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Inferring SO 2 and NO x Emissions from Satellite Remote Sensing Randall Martin with contributions from Akhila Padmanabhan, Gray O’Byrne, Sajeev Philip.

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Presentation on theme: "Inferring SO 2 and NO x Emissions from Satellite Remote Sensing Randall Martin with contributions from Akhila Padmanabhan, Gray O’Byrne, Sajeev Philip."— Presentation transcript:

1 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 Environment Canada Seminar 17 Jan 2011 Chulkyu Lee, Dalhouse U  NIMR, Korea

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

3 Local Air Mass Factor (AMF) Calculation d()d() IoIo IBIB EARTH SURFACE Radiative Transfer Model Scattering weight Atmospheric Chemistry Model “a-priori” Shape factor Calculate w(  ) as function of: solar and viewing zenith angle surface albedo, pressure cloud pressure, aerosol OMI O 3 column INDIVIDUAL OMI SCENES SO 2 mixing ratio C SO2 (  )  (  ) is temperature dependent cross-section sigma (  )

4 Local Air Mass Factor Improves Agreement with Aircraft Observations (INTEX-A and B) Lee et al., JGR, 2009 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 SCIAMACHYOMI

5 Extend Air Mass Factor Calculation to Longer Time Period SCIAMACHYOMI Launch Resolution (km)30x60>13x24 Repeat (days)61-2 Equator Crossing Time10:001:45 Provide daily local SO 2 AMFs and scattering weights so any model can be used in the analysis

6 NO 2 & SO 2 Retrievals Affected by Errors in Surface Reflectance and Clouds Winter OMI NO 2 over Calgary & Edmonton 6 OMI Reported Cloud Fraction ≥ 5cm of snow 0 > snow < 5cm no snow Mean Trop. NO 2 (molec/cm 2 ) O’Byrne et al., JGR, 2010

7 Expected Retrieval Bias OMI NO 2 for Snow-Covered Scenes Due to Errors in Accounting for Transient Snow & Ice 7 With Cloud Fraction Threshold (f < 0.3) O’Byrne et al., JGR, 2010

8 Trend in Summer Tropospheric NO 2 Column over from SCIAMACHY Akhila Padmanabhan & Chris Sioris Bottom-Up Emission Inventories Take Years to Compile

9 Evaluate Hindcast Inventory Versus Bottom-up Hindcast for 2003 Based on Bottom-up for 2006 and Monthly NO 2 for Lamsal et al., GRL, 2011 HindcastBottom-up Application of Satellite Observations for Timely Updates to NO x Emission Inventories Use Model to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions

10 Forecast Inventory for 2009 Based on Bottom-up for 2006 and Monthly SCIAMACHY NO 2 for Temporary Dataset Until Bottom-Up Inventory Available Lamsal et al., GRL, % increase in global emissions 19% increase in Asian emissions 6% decrease in North American emissions

11 Top-Down (Mass Balance) Constraints on Emissions SCIAMACHY Tropospheric NO 2 (10 15 molec cm -2 ) NO x emissions (10 11 atoms N cm -2 s -1 ) Lee et al., Inverse Modeling SO x emissions (10 11 atoms N cm -2 s -1 ) SCIAMACHY SO 2 (10 16 molec cm -2 ) Tg S yr -1 Martin et al., 2006

12 Accuracy of Mass Balance Approach for SO 2 and NO x Emissions? Mass Balance Approach exploits short lifetimesexploits short lifetimes Easily implemented for many forward modelsEasily implemented for many forward models Infer emissions E from local trace gas column ΩInfer emissions E from local trace gas column Ω Box A Box B

13 Accuracy of Mass Balance Approach for SO 2 and NO x Emissions? Test with Adjoint Approach Mass Balance Approach exploits short lifetimesexploits short lifetimes Easily implemented for many forward modelsEasily implemented for many forward models Infer emissions E from trace gas column ΩInfer emissions E from trace gas column Ω Adjoint Approach Explicitly accounts for spatial smearingExplicitly accounts for spatial smearing Minimize Cost Function J~[model(E)-obs(Ω)] 2Minimize Cost Function J~[model(E)-obs(Ω)] 2 Use adjoint model to calculate sensitivities λUse adjoint model to calculate sensitivities λ to produce improve estimate of E


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