in-water spatial features were scanned for one full orbital-repeat cycle of each ocean color satellite. While some view-angle dependent differences in simulated Aqua-MODIS and VIIRS were observed, the average uncertainties (absolute) in product intercomparisons (due to differences in spatial sampling) at regional scales are found to be 1.8%, 1.9%, 2.4%, 4.3%, 2.7%, 1.8%, and 4% for the R(sub rs)(443), R(sub rs)(482), R(sub rs)(561), R(sub rs)(655), Chla, K(sub d)(482), and b(sub bp)(655) products, respectively. It is also found that, depending on in-water spatial variability and the sensor's footprint size, the errors for an in-situ validation station in coastal areas can reach as high as +/- 18%. We conclude that a) expected biases induced by the spatial sampling in product intercomparisons are mitigated when products are averaged over at least 7 km × 7 km areas, b) VIIRS observations, with improved consistency in cross-track spatial sampling, yield more precise calibration/validation statistics than that of MODIS, and c) use of a single pixel centered on in-situ coastal stations provides an optimal sampling size for validation efforts. These findings will have implications for enhancing our understanding of uncertainties in ocean color retrievals and for planning of future ocean color missions and the associated calibration/validation exercises.

Coastal ocean color↗
DOE OSTI2021

The first Hubble diagram and cosmological constraints using superluminous supernovae

In this work, we present the first Hubble diagram of superluminous supernovae (SLSNe) out to a redshift of two, together with constraints on the matter density, Ω M , and the dark energy equation-of-state parameter, w(≡p/ρ). We build a sample of 20 cosmologically useful SLSNe I based on light curve and spectroscopy quality cuts. We confirm the robustness of the peak–decline SLSN I standardization relation with a larger data set and improved fitting techniques than previous works. We then solve the SLSN model based on the above standardization via minimization of the χ2 computed from a covariance matrix that includes statistical and systematic uncertainties. For a spatially flat Λ cold dark matter (ΛCDM) cosmological model, we find $\Omega _{\rm M}=0.38^{+0.24}_{-0.19}$, with an rms of 0.27 mag for the residuals of the distance moduli. For a w 0 w a CDM cosmological model, the addition of SLSNe I to a ‘baseline’ measurement consisting of Planck temperature together with Type Ia supernovae, results in a small improvement in the constraints of w 0 and wa of 4 per cent. We present simulations of future surveys with 868 and 492 SLSNe I (depending on the configuration used) and show that such a sample can deliver cosmological constraints in a flat ΛCDM model with the same precision (considering only statistical uncertainties) as current surveys that use Type Ia supernovae, while providing a factor of 2–3 improvement in the precision of the constraints on the time variation of dark energy, w 0 and w a . This paper represents the proof of concept for superluminous supernova cosmology, and demonstrates they can provide an independent test of cosmology in the high-redshift (z > 1) universe.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2021

Global Intercomparison of hyper-resolution ECOSTRESS coastal sea surface temperature measurements from the Space Station with VIIRS-N20

The ECOSTRESS multi-channel thermal radiometer on the Space Station has an unprecedented spatial resolution of 70 m and a return time of hours to 5 days. It resolves details of oceanographic features not detectable in imagery from MODIS or VIIRS, and has open-ocean coverage, unlike Landsat. We calibrated two years of ECOSTRESS sea surface temperature observations with L2 data from VIIRS-N20 (2019–2020) worldwide but especially focused on important upwelling systems currently undergoing climate change forcing. Unlike operational SST products from VIIRS-N20, the ECOSTRESS surface temperature algorithm does not use a regression approach to determine temperature, but solves a set of simultaneous equations based on first principles for both surface temperature and emissivity. We compared ECOSTRESS ocean temperatures to well-calibrated clear sky satellite measurements from VIIRS-N20. Data comparisons were constrained to those within 90 min of one another using co-located clear sky VIIRS and ECOSTRESS pixels. ECOSTRESS ocean temperatures have a consistent 1.01 ◦C negative bias relative to VIIRS-N20, although deviation in brightness temperatures within the 10.49 and 12.01 µm bands were much smaller. As an alternative, we compared the performance of NOAA, NASA, and U.S. Navy operational split-window SST regression algorithms taking into consideration the statistical limitations imposed by intrinsic SST spatial autocorrelation and applying corrections on brightness temperatures. We conclude that standard bias-correction methods using already validated and well-known algorithms can be applied to ECOSTRESS SST data, yielding highly accurate products of ultra-high spatial resolution for studies of biological and physical oceanography in a time when these are needed to properly evaluate regional and even local impacts of climate change.

Nicolas Weidberg↗
DOE OSTI2025

Processing-dependent chemical ordering in Cu 3 Au characterized via non-destructive Bragg coherent diffraction imaging

Of current importance for alloy design is controlling chemical ordering through processing routes to optimize an alloy's mechanical properties for a desired application. However, characterization of chemical ordering remains an ongoing challenge, particularly when nondestructive characterization is needed. Here, in this study, Bragg coherent diffraction imaging is used to reconstruct morphology and lattice displacement in model Cu 3 Au nanocrystals that have undergone different heat treatments to produce variation in chemical ordering. The magnitudes and distributions of the scattering amplitudes (proportional to electron density) and lattice strains within these crystals are then analyzed to correlate them to the expected amount of chemical ordering present. Nanocrystals with increased amounts of ordering are found to generally have less extreme strains present and reduced strain distribution widths. In addition, statistical correlations are found between the spatial arrangement of scattering amplitude and lattice strains.

Warren, Nathaniel [Pennsylvania State Univ., Unive↗