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Global Navigation Satellite System (GNSS) Final Clock Product (30 second resolution, daily files, generated weekly) from NASA CDDIS
data.nasa.gov | Last Updated 2023-02-28T19:25:26.000ZThis derived product set consists of Global Navigation Satellite System Final Satellite and Receiver Clock Product (30-second granularity, daily files, generated weekly) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined satellite and receiver clock products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.
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Global Navigation Satellite System (GNSS) IGS Clock Combination Product from Real-Time AC Submissions from NASA CDDIS
data.nasa.gov | Last Updated 2022-01-17T05:22:33.000ZThis derived product set consists of Global Navigation Satellite System satellite and receiver clock combination product (30-second granularity, daily files, generated daily) from the real-time IGS analysis center submissions available from NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The CDDIS provides access to products generated from real-time data streams in support of the IGS Real-Time Service. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format. These real-time data are utilized to generate near real-time product streams. The real-time products consist of GNSS satellite orbit and clock corrections to the broadcast ephemeris. These correction streams are formatted according to the RTCM SSR standard for State Space Representation and are broadcast using the NTRIP protocol. IGS analysis centers (ACs) access GNSS real-time data streams to produce GNSS satellite and ground receiver clock values in real-time. The product streams are combination solutions generated by processing individual real-time solutions from participating IGS Real-time ACs. The IGS Real-Time Analysis Center Coordinator (RTACC) uses these individual AC solutions to generate this real-time IGS combined satellite and receiver clock product. The effect of combining the different AC solutions is a more reliable and stable performance than that of any single AC's product. This clock solution is a batch combination based on daily clock submissions by these IGS real-time analysis centers and have been provided since February 2009, shortly after real-time streams were routinely available through the IGS Real-Time Pilot Project and prior to the availability of real-time product streams. Clock solution files consist of decoded clock results from the real time stream at 30-second intervals. This combination is a daily solution available approximately one to three days after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC.
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Global Navigation Satellite System (GNSS) Rapid Clock Product (30 second resolution, daily files, generated daily) from NASA CDDIS
data.nasa.gov | Last Updated 2023-02-28T19:25:38.000ZThis derived product set consists of Global Navigation Satellite System Rapid Satellite and Receiver Clock Product (30-second granularity, daily files, generated daily) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined satellite and receiver clock products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. For most applications the user of IGS products will not notice any significant differences between results obtained using the IGS Final and the IGS Rapid products.
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Global Navigation Satellite System (GNSS) Rapid Orbit/Clock/ERP Product Summary from NASA CDDIS
data.nasa.gov | Last Updated 2023-03-01T00:51:24.000ZThis derived product set consists of Global Navigation Satellite System Rapid Orbit/Reference Frame Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS rapid combined orbit, satellite and receiver clock, and ERP products. The rapid combination is a daily solution available approximately 17 hours after the end of the previous UTC day. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the daily rapid products.
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Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)
data.nasa.gov | Last Updated 2023-04-17T13:04:40.000ZTitle: The Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)Research Team:* Antonio Mannino (PI) - NASA GSFC* Michael Novak - NASA GSFC* Margaret Mulholland (co-PI) - Old Dominion University* Peter Bernhardt - Old Dominion University* CJ Staryk - Old Dominion University* Kimberly Hyde (co-PI) - NOAA NEFSC* Jon Hare (collaborator) - NOAA NEFSC* David Lary (co-I) - University of Texas at DallasObservations from the MODIS and SeaWiFS time series (1997-2012) and measurements from an extensive field campaign are employed to examine how inter-annual and decadal-scale climate variability affects primary productivity and organic carbon distributions along the continental margin of the U.S. northeast coast. Estimates of daily primary productivity (PP) will be computed using the Ocean Productivity from Absorption of Light (OPAL) model. OPAL vertically resolves phytoplankton absorption of photosynthetically active radiation (PAR) and relates the chlorophyll-specific absorption coefficient to sea-surface temperature (SST), where SST is a proxy for seasonal changes in the phytoplankton community. OPAL will be validated with new field measurements of PP including dissolved organic carbon production.Field measurements of particulate (POC) and dissolved organic carbon (DOC) and the absorption coefficients of phytoplankton (aph) and colored dissolved organic matter (aCDOM) will allow us to extend the validation range (temporally and spatially) for our coastal algorithms and reduce the uncertainties in satellite-derived estimates of OPAL PP, POC, DOC, aph and aCDOM. Furthermore, we will apply our extensive field data to derive region-independent ocean color algorithms for PP, POC, DOC aCDOM and aph using machine learning approaches. We will rigorously validate and compare band-ratio and multivariate machine learning algorithms. Algorithms validated from this study will be applied to satellite observations to produce a time series of satellite data productsThe U.S. Middle Atlantic Bight (MAB), George's Bank (GB) and Gulf of Maine (GoM) stand at the crossroads between major ocean circulation features - the Gulf Stream and Labrador slope-sea and shelf currents - and are influenced by highly variable river discharge, summer upwelling, warm core rings, and intense seasonal stratification. Our work will focus on the impacts of variable river discharge, SST and large-scale climate indices on primary production, and POC and DOC distributions. These processes are not unique to the MAB and GoM. Consequently, the results from this activity can be applied to understanding how inter-annual and long-term variability in climate patterns can impact the carbon cycle of continental margins throughout the globe.
