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The JPL Physical Oceanography Distributed Active Archive Center (DAAC) is one of nine centers processing data for the Earth Observation System. As the physical oceanography data center, it archives, processes, and distributes data to researchers from various satellites. Data holdings can be searched through the Global Change Master Directory, and an advanced information management system is now in prototype.
BEACON-Archives, the JPL Archives electronic catalog, is now online. It enables researchers to find a collection or document description using title, author, documnet number, subject terms, or any words and phrases appearing in the catalog entry.
The Solar-B spacecraft was launched from the Uchinoura Space Center into a circular, sun-synchronous, polar orbit by the Japanese Aerospace Exploration Agency in late September 2006. The spacecraft carries thee scientific instruments designed to follow the flow of magnetic energy from the photosphere to the corona to improve our understanding of both steady state and transient energy release. This goal will be achieved through coordinated observations of three highly advanced solar telescopes developed cooperatively by teams from Japan, the United States and the United Kingdom. The three telescopes are a 0.5m aperture, diffraction limited, solar optical telescope (SOT), an X-ray telescope (XRT) designed for full sun imaging with 1.0 arcsec pixels and an EUV imaging spectrometer (EIS) with an order of magnitude improvement in sensitivity over past instruments. The SOT focal plane contains three instruments, a spectropolarimeter for measuring vector magnetic fields, a broadband filter imager for recording images of the photosphere and chromosphere at the highest resolution the telescope is capable of, and a narrow band filter imager that will record Doppler grams and vector magnetograms. The XRT has broad temperature coverage and a spatial a resolution three times as high as Yohkoh. EIS covers a broad range of transition region and coronal temperatures in two spectral bands. Both XRT and EIS have 2 arcsec spatial resolution (1 arcsec pixels). Instrument first light occurred after five weeks on orbit to allow for out gassing and the opening of the telescopes doors. The initial observation sequences are designed to test the functionality of the different operating modes and for calibration. After this commissioning phase is complete a series of observations are planned to demonstrate the ability of the instruments to meet NASA's mission minimum success criteria. Data is downloaded every orbit to the Norwegian high latitude ground station at Svalbard. The data are transmitted to ISAS where they are reformatted into FITS files and archived as Level 0 data on the ISAS DARTS system. Once the initial observation period is complete, approximately six months after launch, the mission data will be open and freely available to researchers shortly after receipt at the DARTS data archive hosted in Japan and at NASA s Solar Data Analysis Center at the Goddard Space Flight Center. Scientific operations will be conducted from the ISAS facility in Sagamihara, Japan and the observatory will become available for performing joint operations with both ground and space based instruments and for conducting observing programs proposed by non-team members. This process will be described together with a status report from the initial operation of the observatory, showing examples of the first observations.
The development, deployment, operation and maintenance of Integrated Systems Health Management (ISHM) applications require the storage and processing of tremendous amounts of low-level data. This data must be shared in a secure and cost-effective manner between developers, and processed within several heterogeneous architectures. Modern database technology allows this data to be organized efficiently, while ensuring the integrity and security of the data. The extensibility and interoperability of the current database technologies also allows for the creation of an associated support database system. A support database system provides additional capabilities by building applications on top of the database structure. These applications can then be used to support the various technologies in an ISHM architecture. This presentation and paper propose a detailed structure and application description for a support database system, called the Health Assessment Database System (HADS). The HADS provides a shared context for organizing and distributing data as well as a definition of the applications that provide the required data-driven support to ISHM. This approach provides another powerful tool for ISHM developers, while also enabling novel functionality. This functionality includes: automated firmware updating and deployment, algorithm development assistance and electronic datasheet generation. The architecture for the HADS has been developed as part of the ISHM toolset at Stennis Space Center for rocket engine testing. A detailed implementation has begun for the Methane Thruster Testbed Project (MTTP) in order to assist in developing health assessment and anomaly detection algorithms for ISHM. The structure of this implementation is shown in Figure 1. The database structure consists of three primary components: the system hierarchy model, the historical data archive and the firmware codebase. The system hierarchy model replicates the physical relationships between system elements to provide the logical context for the database. The historical data archive provides a common repository for sensor data that can be shared between developers and applications. The firmware codebase is used by the developer to organize the intelligent element firmware into atomic units which can be assembled into complete firmware for specific elements.
