IN22A-01
IGMAS+ A New 3D Gravity, FTG and Magnetic Modeling Software
Modern geophysical interpretation requires an interdisciplinary approach, particularly when considering the available amount of 'state of the art' information contained in comprehensive data bases. A combination of different geophysical surveys employing seismics, gravity and geoelectrics, together with geological and petrological studies, can provide new insights into the structures and tectonic evolution of the lithosphere and natural deposits. Interdisciplinary interpretation is essential for any numerical modelling of these structures and the processes acting on them. Three-dimensional (3D) interactive modeling with the IGMAS+ software provides means for integrated processing and interpretation of geoid, gravity and magnetic fields and their gradients (full tensor), yielding improved geological interpretation. IGMAS+ is an acronym standing for "Interactive Geophysical Modelling Application System". It bases on the existing software IGMAS (http://www.gravity.uni-kiel.de/igmas), a tool developed during the past twenty years for potential field modelling. The new IGMAS+, however, will comprise the advantages of the "old" IGMAS (e.g. flexible geometry concept and a fast and stable algorithm) with automated interpretation tools and a modern graphical GUI based on leading edge insights from psychological computer graphics research and thus provide optimal man machine communication. IGMAS+ fully three-dimensional models are constructed using triangulated polyhedra and/or triangulated grids, to which constant density and/or induced and remanent susceptibility are assigned. Interactive modifications of model parameters (geometry, density, susceptibility, magnetization), access to the numerical modeling process, and direct visualization of both calculated and measured fields of gravity and magnetics, enable the interpreter to design the model as realistically as possible. IGMAS+ allows easy integration of constraining data into interactive modeling processes, visualization and combination of geodata with density/susceptibility models. These visual overlays of different 2D and 3D datasets enables quantitative comparison and adjustment and results in models that are constrained by as much independently derived information as possible. The use of the programming language Java/Java3D will ensure that IGMAS+ will be a flexible, platform- independent tool, which, at the same time, can incorporate the interfaces needed for the integration of plugins and user-defined functions. http://www.gravity.uni-kiel.de/igmas
IN22A-02
A Contrast in Use of Metrics in Earth Science Data Systems
In recent years there has been a surge in the number of systems for processing, archiving and distributing remotely sensed data. Such systems, working independently as well as in collaboration, have been contributing greatly to the advances in the scientific understanding of the Earth system, as well as utilization of the data for nationally and internationally important applications. Among such systems, we consider those that are developed by or under the sponsorship of NASA to fulfill one of its strategic objectives: "Study Earth from space to advance scientific understanding and meet societal needs." NASA's Earth science data systems are of varying size and complexity depending on the requirements they are intended to meet. Some data systems are regarded as NASA's Core Capabilities that provide the basic infrastructure for processing, archiving and distributing a set of data products to a large and diverse user community in a robust and reliable manner. Other data systems constitute Community Capabilities. These provide specialized and innovative services to data users and/or research products offering new scientific insight. Such data systems are generally supported by NASA through peer reviewed competition. Examples of Core Capabilities are 1. Earth Observing Data and Information System (EOSDIS) with its Distributed Active Archive Centers (DAACs), Science Investigator-led Processing Systems (SIPSs), and the EOS Clearing House (ECHO); 2. Tropical Rainfall Measurement Mission (TRMM) Science Data and Information System (TSDIS); 3. Ocean Data Processing System (ODPS); and 4. CloudSat Data Processing Center. Examples of Community Capabilities are projects under the Research, Education and Applications Solutions Network (REASoN), and Advancing Collaborative Connections for Earth System Science (ACCESS) Programs. In managing these data system capabilities, it is necessary to have well-established goals and to measure progress relative to them. Progress is measured through metrics, which can be a combination of quantitative as well as qualitative assessments. The specific metrics of interest depend on the user of the metrics as well as the type of data system. The users of metrics can be data system managers, program managers, funding agency or the public. Data system managers need metrics for assessing and improving the performance of the system and for future planning. Program managers need metrics to assess progress and the value of the data systems sponsored by them. Also, there is a difference in the metrics needed for core capabilities that tend to be more complex, larger and longer-term compared to community capabilities and the community capabilities that tend to be simpler, smaller and shorter-term. Even among community capabilities there are differences; hence the same set of metrics does not apply to all. Some provide data products to users, some provide services that enable better utilization of data or interoperability among other systems, and some are a part of a larger project where provision of data or services is only a minor activity. There is also a contrast between metrics used for internal and external purposes. Examples of internal purposes are: ensuring that the system meets its requirements, and planning for evolution and growth. Examples of external purposes are: providing to sponsors indicators of success of the systems, demonstrating the contributions of the system to overall program success, etc. This paper will consider EOSDIS, REASoN and ACCESS programs to show the various types of metrics needed and how they need to be tailored to the types of data systems while maintaining the overall management goals of measuring progress and contributions made by the data systems.
