1 Integrated System Solutions Value & benefits to citizens and society Data Policy Decisions Management Decisions Predictions Observations High Performance Computing, Communication, & Visualization Decision Support Tools -Assessments w/ dynamic scenario ability -Decision Support Systems Monitoring & Measurements -Satellite - Airborne - in situ Science Models -Oceans- Ice -Land- Coupled -Atmosphere InputsOutputs OutcomesImpacts Standards & Interoperability 2 Table 1: Summary of the air quality management processes broken down by timescale and decision support method. Retrospective Analysis Month, Years NOW Analysis, Days Predictive Analysis, Days-Years Data Sources and Types All the real time data + NPS IMPROVE Aerosol Chemistry. EPA PM2.5 Mass, NWS Visibility, WEBCAMs, NASA MODIS, GOES, TOMS NAAPS MODEL Forecast NOAA/EPA, NASA, CMAQ Data Analysis Tools and Methods Full chemical model simulations Diagnostic and inverse modeling Spatio-temporal overlays Multi-sensory data interrogation Back & forward trajectories, CATT Emission and met. forecasts Full chemical model Data assimilation Parcel tagging, tracking Communication Collaboration and Coordination Methods Tech reports reg. Support Peer reviewed scientific Analyst and managers consoles Open inclusive communications Data Assimilation methods Open, public forecasts Model-data comparison Modeler-data analyst comm. Analysis ProductsQuantitative natural aerosol concentration Current aerosol pattern Evolving event summary Future natural emissions Simulated conc. pattern Future location of high conc. Decision SupportJurisdiction: natural/manmade State Implementation Plans, (SIP), PM/Haze Criteria Documents, Regs Jurisdiction: natural/manmade Triggers for management action Public information and decisions Statutory and policy changes Management action triggers Progress tracking 3 a)a) b)b) 4 5 Technological Barriers Finding Accessing Transforming Fusing 6 Science Barriers The science of satellite data use is poor 7 Social Management Barriers Poor community engagement Data and tool reuse sharing Supply (type of info) does not match demand 8 Community Engagement John Townshend University of Maryland SEEDS Workshop Recommendation: It should be the highest priority for the current Formulation Team of the SEEDS project to develop and implement organizational structures facilitating much deeper engagement of key stakeholders. This action itself must involve some of these stakeholders and should start immediately. The success of SEEDS will strongly depend on the degree to which we engage all the communities supplying, analyzing, adding value and using NASAs ESE products Benefits of CE 9 Levels of participation Low High Deep Involvement Community Engagement ParticipationAwarenessOwnership Community Involvement 10 Community Engagement Community engagement is a process, not a program. It is the participation of members of a community in assessing, planning, implementing, and evaluating solutions to problems that affect them. As such, community engagement involves interpersonal trust, communication, and collaboration. Such engagement, or participation, should focus on, and result from, the needs, expectations, and desires of a community's members. 11 Principles of Community Engagement (derived from with some additions from1.Be clear about the purposes or goals of the engagement effort, and the populations and/or communities you want to engage. The implementers of the engagement process need to be able to communicate to the community why participation is worthwhile. 2.Become knowledgeable about the community in terms of its economic conditions, political structures, norms and values, demographic trends, history, and experience with engagement efforts. Learn about the community's perceptions of those initiating the engagement activities. It is important to learn as much about the community as possible, through both qualitative and quantitative methods from as many sources as feasible. 3.Go into the community, establish relationships, build trust, work with the formal and informal leadership, and seek commitment from community organizations and leaders to create processes for mobilizing the community. Engagement is based on community support for whatever the process is trying to achieve. 4. Remember and accept that community self-determination is the responsibility and right of all people who comprise a community. No external entity should assume it can bestow on a community the power to act in its own self-interest. 5. Partnering with the community is necessary to create change and improve information systems.. 6. All aspects of community engagement must recognize and respect community diversity. Awareness of the various cultures of a community and other factors of diversity must be paramount in designing and implementing community engagement approaches. 7. Community engagement can only be sustained by identifying and mobilizing community assets, and by developing capacities and resources for community decisions and action. 8. An engaging organization or individual change agent must be prepared to release control of actions or interventions to the community, and be flexible enough to meet the changing needs of the community. 9. Community collaboration requires long-term commitment by the engaging organization and its partners. NewDISS Petri Dish with Generic Federation Mapping ESIP-1 with LTA in-place LTA ESIP-1, ESIP-2, SIPS or SCF Backbone Data Centers Science Data Centers Long Term Archive Multi-Mission Data Centers Application Centers ESIP-2, ESIP-3, RESAC or RAC ESIP-1 or ESIP-2 ESIP-1 Mission Data Centers ESIP-2 or Pathfinder PI 13 14 15 Technology Infusion Scenarios - for SEEDS working group Based on REASoN project: Systems Integration and Visualization of Yellowstone Fred Watson (PI) California State University Monterey Bay. March 17 th Issues: Bison leaving Yellowstone National Park highly controversial Impacts of winter recreation (e.g. snowmobiles) on National Park wildlife highly controversial Impacts of wolf-reintroduction on wildlife and livestock highly controversial Processes: Large animals moving around large, complex, dynamic landscapes interacting with each other and the landscape Public understanding of processes is relatively poor Product: Visualization of large wildlife systems. 3D animated visualizations integrate the following: terrain data, land cover data other remotely sensed data describing wildlife landscape (e.g. fires, geothermals, greenness) dynamic spatial model output (e.g. climate, snowpack, phenology) wildlife tracking data, wildlife models Technology medium: Interactive DVD videos menu driven, accessible to anyone with a DVD player containing numerous short computer-animated visualization pieces each piece communicates a specific point about the science behind the issues mixed with real film footage, to provide documentary look and feel that is familiar to most people Clients: National Parks Service staff in public outreach, planning, and decision making National Park visitors in Park visitor centers possibly: Web visitors more difficult, because of the very high bandwidth of DVD-quality video Differences to other REASoN projects: NOT based around near-real-time web access to terrabytes of relatively raw NASA imagery RATHER, about developing a novel highly processed medium (3D animations of multiple data types, mixed with film footage) and disseminating products dealing with very specific, carefully selected scenarios Long gap in time between data collection and product dissemination Potential for much more widespread use of this sort of technology in future 16 Example of progress (circa late 2002) 17 SEEDS IT Vision 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 ConcentrationsSmoke Event 18 Smoke Scenario: IT needs and Capabilities 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 19 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 20 Data Flow & Processing in AQ Management 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 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 Web Services 21 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 adopt. 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/ea gif ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y2000/ea gif URL template: ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y[yyyy]/ea[yy][mm][dd].gif 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= &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= &image_width=800&image_height=500NAAPS_GLO_DUST_AOT&lat_min=0&lat_max=70&lon_min=-180&lon_max=-60&datetime= &image_width=800&image_height=500VIEWS_Soil&lat_min=0&lat_max=70&lon_min=-180&lon_max=-60&datetime= &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= &image_width=800&image_height=500 22 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 23 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 24 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