An Integrated Systems Solutionto

Air Quality Data and Decision Support on the Web

GEO Architecture Implementation Pilot – Phase 2 (AIP-2) Kickoff Workshop

NCAR Mesa Laboratory, Boulder, Colorado

September 25-26, 2008

AIP Phase 2 Call For Participation: Sensors and Models

VIEWSThe Visibility Information Exchange Web System

http://vista.cira.colostate.edu/views

Provides integrated online access to:

Monitoring data: from over 3 dozen networks

Modeling data: from a variety of modeling scenarios

Emissions data: inventories and summaries

Satellite data: MODIS, CALIPSO, Aqua, Terra, GOES, etc.

Serves as the primary source for:

IMPROVE Aerosol and Optical data

IMPROVE Regional Haze Rule data

IMPROVE Special Studies data

Has about 1200 registered users from over 300 organizations, institutions,

universities, and companies

TSSWRAP Technical Support System

http://vista.cira.colostate.edu/tss

Built upon the database and software infrastructure of VIEWS

Provides consolidated online access to:

Regional technical data, planning guidance, and analysis results

Decision support for the development of SIPs and TIPs

Source apportionment and visibility projections tools

Ongoing tracking and assessment of emissions control strategies

Documents the technical methods used in implementation plans

How do we compare diverse datasets?

• Ground-based observations

• Model results

• Emissions inventories

• Satellite data

• Airborne samples

• etc…

SO4 = SO4 = SO4 = SO4 ??Measured Modeled Emissions Satellite

Fine Sulfate (SO4) equivalence:

Establishing Dataset Comparability: Method Metadata

• Sampling and collection methods (sensors)

• Analysis methods (IC, XRF, PIXE, etc.) (sensors)

• Calculation methods (EC = 1 + E2 + E3 – OP)

• Processing and aggregation methods

• Data handling and transformation methods (AQS to VIEWS)

• Unit conversion methods

• etc…

Sensor Metadata: Sampling Methods

Model Metadata

• Initial conditions

• Boundary conditions

• Model parameters

• Grid cell size

• Algorithms and processing

• Assumptions

• etc…

Inter-domain “Rosetta Stone”

Model Performance Evaluation

CMAQ Model Performance vs. Monitored Worst 20% Days in 2002

Source Apportionment

Mass source apportionment by source category and region

From regional photochemical model with comprehensive emissions inputs

Species mass for various time periods – directly comparable to monitoring data

Network Inter-comparisons

Parameter: Nitrate Ion Concentrations

Location: Bondville, IL

Networks: IMPROVE, STN, and CASTNet

Graphs: Time Series and Scatter Plot

Method (Sensors and Models) Questions:

• How do we “codify” methods (sensors and models)?

• What is the minimum set of method metadata for each type of dataset?

• How do we codify the degree of equivalence between methods?

• How do we associate method metadata with observations and results?

• How do we educate users about method considerations?

The End…

Thanks!