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  • Loosely coupled data network for Observations, Models and Emissions (OME)AMEN portal that facilitates standards-based access to the distributed OMEsTools for statistical test and analyses through interactive graphic interface

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  • 1. Tools forClosure of Emissions, Observations and Models using Service Oriented ArchitectureRudolf B. Husar, rhusar@wustl.edu, Washington University, USAStefan R. Falke, stefan.falke@ngc.com, NGC, USAGregory J. Frost, gregory.j.frost@noaa.gov, NOAA & U. Colorado, USATerry J. Keating, keating.terry@epa.gov, US EPA/OAR, USA GEIA Conference, Toulouse, FR, June 11-13, 2012
  • 2. Guiding Principles byGlobal Observing System of Systems (GEOSS)Any Single Data Set CanServe Many Applications Any Single ProblemRequires Many Data Sets Interoperability is Required!
  • 3. AQ Networking Requires:Interoperability of People and Machines People & Machines People People People & People AQ Community of Practice Machines & Machines AQ Community Server
  • 4. AQ Data Network: Core Datastes; AQ Community Catalog Exits Many Interop. Issues Unresolved (e.g. Sept. Dublin Metadata Mtg.)20+ Datasets, 12+ Originators
  • 5. GEO Air Quality Community of Practice AQ Data Network ArchitectureBased on Service Oriented Architecture: Loosely Coupled Components
  • 6. GEO Air Quality Community of PracticeAQ Data Network Architecture Atmospheric Model Evaluation Network
  • 7. The Atmospheric Model Evaluation Network (AMEN) Terry Keating, U.S. EPA, keating.terry@epa.gov Air Quality Air Quality Air Quality Obs. Data Obs. Data Obs. Data Air Quality Model Output Air Model Stat Tests & Views Model-Obs; Model-Model Evaluation Model-Emiss; Emiss-Obs Network Portal Obs-Obs Air Quality Model Output Emissions Air Quality Model Output DatabaseTools for statistical & interactive model evaluationBuilt on the federated data infrastructure
  • 8. Still High Variability of Aerosol Model Performance Example: Huneeus et al, 2011: Global dust model intercomparison in AeroCom I 10000 Max Median Min 1000 100 10 NorthAfrica AsiaSouth MiddleEast WorldRest• Transport simulations are are consistent but emissions and transformation/removal processes diverge among models• Dust emissions varied my an order of magnitude, causing similar divergence of the simulated dust surface concentrations
  • 9. Simple Goal: UtopiaBest Available Atmospheric Composition Best Available Atm. Composition Public Health Chem. Climate Ecology, Esthetics By Integrating Best Observations, Emiss ions, Models
  • 10. Approach to Obs. Model Closure: Tool to Iteratively Reduce the Bias Actual closure to be worked out by the AQ community DJF MAM JJA SON Nitrate Organics Fine Dust Bio. Organics Low in DJF Low in DJF Low in MAM High MAM & JJA Add nitrate source Improved smoke by Add Sahara, local dust Reduce biogenic OCInverse modeling of combined Dust and smoke BC for Adjust source trem VIEWS Nitrate chemical, satellite, spa CMAQ – e.g. NAAPS ce-timeVIEWS NO3 DJF CMAQ NAAPS Dust, July
  • 11. Fine Particle Mass, PM 2.5 Obs.: USEPA; Model: Regional-Summer PM2.5 BIASOBSERVATION MODEL
  • 12. Fine Particle Mass, PM 2.5 Obs.: USEPA; Model: Regional-Winter PM2.5 BIASOBSERVATION MODEL
  • 13. Fine Particle Sulfate Obs: IMPROVE; Model: Global SO4 BIASOBSERVATION MODEL
  • 14. Aerosol Optical Thickness Obs.: Aeronet; Model: GlobalAOT Bias
  • 15. Summary: Current State: Future Possibilities:Tools for Emission Obs. Community-based EOM Model Closure convergence & closure Best Available Atm. Composition Public Health Chem. Climate Ecology, Esthetics By Integrating Best Observations, Emiss ions, Models