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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

The Ocean Colour Climate Change Initiative: II. Spatial and Temporal Homogeneity of Satellite Data Retrieval Due to Systematic Effects in Atmospheric Correction Processors

The established procedure to access the quality of atmospheric correction processors and their underlying algorithms is the comparison of satellite data products with related in-situ measurements. Although this approach addresses the accuracy of derived geophysical properties in a straight forward fashion, it is also limited in its ability to catch systematic sensor and processor dependent behaviour of satellite products along the scan-line, which might impair the usefulness of the data in spatial analyses. The Ocean Colour Climate Change Initiative (OC-CCI) aims to create an ocean colour dataset on a global scale to meet the demands of the ecosystem modelling community. The need for products with increasing spatial and temporal resolution that also show as little systematic and random errors as possible, increases. Due to cloud cover, even temporal means can be influenced by along-scanline artefacts if the observations are not balanced and effects cannot be cancelled out mutually. These effects can arise from a multitude of results which are not easily separated, if at all. Among the sources of artefacts, there are some sensor-specific calibration issues which should lead to similar responses in all processors, as well as processor-specific features which correspond with the individual choices in the algorithms. A set of methods is proposed and applied to MERIS data over two regions of interest in the North Atlantic and the South Pacific Gyre. The normalised water leaving reflectance products of four atmospheric correction processors, which have also been evaluated in match-up analysis, is analysed in order to find and interpret systematic effects across track. These results are summed up with a semi-objective ranking and are used as a complement to the match-up analysis in the decision for the best Atmospheric Correction (AC) processor. Although the need for discussion remains concerning the absolutes by which to judge an AC processor, this example demonstrates clearly, that relying on the match-up analysis alone can lead to misjudgement.

Ocean Colour↗
NASA NTRS1994

Remote sensing of forest ecosystem dynamics: Measurements and modeling

The development of an integrated approach to the modeling of forest dynamics encompassing submodels of forest growth and succession, soil processes and radiation interactions, is reported. Remote sensing technology is a key element of this study in that it provides data for developing, initializing, updating, and validating the models. The objectives are reviewed, the data collected and models in use are discussed, and a framework for studying interactions between the forest growth, soil process and energy interaction components, is described. Remote sensing technology used in the study includes optical and microwave field, aircraft and satellite borne instruments. The types of data collected during intensive field and aircraft campaigns included bidirectional reflectance, thermal emittance and multifrequency, multipolarization synthetic aperture radar backscatter. Synthetic imagery of derived products such as forest biomass and NDVI (Normalized Difference Vegetative Index), and collections of ground data are being assembled in a georeferenced data base. These data are used to drive or test multidiscipline simulations of forested ecosystems. Enhancements to the modeling environment permit considerable flexibility in configuring simulations and selecting results for reporting and graphical display.

Williams, Darrel L.↗
NASA NTRS2020

Conducted Susceptibility Data Adaptation Tool

The purpose of this research project is to help NASA scientists increase effectivity in testing by reducing the amount of time required for data conversion from initial measurements to the analysis stage. This project resulted in a data conversion tool developed in python using the openpyxl library. When lab personnel perform the Conducted Susceptibility 101 (CS101) and Conducted Susceptibility 02 (CS02) tests in the Semi-anechoic Electromagnetic Compatibility Test Facility at NASA Langley Research Center, the driving code produces a text file under the .DAT extension.The data must be manually converted the .DAT file into an Excel file line by line for data analysis.The Conducted Susceptibility Data Adaptation Tool (CSDAT) uses python code to convert the .DAT files to spreadsheets automatically which saves lab personnel time which they could use for faster analysis as well as prevents possible human error such as leaving out a line of data in the manual conversion process.

Gabriel Johnson↗
NASA NTRSDate not supplied

Beyond Fair: Engagement, Data Usability, and Open Community Productivity through the NASA Open Science Data Repository

