1. Models are an Integral Part of Field Experiments Flight planning Provide 4-Dimensional context of the observations Facilitate the integration of the different measurement platforms Evaluate processes (e.g., role of biomass burning, heterogeneous chemistry….) Evaluate emission estimates (bottom-up as well as top-down)

2. What does this tell us about the model – Model deficiency? Emissions problem?

3. Back Trajectories from High CO points. --- CO > 700 --- CO > 600 --- CO > 500 --- CO > 450 --- CO > 400

4. Back Trajectories from High CO point (Zoom & CO > 500 ppbv) --- CO > 700 --- CO > 600 --- CO > 500

5. Comparing Modeled and Measured Ratios: We extract all points associated with a specified city and plot measured ratios and plot modeled ratios.  BC/  CO This analysis suggests that there emissions may be related to an underestimation of a specific sector.

6. The Importance of Fossil, Biofuels and Open Burning Varies by Region -- Richness of Emissions Data Base Can be Exploited

7. Using Measurements and Model – We Estimate Contributions of Fossil, Biofuel and Open Burning Sources Domestic Sector May be a Key.

8. Construction of Hg Emissions: Hg emission estimates – bottom up; refined using observed chemical ratios of air masses that pass through specific regions; e.g., using observed ratios of Hg/SO2 to estimate emissions of Hg from known SO2 sources.

9. Predictability – as Measured by Correlation Coefficient Met Parameters are Best

10. Development of a General Computational Framework for the Optimal Integration of Atmospheric Chemical Transport Models and Measurements Using Adjoints (NSF ITR/AP&IM 0205198 – Started Fall 2002) A collaboration between: Greg Carmichael (Dept. of Chem. Eng., U. Iowa) Adrian Sandu (Dept. of Comp. Sci., Mich. Inst. Tech.) John Seinfeld (Dept. Chem. Eng., Cal. Tech.) Tad Anderson (Dept. Atmos. Sci., U. Washington) Peter Hess (Atmos. Chem., NCAR) Dacian Daescu (Inst. of Appl. Math., U. Minn.)

11. Overview of Research in Data Assimilation for Chemical Models. Solid lines represent current capabilities. Dotted lines represent new analysis capabilities that arise through the assimilation of chemical data.

12. We Have Now a Full 4d-VAR Version of STEM and are beginning to use it For Ace-Asia/Trace-P Analysis

13. Thoughts on Forecasting and Modeling Roles of models are expanding Challenge: How to make the best use of having a suite of forecasting products AND modelers in the field Challenge: How best to use the models to meet the mission objectives Challenge: How to optimally integrate measurements and model data

14. Forecasting -- Next Time…. Couple global and regional models – and test the advantages…. Link more closely air-mass/emission markers with measured quantities Think about how to use photochemical/radical products – e.g., forecasts of ozone production efficiencies, indicator ratios.. Much more emphasis on aerosol chemical composition, optical properties, extinction, SSA and how to use this information….e.g., single particle info Identify experiments that can test specific aspects of of our understanding (e.g., point vs integrated impacts), our ability to track air masses…

15. Post-Run with MOZART Boundary Conditions Top and Lateral Boundary Conditions from MOZART II every 3 hours STEM 80x70 domain 13.4km mapped species: O 3, CO, ethane, ethene, propane, propene, ethyne, HCHO, CH 3 CHO, H 2 O 2, PAN, MPAN, isoprene, NO, NO 2, HNO 3, HNO 4, NO 3, and MVK Lateral boundary conditions of other species, included SO 2 and sulfate still come from the large-scale CFORS tracer model

16. P3 Flight on April 25 th P3 Flight on May 2 nd By using MOZART boundary conditions, the variations of some species are improved in the STEM simulations, especially for O 3.

17. Results from Trace-P Intercomparison Study

19. Approach: Develop novel and efficient algorithms for 4D-Var data assimilation in CTMs; Develop general software support tools to facilitate the construction of discrete adjoints to be used in any CTM; Apply these techniques to important applications including: (a) analysis of emission control strategies for Los Angeles; (b) the integration of measurements and models to produce a consistent/optimal analysis data set for the AceAsia intensive field experiment; (c) the inverse analysis to produce a better estimate of emissions; and (d) the design of observation strategies to improve chemical forecasting capabilities.

