Results for “Space Science Data Systems” — Page 43

NASA NTRS1996

Development of Secondary Archive System at Goddard Space Flight Center Version 0 Distributed Active Archive Center

The Goddard Space Flight Center (GSFC) version 0 Distributed Active Archive Center (DAAC) has been developed to support existing and pre Earth Observing System (EOS) Earth science datasets, facilitate the scientific research, and test EOS data and information system (EOSDIS) concepts. To ensure that no data is ever lost, each product received at GSFC DAAC is archived on two different media, VHS and digital linear tape (DLT). The first copy is made on VHS tape and is under the control of UniTree. The second and third copies are made to DLT and VHS media under a custom built software package named 'Archer'. While Archer provides only a subset of the functions available with commercial software like UniTree, it supports migration between near-line and off-line media and offers much greater performance and flexibility to satisfy the specific needs of a data center. Archer is specifically designed to maximize total system throughput, rather than focusing on the turn-around time for individual files. The commercial off the shelf software (COTS) hierarchical storage management (HSM) products evaluated were mainly concerned with transparent, interactive, file access to the end-user, rather than a batch-orientated, optimizable (based on known data file characteristics) data archive and retrieval system. This is critical to the distribution requirements of the GSFC DAAC where orders for 5000 or more files at a time are received. Archer has the ability to queue many thousands of file requests and to sort these requests into internal processing schedules that optimize overall throughput. Specifically, mount and dismount, tape load and unload cycles, and tape motion are minimized. This feature did not seem to be available in many COTS pacages. Archer also uses a generic tar tape format that allows tapes to be read by many different systems rather than the proprietary format found in most COTS packages. This paper discusses some of the specific requirements at GSFC DAAC, the motivations for implementing the Archer system, and presents a discussion of the Archer design that resulted.

Sherman, Mark↗

NASA NTRS1999

Clouds and Radiation from the Earth Observing System

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year, four EOS science missions are scheduled for launch, representing observations of (i) total solar irradiance, (ii) Earth radiation budget, (iii) land cover & land use change, (iv) ocean processes (vector wind, sea surface temperature, and ocean color), (v) atmospheric processes (aerosol and cloud properties, water vapor, and temperature and moisture profiles), and (vi) tropospheric chemistry. In succeeding years many more satellites will be launched that will contribute immeasurably to our understanding of the Earth's environment. In this presentation we will describe how scientists are using MODIS (Moderate Resolution Imaging Spectroradiometer), an earth-viewing cross-track scanning spectroradiometer on the Terra (AM-1) satellite, for the remote sensing of cloud and aerosol properties. MODIS scans a swath width sufficient to provide nearly complete global coverage every two days from a polar-orbiting, sun-synchronous, platform at an altitude of 705 km, and provides images in 36 spectral bands between 0.415 and 14.235 pm with spatial resolutions of 250 m (2 bands), 500 m (5 bands) and 1000 m (29 bands). These bands have been carefully selected to enable advanced studies of land, ocean, and atmosphere processes.

King, Michael D.↗

NASA NTRS2000

Insights on How NASA's Earth Observing System (EOS) Monitors Our World Environment

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year, four EOS science missions were launched, representing observations of (1) total solar irradiance, (2) Earth radiation budget, (3) land cover and land use change, (4) ocean processes (vector wind, sea surface temperature, and ocean color), (5) atmospheric processes (aerosol and cloud properties, water vapor, and temperature and moisture profiles), and (6) tropospheric chemistry. In succeeding years many more satellites will be launched that will contribute immeasurably to our understanding of the Earth's environment. In this presentation I will describe how scientists are using EOS data to examine land use and natural hazards, environmental air quality, including dust storms over the world's deserts, cloud and radiation properties, sea surface temperature, and winds over the ocean.

