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Implementation of a direct sensitivity method into CMAQ

Implementation of a direct sensitivity method into CMAQ. Daniel S. Cohan, Yongtao Hu, Amir Hakami, M. Talat Odman, Armistead G. Russell Georgia Institute of Technology, Atlanta, GA Presentation to Models-3 Users’ Workshop October 22, 2002. SIMULATION. SENSITIVITY. I.C. I.C. B.C. B.C.

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Implementation of a direct sensitivity method into CMAQ

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  1. Implementation of a direct sensitivity method into CMAQ Daniel S. Cohan, Yongtao Hu, Amir Hakami, M. Talat Odman, Armistead G. Russell Georgia Institute of Technology, Atlanta, GA Presentation to Models-3 Users’ Workshop October 22, 2002

  2. SIMULATION SENSITIVITY I.C. I.C. B.C. B.C. Emissions Emissions ( ) Δ CMAQ CMAQ ( ) Δ

  3. Uses of Sensitivity • Policy development: • Impact of emission control measures • Impact of new emitters • Uncertainty analysis: • Dependence of model results on assumptions • Inverse modeling (“Area of Influence”): • Which emitters affect a receptor

  4. Sensitivity Methods • “Brute-Force” Method: • Run CMAQ once for a “base case” • Run CMAQ again for each of N perturbations • Direct Decoupled Method:(Dunker 1981, Yang et al., 1997) • Solve for sensitivities decoupled from concentrations, using the same numerical routines in a single CMAQ run • Local, first-order sensitivities:

  5. DDM andBrute Force Conc. (e.g., O3) DDM BF ∆Ci a2 a1 Sensitivity = tan(a) Sensitivity Parameter (e.g., NOx Emissions) Dpj

  6. Direct Decoupled Method Concentrations (t) Concentrations (t+Δt) Chemistry Advection & Diffusion I.C., B.C., Emissions Chemistry Sensitivities (t) Sensitivities (t+Δt)

  7. Pros & Cons Brute Force: ▲Simple ▲ Captures non-linearities ▼Inefficient for large N ▼Inaccurate for small perturbations DDM: ▲ Efficient for large N ▲ Accurate for small perturbations ▼ Does not capture non-linearities

  8. Demonstration of CMAQ-DDM • Fall-Line Air Quality Study: • Focus on Georgia • 12 km horizontal; 13 layers • SAPRC-99 chemistry • SAMI emissions inventory • DDM (implemented so far): • gas-phase • first-order • emissions, I.C., & B.C.

  9. DDM: O3 to Isoprene & NOx

  10. Sensitivity to point NO emissions DDM to Actual NO Emissions DDM to 1 mol/s, Layer 6 Emission

  11. DDM vs. Brute Force: Ozone Initial Conditions DDM Brute Force

  12. DDM vs. Brute Force: Domainwide NOx Emissions DDM Brute Force

  13. DDM vs. Brute Force:Single Point NO DDM Brute Force

  14. DDM E R AOI DDM DDM E E E Area of Influence • DDM shows impact of one emitter on concentrations domainwide • To compute the receptor-based “Area of Influence”: • Compute DDM for unit emissions from various emitters E • Interpolate to obtain AOI of receptor R to every emitter E • Scale to amount of emissions at each E

  15. AOI: Atlanta Ozone to NO Response to 1 mol/s NO source Scaled by NO emissions

  16. AOI: Macon Ozone to NO Response to 1 mol/s NO source Scaled by NO emissions

  17. Conclusions • DDM and Area of Influence enhance the functionality of CMAQ • Strong agreement with brute force, even for fairly large perturbations • Future work will incorporate: • higher-order sensitivities • aerosols • further exploration of AOI

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