An Integrated Systems Solution to Air Quality Data and Decision Support on the Web GEO Architecture...
-
Upload
rebecca-mccann -
Category
Documents
-
view
217 -
download
0
Transcript of An Integrated Systems Solution to Air Quality Data and Decision Support on the Web GEO Architecture...
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!