King, Michael D.↗

NASA NTRS2001

Earth Observing System: Global Observations to Study the Earth's Environment

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During the last couple of years, four EOS science missions were launched, representing observations of (1) total solar irradiance, (2) Earth radiation budget, (3) land cover & land use change, (4) ocean processes (vector wind, sea surface temperature, and ocean color), (5) atmospheric processes (aerosol and cloud properties, water vapor, and temperature and moisture profiles), and (6) tropospheric chemistry. In succeeding years many more satellites will be launched that will contribute immeasurably to our understanding of the Earth's environment. In this presentation I will describe how scientists are using NASA's Earth science data to examine land use and natural hazards, environmental air quality, including dust storms over the world's deserts, cloud and radiation properties, sea surface temperature, and winds over the ocean.

King, Michael D.↗

NASA NTRS2002

Overview of Terra

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During the last couple of years, seven EOS science missions were launched, representing observations of (i) total solar irradiance, (ii) Earth radiation budget, (iii) land cover & land use change, (iv) ocean processes (vector wind, sea surface temperature, ocean topography, and ocean color), (v) atmospheric processes (aerosol and cloud properties, water vapor, and temperature and moisture profiles), (v) tropospheric chemistry, (vi) sea ice concentration, and (viii) precipitation. In succeeding years many more satellites will be launched that will contribute immeasurably to our understanding of the Earth's environment. In this lecture I will describe how scientists are using NASA's Earth science data to examine land use and natural hazards, environmental air quality, including dust storms over the world's deserts, cloud and radiation properties, sea surface temperature, and tropospheric chemistry. This lecture will describe the Terra satellite, launched in December 1999 and still operating, and each of the five sensors onboard the spacecraft. This overview will highlight the goals and objectives of this mission, and describe the contributions and unique datasets provided by each sensor. This lecture will form the background for an extensive weeklong course on Terra and all the algorithms that have been developed and implemented to process the data from this spacecraft. This lecture will include a description of the Terra orbit, launch, data communication with the spacecraft, and data processing and archival of the data.

King, Michael D.↗

NASA NTRS2002

Measurement Sets and Sites Commonly Used for Characterization

Scientists at NASA's Earth Science Applications Directorate are creating a well-characterized Verification & Validation (V&V) site at the Stennis Space Center. This site enables the in-flight characterization of remote sensing systems and the data they acquire. The data are predominantly acquired by commercial, high spatial resolution satellite systems, such as IKONOS and QuickBird 2, and airborne systems. The smaller scale of these newer high resolution remote sensing systems allows scientists to characterize the geometric, spatial, and radiometric data properties using a single V&V site. The targets and techniques used to characterize data from these newer systems can differ significantly from the techniques used to characterize data from the earlier, coarser spatial resolution systems. Scientists are also using the SSC V&V site to characterize thermal infrared systems and active LIDAR systems. SSC employs geodetic targets, edge targets, radiometric tarps, and thermal calibration ponds to characterize remote sensing data products. This paper presents a proposed set of required measurements for visible through long-wave infrared remote sensing systems and a description of the Stennis characterization. Other topics discussed include: 1) The use of ancillary atmospheric and solar measurements taken at SSC that support various characterizations; 2) Additional sites used for radiometric, geometric, and spatial characterization in the continental United States; 3) The need for a standardized technique to be adopted by CEOS and other organizations.

Pagnutti, Mary↗

NASA NTRS2003

Earth Observing System: Global Observations to Study the Earth's Environment

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During the last couple of years, four EOS science missions were launched, representing observations of (i) total solar irradiance, (ii) Earth radiation budget, (iii) land cover & land use change, (iv) ocean processes (vector wind, sea surface temperature, and ocean color), (v) atmospheric processes (aerosol and cloud properties, water vapor, and temperature and moisture profiles), and (vi) tropospheric chemistry. In succeeding years many more satellites will be launched that will contribute immeasurably to our understanding of the Earth's environment. In this presentation I will describe how scientists are using NASA's Earth science data to examine land use and natural hazards, environmental air quality, including: dust storms over the worlds deserts, cloud and radiation properties, sea surface temperature, and winds over the ocean, with a special emphasis on satellite observations available for studying the southern African environment.

