1. 1 Application Scenario: Smoke Impact REASoN Project: Application of NASA ESE Data and Tools to Particulate Air Quality Management (PPT/PDF)Application of NASA ESE Data and Tools to Particulate Air Quality ManagementPPT/PDF Scenario: Smoke form Mexico causes record PM over the Eastern US. Goal: Detect smoke emission and predict PM and ozone concentration Support air quality management and transportation safety Impacts: PM and ozone air quality episodes, AQ standard exceedance Transportation safety risks due to reduced visibility Timeline: Routine satellite monitoring of fire and smoke The smoke event triggers intensified sensing and analysis The event is documented for science and management use Science/Air Quality Information Needs: Quantitative real-time fire & smoke emission monitoring PM, ozone forecast (3-5 days) based on smoke emissions data Information Technology Needs: Real-time access to routine and ad-hoc data and models Analysis tools: browsing, fusion, data/model integration Delivery of science-based event summary/forecast to air quality and aviation safety managers and to the public Record Smoke Impact on PM Concentrations rhusar@me.wustl.edurhusar@me.wustl.edu, stefan@me.wustl.edu stefan@me.wustl.edu Smoke Event

2. 2 Web Services for Air Quality Management

3. 3 IT needs and Capabilities: Web Services IT need visionCurrent stateNew capabilitiesHow to get there Real-time access to routine and ad-hoc fire, smoke, transport data/ and models Human analysts access a fraction of a subset of qualitative satellite images and some surface monitoring data, Limited real-time data downloaded from providers, extracted, geo-time-param- coded, etc. by each analyst Agents (services) to seamlessly access distributed data and provide uniformly presented views of the smoke. Web services for data registration, geo-time- parameter referencing, non-intrusive addition of ad hoc data; communal tools for data finding, extracting Analysis tools for data browsing, fusion and data/model integration Most tools are personal, dataset specific and ‘hand made’ Tools for navigating spatio- temporal data; User-defined views of the smoke; Conceptual framework for merging satellite, surface and modeling data Services linking tools Service chaining languages for building web applications; Data browsers, data processing chains; Smoke event summary and forecast for managers (air quality, aviation safety) and the public Uncoordinated event monitoring, serendipitous and limited analysis. Event summary by qualitative description and illustration Smoke event summary and forecast suitably packaged and delivered for agency and public decision makers Community interaction during events through virtual workgroup sites; quantitative now-casting and observation-augmented forecasting

4. 4 Project Domain, New Technologies and Barriers REASoN Project Type: Application – Particulate Air Quality Application Environment Participants: NASA as provider; EPA, States, mediators’ as users of data & tech (slide 4) Process Goal: Facilitate use of ESE data and technologies in AQ management Specific application projects: FASTNET, Fires and Biomass Smoke, CATT Current barriers to ESE data use in PM management Technological: Resistances to seamless data flow; user-driven processing is tedious Scientific: Quantitative usage of satellite data for AQ is not well understood Organizational: Lack of tools, skills (and will??) within AQ agencies New Information Technologies Applied in the Project Web service wrappers for ESE data and associated tools (slide 5) Reusable web services for data transformation, fusion and rendering (slide 6) Web service chaining (orchestration) tools, ‘web applications’ (slide 7,8) Virtual community support tools (e.g. virtual workgroup websites for 1998 Asian Dust Event)1998 Asian Dust Event Barriers to IT Infusion (not yet clear) New technologies are at low tech readiness level, TRL 4-5

5. 5 Data Flow & Processing in AQ Management AQ DATA EPA Networks IMPROVE Visibility Satellite-PM Pattern METEOROLOGY Met. Data Satellite-Transport Forecast model EMISSIONS National Emissions Local Inventory Satellite Fire Locs Status and Trends AQ Compliance Exposure Assess. Network Assess. Tracking Progress AQ Management Reports ‘Knowledge’ Derived from Data Primary Data Diverse Providers Data ‘Refining’ Processes Filtering, Aggregation, Fusion Driving Forces: Provider Push User Pull Resistances: Data Access Processing Delivery Information Engineering: Info driving forces, source-transformer-sink nodes, processes (services) in each node, flow & other impediments, overall systems ‘modeling’ and analysis

