1 web services for air quality management air quality management requires data from many distributed...
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Web Servicesfor
Air Quality Management
Air Quality management requires data from many distributed sources
For AQ management, data need to be filtered, aggregated and fused
Loosely coupled web services are a promising technology
The http://DataFed.Net tools follow such a Service Oriented Architecture to access, process and deliver AQ-relevant information
R. Husar, S. Falke, K. Höijärvi
Washington University, St. Louis, MO, [email protected]
ESIP Meeting, Washington DC, January 4-6, 2005
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Application Scenario: Smoke ImpactREASoN Project: Application of NASA ESE Data and Tools to Particulate Air Quality Management (PPT/PDF)
Scenario: Smoke form Mexico causes record PM over the Eastern US.
Goal: Detect smoke emission and predict PM and ozone
concentrationSupport air quality management and transportation safety
Impacts: PM and ozone air quality episodes, AQ standard
exceedanceTransportation safety risks due to reduced visibility
Timeline: Routine satellite monitoring of fire and smokeThe smoke event triggers intensified sensing and analysisThe 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 modelsAnalysis tools: browsing, fusion, data/model integrationDelivery of science-based event summary/forecast to air
quality and aviation safety managers and to the public
Record Smoke Impact on PM Concentrations
[email protected], [email protected]
Smoke Event
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IT needs and Capabilities: Web Services
IT need vision Current state New capabilities How to get thereReal-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 toolsService 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
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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)
Barriers to IT Infusion (not yet clear) • New technologies are at low tech readiness level, TRL 4-5
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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 PullResistances: 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
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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)
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)
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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
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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-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
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Service Oriented Architecture:Data AND Services are Distributed
Control
Data
Process Process
Process
Peer-to-peer network representation
Data ServiceCatalog
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:
Find 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’
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An Application Program: Voyager Data Browser
The web-program consists of a stable core and adoptive input/output layersThe core maintains the state and executes the data selection, access and render servicesThe 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
Dev
ice
Dri
vers
Wra
pp
ers App State Data
Flow Interpreter
Core
Web Services
WSDL
Ports