King, Michael D.↗

NASA NTRS2003

Promise and Capability of NASA's Earth Observing System to Monitor Human-Induced Climate Variations

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. The Moderate Resolution Imaging Spectroradiometer (MODIS), developed as part of the Earth Observing System (EOS) and launched on Terra in December 1999 and Aqua in May 2002, is designed to meet the scientific needs for satellite remote sensing of clouds, aerosols, water vapor, and land and ocean surface properties. This sensor and multi-platform observing system is especially well suited to observing detailed interdisciplinary components of the Earth s surface and atmosphere in and around urban environments, including aerosol optical properties, cloud optical and microphysical properties of both liquid water and ice clouds, land surface reflectance, fire occurrence, and many other properties that influence the urban environment and are influenced by them. In this presentation I will summarize the current capabilities of MODIS and other EOS sensors currently in orbit to study human-induced climate variations.

King, M. D.↗

NASA NTRS2004

NASA's Earth Observing System (EOS): Observing the Atmosphere, Land, Oceans, and Ice from Space

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year, the last of the first series of EOS missions, Aura, was launched. Aura is designed exclusively to conduct research on the composition, chemistry, and dynamics of the Earth's upper and lower atmosphere, employing multiple instruments on a single spacecraft. Aura is the third in a series of major Earth observing satellites to study the environment and climate change and is part of NASA's Earth Science Enterprise. The first and second missions, Terra and Aqua, are designed to study the land, oceans, atmospheric constituents (aerosols, clouds, temperature, and water vapor), and the Earth's radiation budget. The other seven EOS spacecraft include satellites to study (i) land cover & land use change, (ii) solar irradiance and solar spectral variation, (iii) ice volume, (iv) ocean processes (vector wind and sea surface topography), and (v) vertical variations of clouds, water vapor, and aerosols up to and including the stratosphere. Aura's chemistry measurements will also follow up on measurements that began with NASA's Upper Atmosphere Research Satellite and continue the record of satellite ozone data collected from the TOMS missions. In this presentation I will describe how scientists are using EOS data to examine the health of the earth's atmosphere, including atmospheric chemistry, aerosol properties, and cloud properties, with a special but not exclusive look at the latest earth observing mission, Aura.

King, Michael D.↗

NASA NTRS2005

NASA's Earth Observing System (EOS): Observing the Atmosphere, Land, Oceans, and Ice from Space

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by whch scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year, the last of the first series of EOS missions, Aura, was launched. Aura is designed exclusively to conduct research on the composition, chemistry, and dynamics of the Earth's upper and lower atmosphere, employing multiple instruments on a single spacecraft. Aura is the third in a series of major Earth observing satellites to study the environment and climate change and is part of NASA's Earth Science Enterprise. The first and second missions, Terra and Aqua, are designed to study the land, oceans, atmospheric constituents (aerosols, clouds, temperature, and water vapor), and the Earth's radiation budget. The other seven EOS spacecraft include satellites to study (i) land cover & land use change, (ii) solar irradiance and solar spectral variation, (iii) ice volume, (iv) ocean processes (vector wind and sea surface topography), and (v) vertical variations of clouds, water vapor, and aerosols up to and including the stratosphere. Aura's chemistry measurements will also follow up on measurements that began with NASA's Upper Atmosphere Research Satellite and continue the record of satellite ozone data collected from the TOMS missions. In this presentation I will describe how scientists are using EOS data to examine the health of the earth's atmosphere, including atmospheric chemistry, aerosol properties, and cloud properties, with a special look at the latest earth observing mission, Aura.

King, Michael D.↗

NASA NTRS2008

Validation of On-Orbit Methodology for the Assessment of Cardiac Function and Changes in the Circulating Volume Using Ultrasound and "Braslet-M" Occlusion Cuffs

