Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Spatio-temporal”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.

Kuo, Kwo-Sen↗
NASA NTRS2017

Complexities in Subsetting Level 2 Data

Satellite Level 2 data presents unique challenges for tools and services. From nonlinear spatial geometry to inhomogeneous file data structure to inconsistent temporal variables to complex data variable dimensionality to multiple file formats, there are many difficulties in creating general tools for Level 2 data support. At NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are implementing a general Level 2 Subsetting service for Level 2 data to a user-specified spatio-temporal region of interest (ROI). In this presentation, we will unravel some of the challenges faced in creating this service and the strategies we used to surmount them.

Data acces↗
NASA NTRS2018

The Impact of Prior Biosphere Models in the Inversion of Global Terrestrial CO2 Fluxes by Assimilating OCO-2 Retrievals

Atmospheric mixing ratios of carbon dioxide (CO2) are largely controlled by anthropogenic emissions and biospheric fluxes. The processes controlling terrestrial biosphere-atmosphere carbon exchange are currently not fully understood, resulting in terrestrial biospheric models having significant differences in the quantification of biospheric CO2 fluxes. Atmospheric transport models assimilating measured (in situ or space-borne) CO2 concentrations to estimate "top-down" fluxes, generally use these biospheric CO2 fluxes as a priori information. Most of the flux inversion estimates result in substantially different spatio-temporal posteriori estimates of regional and global biospheric CO2 fluxes. The Orbiting Carbon Observatory 2 (OCO-2) satellite mission dedicated to accurately measure column CO2 (XCO2) allows for an improved understanding of global biospheric CO2 fluxes. OCO-2 provides much-needed CO2 observations in data-limited regions facilitating better global and regional estimates of "top-down" CO2 fluxes through inversion model simulations. The specific objectives of our research are to: 1) conduct GEOS-Chem 4D-Var assimilation of OCO-2 observations, using several state-of-the-science biospheric CO2 flux models as a priori information, to better constrain terrestrial CO2 fluxes, and 2) quantify the impact of different biospheric model prior fluxes on OCO-2-assimilated a posteriori CO2 flux estimates. Here we present our assessment of the importance of these a priori fluxes by conducting Observing System Simulation Experiments (OSSE) using simulated OCO-2 observations with known "true" fluxes.

Biosphere↗
NASA NTRS2018

Human-in-the-Loop Evaluation of Dynamic Multi-Flight Common Route Advisories

Flights often experience large delays when they are routed around weather. Multi-flight common route advisories provide delay recovery by suggesting time-saving re-routes for groups of flights whose current weather-avoidance routes have become outdated because the weather has dissipated and/or moved away. A laboratory evaluation of these advisories was conducted by four subject matter experts having extensive experience in traffic flow management operations. These experts provided a total of 120 data points in the airspace of Houston Center. The multi-flight common route tool provides time-saving route change advisories taking into account flight plans, wind fields, and the spatio-temporal evolution of predicted convective weather. It is not designed to account for complex operational factors such as non-standard sector traversal and interactions with local traffic management initiatives; hence a relatively low percentage (37%) of advisories generated by the tool were rated as acceptable. However, a high percentage (81%) of advisories were rated as acceptable after the subject matter experts used the tool's user interface to make route modifications that accounted for relevant operational factors not considered by the tool. The workload associated with using the tool, as measured by the NASA Task Load Index, was quite low (1.1 on a scale of 0 to 10). The results of this evaluation make a good case for human-automation teaming to design operationally valid weather re-routes for delay recovery.

Traffic Flow Management↗
NASA NTRS2018

Human-in-the-Loop Evaluation of Dynamic Multi-Flight Common Route Advisories

Flights often experience large delays when they are routed around weather. Multi-flight common route advisories provide delay recovery by suggesting time-saving re-routes for groups of flights whose current weather-avoidance routes have become outdated because the weather has dissipated and/or moved away. A laboratory evaluation of these advisories was conducted by four subject matter experts having extensive experience in traffic flow management operations. These experts provided a total of 120 data points in the airspace of Houston Center. The multi-flight common route tool provides time-saving route change advisories taking into account flight plans, wind fields, and the spatio-temporal evolution of predicted convective weather. It is not designed to account for complex operational factors such as non-standard sector traversal and interactions with local traffic management initiatives; hence a relatively low percentage (37%) of advisories generated by the tool were rated as acceptable. However, a high percentage (81%) of advisories were rated as acceptable after the subject matter experts used the tool's user interface to make route modifications that accounted for relevant operational factors not considered by the tool. The workload associated with using the tool, as measured by the NASA Task Load Index, was quite low (1.1 on a scale of 0 to 10). The results of this evaluation make a good case for human-automation teaming to design operationally valid weather re-routes for delay recovery.

Traffic Flow Management↗
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↗