Search NASA⌕ Search

SEARCH · Search NASA

Results for “science data processing”

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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the AOS Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David M. Giles↗
NASA NTRS1991

Applications of the massively parallel machine, the MasPar MP-1, to Earth sciences

The computational workload of upcoming NASA science missions, especially the ground data processing for the Earth Observing System, is projected to be quite large (in the 50 to 100 gigaFLOPS range) and corespondingly very expensive to perform using conventional supercomputer systems. High performance, general purpose massively parallel computer systems such as the MasPar MP-1 are being investigated by NASA as a more cost effective alternative. Massively parallel systems are targeted for accelerated development and maturation by NASA's upcoming five-year High Performance Computing and Communications Program. A summary of the broad range of applications currently running on the MP-1 at NASA/Goddard are presented in this paper along with descriptions of the parallel algorithmic techniques employed in five applications that have bearing on Earth sciences.

Fischer, James R.↗
NASA NTRS2005

Science Instruments and Sensors Capability Roadmap: NRC Dialogue

The Science Instruments and Sensors roadmaps include capabilities associated with the collection, detection, conversion, and processing of scientific data required to answer compelling science questions driven by the Vision for Space Exploration and The New Age of Exploration (NASA's Direction for 2005 & Beyond). Viewgraphs on these instruments and sensors are presented.

Barney, Rich↗
NASA NTRS2019

Assimilated Hydrological Data at NASA GES DISC with Examples of Extreme Events

Extreme weather and climate events, such as heavy rainfall, heatwave, floods and droughts, and strong wind, can have devastating impacts on society. NASA and NOAA, based on independent analyses, recently announced that global surface temperatures in 2018 are the fourth warmest since 1880, behind only those of 2016, 2017, and 2015 (nasa.gov). Also in 2018, the United States experienced 14 billion-dollar disasters, ranking as the fourth highest total number of such events, behind only the years 2017, 2011, and 2016 (climate.gov). Many research studies have focused on acquiring observational and modeling data, to reveal linkages between increasing extreme events, global water and energy cycle, and global climate change. However, draw conclusions is still a challenge. NASA Goddard Earth Sciences Data and Information Services Center is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remotely-sensed and model data and provides reliable and robust data access and services to users worldwide. This presentation provides a few examples of extreme event study that use Land Surface Model (LSM) assimilated, quality-controlled, and spatially and temporally consistent, hydrological data from the GES DISC. Also provided is a summary table for the hydrological data holdings, along with discussions of recent updates to data and data services.

Rui, Hualan↗
NASA NTRS1991

Earth observing SAR data processing systems at the Jet Propulsion Laboratory - Seasat to EOS SAR

The evolution of SAR digital data processing and management ground systems developed at the JPL for earth science missions is discussed. Attention is given to the SAR ground data system requirements, the early data processing systems, the Seasat SAR system, and the SIR-B data processing system. Special consideration is given to two currently operational SAR data systems: the JPL aircraft SAR processing system that flies on the NASA DC-8 and the Alaska SAR Facility at Fairbanks.

Nichols, David A.↗
NASA NTRS1994

The development and operation of the international solar-terrestrial physics central data handling facility

