060730 Igarss06 Denver Husar
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060730 Igarss06 Denver Husar

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http://datafedwiki.wustl.edu/index.php/2006-07-30_IGARSS06_Denver

http://datafedwiki.wustl.edu/index.php/2006-07-30_IGARSS06_Denver

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  • We will start out by highlighting significant trends in air pollution monitoring and management Next we will describe an agile information system architecture for air quality decision support Finally, we will show the application of this architecture and technologies for two use cases: a real time smoke event analysis and a long-term model evaluation

060730 Igarss06 Denver Husar 060730 Igarss06 Denver Husar Presentation Transcript

    • Outline
    • Highlight Trends of Air Quality Sensing and Management
    • Describe an Agile IS Architecture for Air Quality Decision Support
    • Show Their Application Through Two Use Cases
    • Smoke Event ppt , flash
    • AQ Policy ppt , flash
    Architecture and Technologies for an Agile, User-Oriented Air Quality Data System Rudolf B. Husar Washington University, St. Louis Presented at the workshop The User and the GEOSS Architecture Applications for North America July 30, 2006, Denver
  • Changes in Air Quality Management Command & Control Weight of Evidence Flexible NAAMS Rigid Monitoring
  • Real-time Air Pollution Sensing and Reporting High Resolution Satellite Data Surface PM25 and Ozone Data Smoke Plumes
  • Generic Decision Support for Air Quality Decisions GEOSS Architecture Framework Knowledge into the Minds of Regulatory Analysts Knowledge into the Minds of Technical Analysts Observations Reports: Model Forecasts, Obs. Evidence Models Decisions Knowledge into the Minds of Decision- making managers Decision Support System
  • Key Technical Challenge: Characterization
    • Pollutant characterization requires many different instruments and analysis tools.
    • Each sensor/network covers only a fraction of the 6-8 dimensional data space.
    • Other sensors provide only integral measures of the pollution, e.g. satellite - vertical integral.
    Satellite-Integral
  • Information Providers: Geography, Content, Agency, Form
    • Data are distributed geographically by autonomous providers
    Emission Ambient Satellite Model EPA NOAA NASA Other Content | Agency | Form
    • Data includes emissions
    Emission Emission Emission Emission Emission Ambient Satellite Model EPA NOAA NASA Other Content | Agency | Form
    • Data includes emissions, ambient data,
    Ambient Ambient Ambient Ambient Emission Emission Emission Emission Emission Ambient Satellite Model EPA NOAA NASA Other Content | Agency | Form
    • Data includes emissions, ambient data, satellite data
    Satellite Satellite Satellite Satellite Ambient Ambient Ambient Ambient Emission Emission Emission Emission Emission Ambient Satellite Model EPA NOAA NASA Other Content | Agency | Form
    • Data includes emissions, ambient data, satellite data and model output
    Model Model Model Model Satellite Satellite Satellite Satellite Ambient Ambient Ambient Ambient Emission Emission Emission Emission Emission Ambient Satellite Model EPA NOAA NASA Other Content | Agency | Form
    • Data are provided by multiple agencies : EPA, NOAA, NASA and others
    NASA Mission NOAA GASP NASA IDEA NASA DAACs NOAA ASOS EPA-AQS DataMart EPA AIRNow RPO VIEWS FS FireInv State/Local Emission EPA NEISGEI EPA NEI NOAA WeaMod EPA AQModel NOAA Forecast Emission Ambient Satellite Model EPA NOAA NASA Other Content | Agency | Form NASA DAACs NOAA GASP NASA IDEA NASA Missions EPA NEI EPA NEISGEI FS FireInv State/Local Emission NOAA ASOS RPO VIEWS EPA AIRNow EPA-AQS AIRS NOAA WeaMod EPA AQModel NASA GloModel NOAA Forecast
    • Furthermore, data are provided in varied formats and access protocols
    Emission Ambient Satellite Model EPA NOAA NASA Other Content | Agency | Form
    • Data on Internet are geography-independent and can be ‘linearized’
    Internet NASA DAACs EPA R&D Model EPA AIRNow others
  • Users: By Types, Agency, Info Needs
    • Users are distributed geographically
    EPA NOAA NASA Other Stakeholder | Agency | Form Policy Manager Public Scientist EPA NOAA NASA Other Stakeholder | Agency | Form Policy Manager Public Scientist Policy Policy Policy
