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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Deciphering Solar Magnetic Activity. I. On the Relationship Between the Sunspot Cycle and the Evolution of Small Magnetic Features

Sunspots are a canonical marker of the Sun's internal magnetic field which flips polarity every ~22 yr. The principal variation of sunspots, an ~11 yr variation, modulates the amount of the magnetic field that pierces the solar surface and drives significant variations in our star's radiative, particulate, and eruptive output over that period. This paper presents observations from the Solar and Heliospheric Observatory and Solar Dynamics Observatory indicating that the 11 yr sunspot variation is intrinsically tied to the spatio-temporal overlap of the activity bands belonging to the 22 yr magnetic activity cycle. Using a systematic analysis of ubiquitous coronal brightpoints and the magnetic scale on which they appear to form, we show that the landmarks of sunspot cycle 23 can be explained by considering the evolution and interaction of the overlapping activity bands of the longer-scale variability.

Sun: interior↗
NASA NTRS2018

Geonex: A NASA-NOAA Collaboration for Producing Land Surface Products from Geostationary Sensors Using Cloud Computing

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement.Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. We estimate the cost of deploying GEONEX to be $400 - 750 a month for processing data (every 30 minutes) and producing products over the conterminous US. For products such as Fire, latency can be as little as 10 minutes from the time of data acquisition.

Geostationary↗
NASA NTRS2018

Helioseismic Observations of Torsional Oscillations Inside the Sun and Their Potential for Predicting Solar Cycles

The helioseismic analysis of torsional oscillations of the Sun, obtained in 1996-2018 from SOHO (Solar and Heliospheric Observatory) and SDO (Solar Dynamics Observatory), reveals the spatio-temporal dynamics associated with the dynamo process. The data reveal new relationships between the migrating magnetic field patterns observed in synoptic magnetograms and the dynamics of torsional oscillations near the surface and in the interior. In particular, it is found that the evolution of torsional oscillations in the deep convection zone is ahead of the surface magnetic evolution by several years, and that it is related to the extended solar cycle phenomenon previously observed in the solar corona. The data show substantial differences in the torsional oscillation properties between Cycles 23 and 24 indicating fundamental changes in the dynamo regime, and also reveal initiation of Cycle 25. The helioseismology observations of the torsional oscillations open new perspectives for understanding the global dynamo processes inside the Sun, and for predicting the next solar cycle.

Oscillations↗
NASA NTRS2018

Ocean Circulation Causes Strong Variability in the Mid-Atlantic Bight Nitrogen Budget

Understanding of nitrogen cycling on continental shelves, a critical component of global nutrient cycling, is hampered by limited observations compared to the strong variability on a wide range of time and space scales. Numerical models have the potential to partially alleviate this issue by filling spatio-temporal data gaps and hence resolving annual area-integrated nutrient fluxes. In this study, a three-dimensional bio geo chemical-circulation model was implemented to simulate the Mid-Atlantic Bight (MAB) nitrogen budget. Model results demonstrate that, on average, MAB net community production (NCP) was positive (plus 0.27 teragrams (Tg) Nitrogen per year), indicating net autotrophy. Inter-annual variability in NCP was strong, with annual values ranging between 0.19 and 0.41 Tg N/year. Along-shelf and across-shelf horizontal transport fluxes were the other dominant terms in the nitrogen budget and were primarily responsible for this NCP variability. The along-shelf current transported nitrogen from the north (0.65 Tg N/year) into the MAB, supplementing the nitrogen entering from terrestrial inputs (0.27 Tg N/year). However, NCP was highest in the year when total water volume transport and inorganic nitrogen input was strongest across the continental slope in the southern MAB, rather than when terrestrial inputs were greatest. Inter-annual variability of NCP appears to be linked to changes in the positions of the Gulf Stream and Slope Water Gyre. Overall, the strong spatio temporal variability of the nitrogen fluxes highlights the importance of observations throughout all seasons and multiple years in order to accurately resolve the current status and future changes of the MAB nitrogen budget.

Friedrichs, Marjorie A. M.↗
NASA NTRS2014

BioSentinel/Mars: Interplanetary Space Radiation Biosensor Experiment in Martian Transit on Mars 2020

Despite significant progress understanding biological radiation effects via terrestrial studies, no terrestrial source duplicates space’s unique radiation environment. Furthermore, no biological experiments have been conducted beyond low Earth orbit since Apollo. Understanding space’s fundamental biological effects requires overcoming these limitations. The BioSentinel 4U payload, under development for flight aboard Exploration Mission-1, measures biological responses to deep space radiation. Traveling to more than 1AU from Earth, BioSentinel/EM-1 will record DNA double-strand breaks (DSBs) repaired using a pathway common to humans and BioSentinel’s bioengineered yeast model organism, responding to as few as one biologically repaired DSB. The BioSentinel/Mars 4U instrument (6-8kg; 5-8W; 0.2-1MB/week) would include eighteen 16-well biosensor fluidic cards, activated biweekly during Mars 2020’s cruise phase, to provide a dose-dependent rate of DSB/repair. The instrument, which includes solid-state sensors for total ionizing dose and linear-energy-transfer spectra, addresses MEPAG SKG-B3 by simultaneously measuring both spectra and biological effects of space radiation. Biological measurements are rendered reliable by independent replicate experiments. The spatio-temporal uniformity of interplanetary galactic cosmic radiation makes BioSentinel/EM1 and BioSentinel/Mars approximate replicates, except for any major differences in solar particle events. Results will be compared to Earth and ISS controls to characterize the radiation/reduced-gravity parameter space by its biological impact.

