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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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This is a digitized geologic map, in shapefile format, including rock unit lithological descriptions, faults, and dikes.
The individual motorist dataset contains data and analysis of consumer electric vehicle (EV) ownership trends in rural Appalachian Ohio in comparison with statewide trends. The data span four years, from Q1 2020 to Q2 2023 (partial). They are sourced from the Ohio Bureau of Motor Vehicles registration records and contain detail on drivetrain type (battery-electric vehicle [BEV] or plug-in hybrid electric vehicle [PHEV]); specific vehicle make and model; and registration location at county, city, and ZIP code levels of spatial resolution. Registration data are analyzed at the county level against such indicators as median income, poverty status, urban-rural status, and density of public charging infrastructure. In addition to tabular data, a GIS shapefile with many analysis fields joined is included.
A patient in the United States has been diagnosed with Ebola. Fear and panic kicks in across the country, and hospitals are inundated with hundreds of people, some infected with the highly contagious disease and others not. Blood tests are needed for positive diagnoses, but the diagnostic labs are overwhelmed with blood samples to test, and staff are overworked and stressed. Infected people need to be quarantined and treated, but it's hard to find rooms to quarantine so many patients. Sick people who need triage and regular care for other emergencies are afraid to go to hospitals for fear of Ebola, which has a 50% fatality rate. And since hospitals are so overwhelmed, sick people often stay home, infecting heathy people around them; the U.S. is now in the grips a full-blown Ebola outbreak. Sandia's high-performance computers simulated such a nightmare scenario recently, and with good reason. An Ebola outbreak in the United States could be devastating if hospitals are not prepared. When an Ebola outbreak in West Africa became a global concern in 2014, health advisers were alarmed at the length of time it took to properly diagnose infected people. In rural areas in Liberia, for example, blood samples from ailing people would be sent to a laboratory for testing, but the closest lab was hundreds of miles away through difficult and sometimes impassable roads. In more urban areas, blood samples would be sent to nearby labs, but those labs were often already overburdened by the sheer volume of samples to test. Staff at some treatment centers were unaware that a lab a little farther away might have the capacity to take in more samples. Meanwhile, undiagnosed infected people were unknowingly spreading the disease to many others around them, worsening the outbreak. The U.S. Defense Threat Reduction Agency (DTRA) and Centers for Disease Control and Prevention (CDC) posed a serious question: how do we improve blood-sample transportation routes in Liberia to ensure that samples taken from ill people are tested as quickly as possible, ensuring a proper diagnosis and faster treatment? Sandia scientists, already experts in transportation modeling for nuclear materials, quickly swarmed on this problem. The Sandia Ebola response team immediately set out to collect data from the region using available maps and local information, and transformed the raw data to GIS maps. Then, applying Sandia transportation routing algorithms, the team identified the optimal routes to get blood samples to the best laboratory for testing, even if that lab was not geographically the closest. The models also showed the best possible locations for mobile diagnostic laboratories that would better support the very rural regions that were most affected by the Ebola outbreak.
The National Renewable Energy Laboratory's (NREL) Photovoltaic (PV) Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The PVRDB data are organized by city and year of lidar collection. Five geospatial layers are available for each city and year: 1) the raster extent of the lidar collection, 2) buildings identified from the lidar data, 3) suitable developable planes for each building, 4) aspect values of the developable planes, and 5) the technical potential estimates of the developable planes.
This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.
Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted, such as model inputs from satellite, or extreme events (such as volcano eruptions, dust storms, etc.).Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. Such obstacles may be avoided by allowing users to visualize satellite data as images, with accurate pixel-level (Level-2) information, including pixel coverage area delineation and science team recommended quality screening for individual geophysical parameters. We present a prototype service from the Goddard Earth Sciences Data and Information Services Center (GES DISC) supporting Aura OMI Level-2 Data with GIS-like capabilities. Functionality includes selecting data sources (e.g., multiple parameters under the same scene, like NO2 and SO2, or the same parameter with different aggregation methods, like NO2 in OMNO2G and OMNO2D products), user-defined area-of-interest and temporal extents, zooming, panning, overlaying, sliding, and data subsetting, reformatting, and reprojection. The system will allow any user-defined portal interface (front-end) to connect to our backend server with OGC standard-compliant Web Mapping Service (WMS) and Web Coverage Service (WCS) calls. This back-end service should greatly enhance its expandability to integrate additional outside data-map sources.
Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted, such as model inputs from satellite, or extreme events (such as volcano eruptions, dust storms,... etc.). Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. Such obstacles may be avoided by allowing users to visualize satellite data as "images", with accurate pixel-level (Level-2) information, including pixel coverage area delineation and science team recommended quality screening for individual geophysical parameters. We present a prototype service from the Goddard Earth Sciences Data and Information Services Center (GES DISC) supporting Aura OMI Level-2 Data with GIS-like capabilities. Functionality includes selecting data sources (e.g., multiple parameters under the same scene, like NO2 and SO2, or the same parameter with different aggregation methods, like NO2 in OMNO2G and OMNO2D products), user-defined area-of-interest and temporal extents, zooming, panning, overlaying, sliding, and data subsetting, reformatting, and reprojection. The system will allow any user-defined portal interface (front-end) to connect to our backend server with OGC standard-compliant Web Mapping Service (WMS) and Web Coverage Service (WCS) calls. This back-end service should greatly enhance its expandability to integrate additional outside data/map sources.
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The Prediction Of Worldwide Energy Resources (POWER) Project 25th year of providing global solar and meteorology data products to the world. The goals of this event were to inform users of new, enhanced features, gather feedback on services, identify new requests and requirements, and capture more thorough user stories while identifying new user communities and partnership opportunities. The event was “virtual” and had over 580 registrants. Over the two-day event, the Summit was attended by 162 unique attendees representing 24 countries.
