- 1. Application Scenario: Smoke Impact REASoN
Project:Application of NASA ESE Data and Tools to Particulate Air
Quality Management ( PPT/PDF )
- Smoke form Mexico causes record PM over the Eastern US.
- Detect smoke emission and predict PM and ozone
concentration
- Support air quality management and transportation safety
- PM and ozone air quality episodes, AQ standard exceedance
- Transportation safety risks due to reduced visibility
- 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 andad-hocdata 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 PMConcentrations [email_address] ,stefan
@me.wustl. edu SmokeEvent 2. Web Services for Air Quality
Management 3. IT needs and Capabilities:Web Services 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, serendipitousand 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
dataMost tools are personal, dataset specific and hand made
Analysis tools for data browsing, fusion anddata/model integration
Web servicesfor data registration, geo-time-parameter
referencing,non-intrusive addition ofad hocdata; 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 somesurface monitoring data, Limited real-time
data downloaded from providers, extracted, geo-time-param-coded,
etc. by each analystReal-time access to routine andad-hocfire,
smoke, transport data/ and models How to get there New capabilities
Current state IT need vision 4. Project Domain, New Technologies
and Barriers
- REASoN Project Type: ApplicationParticulate Air Quality
-
- 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 servicewrappers for ESE dataand associated tools (slide
5)
-
- Reusableweb servicesfor data transformation, fusion and
rendering (slide 6)
-
- Webservice chaining(orchestration) tools, web applications
(slide 7,8)
-
- Virtual communitysupporttools(e.g. virtual workgroup websites
for1998 Asian Dust Event )
- Barriers to IT Infusion (not yet clear)
-
- New technologies are atlow tech readiness level , TRL 4-5
5. Data Flow & Processing in AQ Management
- Resistances : Data AccessProcessing Delivery
AQ DATA EPA Networks IMPROVE VisibilitySatellite-PM
PatternMETEOROLOGY Met. DataSatellite-TransportForecast model
EMISSIONS National EmissionsLocal InventorySatellite Fire Locs
Status and Trends AQ Compliance Exposure Assess. Network Assess.
Tracking Progress AQManagementReports Knowledge Derived from Data
Primary DataDiverse Providers Data Refining ProcessesFiltering,
Aggregation, Fusion Driving Forces :Provider Push User Pull
Information Engineering: Info driving forces,
source-transformer-sink nodes, processes (services) in each node,
flow & other impediments, overall systems modeling and analysis
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 datathrough a web
services-based Viewer)
- 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 rdparty
- Check theDataFed.Net Catalogfor the data wrapped by data access
web services (not yet fully functional)
src_img_width src_img_height src_margin_right src_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 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=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=500
7. Generic Data Flow and Processing for Browsing DataView 1 Data
Processed 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-specificwrappers Abstract Data
Abstract data slices are requested by viewers; uniform data are
delivered bywrapperservices DataView 2 DataView 3 View Data
Processed data are delivered to the user as multi-layer views
byportrayal and overlay web services Processed Data Data passed
through filtering, aggregation, fusion and otherprocessing
webservices 8. Service Oriented Architecture: Data AND Services are
Distributed
- Peer-to-peer network representation
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
outputUser Carries less Burden In service-oriented peer-to peer
architecture, the user is aided by software agents ControlData
Process Process Process Data Service Catalog Process Chain 2 Chain
1 Chain 3 Data Service 9. An Application Program: Voyager Data
Browser
- The web-program consists of a stablecoreand
adoptiveinput/outputlayers
- 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:
-
- Wrappersencapsulate the heterogeneous external data sources and
homogenize the access
-
- Device Driverstranslate generic, abstract graphic objects to
specific devices and formats
-
- Portsconnect the internal parameters of the program to external
controls
-
- WDSLweb service description documents
Data Sources Controls Displays I/O Layer Device Drivers Wrappers
App State Data Flow Interpreter Core Web Services WSDL Ports