BioSentinel↗
NASA NTRS2019

Unravelling Complex Geologic Histories Using U–Pb and Trace Element Systematics of Titanite

Unravelling the spatio-temporal evolution of orogenic terranes requires a comprehensive understanding of the duration and extent of metamorphic events and hydrothermal alteration. Commonly used minerals such as zircon and monazite may not fully record geological histories in complex tectonic settings because their elemental constituents do not react under many metamorphic and metasomatic conditions. Here, we complement the current geochronological record of the Capricorn Orogen, Western Australia, with titanite U–Pb geochronology and geochemistry of felsic intrusive rocks to draw conclusions about the use of titanite in understanding the evolution of orogenic terranes. Because titanite usually incorporates common-Pb and may be variably reset by multiple metamorphic and hydrothermal events, a workflow is provided here for the systematic and robust interpretation of titanite U-Pb data. The addition of trace element data in titanite is particularly effective for differentiating whether a grain is igneous, recrystallized or metamorphic. We have developed several petrogenetic indices to differentiate these three types of titanite using Zr-in-titanite temperature, Th/U, Th/Pb, Al/(Al + Fe), light to heavy rare earth element ratio, and Eu anomalies. The addition of trace element geochemistry can also highlight anomalously radiogenic (sup 207)Pb/(sup 206)Pb reservoirs. Utilization of our workflow in the Capricorn Orogen reveals that titanite ages from the same samples as published zircon U–Pb data range from coeval to several hundreds of Myr of age difference between the two minerals. Titanite geochronology and trace element geochemistry indicates ~20 Myr of previously unrecognized prolonged cooling for the Capricorn Orogeny to ca. 1750 Ma. The spatial extent of the ca. 1210–1170 Ma part of the Mutherbukin Tectonic Event is also broadened significantly farther north and south than previously recognized. Incorporating titanite geochronology and trace geochemistry with more commonly used techniques (e.g., zircon and monazite petrochronology) extends our ability to resolve the complete history of large-scale orogenic terranes.

Hugo K. H. Olierook↗
NASA NTRS2019

Geonex: Land Surface Monitoring from a New Generation of Geostationary Sensors

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement. Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. By making the GEONEX pipeline available on the cloud, we hope to engage a broad community of Earth scientists from around the world in utilizing this new source of data for Earth monitoring.

Nemani, Ramakrishna R.↗
NASA NTRS2014

Evaluation of Temporal Spacing Errors Associated with Interval Management Algorithms

This paper seeks to characterize the temporal spacing errors resulting from the use of Interval Management (IM) algorithms. The focus of the current paper is IM concepts and algorithms that realize a specified temporal spacing between a Target aircraft and an Ownship aircraft at the runway threshold. The paper presents an IM algorithm consisting of the following four modules: (i) Target-Landing-Time Estimation Module, (ii) Ownship-Landing-Time Estimation Module, (iii) Ownship Speed Command Computation Module, and (iv) Ownship Thrust Command Computation Module. The overall guidance module is evaluated on a simulation that models aircraft point-mass dynamics, bank-angle auto-pilot dynamics, pitch-axis auto-pilot dynamics, and engine lag dynamics. The simulation environment also consists of actual atmospheric forecasts and realistic spatio-temporally correlated wind uncertainty models. Results obtained from single case simulation as well as Monte-Carlo simulations are presented in the paper. The modeled scenario consisted of an A320 Target equipped with “Lateral Navigation”/“Vertical Navigation” (LNAV/VNAV) capabilities followed by an A320 Ownship equipped with the IM algorithm. Both aircraft fly the BIGSUR route to SFO airport using a RAP-13 1-hr wind forecast. 500 Monte-Carlo simulations were conducted with realistic wind uncertainty models. The IM algorithm for this case is seen to have a 90% probability landing time error range of 5.9 seconds, compared to the no-IM solution, which has a 90% probability landing time error range of 33.4 seconds.

Bai, Xiaoli↗
NASA NTRS2018

Identifying and Forecasting Potential Biophysical Risk Areas within a Tropical Mangrove Ecosystem Using Multi-Sensor Data

Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities.

Shanti Shrestha↗
NASA NTRS2019

Earth Observations from Geostationary Satellites

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GeoNEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GeoNEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement. Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GeoNEX pipeline including choice CPUs, storage media, and automation. By making the GEONEX pipeline available on the cloud, we hope to engage a broad community of Earth scientists from around the world in utilizing this new source of data for Earth monitoring.