    • Users includes policy makers
    EPA NOAA NASA Other Stakeholder | Agency | Form Policy Manager Public Scientist
    • Users includes policy makers, the public
    Policy Policy Policy Public Public EPA NOAA NASA Other Stakeholder | Agency | Form Policy Manager Public Scientist
    • Users includes policy makers, the public , AQ managers
    Policy Policy Policy Public Public Manager Manager EPA NOAA NASA Other Stakeholder | Agency | Form Policy Manager Public Scientist and scientist Policy Policy Policy Public Public Manager Manager Scientist Scientist Scientist EPA NOAA NASA Other Stakeholder | Agency | Form Policy Manager Public Scientist
    • Users are affiliated with multiple agencies : EPA, NOAA, NASA, as well as others
    Policy Policy Policy Public Public Manager Manager Scientist Scientist Scientist EPA NOAA NASA Other Stakeholder | Agency | Form Policy Manager Public Scientist
    • Furthermore, users need various types of information provided in multiple formats
    Policy Manager Policy Scientist Manager Scientist Scientist Policy Public Public EPA NOAA NASA Other Stakeholder | Agency | Form Policy Manager Public Scientist
    • Since the users are also on the Internet, their geographic location is irrelevant
    Public Manager Scientist Internet other
  • Data Acquisition and Usage Activities (Select View Show, click to step through PPT)
    • The data life cycle consists of the acquisition and the usage parts
    Usage Activities Data Acquisition
    • The acquisition part processes the sensory data by firmly linked procedures
    The focus is on data usage activities
    • The usage activities are more iterative, dynamic procedures
    • The collected and cleaned data are stored in the repository
    Data Repository
    • The usage cycle transform data into knowledge for decision making
    Decisions
  • ‘ Stovepipe’ and Federated Usage Architectures Landscape Scientist Science DAACs
    • Current info systems are project/program oriented and provide end-to-end solutions
    Info Users Data Providers Info System AIRNow Public AIRNow Model Compliance Manager
    • Part of the data resources of any project can be shared for re-use through DataFed
    • Through the Federation, the data are homogenized into multi-dimensional cubes
    • Data processing and rendering can then be performed through web services
    • Each project/program can be augmented by Federation data and services
  • The Network Effect: Less Cost, More Benefits through Data Multi-Use Program Public Data Organization Data Data Program Program Organization Data Data Program Data Orgs Develop Programs Programs ask/get Data Public sets up Orgs Pay only once Richer content Data Re-Use Network Effect Data are costly resource – should be reused (recycled) for multiple applications Data Reuse Less Prog. Cost More Knowledge Data reuse saves $$ to programs and allows richer knowledge creation Less Soc. Cost More Soc. Benefit Data reuse, like recycling takes some effort : labeling, organizing, distributing
  • Agile Information System: Data Access, Processing and Products Providers NASA DAACs EPA R&D Model EPA AIRNow others Public Manager Scientist Users other
    • The info system transforms the data into info products for each user
    • In the first stage the heterogeneous data are prepared for uniform access
    Uniform Access
    • The second stage performs filtering, aggregation, fusion and other operations
    Data Processing Web Service Chain Custom Processing SciFlo DataFed Info Products Reports, Websites Forecasting Compliance Other Sci. Reports
    • The third stage prepares and delivers the needed info products
  • DSS for Exceptional Event Decisionsapping of Event Knowledge into the Minds of EPA Analysts Knowledge into the Minds of State Analysts Observations Event Reports: Model Forecasts, Obs. Evidence Models Decisions Event Knowledge into the Minds of EPA Regulators Decision Support System Decision Support System Data Sharing Std. Interface Data Obs. & Models Characterization Std. Interface Reporting Domain Processing Control Reports