The succession of US and international Earth observing satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000). The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot (ATDD), to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The ATDD will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archiving for fast data access, access to remote data without unnecessary data transfers, and data retrieval by users finding data desirable for further study. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be foilowed by associated measurements from TVILS, =MI, HIRDLS, sad TES. Given the independent nature of instrumentlplatform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be illustrated and discussed. The project's early visualizations and analysis efforts illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.
This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major components--the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products.
This paper describes the guiding vision, progress to date and the plan forward for development of the Marshall Space Flight Center (MSFC) Systems Engineering Guide (SEG), a virtual systems engineering handbook and archive that describes the system engineering processes used by MSFC in the development of ongoing complex space systems such as the Ares launch vehicle and forthcoming ones as well. It is the intent of this website to be a "One Stop Shop' for MSFC systems engineers that will provide tutorial information, an overview of processes and procedures and links to assist system engineering with guidance and references, and provide an archive of relevant systems engineering artifacts produced by the many NASA projects developed and managed by MSFC over the years.
As systems and subsystems requirements become more complex in the pursuit of the exploration of space, advanced technology will demand and require an integrated approach to the design and development of safe and successful space vehicles and there products. System engineers play a vital and key role in transforming mission needs into vehicle requirements that can be verified and validated. This will result in a safe and cost effective design that will satisfy the mission schedule. A key to successful vehicle design within systems engineering is communication. Communication, through a systems engineering infrastructure, will not only ensure that customers and stakeholders are satisfied but will also assist in identifying vehicle requirements; i.e. identification, integration and management. This vehicle design will produce a system that is verifiable, traceable, and effectively satisfies cost, schedule, performance, and risk throughout the life-cycle of the product. A communication infrastructure will bring about the integration of different engineering disciplines within vehicle design. A system utilizing these aspects will enhance system engineering performance and improve upon required activities such as Development of Requirements, Requirements Management, Functional Analysis, Test, Synthesis, Trade Studies, Documentation, and Lessons Learned to produce a successful final product. This paper will describe the guiding vision, progress to date and the plan forward for development of the Marshall Space Flight Center (MSFC) Systems Engineering Guide (SEG), a virtual systems engineering handbook and archive that will describe the system engineering processes that are used by MSFC in the development of complex systems such as the Ares launch vehicle. It is the intent of this website to be a "One Stop Shop" for our systems engineers that will provide tutorial information, an overview of processes and procedures and links to assist system engineering with guidance and references, and provide an archive of systems engineering artifacts produced by the many NASA projects developed and managed by MSFC over the years.
The Physical Oceanography Distributed Active Archiving Center (PO.DAAC), NASA's primary data center for archiving and distributing oceanographic data, is supporting the Jason and TOPEX/Poseidon satellite tandem missions by providing a variety of data products, tools, and distribution methods to the wider scientific and general community. PO.DAAC has developed several new data products for sea level residual measurements, providing a longterm climate data record from 1992 to the present These products provide compatible measurements of sea level residuals for the entire time series including the tandem TOPEX/Poseidon and Jason mission. Several data distribution tool. are available from NASA PO.DAAC. The Near-Real-Time Image Distribution Server (NEREIDS) provides quicklook browse images and binary data files The PO.DAAC Ocean ESIP Tool (POET) provides interactive, on-tine data subsetting and visualization for several altimetry data products.
