1. Tools for Closure of Emissions, Observations and Models using Service Oriented Architecture Rudolf B. Husar, rhusar@wustl.edu, Washington University, USA Stefan R. Falke, stefan.falke@ngc.com, NGC, USA Gregory J. Frost, gregory.j.frost@noaa.gov, NOAA & U. Colorado, USA Terry J. Keating, keating.terry@epa.gov, US EPA/OAR, USA GEIA Conference, Toulouse, FR, June 11-13, 2012

2. Any Single Data Set Can Serve Many Applications Any Single Problem Requires Many Data Sets Guiding Principles by Global Observing System of Systems (GEOSS) Interoperability is Required!

3. AQ Networking Requires: Interoperability of People and Machines People People & Machines 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 Architecture Based on Service Oriented Architecture: Loosely Coupled Components

6. GEO Air Quality Community of Practice AQ Data Network Architecture Atmospheric Model Evaluation Network

7. The Atmospheric Model Evaluation Network (AMEN) Terry Keating, U.S. EPA, keating.terry@epa.gov Tools for statistical & interactive model evaluation Built on the federated data infrastructure Air Model Evaluation Network Portal Stat Tests & Views Model-Obs; Model-Model Model-Emiss; Emiss-Obs Obs-Obs Air Quality Model Output Air Quality Obs. Data Air Quality Obs. Data Air Quality Obs. Data Air Quality Model Output Emissions Database Air Quality Model Output

8. Still High Variability of Aerosol Model Performance Example: Huneeus et al, 2011: Global dust model intercomparison in AeroCom I 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: Utopia Best Available Atmospheric Composition Public Health Chem. Climate Ecology, Esthetics Best Available Atm. Composition By Integrating Best Observations, Emissions, Models

10. Approach to Obs. Model Closure: Tool to Iteratively Reduce the Bias Actual closure to be worked out by the AQ community DJF JJA SON MAM Nitrate Low in DJF Add nitrate source Inverse modeling of VIEWS Nitrate Organics Low in DJF Improved smoke by combined chemical, satellite, space-time Fine Dust Low in MAM Add Sahara, local dust Dust and smoke BC for CMAQ – e.g. NAAPS Bio. Organics High MAM & JJA Reduce biogenic OC Adjust source trem NAAPS Dust, July VIEWS NO3 DJF CMAQ

11. OBSERVATION MODEL PM2.5 BIAS Fine Particle Mass, PM 2.5 Obs.: USEPA; Model: Regional-Summer

12. Fine Particle Mass, PM 2.5 Obs.: USEPA; Model: Regional-Winter OBSERVATION MODEL PM2.5 BIAS

13. Fine Particle Sulfate Obs: IMPROVE; Model: Global OBSERVATION MODEL SO4 BIAS

14. Aerosol Optical Thickness Obs.: Aeronet; Model: Global AOT Bias

15. Summary: Public Health Chem. Climate Ecology, Esthetics Best Available Atm. Composition By Integrating Best Observations, Emissions, Models Current State: Tools for Emission Obs. Model Closure Future Possibilities: Community-based EOM convergence & closure