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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Sea Ice Kinematics and Thickness from RGPS: Observations and Theory

The RADARSAT Geophysical Processor System (RGPS) has produced a wealth of data on Arctic sea ice motion, deformation, and thickness with broad geographical coverage and good temporal resolution. These data provide unprecedented spatial detail of the structure and evolution of the sea ice cover. The broad purpose of this study was to take advantage of the strengths of the RGPS data set to investigate sea ice kinematics and thickness, which affect the climate through their influence on ice production, ridging, and transport (i.e. mass balance); heat flux to the atmosphere; and structure of the upper ocean mixed layer. The objectives of this study were to: (1) Explain the relationship between the discontinuous motion of the ice cover and the large-scale, smooth wind field that drives the ice; (2) Characterize the sea ice deformation in the Arctic at different temporal and spatial scales, and compare it with deformation predicted by a state-of-theart ice/ocean model; and (3) Compare RGPS-derived sea ice thickness with other data, and investigate the thinning of the Arctic sea ice cover as seen in ULS data obtained by U.S. Navy submarines. We briefly review the results of our work below, separated into the topics of sea ice deformation and sea ice thickness. This is followed by a list of publications, meetings and presentations, and other activities supported under this grant. We are attaching to this report copies of all the listed publications. Finally, we would like to point out our community service to NASA through our involvement with the ASF User Working Group and the RGPS Science Working Group, as evidenced in the list of meetings and presentations below.

Stern, Harry↗
NASA NTRS1996

(abstract) Alaska SAR Facility Ocean and Ice Data Opportunities Over the Next Decade

The Alaska SAR Facility (ASF) is currently processing data from four satellites. All of these generate SAR data useful for ice observations and three of them generate data useful for ocean observations. The data sets from these satellites are available to approved US and other investigators although the approval mechanisms are different. More satellites are planned. These will carry SAR instruments of increased capability for ocean and ice observations. We will discuss the anticipated situation for access to these data sets by the scientific community and we will speculate on what the major oceanographic applications may be.

ice ocean observations SAR satellite observations ↗
NASA NTRS1995

ScanSAR and Precision Processor Implementation at the Alaska SAR Facility

This paper summarizes the algorithm and hardware selection phases of the ScanSAR Processor (SSP) and Precision Processor (PP) implementation task for the Alaska SAR Facility (ASF). The SSP is being designed to specifically process RADARSAT ScanSAR mode SAR data while the PP is being designed to produce high precision image products from continuous mode SAR data from RADARSAT as well as ERS-1,2 and JERS-1. This paper describes the algorithms selected for the SSP and the PP; and reports on the hardware selection process in arriving at the target computing platform for these processors.

SanSAR Alaska SAR Facility↗
NASA NTRS1994

Ice Lead Orientation Characteristics in the Winter Beaufort Sea

The directional orientations of leads in the winter ice pack of the Beaufort Sea are studied both spatially and temporally. Data from the European Earth Resources Satellite-1 (ERS-1) Synthetic Aperture Radar (SAR) was used. The SAR data was produced in image form at the Alaska SAR Facility (ASF) with 100 x 100 m pixel resolution. The lead ice pixels, which included all non-multiyear ice, were defined using a simple thresholding of the radar backscatter values. The orientations of the leads covering the Beaufort Sea during the period of January through March of 1992 were derived using a lead skeletonization technique. Results show a strong temporal persistence in the distribution and orientation of the leads during this period.

SAR ice lead orientation↗
NASA NTRS1998

Data Quality of the JERS-1 SAR Global Rain Forest Mapping (GRFM) Project

The National Space Development Agency of Japan's (NASDA) JERS-1 SAR began collecting data in 1995 for the Global Rain Forest Mapping Project (GRFM). The GRFM data quality has been examined for products resulting from both the NASDA and Alaska SAR facility's (ASF) processing facilities.

JERS-1 SAR Rain Forest Mapping↗
NASA NTRS1994

Alaska Satellite Facility ASF5 SAR Data Processing System Overview

The Alaska SAR Facility (ASF) situated at the University of Alaska Fairbanks (UAF) is currently undergoing an extensive development effort to increase its capabilities in handling new sensors and in the areas of throughput, data handling, archive, and distribution [15].