NASA's traditional science data processing systems have focused on specific missions, and providing data access, processing and services to the funded science teams of those specific missions. Recently NASA has been modifying this stance, changing the focus from Missions to Measurements. Where a specific Mission has a discrete beginning and end, the Measurement considers long term data continuity across multiple missions. Total Column Ozone, a critical measurement of atmospheric composition, has been monitored for'decades on a series of Total Ozone Mapping Spectrometer (TOMS) instruments. Some important European missions also monitor ozone, including the Global Ozone Monitoring Experiment (GOME) and SCIAMACHY. With the U.S.IEuropean cooperative launch of the Dutch Ozone Monitoring Instrument (OMI) on NASA Aura satellite, and the GOME-2 instrumental on MetOp, the ozone monitoring record has been further extended. In conjunction with the U.S. Department of Defense (DoD) and the National Oceanic and Atmospheric Administration (NOAA), NASA is now preparing to evaluate data and algorithms for the next generation Ozone Mapping and Profiler Suite (OMPS) which will launch on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) in 2010. NASA is constructing the Science Data Segment (SDS) which is comprised of several elements to evaluate the various NPP data products and algorithms. The NPP SDS Ozone Product Evaluation and Test Element (PEATE) will build on the heritage of the TOMS and OM1 mission based processing systems. The overall measurement based system that will encompass these efforts is the Atmospheric Composition Processing System (ACPS). We have extended the system to include access to publically available data sets from other instruments where feasible, including non-NASA missions as appropriate. The heritage system was largely monolithic providing a very controlled processing flow from data.ingest of satellite data to the ultimate archive of specific operational data products. The ACPS allows more open access with standard protocols including HTTP, SOAPIXML, RSS and various REST incarnations. External entities can be granted access to various modules within the system, including an extended data archive, metadata searching, production planning and processing. Data access is provided with very fine grained access control. It is possible to easily designate certain datasets as being available to the public, or restricted to groups of researchers, or limited strictly to the originator. This can be used, for example, to release one's best validated data to the public, but restrict the "new version" of data processed with a new, unproven algorithm until it is ready. Similarly, the system can provide access to algorithms, both as modifiable source code (where possible) and fully integrated executable Algorithm Plugin Packages (APPs). This enables researchers to download publically released versions of the processing algorithms and easily reproduce the processing remotely, while interacting with the ACPS. The algorithms can be modified allowing better experimentation and rapid improvement. The modified algorithms can be easily integrated back into the production system for large scale bulk processing to evaluate improvements. The system includes complete provenance tracking of algorithms, data and the entire processing environment. The origin of any data or algorithms is recorded and the entire history of the processing chains are stored such that a researcher can understand the entire data flow. Provenance is captured in a form suitable for the system to guarantee scientific reproducability of any data product it distributes even in cases where the physical data products themselves have been deleted due to space constraints. We are currently working on Semantic Web ontologies for representing the various provenance information. A new web site focusing on consolidating informaon about the measurement, processing system, and data access has been established to encourage interaction with the overall scientific community. We will describe the system, its data processing capabilities, and the methods the community can use to interact with the standard interfaces of the system.
The International Space Station (ISS) represents a semi-closed environment with a high level of crewmember interaction. As community-acquired methicillin-resistant Staphylococcus aureus (MRSA) has emerged as a health concern in environments with susceptible hosts in close proximity, an evaluation of isolates of clinical and environmental Staphylococcus aureus and coagulase negative Staphylococcus was performed to determine if this trend was also present in astronauts aboard ISS or the space station itself. Rep-PCR fingerprinting analysis of archived ISS isolates confirmed our earlier studies indicating a transfer of S. aureus between crewmembers. In addition, this fingerprinting also indicated a transfer between crewmembers and their environment. While a variety of S. aureus were identified from both the crewmembers and the environment, phenotypic evaluations indicated minimal methicillin resistance. However, positive results for the Penicillin Binding Protein, indicative of the presence of the mecA gene, were detected in multiple isolates of archived Staphylococcus epidermidis and Staphylococcus haemolyticus. Phenotypic analysis of these isolates confirmed their resistance to methicillin. While MRSA has not been isolated aboard ISS, the potential exists for the transfer of the gene, mecA, from coagulase negative environmental Staphylococcus to S. aureus creating MRSA strains. This study suggests the need to expand environmental monitoring aboard long duration exploration spacecraft to include antibiotic resistance profiling.