Recent advances in remotely guided imaging techniques on ISS allow the acquisition of high quality ultrasound data using crewmember operators with no medical background and minimal training. However, ongoing efforts are required to develop and validate methodology for complex imaging protocols to ensure their repeatability, efficiency, and suitability for use aboard the ISS. This Station Developmental Test Objective (SDTO) tests a cardiovascular evaluation methodology that takes advantage of the ISS Ultrasound capability, the Braslet-M device, and modified respiratory maneuvers (Valsalva and Mueller), to broaden the spectrum of anatomical and functional information on human cardiovascular system during long-duration space missions. The proposed methodology optimizes and combines new and previously demonstrated methods, and is expected to benefit medically indicated assessments, operational research protocols, and data collections for science. Braslet-M is a current Russian operational countermeasure that compresses the upper thigh to impede the venous return from lower extremities. The goal of the SDTO is to establish and validate a repeatable ultrasound-based methodology for the assessment of a number of cardiovascular criteria in microgravity. Braslet-M device is used as a means to acutely alter volume distribution while focused ultrasound measurements are performed. Modified respiratory maneuvers are done upon volume manipulations to record commensurate changes in anatomical and functional parameters. The overall cardiovascular effects of the Braslet-M device are not completely understood, and although not a primary objective of this SDTO, this effort will provide pilot data regarding the suitability of Braslet-M for its intended purpose, effects, and the indications for its use.

Bogomolov, V. V.↗

NASA NTRS2010

The Heliophysics Data Environment, Virtual Observatories, NSSDC, and SPASE

Heliophysics (the study of the Sun and its effects on the Solar System, especially the Earth) has an interesting data environment in that the data are often to be found in relatively small data sets widely scattered in archives around the world. Within the last decade there have been more concentrated efforts to organize the data access methods and create a Heliophysics Data and Model Consortium (HDMC). To provide data search and access capability a number of Virtual Observatories (VO's) have been established both via funding from the U.S. National Aeronautics and Space Administration (NASA) and through other funding agencies in the U.S. and worldwide. At least 15 systems can be labeled as Heliophysics Virtual Observatories, 9 of them funded by NASA. Other parts of this data environment include Resident Archives, and the final, or "deep" archive at the National Space Science Data Center (NSSDC). The problem is that different data search and access approaches are used by all of these elements of the HDMC and a search for data relevant to a particular research question can involve consulting with multiple VO's - needing to learn a different approach for finding and acquiring data for each. The Space Physics Archive Search and Extract (SPASE) project is intended to provide a common data model for Heliophysics data and therefore a common set of metadata for searches of the VO's and other data environment elements. The SPASE Data Model has been developed through the common efforts of the HDMC representatives over a number of years. We currently have released Version 2.1. of the Data Model. The advantages and disadvantages of the Data Model will be discussed along with the plans for the future. Recent changes requested by new members of the SPASE community indicate some of the directions for further development.

Thieman, James↗

NASA NTRS2012

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/ deviation so that further analysis can be directed and corrective actions followed.

Pham, Timothy T.↗

NASA NTRS2012

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/deviation so that further analysis can be directed and corrective actions followed.

Pham, Timothy T.↗

NASA NTRS2018

Enabling Technologies for Deep Space Motion Imagery

From the beginning of the Space Age, imagery, particularly motion imagery, has been a part of crewed and uncrewed missions. As technologies have evolved the imagery gets better, more compelling, and more useful for operations and monitoring of systems, crew, and spacecraft. Film, video and live television have engaged the public and sparked the imaginations of engineers, scientists, and artists alike. As we look forward now to crewed missions beyond low-Earth orbit, such as the Deep Space Gateway being considered as a pre-cursor to future crewed Mars missions, there are both opportunities and challenges in implementing a multi-faceted imaging system that advances mission capabilities and technology.This paper will present a vision for an imaging system that is relevant for operations of the ISS and future crewed missions in deep space, with a detailed look at some of the key innovative technologies required to enable such a system. In addition, impacts to Space Operations, video distribution to the public and to science investigators, and video system interoperability will be presented. Opportunities where standardization can enable interoperability are also identified for those imagery and communications functions planned for inclusion in the Consultative Committee for Space Data Systems (CCSDS) Blue Book.