The FAIR principle (findable, accessible, interoperable, and reusable) governs the storage and sharing of NASA space biology and health data[1]. These guiding principles maximize reuse of data and the reproducibility of scientific findings. The NASA Open Science Data Repository (OSDR; an expansion of NASA GeneLab) was built on the FAIR principles and houses over 500 studies and close to 1000 datasets from decades of space life sciences experiments. OSDR embodies the FAIR principles through data governance that includes mediated, embargoed, and fully open access data. The FAIR data governance principles were recently proposed to be expanded to encompass a FAIREST framework for assessing research data repositories (FAIR + Engagement, Social connections, and Trust)[2]. FAIREST emphasizes the importance of data repositories engaging with the scientific community and gaining the trust of researchers regarding data quality. Trust also refers to the TRUST principles developed for assessment of digital repositories: Transparency, Responsibility, User Focus, Sustainability, Technology[3]. We present the “Open Science for Life in Space” Analysis Working Groups (AWGs) as evidence regarding the power of engagement, social connections, and trust which has enhanced OSDR’s capabilities and productivity. AWG members engage in two main activities. One, members provide feedback on OSDR scientific standards for data ingestion, curation, and reuse (study, subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability). Two, AWG members collaborate to mine-reuse OSDR data to conduct scientific analysis. With nearly 800 active members, the AWGs have resulted in 32 publications re-using OSDR data and contributed many papers in two major special issues in Cell (2020) and Nature (2024). AWGs also serve as networking groups, facilitate social connections between researchers at all levels of experience, and also have a social online ‘Forum’ used to keep members informed on projects and opportunities. This community-centric, productive, and trustworthy data culture has resulted in a broader effect with international space agencies, academics, and the commercial space sector wanting to submit their data to OSDR. Ten studies of Inspiration 4 data were recently publicly released by OSDR, as were some JAXA human data. Coming up soon in OSDR are data submissions from the European Space Agency, Virgin Galactic PIs, and SpaceX Polaris Dawn. A major benefit of OSDR is the array of standardized and uniformly formatted data (which was developed through AWG member consensus), from which visualization tools, analysis tools, and machine learning models can be built or trained. This talk will cover the Multi-Study Visualization Tool, the Environmental Data Application, RadLab, and a UCSF-NSF funded knowledge graph biomedical health discovery tool ‘SPOKE’ currently being integrated with OSDR. OSDR also provides training programs in bioinformatics and machine learning to improve the scientific community’s awareness of data availability and to boost their ability to perform data analysis. The increasing engagement of the scientific community and the public with technologies powered by artificial intelligence (AI) heightens the need for data analysis to be transparent. The AI for Life in Space initiative leverages the data products provided in OSDR to train AI models, with an emphasis on explainable and trustworthy AI, which would not be possible without FAIR data and metadata. Overall, here we will demonstrate the importance for NASA life sciences data repositories to adhere to the FAIREST framework, by providing examples and success stories from different aspects of OSDR.

data↗
NASA NTRS1993

An application of artificial neural networks to experimental data approximation

As an initial step in the evaluation of networks, a feedforward architecture is trained to approximate experimental data by the backpropagation algorithm. Several drawbacks were detected and an alternative learning algorithm was then developed to partially address the drawbacks. This noniterative algorithm has a number of advantages over the backpropagation method and is easily implemented on existing hardware.

Meade, Andrew J., Jr.↗
NASA NTRSDate not supplied

Life After Launch: A Snapshot of the First 6 Months of NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission launched from Kennedy Space Center in the early morning of February 8, 2024. Just 63 days later, data from NASA’s newest Earth-observing satellite became available to the public. These data will extend and improve upon NASA’s 20+ years of global satellite observation of our living oceans, atmospheric aerosols, and cloud and initiate an advanced set of climate-relevant data records. Ultimately, PACE is the first mission to provide daily, global measurements that will enable prediction of the “boom-bust” cycle of fisheries, the appearance of harmful algae, and other factors that affect commercial and recreational industries. PACE also observes clouds and tiny airborne particles known as aerosols that influence air quality and absorb and reflect sunlight, thus warming and cooling the atmosphere. In the months since launch and initial data release, the PACE Project pursued instrument temporal and system vicarious calibrations, executed cross-instrument comparisons, conducted performance assessments, explored synergies with other missions, and released advanced science data products. In parallel, the PACE Validation Science Team left for the field and the Post-launch Airborne eXperiment (PACE-PAX) prepared for its mission. And, most importantly, preliminary science results were realized. Here, we present a snapshot of these activities and their impacts and outcomes, encompassing the first half year of the PACE mission.

PACE↗
NASA NTRS2017

Biogeochemical Protocols and Diagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP)