  • Stages of AQ Data Flow and Value-Adding Processes Domain Processing Data Sharing Std. Interface Gen. Processing Std. Interface Data Control Reports Reporting Obs. & Models Decision Support System Analyzing Filter/Integrate Aggregate/Fuse Custom Analysis Organizing Document Structure/Format Interfacing Characterizing Display/Browse Compare/Fuse Characterize Value-Adding Processes Reporting Inclusiveness Iterative/Agile Dynamic Report
  • Loosely Coupled Data Access through Standard Protocols The next three slides describe the key technologies used in the creation of an adaptable and responsive air quality information system. OGC data access protocols and standard formats facilitate loose coupling between data on the internet and processing services. For air quality, the Web Coverage Service (WCS), provides a universal simple query language for requesting data as where, when, what. That is: geographic (3D bounding box), time range and parameter. The Web Map Service (WMS) and Web Feature Service (WFS) are also useful. The use of standard data physical data formats and naming conventions elevates the syntactic and semantic interoperability. Within DataFed all data access services are implemented as WCS or WMS and optionally WFS. General format adapter components permit data request in a variety of standard formats. GetCapabilities GetData Capabilities, ‘Profile’ Data Where? When? What? Which Format? Server Back End Std. Interface Client Front End Std. Interface T2 T1 Domain Processing Data Sharing Std. Interface Gen. Processing Std. Interface Data Control Reports Reporting Obs. & Models Decision Support System CF, EOS, OGC CF OGC, ISO OGC, ISO Standards netCDF, HDF.. Format Temperature What? Time When? BBOX Where? GetData Query
  • Web Services and Workflow for Loose Coupling Service Broker Service Provider Publish Find Bind Service User Web Service Interaction Domain Processing Data Sharing Std. Interface Gen. Processing Std. Interface Data Control Reports Reporting Obs. & Models Decision Support System Web Services Triad: Publish – Find – Bind Workflow Software: Dynamic Programming Service Chaining & Workflow
  • Collaborative Reporting and Dynamic Delivery Analysis Reports: Information supplied by many Needs continuous program feedback Report needs many authors Wiki technologies are for collaborative writing Dynamic Delivery: Much of the content is dynamic Animated presentations are compelling Movies and screencasts are for dynamic delivery Domain Processing Data Sharing Std. Interface Gen. Processing Std. Interface Data Control Reports Reporting Obs. & Models Decision Support System Co Writing - Wiki ScreenCast
  • Summary
    • The current challenges for air quality information systems include data delivery in real time, pollution characterization through the integration of multi-sensory data and providing agile support to regulatory management. The talk describes the architecture and implementation of a standards based system for accessing and processing air quality data to satisfy the above needs. The agile federated data system, DataFed, system has been in use for science and management support since 2005.
    • DataFedis composed of distributed data and web services and integrated by user-configurable workflow software.The use of DataFed is illustrated through two use cases: (1) A real time monitoring example of a smoke event uses surface,satellite and model forecast information to inform air quality managers and the public. (2) Hemispheric aerosol transport model is compared to surface monitoring data to estimate the uncertainty and to improve themodel estimates.
  • Links 800 / 1200 AQ Policy ppt 800 / 1200 Smoke Event ppt 800 / 1200 DataFed Architecture 800 / 1200 Background Flash Powerpoint
  • Acknowledgements
    • The presentation on Air Quality Background and Information Architecture benefited greatly from ideas, and challenges posed by a number of experienced individuals, from EPA (Rich Scheffe, Steve Young, Terry Keating), NASA (Lawrence Friedl, Kathy Fontaine).
    • The participation in the NASA Information Technology Infusion workgroup (Karen Moe, Bran Wilson, Liping Di and others) was an intense collective learning experience.
    • At CAPITA, Kari Hoijarvi engineered and implemented DataFed; Stefan Falke contributed datasets and application software. This presentation was prepared with help from Erin Robinson.