From Event to Action: Accelerate Your Decision Making with Real-Time Automation
050405 Epa Info System
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2. CATT: A Community Tool! Part of an Analysis Value Chain Next Process Next Process Why? How? When? Where? Aerosol Data Collection IMP. EPA Aerosol Sensors Integration VIEWS Integrated AerData AEROSOL Weather Data Assimilate NWS Gridded Meteor. Trajectory ARL Traject.Data TRANSPORT TrajData Cube Aggreg. Traject. AerData Cube CATT Aggreg.Aerosol CATT-In CAPITA CATT-In CAPITA There! Not There! Further Analysis GIS Grid Processing Emission Comparison
3. Direction of Dust Origin at 5 IMPROVE Sites Ad hoc Data Processing Value Chain High ‘dust’ concentration at 5 sites indicate the same airmass pathway from the tropical Atlantic Weather Serv. Upper Air Data NOAA ARL ATAD ATAD Traject Gebhart (2002) NPS-CIRA IMPROVEData PMF Tool Pareto (2001) PMF “Sources” Coutant (2002) CATT Tool Husar (2003) Aggregation Poirot (2003)
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7. Earth Science Data to Knowledge Transformation: Value-Adding Processes Petabytes 10 15 Terabytes 10 12 Gigabytes 10 9 Megabytes 10 6 Calibration, Transformation To Characterized Geophysical Parameters Filtering, Aggregation, Fusion, Modeling, Trends, Forecasting Interactive Dissemination ACCESS Multi-platform/parameter, high space/time resolution, remote & in-situ sensing Sensing Analysis & Synthesis Data Acquisition Value Chain (Network) InfoSystem Goal: Add as much value to the data as possible to benefit all users Data Usage Value Network Flexible data selection, and processing to to deliver right knowledge, right place right time Data - L1 Information – L2 Knowledge – L3-6? Usable Knowledge Query Data Distributed, Dynamic More Local, DAAC Processing Knowledge Use
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12. Smoke Scenario: IT needs and Capabilities Community interaction during events through virtual workgroup sites; quantitative now-casting and observation-augmented forecasting Smoke event summary and forecast suitably packaged and delivered for agency and public decision makers Uncoordinated event monitoring, serendipitous and limited analysis. Event summary by qualitative description and illustration Smoke event summary and forecast for managers (air quality, aviation safety) and the public Services linking tools Service chaining languages for building web applications; Data browsers, data processing chains; Tools for navigating spatio-temporal data; User-defined views of the smoke; Conceptual framework for merging satellite, surface and modeling data Most tools are personal, dataset specific and ‘hand made’ Analysis tools for data browsing, fusion and data/model integration Web services for data registration, geo-time-parameter referencing, non-intrusive addition of ad hoc data; communal tools for data finding, extracting Agents (services) to seamlessly access distributed data and provide uniformly presented views of the smoke. Human analysts access a fraction of a subset of qualitative satellite images and some surface monitoring data Limited real-time datasets are downloaded from providers, extracted, geo-time-param-coded, etc. by each analyst Real-time access to routine and ad-hoc fire, smoke, transport data/ and models How to get there New capabilities Current state IT need vision
13. Data Analysis and Decision Support Retrospective Anal. Months-years Now Analysis Days Predictive Analysis Days-years Data Sources & Types All the Real-Time data + NPS IMPROVE Aer. Chem. EPA Speciation EPA PM10/PM2.5 EPA CMAQ Full Chem. Model EPA PM2.5Mass NWS ASOS Visibility, WEBCAMs NASA MODIS, GOES, TOMS, MPL NOAA Fire, Weather & Wind NAAPS MODEL Simulation NAAPS MODEL Forecast NOAA/EPA CMAQ? Data Analysis Tools & Methods Full chemical model simulation Diagnostic & inverse modeling Chemical source apportionment Multiple event statistics Spatio-temporal overlays Multi-sensory data integration Back & forward trajectories, CATT Pattern analysis Emission and met. forecasts Full chemical model Data assimilation Parcel tagging, tracking Communication Collab. & Coord. Methods Tech Reports for reg. support Peer reviewed scientific papers Science-AQ mgmt. interaction Reconciliation of perspectives Analyst and managers consoles Open, inclusive communication Data assimilation methods Community data & idea sharing Open, public forecasts Model-data comparison Modeler-data analyst comm. Analysis Products Quantitative natural aer. concr. Natural source attribution Comparison to manmade aer. Current Aerosol Pattern Evolving Event Summary Causality (dust, smoke, sulfate) Future natural emissions Simulated conc. pattern Future location of high conc. Decision Support Jurisdiction: nat./manmade State Implementation Plans, (SIP) PM/Haze Crit. Documents, Regs Jurisdiction: nat./manmade Triggers for management action Public information & decisions Statutory & policy changes Management action triggers Progress tracking
14. Data Acquisition and Usage Value Chain Monitor Store Data 1 Monitor Store Data 2 Monitor Store Data n Monitor Store Data m IntData 1 IntData n IntData 2 Virtual Int. Data
15. Information ‘Refinery’ Value Chain (Taylor, 1985) Organizing Grouping Classifying Formatting Displaying Analyzing Separating Evaluating Interpreting Synthesizing Judging Options Quality Advantages Disadvantages Deciding Matching goals, Compromising Bargaining Deciding e.g. CIRA VIEWS e.g. Langley IDEA FASTNET Summary Rpt e.g. RPO Manager Informing Knowledge Action Productive Knowledge Information Data
Notas del editor
AQ data arise from diverse sources, each having specific history, driving forces, formats, quality, etc. Data analysis, i.e. turning the raw data into ‘actionable’ knowledge, requires combining data from these sources The three major data ‘processing’ operations (services) are filtering, aggregation and fusion