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NO x Emission Inversion Using the Adjoint of CMAQ

NO x Emission Inversion Using the Adjoint of CMAQ. Farid Amid and Amir Hakami. Carleton University. Overview. - Inversion Methodology OMI retrievals Observational operators Adjoint inversion Category-specific emission inversion - Results. Adjoint-based inversion. Observations.

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NO x Emission Inversion Using the Adjoint of CMAQ

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  1. NOx Emission Inversion Using the Adjoint of CMAQ Farid Amid and Amir Hakami Carleton University

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

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

  4. Model application • North America • 36 km • 13 layers • Summer of 2007 • OMI NO2 column and surface ozone • For now won’t use surface NO2 • Category-specific as much as possible • SAPRC-99 • Parallel (?) • Bott scheme (?) Oct 20, 2009

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

  6. Domain regridding (forward and backward) Averaging Kernel -- A VCDCMAQ AT CMAQ forcing VCDCMAQ- VCDOMI Oct 20, 2009

  7. 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. Oct 20, 2009

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

  9. Forward sensitivity analysis • Using DDM for screening receptor sites where a single source category dominates (NO2 sensitivity wrt to anthropogenic NOx emissions) Oct 20, 2009

  10. 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. Oct 20, 2009

  11. 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. • NO2 may not be the best candidate for this type of analysis (biogenics?) Oct 20, 2009

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