The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) International Solar-Terrestrial Physics (ISTP) Program is committed to the development of a comprehensive, multi-mission ground data system which will support a variety of national and international scientific missions in an effort to study the flow of energy from the sun through the Earth-space environment, known as the geospace. A major component of the ISTP ground data system is an ISTP-dedicated Central Data Handling Facility (CDHF). Acquisition, development, and operation of the ISTP CDHF were delegated by the ISTP Project Office within the Flight Projects Directorate to the Information Processing Division (IPD) within the Mission Operations and Data Systems Directorate (MO&DSD). The ISTP CDHF supports the receipt, storage, and electronic access of the full complement of ISTP Level-zero science data; serves as the linchpin for the centralized processing and long-term storage of all key parameters generated either by the ISTP CDHF itself or received from external, ISTP Program approved sources; and provides the required networking and 'science-friendly' interfaces for the ISTP investigators. Once connected to the ISTP CDHF, the online catalog of key parameters can be browsed from their remote processing facilities for the immediate electronic receipt of selected key parameters using the NASA Science Internet (NSI), managed by NASA's Ames Research Center. The purpose of this paper is twofold: (1) to describe how the ISTP CDHF was successfully implemented and operated to support initially the Japanese Geomagnetic Tail (GEOTAIL) mission and correlative science investigations, and (2) to describe how the ISTP CDHF has been enhanced to support ongoing as well as future ISTP missions. Emphasis will be placed on how various project management approaches were undertaken that proved to be highly effective in delivering an operational ISTP CDHF to the Project on schedule and within budget. Examples to be discussed include: the development of superior teams; the use of Defect Causal Analysis (DCA) concepts to improve the software development process in a pilot Total Quality Management (TQM) initiative; and the implementation of a robust architecture that will be able to support the anticipated growth in the ISTP Program science requirements with only incremental upgrades to the baseline system. Further examples include the use of automated data management software and the implementation of Government and/or industry standards, whenever possible, into the hardware and software development life-cycle. Finally, the paper will also report on several new technologies (for example, the installation of a Fiber Data Distribution Interface network) that were successfully employed.

Lehtonen, Kenneth↗
NASA NTRS1985

Science opportunities using the NASA scatterometer on N-ROSS

The National Aeronautics and Space Administration scatterometer (NSCAT) is to be flown as part of the Navy Remote Ocean Sensing System (N-ROSS) scheduled for launch in 1989. The NSCAT will provide frequent accurate and high-resolution measurements of vector winds over the global oceans. NSCAT data will be applicable to a wide range of studies in oceanography, meteorology, and instrument science. The N-ROSS mission, is outlined, are described. The capabilities of the NSCAT flight instrument and an associated NASA research ground data-processing and distribution system, and representative oceanographic meteorological, and instrument science studies that may benefit from NSCAT data are surveyed.

Freilich, M. H.↗
NASA NTRS2006

The Mission Operations System for Wide-field Infrared Survey Explorer (WISE)

The goal of the Wide-field Infrared Survey Explorer (WISE) mission is to perform a highly sensitive all-sky survey in 4 wavebands from 3 to 25(mu)m. Launched on a Delta II rocket into a 500km Sun-synchronous orbit in June 2009, during its 7 months of operations, WISE will acquire about 50GBytes of raw science data every day, which will be down-linked via the TDRSS relay satellite system and processed into an astronomical catalogue and image atlas. The WISE mission operations system is being implemented in collaboration between UCLA, JPL and IPAC (Caltech). In this paper we describe the challenges to manage a high data rate, cryogenic, low earth-orbit mission; maintaining safe on-orbit operations, fast anomaly recoveries (mandated by the desire to provide complete sky coverage in a limited lifetime), production and dissemination of high quality science products, given the constraints imposed by funding profiles for small space missions.

Science Data Processing↗
NASA NTRS1988

Research and technology 1988

This report presents the on-going research activities at the NASA Marshall Space Flight Center for the year 1988. The subjects presented are space transportation systems, shuttle cargo vehicle, materials processing in space, environmental data base management, microgravity science, astronomy, astrophysics, solar physics, magnetospheric physics, aeronomy, atomic physics, rocket propulsion, materials and processes, telerobotics, and space systems.

Source record↗
NASA NTRS2010

Tracking Provenance of Earth Science Data

Tremendous volumes of data have been captured, archived and analyzed. Sensors, algorithms and processing systems for transforming and analyzing the data are evolving over time. Web Portals and Services can create transient data sets on-demand. Data are transferred from organization to organization with additional transformations at every stage. Provenance in this context refers to the source of data and a record of the process that led to its current state. It encompasses the documentation of a variety of artifacts related to particular data. Provenance is important for understanding and using scientific datasets, and critical for independent confirmation of scientific results. Managing provenance throughout scientific data processing has gained interest lately and there are a variety of approaches. Large scale scientific datasets consisting of thousands to millions of individual data files and processes offer particular challenges. This paper uses the analogy of art history provenance to explore some of the concerns of applying provenance tracking to earth science data. It also illustrates some of the provenance issues with examples drawn from the Ozone Monitoring Instrument (OMI) Data Processing System (OMIDAPS) run at NASA's Goddard Space Flight Center by the first author.