Earth↗
NASA NTRS2019

Mitigation of Silver Ion Loss from Solution by Polymer Coating of Metal Surfaces

Spacecraft potable water systems require a biocidal agent that effectively provides both immediate and residual disinfection over long periods of time. Ionic silver (Ag+) is a leading candidate for this application, but suffers from rapid concentration loss due interactions with the metal storage containers and tubing. In order to maintain biocidal efficacy in systems with long periods of dormancy and to reduce the required rate of Ag+ injection, it is necessary to develop alternative materials and coatings for certified metal alloys that significantly reduce the Ag+ loss. In this work, we investigate the performance of two unusual polymers, Teflon AF 2400 and parylene-AF4, as barrier coatings under immersion in 400 ppb Ag+. We show that 25 m-thick parylene-AF4 is a promising coating with Ag+ losses similar to PTFE. Key challenges with this material include maintenance of adhesion under long-term immersion and control of deposition during the internal coating of high-aspect ratio tubing. Solutions to these problems may including surface structuring of substrates for mechanical anchoring or an alternative parylene-C chemistry (with more stable adhesion), and dynamic spatio-temporal control of substrate temperature, respectively.

water↗
NASA NTRS2019

The Role of Cyclone Activity in Snow Accumulation on Arctic Sea Ice

Identifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

Webster, M. A.↗
NASA NTRS2019

Simplifying Satellite and Ground Data Validation with Level-2 Subsetting

We demonstrate that scientists can simplify their satellite data validation workflow with the use of NASA Godddard Earth Sciences Data and Information Services Center (GES DISC) subsetting services. We perform a sample validation of Aura ozone products collocated with ground-based ozone measurements using subsetting services to trim satellite data to only the relevant user-defined variables and spatio-temporal region. Because the subsetting service automatically returns only relevant data granules that adhere to a set of user-defined coincidence criteria, user workload is greatly reduced. Moreover, the resultant data files are substantially smaller than full data granules due to the subsetting service further culling the data to the relevant geospatio-temporal coincidence criteria, user-defined variables, and user-defined dimensions of variables. This decreases data download throughput and file storage requirements. The validation presented here quantifies the time and file size savings that can be achieved by utilizing subsetting services within the satellite data validation workflow.

Johnson, James↗
NASA NTRS2013

Processing of KaRIn/SWOT Data

The Surface Water and Ocean Topography (SWOT) mission [1] is part of NASA’s Decadal Survey Program, and prepared jointly by NASA’s Jet Propulsion Laboratory (JPL), the French space agency (CNES), and the Canadian space agency (CSA). Launch is currently foreseen in 2020. The principal instrument KaRIn (Ka-band Radar Interferometer) would be a bistatic SAR system operating at Kaband, with 200 MHz bandwidth, covering 50 km wide near-nadir swaths (1-4° incidence) on both sides of the satellite track. The nominal payload also would include a conventional Ku/C-band nadir altimeter, a water vapor radiometer, and a precise orbit determination suite. Moreover, additional near-nadir channels from KaRIn to reduce the gap between the nominal swaths may be considered. The main mission goals would be to improve the spatio-temporal coverage of the oceans with respect to today’s nadir-looking altimeters (such as the Jason series), by providing measurements on a 1 km2 grid with virtually complete coverage up to ±78° latitude, while keeping a height precision of 1-2 cm, and to extend the altimetry measurements to continental water surfaces, including lakes, reservoirs and wetlands bigger than 250x250 m2 and rivers down to a width of 100 m (50 m as a goal), with a height precision of the order of 10 cm, represented with a horizontal point spacing of about 50 m. The fine scale of the measurements, with continuous acquisition over both oceans and continents, will result in huge data volumes, starting at the spacecraft and flowing through all links of the ground system. Furthermore, the centimetric height accuracy required means that all aspects of the processing must be considered carefully. In this article we give an overview of the foreseen data products and data processing chains for KaRIn on SWOT. Some particularly challenging processing steps are (will be) described in more detail.

Desroches, Damien↗
NASA NTRS2017

Gaining the most utility from our geospace observational system: Network analysis of total electron content as a means to understand space weather to the point of prediction

We present the first network analysis of interplanetary magnetic field (IMF) clock angle dependent, high-latitude, hemispheric-specific total electron content (TEC) data. We examine network parameters to describe spatio-temporal correlations in the TEC data for January 2016. We find that significant network structure exists distinguishing the dayside and nightside ionosphere, and specific features in the high-latitudes (cusp/ionospheric footpoints of magnetospheric boundary layers, polar cap, and auroral zone), and that these features vary with IMF clock angle. In this brief summary paper, we provide proof of concept results and identify important areas of future research, providing a basis for the discussion of network analysis and machine learning approaches for space weather applications.

Malik, Nishant↗
DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Discovering Physically Meaningful Structures from Climate Extreme Data

The original proposal described an interdisciplinary team spanning UC San Diego (lead), Columbia University, and UC Irvine, with Columbia investigators including Pierre Gentine, Elias Bareinboim, and Marcus van Lier-Walqui. The proposal further specified a leadership structure in which Columbia co-investigators contributed across the three aims, with Co-PI Gentine serving as a point of contact with science teams and with responsibilities distributed across aims.

42 ENGINEERING↗
NASA NTRS2011

Space-Time Data fusion for Remote Sensing Applications

NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets.

carbon dioxide↗