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Inferring SO 2 and NO x Emissions from Satellite Remote Sensing

Inferring SO 2 and NO x Emissions from Satellite Remote Sensing. Randall Martin with contributions from Akhila Padmanabhan , Gray O’Byrne, Sajeev Philip Dalhousie U. Chulkyu Lee, Dalhouse U  NIMR, Korea. Environment Canada Seminar 17 Jan 2011.

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Inferring SO 2 and NO x Emissions from Satellite Remote Sensing

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  1. Inferring SO2 and NOx Emissions from Satellite Remote Sensing Randall Martin with contributions from AkhilaPadmanabhan, Gray O’Byrne, Sajeev Philip Dalhousie U Chulkyu Lee, Dalhouse U  NIMR, Korea Environment Canada Seminar 17 Jan 2011

  2. Information about Anthropogenic SO2 Sources?Need Accurate SO2 Retrieval Algorithm Lee et al., JGR, 2009

  3. IB Io dt() EARTH SURFACE Local Air Mass Factor (AMF) Calculation Radiative Transfer Model Atmospheric Chemistry Model “a-priori” Shape factor sigma () SO2 mixing ratio CSO2() Scattering weight () is temperature dependent cross-section INDIVIDUAL OMI SCENES • Calculate w() as function of: • solar and viewing zenith angle • surface albedo, pressure • cloud pressure, aerosol • OMI O3 column

  4. Local Air Mass Factor Improves Agreement with Aircraft Observations (INTEX-A and B) OMI SCIAMACHY Uniform AMF:slope = 1.6, r = 0.71 Local AMF:slope = 0.95, r = 0.92 Uniform AMF:slope = 1.3, r = 0.78 Local AMF:slope = 1.1, r = 0.89 Lee et al., JGR, 2009

  5. Extend Air Mass Factor Calculationto Longer Time Period Provide daily local SO2 AMFs and scattering weights so any model can be used in the analysis

  6. ≥ 5cm of snow 0 > snow < 5cm no snow Mean Trop. NO2 (molec/cm2) OMI Reported Cloud Fraction NO2 & SO2 Retrievals Affected by Errors in Surface Reflectance and CloudsWinter OMI NO2 over Calgary & Edmonton O’Byrne et al., JGR, 2010

  7. With Cloud Fraction Threshold (f < 0.3) -0.5 0 1.0 Expected Retrieval Bias OMI NO2 for Snow-Covered ScenesDue to Errors in Accounting for Transient Snow & Ice O’Byrne et al., JGR, 2010

  8. Bottom-Up Emission Inventories Take Years to Compile Trend in Summer Tropospheric NO2 Column over 2003-2009 from SCIAMACHY AkhilaPadmanabhan & Chris Sioris

  9. Evaluate Hindcast Inventory Versus Bottom-upHindcast for 2003 Based on Bottom-up for 2006 and Monthly NO2 for 2003-2006 Application of Satellite Observations for Timely Updates to NOx Emission Inventories Use Model to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions Bottom-up Hindcast Lamsal et al., GRL, 2011

  10. Forecast Inventory for 2009 Based on Bottom-up for 2006 and Monthly SCIAMACHY NO2 for 2006-2009 Temporary Dataset Until Bottom-Up Inventory Available 9% increase in global emissions 19% increase in Asian emissions 6% decrease in North American emissions Lamsal et al., GRL, 2011

  11. Top-Down (Mass Balance) Constraints on Emissions Inverse Modeling 2004-2005 SCIAMACHY Tropospheric NO2 (1015 molec cm-2) NOx emissions (1011 atoms N cm-2 s-1) Martin et al., 2006 52.4 Tg S yr-1 2006 SOx emissions (1011 atoms N cm-2 s-1) SCIAMACHY SO2 (1016 molec cm-2) Lee et al., 2011

  12. Accuracy of Mass Balance Approach for SO2 and NOx Emissions? • Mass Balance Approach • exploits short lifetimes • Easily implemented for many forward models • Infer emissions E from local trace gas column Ω Box A Box B

  13. Accuracy of Mass Balance Approach for SO2 and NOx Emissions? Test with Adjoint Approach • Mass Balance Approach • exploits short lifetimes • Easily implemented for many forward models • Infer emissions E from trace gas column Ω • Adjoint Approach • Explicitly accounts for spatial smearing • Minimize Cost Function J~[model(E)-obs(Ω)]2 • Use adjoint model to calculate sensitivities λ • to produce improve estimate of E

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