IN22A-03
Provenance Tracking for Earth Science Data and Its Metadata Representation
In many cases Earth science data production involves long and complex chains of processes that accept files as input and create new files. It can be demonstrated that these chains form a mathematical graph in which the files and processes are the vertices and the relations between the files and vertices form the edges. There are four types of edges, which can be represented by the relations "this file was produced by that process," "this file is used by those processes," "this process needs these files for input," and "this process produces those files as output." Because Earth science data production often involves using previous data for statistical quality control, provenance graphs can be very large. For example, if previous data are used to develop statistics of clear sky radiances, a particular file may depend on statistics collected on many months of data. For EOS data or for the upcoming NPP and NPOESS missions, the number of files being ingested per day can be in the range of 10,000 to 100,000. As a result, the number of vertices and links can easily be in the millions to hundreds of millions of objects. This suggests that routine inclusion of complete provenance graphs in single files may be prohibitively voluminous, although the increasingly stringent requirements for provenance tracking require maintenance of the information from which the graph can be reliably constructed. The fact that provenance tracking requires traversal of the vertices and edges of the graph makes it difficult to uniquely fit into eXtensible Markup Language (XML). It also makes the construction of the graph difficult to do in standard Structured Query Language (SQL) because the tables for representing the graph require recursive queries. Both of these difficulties require care in constructing the data structures and the software that stores and makes the metadata accessible. This paper will then discuss the representation of this structure in metadata, including the possibilities in the ISO 19115 family of geospatial standards.
IN22A-04
Geoscientific Model Development: A new EGU Journal for Descriptions of Numerical Models of the Earth System and its components
Geoscientific Model Development (GMD), launching in January 2008, will be an international scientific journal dedicated to the publication and public discussion of the description, development and benchmarking of numerical models of the Earth System and its components. Manuscript types considered for peer-reviewed publication will be: model descriptions, model inter-comparisons, benchmarking papers, and technical papers. In encouraging full publication of Earth System Models we have two main goals. The primary goal is to promote the efficient and effective development of the models, through the clear presentation of the techniques from which all other developers can improve their own models. A secondary goal is to provide increased credibility to the Earth System Science field by creating a space within which models can be openly presented and critically discussed, and their results reproduced and validated. A welcome side-effect will be the formal, peer-reviewed, recognition of the work of Earth System Model developers. It is anticipated that model description papers will form the backbone of GMD. These will comprehensively describe the underlying science behind the models, and will also include details often omitted from more traditional papers, such as the numerical schemes employed. The papers should be somewhat more advanced than internal technical reports. For example, the inclusion of discussion of the scope of applicability and limitations of the approach adopted is expected. In order to enable full peer review of the models, evidence of model output should also be provided, with comparison to standard benchmarks, observations and/or other model output included as appropriate. The publication will potentially consist of three parts: the main paper, a user manual, and the source code (ideally supported by some summary outputs from test case simulations). http://www.paleo.bris.ac.uk/~ggdjl/GMD/GMD.pdf
IN22A-05
Combining Machine Learning and Mesoscale Modeling for Atmospheric Releases Hazard Assessment
In applications such as homeland security and hazards response, it is necessary to know in real time which areas are most at risk from a potentially harmful atmospheric pollutant. Using high resolution remote sensing measurements and atmospheric mesoscale numerical models, it is possible to detect and study the transport and dispersion of particles with great accuracy, and to determine the ground concentrations which might pose a threat to people and properties. Satellite observations from different sensors must be fused together to compensate for different spatial, temporal and spectral resolutions and data availability. Such observations are used to initialize and validate atmospheric mesoscale models, which can provide accurate estimates of ground concentrations. Such numerical models are, however, usually slow due to the complex nature of the computations, and do not provide real time answers. We will define probability maps of risks by running several atmospheric mesoscale and T&D simulations spanning the climatological input conditions of an entire year, observed using high resolution remote sensing instruments. Such maps provide an immediate risk assessment area associated with a given source location. If a release indeed occurs, the computed risk maps can be used for first assessment and rapid response. We analyze the output of the mesoscale model runs using machine learning algorithms to find characteristic patterns which relate potential risk areas with atmospheric parameters which can be observed using remote sensing instruments and ground measurements. Therefore, when a release occurs, it is possible to give a quick hazard assessment without running a time consuming model, but by comparing the current atmospheric conditions with those associated with each identified risk area. The offline learning provides knowledge that can later be used to protect people and properties.
