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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Examining 18 Years of Journal Publications to Characterize Usage Modes of Giovanni, a Versatile Earth Science Data Web Service

Introduction to Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) Giovanni … is a Web-based visualization and analysis system that provides 22 different visualization and analysis options, operating on thousands of Earth science data variables generated by satellite instrument observations and from related model datasets Giovanni … was originally conceived as a data exploration tool, but its ease-of-use, analytical capabilities (spatial and temporal subsetting, multi-period averaging, data mapping and time-series, and more) have led to its use as a multi-discipline research tool Giovanni … provided unprecedented access to NASA Earth science data for many different disciplines, AND is still providing a simple way to find, analyze, visualize, and utilize such data for a wide spectrum of research topics

James Acker↗
DOE OSTI2024

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

A physically-based transformation of Thematic Mapper data The TM tasseled cap

In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space.

Crist, E. P.↗
NASA NTRS1999

The Goddard Three Dimensional Chemistry and Transport Model-A Waystation on the Road to Predicting the Future of Stratospheric Ozone

The Goddard 3D Chemistry and Transport Model (CTM) is an off-line model which includes a full description of stratospheric photochemistry and calculates constituent transport using winds and temperatures from the Goddard Earth Observing System Data Assimilation System. The model can be used to understand and quantify the photochemical and transport processes which produce observed constituent evolution and variability. Data from any platform - ground based, aircraft, balloon, satellite - can play a role. Since any measurement systems has its own set of advantages and disadvantages (e.g., good temporal sampling but poor spatial sampling; good spatial and temporal sampling but poor vertical resolution; high vertical resolution and spatial sampling but poor temporal sampling), using data from disparate sources and a CTM can provide a more complete picture of atmospheric processes.

Douglass, Anne↗
NASA NTRS1999

Keplerian Motion of Broad-Line Region Gas as Evidence for Supermassive Black Holes in Active Galactic Nuclei

Emission-line variability data on NGC 5548 argue strongly for the existence of a mass of order 7 x l0(exp 7) solar mass, within the inner few light-days of the nucleus in the Seyfert 1 galaxy NGC 5548. The time-delayed response of the emission lines to continuum variations is used to infer the size of the line-emitting region, and these determinations are combined with measurements of the Doppler widths of the variable line components to estimate a virial mass. The data for several different emission lines spanning an order of magnitude in distance from the central source show the expected V proportional to r(sup -1/2) correlation and are consistent with a single value for the mass.

Peterson, Bradley M.↗
DOE OSTI2026

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗
NASA NTRS2017

Giovanni in the Cloud: Earth Science Data Exploration in Amazon Web Services

Giovanni is an exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of data centers. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.

Giovanni↗
NASA NTRS1988

Model-scale sound propagation experiment

The results of a scale model propagation experiment to investigate grazing propagation above a finite impedance boundary are reported. In the experiment, a 20 x 25 ft ground plane was installed in an anechoic chamber. Propagation tests were performed over the plywood surface of the ground plane and with the ground plane covered with felt, styrofoam, and fiberboard. Tests were performed with discrete tones in the frequency range of 10 to 15 kHz. The acoustic source and microphones varied in height above the test surface from flush to 6 in. Microphones were located in a linear array up to 18 ft from the source. A preliminary experiment using the same ground plane, but only testing the plywood and felt surfaces was performed. The results of this first experiment were encouraging, but data variability and repeatability were poor, particularly, for the felt surface, making comparisons with theoretical predictions difficult. In the main experiment the sound source, microphones, microphone positioning, data acquisition, quality of the anechoic chamber, and environmental control of the anechoic chamber were improved. High-quality, repeatable acoustic data were measured in the main experiment for all four test surfaces. Comparisons with predictions are good, but limited by uncertainties of the impedance values of the test surfaces.