Grubbs, Rodney↗

NASA NTRSDate not supplied

Using Artificial Intelligence and Machine Learning to Enhance Mission Design and Operations of the Habitable Worlds Observatory (HWO)

One key aspect in the development of HWO is the early deployment of artificial intelligence (AI) and machine learning (ML) to enhance mission science and operations. Our subtask group is part of the HWO AI/ML working group and focuses on AI and ML for mission operations. Our task group seeks to educate other HWO working groups about AI and ML capabilities for mission operations, investigate how to bridge technology gaps, and enable new capabilities particularly in the areas of observational scheduling, instrument health monitoring, and downlink operations. We focus on mission tasking / scheduling both for mission analysis in development and operations. AI and ML for mission scheduling includes: tools to support proposal calls and review, ensuring fairness in calls for proposals, community peer reviews and ease workloads, as well as in-flight and ground software development (e.g., using natural language processing (NLP) to support process automation from requirements). AI and ML for the mission’s development and operations include 1) anomaly detection and prediction (from onboard and ground based tools) to monitor the spacecraft’s health, 2) ground-based automated scheduling for mission operations including long-term and short-term planning and maintenance, and 3) flight system flexible execution (as flight proven for Spitzer and JWST) to enable robust execution despite execution variations, and 4) data analysis for prioritization (e.g., real-time data evaluation leading to autonomous actions and adjustments, high-priority identification, onboard data compression, etc.). Incorporation of ML and AI will enable HWO to address the major science questions related to exoplanet characterization, general astrophysics, and solar system exploration and also extend the boundaries of space mission technologies.

Mark Moussa↗

NASA NTRSDate not supplied

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

As science, exploration, and commercial space missions become increasingly complex, so does the need for efficient, autonomous, and integrated spacecraft navigation and operations techniques. Key operational functions, including data collection and transmission, environment characterization, systems constraints, human factors, and navigation, often are intertwined and conflicted. Deep Reinforcement Learning (DRL) offers a framework for addressing integrated spacecraft navigation and planning in an uncertain dynamical environment. The goal of this study is to evaluate the utility of DRL for integrated spacecraft navigation and planning. This is achieved by developing a simple environmental characterization training environment in the Planar-Restricted 2-Body Problem (PR2BP), establishing benchmarks and heuristic baselines, and designing a previously unstudied Markov Decision Process (MDP) formulation. This MDP formulation enables the spacecraft DRL agents to appropriately balance navigation and actuation capabilities. The resulting DRL-derived policy exceeds a random or untrained policy and meets or exceeds the level of performance of a heuristic without actuation. In the process, valuable intuition is gained about the problem with insight into how DRL methods could scale to increasingly more realistic scenarios, including net-work design and training architectures, efficient state space representations, and methods for encouraging exploration in a parametric action space, among others.

navigation↗

DOE OSTI2016

VERSION 1 OF THE HUBBLE SOURCE CATALOG

The Hubble Source Catalog is designed to help optimize science from the Hubble Space Telescope (HST) by combining the tens of thousands of visit-based source lists in the Hubble Legacy Archive (HLA) into a single master catalog. Version 1 of the Hubble Source Catalog includes WFPC2, ACS/WFC, WFC3/UVIS, and WFC3/IR photometric data generated using SExtractor software to produce the individual source lists. The catalog includes roughly 80 million detections of 30 million objects involving 112 different detector/filter combinations, and about 160,000 HST exposures. Source lists from Data Release 8 of the HLA are matched using an algorithm developed by Budavári and Lubow. The mean photometric accuracy for the catalog as a whole is better than 0.10 mag, with relative accuracy as good as 0.02 mag in certain circumstances (e.g., bright isolated stars). The relative astrometric residuals are typically within 10 mas, with a value for the mode (i.e., most common value) of 2.3 mas. The absolute astrometric accuracy is better than 0{sup ″} .1 for most sources, but can be much larger for a fraction of fields that could not be matched to the PanSTARRS, SDSS, or 2MASS reference systems. In this paper we describe the database design with emphasis on those aspects that enable the users to fully exploit the catalog while avoiding common misunderstandings and potential pitfalls. We provide usage examples to illustrate some of the science capabilities and data quality characteristics, and briefly discuss plans for future improvements to the Hubble Source Catalog.

79 ASTRONOMY AND ASTROPHYSICS↗