The National Polar-orbiting Operational Environmental Satellite System (NPOESS), the U.S. Government's future low-Earth orbiting satellite system, will monitor global weather and environmental conditions. Serving as a risk reduction for NPOESS, the NPOESS Preparatory Project (NPP) will provide remotely sensed atmospheric, land, ocean, ozone, and sounder data that will serve the meteorological and global climate change scientific communities. The National Aeronautics and Space Administration (NASA) NPP Science Data Segment's (SDS) primary role is to independently assess the quality of the NPP science and environmental data records for their ability to support climate research. The SDS is composed of nine elements; an input element that receives data from the operational agencies and acts as a buffer, a calibration analysis element, five elements devoted to measurement based quality assessment, an element used to test algorithmic improvements, and an element that provides overall science direction. Each element requires a set of sensor specific science data products for their evaluation. There are four NPP sensors that will be flown on the NPP observatory. They are the Visible Infrared Imagining Radiometer Suite (VIIRS), the Advanced Technology Microwave Sounder (ATMS), the Cross-Track Infrared Sounder (CrIS), and the Ozone Mapper/Profiler Suite (OMPS). It is estimated that these four sensors combined will make daily data requests for approximately six terabytes of NPP science products from the operational data providers. As a result, issues associated with duplicate data requests, data transfers of large volumes of diverse products, and data transfer failures raised concerns with respect to the network traffic and bandwidth consumption. Therefore, a central data broker system for receiving and buffering data requests and data products for the SDS was developed. The data element for this system is called the SDS Data Depository and Distribution Element (SD3E). It supports science mission data assessment by assuring the timely and validated acquisition and subsequent transfer of the NPP Science Mission data to the SDS Elements and NPP Science Team. The six science elements that interface with the SD3E span across the NASA Goddard Space Flight Center (GSFC), the NASA Jet Propulsion Laboratory (JPL), and the University of Wisconsin. As the primary communication vehicle for the science elements and science team, the SD3E has an interface to the operational data providers: National Environment Satellite, Data, and Information Service (NESDIS) Interface Data Processing System (IDPS) and the National Oceanic Atmospheric Administration's (NOAA) Comprehensive Large Array-data Stewardship system (CLASS) Archive Data System (ADS), that are responsible for product generation and archive and distribution respectively. The SD3E is designed to be a semi-customizable and semi-automated system. This system is designed to provide flexibility and ease of use for the science users in accessing the latest data products by creating a rolling data cache that temporarily stores the products locally before transferring the data to the SDS Measurement based elements for the land, ocean, atmosphere, sounder, and ozone. This paper describes the design and architecture of one of the nine SDS elements, the SD3E, and how this system has provided a mechanism for efficient data exchange, how it has helped in alleviating some of the network traffic and usage, and how it has contributed to reducing operational costs.
The broad-line radio galaxy 3C 111 has been suggested as the counterpart of the y-ray source 3EG J0416+3650. While 3C 111 meets most of the criteria for a high-probability identification, like a bright flat-spectrum radio core and a blazar-like broadband SED, in the Third EGRET Catalog, the large positional offset of about 1.5' put 3C 111 outside the 99% probability region for 3EG J0416+3650, making this association questionable. We present a re-analysis of all available archival data for 3C 111 from the EGRET archives, resulting in detection of variable hard-spectrum high-energy gamma-ray emission above 1000 MeV from a position close to the nominal position of 3C 111, in three separate viewing periods (VPs), at a 3sigma level in each. A second variable hard-spectrum source is present nearby. At >100 MeV, one variable soft-spectrum source seems to account for most of the EGRET-detected emission of 3EG J0416+3650. A follow-up Swift UVOT/XRT observation reveals one moderately bright X-ray source in the error box of 3EG J0416+3650, but because of the large EGRET position uncertainty, it is not certain that the X-ray and gamma-ray sources are associated. Another Swift observation near the second (unidentified) hard gamma-ray source detected no X-ray source nearby.