Willshire, William L., Jr.↗
DOE OSTI2025

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Goddard High Resolution Spectrograph Observations of Variability in the RS Canum Venaticorum System V711 Tauri (HR 1099)

Goddard High Resolution Spectrograph (GHRS) observations of the RS CVn-type binary V711 Tau (Kl IV+G5 IV) were obtained at several phases over two consecutive stellar orbital cycles in order to study ultraviolet emission-line profile and flux variability. Spectra cover the Mg II h and k lines, C IV doublet, and Si IV region, as well as the density-sensitive lines of C III] (1909 A) and Si III] (1892 A). IUE spectra, Extreme Ultra Violet (EUV) data, and Ultraviolet, Blue, Visual (UBV) photometry were obtained contemporaneously with the GHRS data. Variable extended wings were detected in the Mg II lines. We discuss the Mg II line profile variability using various Gaussian emission profile models. No rotational modulation of the line profiles was observed, but there were several large flares. These flares produced enhanced emission in the extended line wings, radial velocity shifts, and asymmetries in some line profiles. Nearly continuous flaring for more than 24 hr, as indicated in the IUE data, represents the most energetic and long-lived chromospheric and transition region flare ever observed with a total energy much greater than 5 x 10(exp 35) ergs. The C III] to Si III] line ratio is used to estimate the plasma density during the flares.

Dempsey, Robert C.↗
NASA NTRS2011

Reconciling Satellite-Derived Atmospheric Properties with Fine-Resolution Land Imagery: Insights for Atmospheric Correction

This paper investigates to what extent satellite measurements of atmospheric properties can be reconciled with fine-resolution land imagery, in order to improve the estimates of surface reflectance through physically based atmospheric correction. The analysis deals with mountainous area (Landsat scene of Peruvian Amazon/Andes, 72 E and 13 S), where the atmosphere is highly variable. Data from satellite sensors were used for characterization of the key atmospheric constituents: total water vapor (TWV), aerosol optical depth (AOD), and total ozone. Constituent time series revealed the season-dependent mean state of the atmosphere and its variability. Discrepancies between AOD from the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS) highlighted substantial uncertainty of atmospheric aerosol properties. The distribution of TWV and AOD over a Landsat scene was found to be exponentially related to ground elevation (mean R(sup 2) of 0.82 and 0.29, respectively). In consequence, the atmosphere-induced and seasonally varying bias of the top-of-atmosphere signal was also elevation dependent (e.g., mean Normalized Difference Vegetation Index bias at 500 m was 0.06 and at 4000 m was 0.01). We demonstrate that satellite measurements of key atmospheric constituents can be downscaled and gap filled with the proposed "background + anomalies" approach, to allow for a better compatibility with fine-resolution land surface imagery. Older images (i.e., predating the MODIS/ATSR era), without coincident atmospheric data, can be corrected using climatologies derived from time series of satellite retrievals. Averaging such climatologies over space compromises the quality of correction result to a much greater degree than averaging them over time. We conclude that the quality of both recent and older fine-resolution land surface imagery can be improved with satellite-based atmospheric data acquired to date.

Zelazowski, Przemyslaw↗
NASA NTRS2001

Response Surface Modeling Using Multivariate Orthogonal Functions

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

Morelli, Eugene A.↗
NASA NTRS1987

Transformation Aids Crop Analysis From Spectral Data

Crop analysis aided by mathematical transformation that optimizes perspective of six-dimensional, six-band, spectral data taken from spacecraft or aircraft. Transformation applied to any temperatureclimate vegetated scene, providing direct view of regions of data concentration resulting from band correlations and fundamental reflectance properties of scene classes. Almost all of data variability captured in three spectral features, thus reducing by factor of 2 number of spectral features carried, incurring minimal loss of important information. Three-dimensional representation with two principal planes retains about 95 percent of six-dimensional spectral data used to distinguish among scenes containing green plants and bare soil with varying degrees of moisture.