The Ocean Model Intercomparison Project (OMIP) focuses on the physics and biogeochemistry of the ocean component of Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). OMIP aims to provide standard protocols and diagnostics for ocean models, while offering a forum to promote their common assessment and improvement. It also offers to compare solutions of the same ocean models when forced with reanalysis data (OMIP simulations) vs. when integrated within fully coupled Earth system models (CMIP6). Here we detail simulation protocols and diagnostics for OMIP's biogeochemical and inert chemical tracers. These passive-tracer simulations will be coupled to ocean circulation models, initialized with observational data or output from a model spin-up, and forced by repeating the 1948-2009 surface fluxes of heat, fresh water, and momentum. These so-called OMIP-BGC simulations include three inert chemical tracers (CFC-11, CFC-12, SF [subscript] 6) and biogeochemical tracers (e.g., dissolved inorganic carbon, carbon isotopes, alkalinity, nutrients, and oxygen). Modelers will use their preferred prognostic BGC model but should follow common guidelines for gas exchange and carbonate chemistry. Simulations include both natural and total carbon tracers. The required forced simulation (omip1) will be initialized with gridded observational climatologies. An optional forced simulation (omip1-spunup) will be initialized instead with BGC fields from a long model spin-up, preferably for 2000 years or more, and forced by repeating the same 62-year meteorological forcing. That optional run will also include abiotic tracers of total dissolved inorganic carbon and radiocarbon, CTabio and 14CTabio, to assess deep-ocean ventilation and distinguish the role of physics vs. biology. These simulations will be forced by observed atmospheric histories of the three inert gases and CO2 as well as carbon isotope ratios of CO2. OMIP-BGC simulation protocols are founded on those from previous phases of the Ocean Carbon-Cycle Model Intercomparison Project. They have been merged and updated to reflect improvements concerning gas exchange, carbonate chemistry, and new data for initial conditions and atmospheric gas histories. Code is provided to facilitate their implementation.

Carbon↗
NASA NTRS1974

A review of initial investigations to utilize ERTS-1 data in determining the availability and distribution of living marine resources

The National Marine Fisheries Service has been studying the application of aerospace remote sensing to fisheries management and utilization for many years. The 15-month ERTS study began in July 1972 to: (1) determine the reliability of satellite and high altitude sensors to provide oceanographic parameters in coastal waters; (2) demonstrate the use of remotely-sensed oceanographic information to predict the distribution and abundance of adult menhaden; and (3) demonstrate the potential use of satellites for acquiring information for improving the harvest and management of fisheries resources. The study focused on a coastal area in the north-central portion of the Gulf of Mexico, including parts of Alabama, Mississippi, and Louisiana. The test area used in the final analysis was the Mississippi Sound and the area outside the barrier islands to approximately the 18-meter (10-fathom) curve.

Stevenson, W. H.↗
NASA NTRS1978

Analysis of stellar occultation data - Effects of photon noise and initial conditions

An occultation light curve can be analyzed to provide information about a planetary atmosphere. Temperature, pressure, and number density profiles for the atmosphere of Mars are derived from a series of boundary layer equations, which invert equal increments of altitude (as opposed to time) in order to predict the noise quality of the occultation. Numerical results are given for a noisy isothermal light curve, with special attention to error analysis.

French, R. G.↗
NASA NTRS1979

Thermal contrast in the atmosphere of Venus - Initial appraisal from Pioneer Venus probe data

The altitude profiles of temperature and pressure were measured during the descent of four Pioneer Venus probes, showing small contrasts below the clouds, but significant differences within the clouds at altitudes from 45 to 61 km. Measurements of pressure differences were found to be consistent with the cyclostrophic balance of zonal winds ranging from 110 to 150 m/sec at 60 km and from 43 to 77 m/sec at 40 km. The clouds were 10 to 20 K warmer than the extended profiles of the lower atmosphere and the middle cloud is convectively unstable. Both phenomena are due to thermal radiation from below. Meridional wind velocities were studied, concluding that significant planetary scale non-axisymmetric motions were present at latitudes below 30 degrees. This result was consistent with the day-night pressure difference. Indications of flow oscillations in the lower atmosphere were noted and the inference of wave motions in the lower atmosphere was supported by analysis of oscillations in the Doppler residuals.

Seiff, A.↗
NASA NTRS1984

Severe storm identification with satellite microwave radiometry: An initial investigation with Nimbus-7 SMMR data

The severe weather characteristics of convective storms as observed by the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) are investigated. Low 37 GHz brightness temperatures (due to scattering of upwelling radiation by precipitation size ice) are related to the occurrence of severe weather (large hail, strong winds or wind damage, tornadoes and funnel clouds) within one hour of the satellite observation time. During 1979 and 1980 over the United States there were 263 storms which had very cold 37 GHz signatures. Of these storms 15% were severe. The SMMR detected hail, wind, and tornadic storms equally well. Critical Success Indices (CSI's) of 0.32, 0.48, and 0.38 are achieved for the thresholding of severe vs. nonsevere low brightness temperature events during 1979, 1980, and the two years combined, respectively. Such scores are comparable to skill scores for early radar detection methods. These results suggest that a future geostationary passive microwave imaging capability at 37 GHz, with sufficient spatial and temporal resolution, would allow the detection of severe convective storms. This capability would provide a useful complement to radar, especially in areas not covered by radar.