IN22A-06
Insights Gained From 4 Years of EOSDIS User Surveys
The Earth Observation System Data and Information System (EOSDIS) is a large, complex data system currently supporting over 18 operational NASA satellite missions including the flagship EOS missions: Terra, Aqua, and Aura. A critical underpinning for management of EOSDIS is developing a thorough knowledge of the EOSDIS user community and how they use the EOSDIS products in their research. It is important to know whether the system is meeting the users' needs and expectations. Thus, in 2004 NASA commissioned a comprehensive survey to determine user satisfaction using the American Customer Satisfaction Index (ACSI) approach. NASA has continued to survey users yearly since. Users continue to rate EOSDIS systems and services highly as the EOSDIS ACSI score has outperformed both the averages for U.S. companies and for Federal Agencies. In addition, users' comments have provided valuable insight into the effect of data center processes on users' experiences. Although their satisfaction has remained high, their preferences have changed with the rapid advances in web-based services. We now have four years of data on user satisfaction from these surveys. The results of each survey highlight areas that, if improved, could lead to increased user satisfaction, including overall product quality, product documentation, and product selection and ordering processes. This paper will present the survey results and how they compare from year to year.
IN22A-07
SOAR: A System for the Analysis of Atmospheric Radiances
We have used a Service Oriented Architecture (SOA) approach to develop a system to produce multi-year, multi-sensor gridded atmospheric radiances on-demand. NASA's Aqua spacecraft launched in 2002 and has provided 5 years of calibrated atmospheric radiances from the AIRS, AMSU and MODIS instruments that are available through SOAR. The system utilizes an IBM power pc compute cluster consisting of a 44 dual and quad blade system. The high resolution spatial, temporal and hyperspectral arrays required to process multiple years of these radiance data from the three sensors required large volumes of local processor memory. In order to reduce virtual memory swapping, leading to large disk I/O times, the observational data was distributed onto multiple processor memories thus making on-demand processing feasible. The on-demand SOAR system processing includes rigorous configuration management and captures the complete data provenance information to ensure scientific reproducibility of the ephemeral on-demand datasets. The system can be used to overlay multi sensor data fields to analyze, explore and visualize the consistency of these multi year radiance data records. In particular, the data were converted to a set of canonical units based on the Brightness Temperature of the radiance fields at correlated wavelengths. This capability greatly simplifies intercomparison across the various sensors. NASA's scientific emphasis has been moving from a "mission" based focus to "measurement" based focus. SOAR crosses the mission boundaries to analyze multiple sensors that capture similar measurements. NASA and NOAA have over 30 years of atmospheric radiance data from various missions. In conjunction with the DoD, the next generation of Earth Observing satellites in the National Polar-orbiting Operational Environmental Satellite System (NPOESS), will acquire similar data for the next 30 years. Applying our techniques to the complete data record is an important first step toward the development of consistent, long term Climate Data Record (CDR) based on the Atmospheric Radiance measurement. The service oriented approach of this system also allows it to be used as a building block layer in other applications by appropriate researchers.
IN22A-08
The LASP Interactive Solar IRradiance Datacenter (LISIRD)
The Laboratory for Atmospheric and Space Physics (LASP) has been making space-based measurements of solar irradiance for many decades, and thus has established an extensive catalog of past and ongoing space- based solar irradiance measurements. In order to maximize the accessibility and usability of solar irradiance data and information from multiple missions, LASP is developing the LASP Interactive Solar IRradiance Datacenter (LISIRD) to better serve the needs of researchers, educators, and the general public. This data center is providing interactive and direct access to a comprehensive set of solar spectral irradiance measurements from the soft X-ray (XUV) at 0.1 nm up to the near infrared (NIR) at 2400 nm, as well as state-of-the-art measurements of Total Solar Irradiance (TSI). LASP researchers are also responsible for an extensive set of solar irradiance models and historical solar irradiance reconstructions, which will also be accessible via this data center over time. LISIRD currently provides access to solar irradiance data sets from the SORCE, TIMED-SEE, UARS-SOLSTICE, and SME instruments, spanning 1981 to the present, as well as a Lyman Alpha composite that is available from 1947 to the present. LISIRD also provides data products of interest to the space weather community, whose needs demand high time cadence and near real-time data delivery. This poster provides an overview of the LISIRD system, summarizes the data sets currently available, describes future plans and capabilities, and provides details on how to access solar irradiance data through LISIRD's various interfaces. http://lasp.colorado.edu/lisird/