Crist, E. P.↗
NASA NTRS2004

AGN Black Hole Masses from Reverberation Mapping

Emission-line variability data on bright AGNs indicates that the central objects in these sources have masses in the million to few-hundred million solar mass range. The time-delayed response of the emission lines to continuum variations can be used to infer the size of the line-emitting region via light travel-time arguments. By combining these sizes with the Doppler widths of the variable part of the emission lines, a virial mass estimate can be obtained. For three especially well-studied sources, NGC 5548, NGC 7469, and 3C 390.3, data on multiple emission lines can be used to test the virial hypothesis. In each of these cases, the response time of the various emission lines is anticorrelated with the line width, with the dependence as expected for gravitationally bound motion of the line-emitting clouds, i.e., that the square of the Doppler line width is inversely proportional to the emission-line time delay. Virial masses based on the Balmer lines have now been measured for about three dozen AGNs. Systematic effects currently limit the accuracy of these masses to a factor of several, but characteristics of the radius-luminosity and mass-luminosity relationships for AGNs are beginning to emerge.

Peterson, B. M.↗
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

X-ray spectral constraints on the broad-line cloud geometry of NGC 4151

X-ray spectral data from NGC 4151 taken with the Einstein Solid-State Spectrometer (SSS) and the HEAO-1 A-2 experiment cannot be simply reconciled with absorption from a uniform column of cold gas. The SSS data can, however, be explained in terms of a clumped absorber with approximately 10% uncovered fraction and factor-of-two overabundances in Z equal to or greater than 14 elements relative to solar oxygen. It is shown that these and previously reported spectral and variability data can be quantitatively reconciled with absorption arising in the cold clouds responsible for the broad optical line emission if the cloud dimensions are small compared to the central source size. It is suggested that the lack of significant X-ray absorption observed from much higher luminosity Seyferts and quasars is a natural consequence of our picture for NGC 4151.

Holt, S. S.↗
NASA NTRS1980

X-ray spectral constraints on the broad-line cloud geometry of NGC 4151

X-ray spectral data from NGC 4151 taken with the Einstein Solid-State Spectrometer (SSS) and the HEAO 1 A-2 experiment cannot be simply reconciled with absorption from a uniform column of cold gas. The SSS data can, however, be explained in terms of a clumped absorber with approximately 10% uncovered fraction and factor-of-two overabundances in Z equal to or greater than 14 elements relative to solar oxygen. It is shown that these and previously reported spectral and variability data can be quantitatively reconciled with absorption arising in the cold clouds responsible for the broad optical line emission if the cloud dimensions are small compared to the central source size. It is suggested that the lack of significant X-ray absorption observed from much higher luminosity Seyferts and quasars is a natural consequence of the proposed picture for NGC 4151.

Holt, S. S.↗
NASA NTRS2021

Western Montana Ecological Forecasting: Modeling Habitat Suitability of Mustelid Species to Guide Detection Dog Surveys for Contaminants Monitoring, via Collected Scats in River Systems of Western Montana

Environmental contaminants are becoming increasingly prevalent in riverine ecosystems. The status of contaminants in western Montana’s relatively pristine river systems is largely unknown. Monitoring for heavy metals, brominated flame-retardants (BFRs), and pharmaceuticals is important due to their negative effects on ecosystems. Exposure to these contaminants can have significant endocrine, neurological, and reproductive effects. Contaminants easily travel up the food chain and bioaccumulate in apex predators. As predators with a largely aquatic diet, American mink (Mustela vison) and North American river otter (Lontra canadensis) serve as reliable indicator species of environmental health and the status of contaminants. Analysis of scat from these species is a noninvasive method to measure contaminant levels, and detection dogs from Working Dogs for Conservation (WD4C) have been used to locate these scat samples. To aid in the search of these samples, habitat suitability models were created for mink and otter for the years 2013-2020 and projected to 2040 using the random forest algorithm in the Software for Assisted Habitat Modeling (SAHM). Predictor variable data were acquired from Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG), Shuttle Radar Topography Mission (SRTM), and Soil Moisture Active Passive (SMAP). Within these models, the most important variable for mink and otter habitat was distance to river. Suitable habitat also corresponded with emergent herbaceous land cover and deeper river locations. These habitat suitability models will inform sampling site section for further contaminant analysis.

Anna Winter↗