Spencer, R. W.↗
NASA NTRS1987

Severe storm identification with satellite microwave radiometry - An initial investigation with Nimbus-7 SMMR data

The use of satellite passive microwave observations for the detection of severe thunderstorms is investigated. Nimbus 7 SMMR data obtained from 1979 to 1980 over the U.S. east of 105 deg W are analyzed. The relative temperature brightnesses of the severe storms are examined; it is observed that temperature brightness decreases as the storm severity increases. The temperature brightness data were transformed into a quantitative measure of storm detection ability using the critical success index of Donaldson et al. (1975). Critical success indices of 0.32, 0.48, and 0.38 were obtained for the low-brightness-temperature thresholding of severe versus nonsevere storms during 1979, 1980, and 1979 and 1980 combined, respectively. The data reveal that the geostationary passive microwave imaging capability at 37 GHz is applicable to the detection and monitoring of severe convective storms.

Howland, Michael R.↗
NASA NTRS2001

Galilean Satellite Surface Non-Ice Constituents: New Results from the Cassini/Huygens VIMS Jupiter Flyby in the Context of the Galileo NIMS Results

The Cassini mission Visible and Infrared Mapping Spectrometer (VIMS) is currently returning data for the Galilean satellites. Examples of the new satellite data and the initial interpretations will be presented in the context of the Galileo NIMS data and results. Additional information is contained in the original extended abstract.

McCord, T. B.↗
NASA NTRS2014

Challenges in Evaluating Relationships Between Quantitative Data (Carbon Dioxide) and Qualitative Data (Self-Reported Visual Changes)

Understanding the nuances in clinical data is critical in developing a successful data analysis plan. Carbon dioxide (CO2) data are collected on board the International Space Station (ISS) in a continuous stream. Clinical data on ISS are primarily collected via conversations between individual crewmembers and NASA Flight Surgeons during weekly Private Medical Conferences (PMC). Law, et.al, 20141 demonstrated a statistically significant association between weekly average CO2 levels on ISS and self-reported headaches over the reporting period from March 14, 2001 to May 31, 2012. The purpose of this analysis is to describe the evaluation of a possible association between visual changes and CO2 levels on ISS and to discuss challenges in developing an appropriate analysis plan. METHODS & PRELIMINARY RESULTS: A first analysis was conducted following the same study design as the published work on CO2 and self-reported headaches1; substituting self-reported changes in visual acuity in place of self-reported headaches. The analysis demonstrated no statistically significant association between visual impairment characterized by vision symptoms self-reported during PMCs and ISS average CO2 levels over ISS missions. Closer review of the PMC records showed that vision outcomes are not well-documented in terms of clinical severity, timing of onset, or timing of resolution, perhaps due to the incipient nature of vision changes. Vision has been monitored in ISS crewmembers, pre- and post-flight, using standard optometry evaluations. In-flight visual assessments were limited early in the ISS program, primarily consisting of self-perceived changes reported by crewmembers. Recently, on-orbit capabilities have greatly improved. Vision data ranges from self-reported post-flight changes in visual acuity, pre- to postflight changes identified during fundoscopic examination, and in-flight progression measured by advanced on-orbit clinical imaging capabilities at predetermined testing intervals. In contrast, CO2 data are recorded in a continuous stream over time; however, for the initial analysis this data was categorized into weekly averages.

Mendez, C. M.↗
NASA NTRS1989

Initial Scientific Assessment of the EOS Data and Information System (EOSDIS)

Crucial to the success of the Earth Observing System (Eos) is the Eos Data and Information System (EosDIS). The goals of Eos depend not only on its instruments and science investigations, but also on how well EosDlS helps scientists integrate reliable, large-scale data sets of geophysical and biological measurements made from Eos data, and on how successfully Eos scientists interact with other investigations in Earth System Science. Current progress in the use of remote sensing for science is hampered by requirements that the scientist understand in detail the instrument, the electromagnetic properties of the surface, and a suite of arcane tape formats, and by the immaturity of some of the techniques for estimating geophysical and biological variables from remote sensing data. These shortcomings must be transcended if remote sensing data are to be used by a much wider population of scientists who study environmental change at regional and global scales.

Source record↗
NASA NTRS2019

Role of CEOS Working Group on Calibration and Validation in Analysis Ready Data Products

The Committee on Earth Observation Satellites (CEOS) is leading the CEOS Analysis Ready Data for Land (CARD4L) initiative. A goal of analysis ready data products is to limit the effort needed by users to pre-process the data allowing them to concentrate on the end products. CARD4L provides a set of specifications that data providers need to meet to be considered to satisfy CARD4L. One of the working groups within CEOS, the Working Group on Calibration and Validation (WGCV) is providing a peer review process to evaluate the documentation of the data providers validation and data product accuracy assessment. The approach makes use of the expertise within WGCV to collaborate with both the CEOS Land Surface Imaging Virtual Constellation and the data providers to work towards acceptance of the...

Thome, K.↗