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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, rhusar@me.wustl.edu

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

rhusar@me.wustl.edu, stefan@me.wustl.edu

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)

src_img_width

src_

img_

heig

h t

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

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