Tilmes, Curt↗
DOE OSTI2024

Massive all-atom analysis of 2D materials with quantum properties (Final report)

Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1975

Data management for Large Space Telescope

The data management system for the Large Space Telescope (LST) must be capable of meeting requirements of 160 million bits per 95 min orbit with a bit error rate of less than 0.00001 for data. The system will be supported by the Tracking and Data Relay Satellite System of the Space Tracking and Data Network. The on-board system includes a general purpose computer that controls the vehicle as a stable observation platform and the array of instruments used for data collection. The ground-based system comprises a Mission Operations Center (MOC), a Science Institute where instrument data is processed, and a communications service for the space and point-to-point data flow. The allocation of hardware and software between on-board and ground-based components to achieve design objectives of maximum flexibility at minimum cost is discussed.

Hope, G. R., Jr.↗
NASA NTRS1977

Study of alternative probe technologies

A number of implied technologies for a deep probe mission was examined; i.e., one that would provide the capability to scientifically examine planetary atmospheres at the 1000 bar level. Conditions imposed by current Jupiter, Saturn, and Uranus atmospheric models were considered. The major thrust of the measurements was to determine lower atmosphere composition, even to trace constituents of one part per billion. Two types of instruments having the necessary accuracy to meet the science objectives were considered and integrated into a deep probe configuration. One deep probe option that resulted was identified as a Minimum Technology Development approach. The significant feature of this option is that only three technology developments are required to enable the mission, i.e., (1) science instrument development, (2) advanced data processing, and (3) external high pressure/thermal insulation. It is concluded that a probe designed for a Jupiter mission could, with minor changes, be used for a Saturn or Uranus mission.

Source record↗
NASA NTRS1991

NASA/Ames Research Center's science and applications aircraft program

NASA-Ames Research Center operates a fleet of seven Science and Applications Aircraft, namely the C-141/Kuiper Airborne Observatory (KAO), DC-8, C-130, Lear Jet, and three ER-2s. These aircraft are used to satisfy two major objectives, each of equal importance. The first is to acquire remote and in-situ scientific data in astronomy, astrophysics, earth sciences, ocean processes, atmospheric physics, meteorology, materials processing and life sciences. The second major objective is to expedite the development of sensors and their attendant algorithms for ultimate use in space and to simulate from an aircraft, the data to be acquired from spaceborne sensors. NASA-Ames Science and Applications Aircraft are recognized as national and international facilities. They have performed and will continue to perform, operational missions from bases in the United States and worldwide. Historically, twice as many investigators have requested flight time than could be accommodated. This situation remains true today and is expected to increase in the years ahead. A major advantage of the existing fleet of aircraft is their ability to cover a large expanse of the earth's ecosystem from the surface to the lower stratosphere over large distances and time aloft. Their large payload capability allows a number of scientists to use multi-investigator sensor suites to permit simultaneous and complementary data gathering. In-flight changes to the sensors or data systems have greatly reduced the time required to optimize the development of new instruments. It is doubtful that spaceborne systems will ever totally replace the need for airborne science aircraft. The operations philosophy and capabilities exist at NASA-Ames Research Center.

Hall, G. Warren↗
NASA NTRS1994

Galileo spacecraft modeling for orbital operations

The Galileo Jupiter orbital mission using the Low Gain Antenna (LGA) requires a higher degree of spacecraft state knowledge than was originally anticipated. Key elements of the revised design include onboard buffering of science and engineering data and extensive processing of data prior to downlink. In order to prevent loss of data resulting from overflow of the buffers and to allow efficient use of the spacecraft resources, ground based models of the spacecraft processes will be implemented. These models will be integral tools in the development of satellite encounter sequences and the cruise/playback sequences where recorded data is retrieved.

Mclaughlin, Bruce A.↗