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Global Navigation Satellite System (GNSS) Combined Final Clock Solution Comparison Summary Product from NASA CDDIS
data.nasa.gov | Last Updated 2023-03-01T00:51:18.000ZThis derived product set consists of Global Navigation Satellite System Final Clock Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final combined satellite and receiver clock products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the final combined clock products and comparison with the individual AC solutions.
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PADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY
data.nasa.gov | Last Updated 2020-01-29T04:18:17.000ZPADMINI: A PEER-TO-PEER DISTRIBUTED ASTRONOMY DATA MINING SYSTEM AND A CASE STUDY TUSHAR MAHULE*, KIRK BORNE**, SANDIPAN DEY*, SUGANDHA ARORA*, AND HILLOL KARGUPTA*** Abstract. Peer-to-Peer (P2P) networks are appealing for astronomy data mining from virtual observatories because of the large volume of the data, compute-intensive tasks, potentially large number of users, and distributed nature of the data analysis process. This paper offers a brief overview of PADMINI—a Peer-to-Peer Astronomy Data MINIng system. It also presents a case study on PADMINI for distributed outlier detection using astronomy data. PADMINI is a webbased system powered by Google Sky and distributed data mining algorithms that run on a collection of computing nodes. This paper offers a case study of the PADMINI evaluating the architecture and the performance of the overall system. Detailed experimental results are presented in order to document the utility and scalability of the system.
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Global Navigation Satellite System (GNSS) Final Orbit/Reference Frame Product Summary from NASA CDDIS
data.nasa.gov | Last Updated 2024-10-07T13:04:40.000ZThis derived product set consists of Global Navigation Satellite System Final Orbit/Reference Frame Product Summary from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce GNSS satellite and ground receiver clock values. The IGS Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS final orbit, reference frame, combined satellite and receiver clock, and ERP products. The final products are considered the most consistent and highest quality IGS solutions; they consist of daily orbit files, generated on a weekly basis with a delay up to 13 (for the last day of the week) to 20 (for the first day of the week) days. All satellite and receiver clock solution files utilize the clock RINEX format and span 24 hours from 00:00 to 23:45 UTC. The solution summary file details information about the generation of the daily final products.
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Transformational Tools and Technologies Project
data.nasa.gov | Last Updated 2020-01-29T05:00:40.000Z<p>The Transformational Tools and Technologies (TTT) Project advances state-of-the-art computational and experimental tools and technologies that are vital to aviation applications in the six strategic thrusts. The project develops new computer-based tools, computational fluid dynamics models, and associated scientific knowledge that will provide first-of-a-kind capabilities to analyze, understand, and predict aviation concept performance. These revolutionary tools will be applied to accelerate NASA&rsquo;s research and the community&rsquo;s design and introduction of advanced concepts. The Project also explores technologies that are broadly-critical to advancing ARMD strategic outcomes.&nbsp; Such technologies include the understanding of new types of strong and lightweight materials, innovative controls techniques, and experimental methods.&nbsp; TTT also develops improved MDAO and systems analysis tools to enable multi-disciplinary integration. All of these technologies will support and enable concept development and benefits assessment across multiple ARMD programs and disciplines.</p><p>&nbsp;</p><p>The tools and technologies of interest span many disciplines.&nbsp; The Fluid Mechanics Discipline encompasses advanced turbulence modeling, boundary layer transition prediction and modeling, numerical methods, and flow control development and prediction for a wide range of airframe and propulsion system flow problems of interest.&nbsp; Canonical data is developed and used to validate the modeling improvements developed in this discipline.&nbsp; Development of more accurate physics-based methods such as large eddy simulation (LES) is emphasized.</p><p>The Structures and Materials Discipline emphasizes improved multifunctional and high temperature materials for airframe and engine application, as well as modeling and simulation tool development to improve validated first-principles materials and structural modeling.&nbsp; Development of ceramic matrix composite (CMC) materials for high-temperature engine application is of particular emphasis in the discipline.</p><p>The MDAO (Multi-Disciplinary Design, Analysis &amp; Optimization) and Systems Analysis Discipline develops MDAO and aircraft system-level tools to improve integration of discipline-based technologies and enable improved assessment of system-level benefits.&nbsp; An open-source framework is emphasized to better leverage external partners and increase interaction and benefit to the community.</p><p>The Combustion Discipline is developing more accurate physics-based models for complex multi-species reacting flows representative of aircraft engine combustors.&nbsp; This is done through a combination of high-fidelity benchmark experiments and the use of advanced unsteady turbulence modeling and large eddy simulation (LES) methods.&nbsp; Advanced concepts such as active combustion control and pressure-gain combustion cycles are also investigated.</p><p>The Controls Discipline encompasses work across aircraft flight controls and advanced propulsion controls.&nbsp; Development of technologies to enable distributed engine control systems are an area of emphasis in this discipline.</p><p>The Innovative Measurements Discipline conducts research to advance the state-of-the-art in cross-cutting sensing and measurement technologies for aircraft and propulsion systems.&nbsp; Areas of development include advanced optical measurements, enhanced sensing, and improved data acquisition.</p>
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Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms
data.nasa.gov | Last Updated 2020-01-29T01:43:04.000ZThe worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.