21. Surface reflection Ice cloud Water cloud EP/TOMS Total Ozone (Dobson) Dust Black Carbon Organic Carbon Sulfate Other PM2.5 and Other PM10 Sea Salt absorption by gas-phase species O 3, SO 2 and NO 2 Inputs from STEM 3-D field STEM TOP 15km O 3 (Dobson) below STEM top height TUV TOP 80km Overtop O 3 = Output: 30 kinds of J-values for SAPRC99 mechanism Framework for Analyzing Chemistry/Aerosol Interactions: Model (STEM+TUV) + Laboratory Studies + Field Experiment Heterogeneous rxns on dust for NO x, O 3, SO 2, HNO 3

23. Cloud Top Temperature (°C) Flight Altitude (m) A example: TRACE-P flights on March 27 DC-8 #15 P-3 #17 P-3 flight #17: volcanic plume observation DC-8 flight #15: frontal study DC-8 J[NO 2 ] P-3 J[NO 2 ]

24. April 11 & 12– Best Conditions for Observing Dust Effects. Twin Otter and C-130 Sampled This outflow Dust BC Sulfate

25. We run back-trajectories from each 5 minute leg of merge data set. Keep track of each time a trajectory passes in the grid cell of the city and below 2 km. Classification of trajectory by the Source of Megacity. Age as determined by trajectory is also shown Before Big difference !!! We catch more number of fresh airmass from Shanghai and Seoul.

28. Comparison of Modeled and Observed Results from China’s Mega Cities Shanghai model measured Shanghai emissions Hong Kong model measured Hong Kong emissions Beijing model measured Beijing emissions HCHO/CO.0072.0080.002490.0045 0.00180.00960.007 0.00720.00251 C2H6/CO.0106.01010.004560.0043 0.00490.011430.0058 0.00510.00452 SO2/C2H2 4.613 3.7116.262.251 1.15038.6724.07 4.108.076 SO2/CO.0179.01950.10490.0031 0.26180.0236 0.02140.0575 N0x/SO2.222.2290.9970.468 0.4162.7050.299 0.2960.884 C2H6/C2H2 1.18 1.140.70571.657 0.7361.6891.21 1.220.634 BC/CO.0105.01120.008380.0058 0.00550.010.0074 0.00790.0080 BC/SO2.245.300.07991.299 1.3010.060.138 0.1860.14

29. Goal: To develop general computational tools, and associated software, for assimilation of atmospheric chemical and optical measurements into chemical transport models (CTMs). These tools are to be developed so that users need not be experts in adjoint modeling and optimization theory.

31. Application: The Design of Better Observation Strategies to Improve Chemical Forecasting Capabilities. Example flight path of the NCAR C-130 flown to intercept a dust storm in East Asia that was forecasted using chemical models as part of the NSF Ace-Asia (Aerosol Characterization Experiment in Asia) Field Experiment Data Assimilation Will help us Better Determine Where and When to Fly and How to More Effectively Deploy our Resources (People, Platforms, $s) Shown are measured CO along the aircraft flight path, the brown isosurface represents modeled dust (100 ug/m3), and the blue isosurface is CO (150 ppb) shaded by the fraction due to biomass burning (green is more than 50%).

32. Urban Photochemistry NO x -VOC Sensitivity to O 3 Production VOC sensitive NOx sensitive Loss(N)/(Loss(N)+Loss(R)) Model NOx (ppbv) Model results along the flight path Megacity points from back trajectories Klienman et al., 2000 Less than 2 day old plumes

33. Forecasting – Next Time Important to get models more involved and forecasting well before the experiment – deploy some models before – to dry run the experiment and develop specific hypotheses to be tested Be more focused with specific primary objectives – e.g., aerosol ageing, emissions testing, evolution opportunities…..

34. Climate : Air Quality Analysis Framework