1. NO x Emission Inversion Using the Adjoint of CMAQ Farid Amid and Amir Hakami Carleton University

2. Oct 20, 2009 Overview - Inversion Methodology -OMI retrievals -Observational operators -Adjoint inversion - Category-specific emission inversion - Results

3. Oct 20, 2009 Adjoint-based inversion -Grid-based emission inference Forward Model OptimizationAdjoint Model Observations Cost/Forcing Gradients Adjustments (Final)

4. Oct 20, 2009 Model application -North America -36 km -13 layers -Summer of 2007 -OMI NO 2 column and surface ozone -For now won’t use surface NO 2 -Category-specific as much as possible -SAPRC-99 -Parallel (?) -Bott scheme (?)

5. Oct 20, 2009 OMI retrievals -KNMI product -Filtered to remove -Large pixels (> 36 km) -High errors (> 70%) -Domain edge (?) -Horizontal and vertical regridding (mapping) -Observational operators

6. Oct 20, 2009 Domain regridding (forward and backward) Averaging Kernel -- A VCD CMAQ VCD CMAQ - VCD OMI ATAT CMAQ forcing

7. Oct 20, 2009 Category-specific inversion -Grid-based inversion does not distinguish between source categories when sources are collocated in one grid. -Emission adjustments are only applied to the total emissions, i.e. adjoint gradients are scaled by emission shares. -Not necessarily a problem -It would be useful to track areas where specific sources dominate.

8. Oct 20, 2009 Source contributions to the gradients B A B C

9. Oct 20, 2009 Forward sensitivity analysis -Using DDM for screening receptor sites where a single source category dominates (NO 2 sensitivity wrt to anthropogenic NO x emissions)

10. Oct 20, 2009 Steps -Identify sources with potentially high spatial correlation in emission bias. -Natural sources, mobile (?) -Screen for receptors where these sources dominate. -Invert for those emissions. -Extend the adjustments to other locations. -Invert for other sources.

11. Oct 20, 2009 Conclusions - Spatial correlations that are intuitive are a potential source of information which should not be neglected, particularly in absence of good understanding of the true covariance matrices. - Such information can be applied to distinguish between sources using forward sensitivities. -Major sources are typically collocated. - NO 2 may not be the best candidate for this type of analysis (biogenics?)