The Sequence History Update Tool performs Web-based sequence statistics archiving for Mars Reconnaissance Orbiter (MRO). Using a single UNIX command, the software takes advantage of sequencing conventions to automatically extract the needed statistics from multiple files. This information is then used to populate a PHP database, which is then seamlessly formatted into a dynamic Web page. This tool replaces a previous tedious and error-prone process of manually editing HTML code to construct a Web-based table. Because the tool manages all of the statistics gathering and file delivery to and from multiple data sources spread across multiple servers, there is also a considerable time and effort savings. With the use of The Sequence History Update Tool what previously took minutes is now done in less than 30 seconds, and now provides a more accurate archival record of the sequence commanding for MRO.
The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla
The Modern Era Retrospective-analysis for Research and Applications (MERRA) dataset is a NASA satellite era, 30 year (1979 - present), reanalysis using the Goddard Earth Observing System Data Assimilation System, Version 5 (GEOS-5). The project, run out of NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center, provides the science and application communities with a state-of-the-art global analysis with emphasis on improved estimates of the hydrological cycle over a broad range of weather and climate time scales. MERRA products are generated as a long-term synthesis that places the NASA EOS suite of observations in a climate context. The MERRA analysis is performed at a horizontal resolution of 2/3 longitude x 1/2 latitude (540x361 global gridpoints) with observational analyses every 6 hours. The MERRA output data will include 3 dimensional state fields for every 6 hourly analysis cycle on 42 pressure levels (or 72 terrain following model coordinate levels) from the surface through the stratosphere. Several data products are specifically designed to support chemistry and stratosphere transport modeling. The 2 dimensional surface and atmospheric diagnostics (numbering 259) are being stored on the native grid at 1 hourly intervals. These include radiation and vertical integrals of the atmosphere for water and energy budget studies and also surface diagnostics where the diurnal cycle is important. The one hourly surface and near surface data product will also facilitate research on the integrated analysis of Earth system observations in the land, ocean and cryosphere. The MERRA products are archived and distributed by the Goddard Earth Sciences Data and Information Services Center (GES DISC) through its Modeling DISC Web (MDISC) portal. Multiple data access methods and services are available for MERRA data through MDISC: (1) Mirador offers a quick, comprehensive search of MERRA and all GES DISC archived data holdings, allowing searches on keywords, location names or latitude/longitude box, and date/time, with responses within a few seconds. (2) Giovanni is a GES DISC developed Web application that provides data visualization and analysis online. Giovanni features popular visualizations such as latitude-longitude maps, animations, cross sections, profiles, time series, etc. and some basic statistical analysis functions such as scatter plots and correlation coefficient maps. Users are able to download results in several different formats, including Google Earth. (3) On-the-fly parameter subsetting of data within a spatial/temporal window is provided through a simple select and click Web page. (4) MERRA data are also available via OPeNDAP, GrADS Data Server (GDS) and can be converted to netCDF on the fly.
In this paper, we present an integrated, end-to-end synthetic aperture radar (SAR) processing system that accepts data processing requests, submits processing jobs, performs quality analysis, delivers and archives processed data. This fully automated SAR processing system utilizes database and internet/intranet web technologies to allow external users to browse and submit data processing requests and receive processed data. It is a cost-effective way to manage a robust SAR processing and archival system. The integration of these functions has reduced operator errors and increased processor throughput dramatically.
Overview of talk: a) Content of Human Life Science data; b) Data archive structure; c) Applicable legal documents and policies; and d) Methods for data access. Life Science Data Archive (LSDA) contains research data from NASA-funded experiments, primarily data from flight experiments and ground analog data collected at NASA facilities. Longitudinal Study of Astronaut Health (LSAH) contains electronic health records (medical data) of all astronauts, including mission data. Data are collected for clinical purposes. Clinical data are analyzed by LSAH epidemiologists to identify trends in crew health and implement changes in pre-, in-, or post-flight medical care.