Improving Retrievals of Tropospheric NO 2

1. Improving Retrievals of Tropospheric NO2 Randall Martin, Dalhousie and Harvard-Smithsonian Lok Lamsal, Gray O’Byrne, Aaron van Donkelaar, Dalhousie Ed Celarier, Eric Bucsela, Joanna Joiner, NASA Folkert Boersma, Ruud Dirksen, KNMI Chao Luo, Yuhang Wang, Georgia Tech September 14, 2009 Air Quality Working Group Aura Meeting Leiden, Netherlands

2. Seasonal Differences Between OMI NO2 Products Direct Validation Has Not Arbitrated Standard (SP) DOMINO (DP) DP-SP DJF 2005 JJA 2005 0.1 1 2 3 4 5 6 7 8 9 10 Tropospheric NO2 Column (1015 molecules cm-2) -5 -3 -1 1 3 5 Δ(1015 molecules cm-2) Lamsal et al., JGR, submitted

3. Indirect Validation of OMI (A) In-situ surface NO2 measurements from the SEARCH (photolytic) and EPA/AQS (molybdenum) networks at rural sites in Eastern US Use GEOS-Chem NO2 profiles to estimate surface-level NO2 from OMI (Lamsal et al., JGR, 2008) • (B) Updated bottom-up emission inventories for 2005-2006 Apply GEOS-Chem to infer top-down emissions from OMI by mass balance (Martin et al., JGR,2003)

4. Multiple Approaches Yield Similar Results SEARCH “True” NO2”, Southeast U.S. AQS/EPA “Corrected” NO2, Eastern U.S. NOx Emissions, US + Canada NOx Emissions, SEARCH domain Lamsal et al., JGR, submitted

5. Stratosphere-troposphere Separation and AMF Together Explain Difference Between DP and SP Air mass factor Strat-trop separation Combined ΔTropospheric NO2 Column DP – SP (1015 molecules cm-2) Lamsal et al., JGR, submitted

6. Produce DP_GC From DP Averaging Kernels and GEOS-Chem NO2 Profiles SEARCH “True” NO2”, Southeast U.S. AQS/EPA “Corrected” NO2, Eastern U.S. NOx Emissions, US + Canada NOx Emissions, SEARCH domain Lamsal et al., JGR, submitted

7. Surface ReflectivityLambertian Equivalent Reflectivity (LER)

8. OMI LER (Kleipool et al. 2008) Best Represents Surface LER Cloud-, Snow-, and Aerosol- Free LER (2005-2007) Use MODIS/Aqua to Eliminate Cloud and Aerosol from OMI Scenes Use NISE Snow Flag to Eliminate Snow LER Difference of 2%  15-30% Bias in NO2 (Martin et al., 2002; Boersma et al., 2004) O’Byrne et al., JGR, submitted

9. Unrealistic Relation in OMI NO2 versus Cloud & Snow ≥ 5cm of snow 0 > snow < 5cm no snow (In situ NO2 data show variation < 15%) Winter OMI NO2 over Calgary & Edmonton Winter Mean Trop. NO2 (molec/cm2) OMI Reported Cloud Fraction O’Byrne et al., JGR, submitted

10. Large Spatial Variation in Snow-Covered Surface LERCurrent Algorithms Assume Snow Reflectivity = 0.6 0 0.2 0.4 0.6 0.8 1 Snow-covered Surface LER (unitless) Snow Weakly Represented in Previous ClimatologiesLeads to Ambiguity in Accounting for Snow OMI LER -0.6 -0.8 -0.4 -0.2 0 0.2 Snow-Covered LER Difference (Previous Climatology – Snow-Covered Surface LER) O’Byrne et al., JGR, submitted

11. Spatially-Varying Biases in OMI NO2 over Snow • To correct NO2 retrieval for snow • Use snow-covered surface reflectivity • Use MODIS-determined cloud-free scenes to correct clouds • NO2 bias for MODIS-determined cloud-free scenes • Positive (negative) bias from underestimated (overestimated) surface LER • OMI reports clouds when surface LER is underestimated With All Cloud Fractions With Cloud Fraction Threshold (f < 0.3) -50 0 100 O’Byrne et al., JGR, submitted

12. Recommendations • Remote Sensing Community: • Use two reflectivity databases: one snow-free, one for snow • Switch from TOMS or GOME reflectivity databases to OMI • Switch from annual mean to monthly mean NO2 profiles for SP • Evaluate Stratosphere-Troposphere Separation • Develop instrumentation with finer spatial resolution • (more cloud-free scenes reduces dependence on assumed profile ) • Following DP, include Averaging Kernels (or Scattering Weights) in trace gas products so the user can remove the effect of the assumed profile • Ground-based Measurement Needs: • span satellite footprint • full year • research quality (e.g. NO2) • vertical profile Modeling Community: Continue develop representation of vertical profile