SAR↗
NASA NTRS2012

EDOS Data Capture for ALOS

In 2008, NASA's Earth Sciences Missions Operations (ESMO) at Goddard Space Flight Center (GSFC) directed the Earth Observing System Data Operations System (EDOS) project to provide a prototype system to assess the feasibility of high rate data capture for the Japan Aerospace Exploration Agency's (JAXA) Advanced Land Observing Satellite (ALOS) spacecraft via NASA's Tracking and Data Relay Satellite System (TDRSS). The key objective of this collaborative effort between NASA and JAXA was to share science data collected over North and South America previously unavailable due to limitations in ALOS downlink capacity. EDOS provided a single system proof-of-concept in 4 months at White Sands TDRS Ground Terminal The system captured 6 ALOS events error-free at 277 Mbps and delivered the data to the Alaska Satellite Facility (ASF) within 3 hours (May/June '08). This paper describes the successful rapid prototyping approach which led to a successful demonstration and agreement between NASA and JAXA for operational support. The design of the operational system will be discussed with emphasis on concurrent high-rate data capture, Level-O processing, real-time display and high-rate delivery with stringent latency requirements. A similar solution was successfully deployed at Svalbard, Norway to support the Suomi NPP launch (October 2011) and capture all X-band data and provide a 30-day backup archive.

McLemore, Bruce↗
NASA NTRS2014

Activate/Inhibit KGCS Gateway via Master Console EIC Pad-B Display

My internship consisted of two major projects for the Launch Control System.The purpose of the first project was to implement the Application Control Language (ACL) to Activate Data Acquisition (ADA) and to Inhibit Data Acquisition (IDA) the Kennedy Ground Control Sub-Systems (KGCS) Gateway, to update existing Pad-B End Item Control (EIC) Display to program the ADA and IDA buttons with new ACL, and to test and release the ACL Display.The second project consisted of unit testing all of the Application Services Framework (ASF) by March 21st. The XmlFileReader was unit tested and reached 100 coverage. The XmlFileReader class is used to grab information from XML files and use them to initialize elements in the other framework elements by using the Xerces C++ XML Parser; which is open source commercial off the shelf software. The ScriptThread was also tested. ScriptThread manages the creation and activation of script threads. A large amount of the time was used in initializing the environment and learning how to set up unit tests and getting familiar with the specific segments of the project that were assigned to us.

Computer Programming↗
NASA NTRSDate not supplied

DAAC Collaboration Overview for AGU

The Atmospheric Science Data Center (ASDC), Goddard Earth Sciences Data and Information Systems Center (GES DISC), Socioeconomic Data and Applications Center (SEDAC), Oak Ridge National Laboratory (ORNL), Land Processes DAAC, Alaska Satellite Facility (ASF), as well as NASA Global Imagery Browse Service (GIBS), Earth Science Data Systems (ESDS) Geographic Information Systems Team (EGIST), Earthdata Content Delivery Team, ArcGIS Online Governance Team, and the Systematic Data Transformation ACCESS team have come together to establish the ArcGIS DAAC Collaboration. This coalition of participants will demonstrate their use of the ArcGIS Enterprise to support Earth science research, applied science, and outreach using Earth Observing System Data and Information System (EOSDIS) data. This includes the use of web services to fuse data products across space and time through services (e.g. ArcGIS Image Services, Open Geospatial Consortium (OGC) Web Coverage Service (WCS), and OGC Web Mapping Services (WMS)) for analysis in Jupyter notebooks, desktop tools, and web based applications.

Matthew Steven Tisdale↗
NASA NTRS1993

Alaska SAR Facility Mass Storage, Current System

This paper examines the mass storage systems that are currently in place at the Alaska SAR facility (ASF). The architecture of the facility will be presented including specifications of the mass storage media that are currently used and the performances that we have realized from the various media. The distribution formats and media will also to be discussed. Because the facility is expected to survice future sensors, the new requriements and possible solutions to these requriements will also be discussed.

Mass↗
NASA NTRS2021

NASA Ames Arc Jet Complex Measurements Handbook

The Arc Jet Measurement System Handbook will serve as a reference of the calculations and formulas used by the Arc Jet Complex Data Acquisition System. It is the objective that Code ASF personnel and experimenters will be able to manually reproduce data calculations performed by the Arc Jet Data Acquisition System using these formulas as a guide.