6. 6 A Wrapper Service: TOMS Satellite Image Data Given the URL template and the image description, the wrapper service can access the image for any day, any spatial subset using a HTTP URL or SOAP protocol, (see TOMS image data through a web services-based Viewer)see TOMS image data For web-accessible data, the wrapping is ‘non-intrusive’, i.e. the provider does not have to change, only expose the data in structured manner. Interoperability (value) can be added retrospectively and by 3 rd party Check the DataFed.Net Catalog for the data ‘wrapped’ by data access web services (not yet fully functional)DataFed.Net Catalog src_img_width src_img_height src_margin_rightsrc_margin_left src_margin_top src_margin_bottom src_lon_min src_lat_max src_lat_min src_lon_max Image Description for Data Access: src_image_width=502 src_image_height=329 src_margin_bottom=105 src_margin_left=69 src_margin_right=69 src_margin_top=46 src_lat_min=-70 src_lat_max=70 src_lon_min=-180 src_lon_max=180 The daily TOMS images (virtually no metadata) reside on the FTP archive, e.g. ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/Y2000/IM_aersl_ept_20000820.png ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/Y2000/IM_aersl_ept_20000820.png URL template: ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y[yyyy]/IM_aersl_ept_[yyyy][mm][dd].png Transparent colors for overlays RGB(89,140,255) RGB(41,117,41) RGB(23,23,23) RGB(0,0,0) ttp://capita.wustl.edu/dvoy_2.0.0/dvoy_services/cgi.wsfl?view_state= TOMS_AI&lat_min=0&lat_max=70&lon_min=-180&lon_max=-60&datetime=2001-04-13&image_width=800&image_height=500ttp://capita.wustl.edu/dvoy_2.0.0/dvoy_services/cgi.wsfl?view_state= TOMS_AI&lat_min=0&lat_max=70&lon_min=-180&lon_max=-60&datetime=2001-04-13&image_width=800&image_height=500 http://capita.wustl.edu/dvoy_2.0.0/dvoy_services/cgi.wsfl?view_state= NAAPS_GLO_DUST_AOT&lat_min=0&lat_max=70&lon_min=-180&lon_max=-60&datetime=2001-04-13&image_width=800&image_height=500 http://capita.wustl.edu/dvoy_2.0.0/dvoy_services/cgi.wsfl?view_state= VIEWS_Soil&lat_min=0&lat_max=70&lon_min=-180&lon_max=-60&datetime=2001-04-13&image_width=800&image_height=500http://capita.wustl.edu/dvoy_2.0.0/dvoy_services/cgi.wsfl?view_state= VIEWS_Soil&lat_min=0&lat_max=70&lon_min=-180&lon_max=-60&datetime=2001-04-13&image_width=800&image_height=500

7. 7 Generic Data Flow and Processing for Browsing DataView 1 DataProcessed Data Portrayed Data Process Data Portrayal/ Render Abstract Data Access View Wrapper Physical Data Abstract Data Physical Data Resides in autonomous servers; accessed non- intrusively by data and view- specific wrappers Abstract Data Abstract data slices are requested by viewers; uniform data are delivered by wrapper services DataView 2 DataView 3 View Data Processed data are delivered to the user as multi-layer views by portrayal and overlay web services Processed Data Data passed through filtering, aggregation, fusion and other processing web services

8. 8 Service Oriented Architecture: Data AND Services are Distributed Control Data Process Peer-to-peer network representation Data Service Catalog Process Data, as well as services and users (of data and services) are distributed Users compose data processing chains form reusable services Intermediate and resulting data are also exposed for possible further use Processing chains can be further linked into complex value-adding data ‘refineries’ Service chain representation User Tasks: Fi nd data and services Compose service chains Expose output Chain 2 Chain 1 Chain 3 Data Service User Carries less Burden In service-oriented peer-to peer architecture, the user is aided by software ‘agents’

9. 9 An Application Program: Voyager Data Browser The web-program consists of a stable core and adoptive input/output layers The core maintains the state and executes the data selection, access and render services The adoptive, abstract I/O layers connects the core to evolving web data, flexible displays and to the a configurable user interface: Wrappers encapsulate the heterogeneous external data sources and homogenize the access Device Drivers translate generic, abstract graphic objects to specific devices and formats Ports connect the internal parameters of the program to external controls WDSL web service description documents Data Sources Controls Displays I/O Layer Device Drivers Wrappers App State Data Flow Interpreter Core Web Services WSDL Ports