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Global Ionospheric Specification (GIS) is based on the Gauss–Markov Kalman filter to assimilate the slant total electron content (TEC) observed from ground-based GPS receivers and space-based radio occultation instrumentations in order to reconstruct three-dimensional (3D) ionospheric electron density structure, and it can remotely sense and monitor the weather condition in space. In this study, five minutes of high temporal resolution GIS is implemented in order to reconstruct the 3D electron density structure on the 21 August 2017 total solar eclipse and analyze the variations induced by the moon’s shadow. To obtain more information of the ionosphere, from the extend 2200 GPS stations on the continental United States, are added for assimilation. The results show the ionosphere peak height (hmF2) uplift was 30–50 km altitude in latitude 25–40°N, and that the electron density depletion at higher altitudes (400 km) has a more noticeable time delay than at low altitudes (200 km), especially in low-latitude regions.
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Jesse Piburn and April Morton of Oak Ridge National Laboratory's Geographic Information Science & Technology Group discuss the science and technology Pokémon GO and how it is used every day to solve real-world problems. (Pokémon GO photos/videos courtesy of Pokémon/Nintendo).
The Applied Research Center at Florida International University (ARC-FIU) is supporting the soil and groundwater remediation efforts of the U.S. Department of Energy's (DOE's) Savannah River Site (SRS) by developing a surface water model to simulate the hydrology and the fate and transport of contaminants and sediment in the Tims Branch watershed. Advances in ArcGIS software through the development of spatial GIS database (geo-database) technology, coupled with the development of data models such as ArcHydro which possesses a spatial relational database management system (RDMS) schema and relationship structure specific to hydrologic systems, provides modelers with tools and applications to assist in the processing, analysis and visualization of flow and contaminant transport data. In addition, the coupling of this type of geo-database structure with a numerical modeling package such as MIKE SHE/MIKE 11 can serve as an efficient tool that significantly reduces the time needed for data preparation. The MIKE modeling system has a geographic information systems (GIS) user interface built into its system that can directly use GIS data for model inputs. GIS-based hydrologic models can provide a spatial element that other hydrologic models lack. GIS is important for processing and analysis of large spatio-temporal datasets derived from multiple sources that are being used for setting up of models, model calibration and simulation of flow and contaminant fate and transport. GIS-based approaches in hydrological modeling can also provide the benefit of combining different layers of geographic data to create new integrated information which can be quite useful for creating dependent or independent hydrological variables. ARC-FIU has developed an ArcGIS geo-database to support the hydrological modeling work being conducted at SRS. The SRS geo-database was developed based on the ArcHydro and ArcGIS Base Map data models with modifications made for project specific input parameters. The significance of this approach was to ensure its replicability for potential application at other DOE sites. The SRS geo-database assists in the storage and management of GIS and time series data being used for hydrological model development at Savannah River Site. Other GIS tools such as ArcGIS Model Builder coupled with Python scripting have enabled the automation of many of the repetitive geo-processing tasks required for pre- and post-processing of the hydrological modeling data. Automation of many of the data processing tasks has facilitated faster and hence more complex analyses of field test data. The toolbox created is also a scalable and reusable application that can be implemented in other watersheds. This paper describes the use of ArcGIS tools for the pre-processing, spatial delineation and analysis of hydrological model data; for automation of repetitive geo-processing tasks; and for visualization of different hydrological components of the Tims Branch watershed. (authors)
The convergence of data-intensive and extreme-scale computing enables an integrated software and data ecosystem for scientific discovery. Developments in this realm will fuel transformative research in data-driven interdisciplinary domains. Geocomputation provides computing paradigms in Geographic Information Systems (GIS) for interactive computing of geographic data, processes, models, and maps. Because GIS is data-driven, the computational scalability of a geocomputation workflow is directly related to the scale of the GIS data layers, their resolution and extent, as well as the velocity of the geo-located data streams to be processed. Unique in high user interactivity and low end-to-end latency requirements, geocomputation applications will dramatically benefit from the convergence of high-end data analytics (HDA) and high-performance computing (HPC). The application level challenge, however, is to identify and eliminate computational bottlenecks that arise along a geocomputation workflow. Indeed, poor scalability at any of the workflow components is detrimental to the entire end-to-end pipeline. Here, we study a large geocomputation use case in flood inundation mapping that handles multiple national-scale geospatial datasets and targets low end-to-end latency. We discuss benefits and challenges for harnessing both HDA and HPC for data-intensive geospatial data processing and intensive numerical modeling of geographic processes. We propose an HDA+HPC geocomputation architecture design that couples HDA (e.g., Spark)-based spatial data handling and HPC-based parallel data modeling. Key techniques for coupling HDA and HPC to bridge the two different software stacks are reviewed and discussed.
Information and activities are provided to: (1) enhance the ability to distinguish between a Geographic Information System (GIS) and a data management system; (2) develop understanding of spatial data handling by conventional methods versus the automated approach; (3) promote awareness of GIS design and capabilities; (4) foster understanding of the concepts and problems of data base development and management; (5) facilitate recognition of how a computerized GIS can model conditions in the present "real world" to project conditions in the future; and (6) appreciate the utility of integrating LANDSAT and other remotely sensed data into the GIS.
The pages in this report portray images and geospatial data embedded in an ESRI, web-based interactive Story Map presentation that will be delivered on November 17, 2021 in recognition for International GIS Day.
This data is associated with the Nevada Play Fairway project and includes excel files containing raw 2-meter temperature data and corrections. GIS shapefiles and layer files contain ing location and attribute information for the data are included. Well data includes both deep and shallow TG holes, GIS shapefiles and layer files.