Jerry Cheng↗
NASA NTRSDate not supplied

Evaluating SAR Radiometric Terrain Correction products: Optimal products for applied users

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has been providing since 2014. Groups like SERVIR, a joint initiative between NASA and USAID, are at the forefront of remote sensing applied uses, and have made many significant contributions to lower the barrier to access, process, and apply SAR for ecosystem services. A takeaway from the SERVIR experience in using SAR is the need to use the appropriate SAR polarimetric product. Radiometric Terrain Correction (RTC) is a key entry-level product for multiple applications that range from ecosystems to hazards. Many software packages exist to create RTC products from SLC or GRD-type Level-1 SAR data, some of which were released only recently, e.g. Interferometric SAR Computing Environment (ISCE) added an RTC module in April 2020. In addition, new versions of open source softwares are expected to address known issues from previous versions, such as Sentinel-1 Toolbox from the European Space Agency (SNAP-7). Despite the growing availability of RTC software solutions, little work has been done to identify differences between RTC products from different softwares. And to address the question, which open-source software produces the most accurate RTC product? This work evaluates Sentinel-1 RTC products created with three different softwares and approaches, including SNAP-7, ISCE-2, and a pseudo RTC product derived from GEE. The GAMMA-derived RTC product, a known optimal RTC and implemented by Alaska Satellite Facility (ASF), is used as a reference. Time series stacks over ten different sites representing varied terrain and ecosystems are evaluated. Products are evaluated for geolocation quality, absolute radiometric calibration, and for the fidelity of the radiometric terrain flattening. The results provide direct guidance and recommendations about the quality of the RTC products obtained from open source methods. This understanding is key to develop operational applications that rely on SAR Sentinel-1 data that need affordable and scalable solutions.

Africa Flores-Anderson↗
NASA NTRSDate not supplied

Harmonized Sentinel-1 SAR Global River Geometry and Inundation Database

Satellite-based observations on river geometries are sporadic in time, space, or both. Most satellite-based surface water maps, river widths, water surface elevations (WSE), slopes, and bathymetry are asynchronized in time and space. The current configuration of satellites such as Sentinel-6 measured the WSE but is missing the river width, slopes, and depths. To advance hydrological sciences research, there is a need to produce a harmonized time series of river geometry data of non-SWOT satellites in partnership with the upcoming SWOT mission. The SWOT satellite will measure river width, height, and slope but missing river depth measurements in space and time. Further, none of these current satellites measure the WSE, river width, and slopes synchronously. In this work, we use the Sentinel-1 SAR satellite data archive from 2015 to the present to create a global river width and surface water database at the reach scale. A modified version of the Sentinel SAR surface water classification algorithm from ASF is used to quantify the surface water extent on the stream approximately every six days (at the equator) at 10m spatial resolution globally. This 10m water mask is fed into a workflow to quantify the river widths, surface water inundations, slopes, and synthetic bathymetry in SWORD (SWOT River Database) stream networks. A Satellite HAND is used to address the cloud obscured surface water observations using a trained machine learning algorithm. We use WSE derived from the Global Water Monitor from NASA GSFC, Hydroweb from LEGOS, and ICESat-2 to harmonize the WSE observation. And Landsat-8/9 and Sentinel-2 water observations to fill the gaps in the Sentinel-1 SAR database. We use Congo River Basin as a test case where we have more than 500 radar altimetry-based WSE, continuous series of Sentinel-1, ICESat-2, Landsat-8/9, and Sentinel-2 observations. A Congo River hydrologic model is used to generate the streamflow discharge. The satellite observed river reaches are assimilated with the stream flows computed by the routing models. And the downstream reaches in the river network without satellite observations get optimized for discharge/river geometry at each observation cycle. Our final product is a harmonized river geometry dataset (reach's water extent, WSE, slope, synthetic bathymetry) for Congo Basin's SWORD reaches.

Chandana Gangodagamage↗
DOE OSTI2024

African Swine Fever Virus Protein–Protein Interaction Prediction

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein–protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein–protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein–protein interactions, further validating their relevance. Two protein–protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