1. Intercomparison methods for satellite sensors: application to tropospheric ozone and CO measurements from Aura Daniel J. Jacob, Lin Zhang, Monika Kopacz and funding from NASA ACMAP with Xiong Liu (NASA/GSFC), Jennifer A. Logan (Harvard), Kelly V. Chance (SAO), and the TES, OMI, AIRS, MOPITT, and SCIAMACHY Science Teams Zhang, L., et al., Intercomparison methods for satellite measurements of atmospheric composition: application to tropospheric ozone from TES and OMI to be submitted Kopacz, M., et al., Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES) submitted to Atmos. Chem. Phys.

2. Tropospheric ozone measurements from TES and OMI TES (V003) Thermal IR (3.3-15.4  m) Retrieve log mixing ratio at 67 levels Along-track 5x8 km 2 pixels every 1.6 o latitude OMI (Xiong Liu, GSFC) UV (0.27-0.5  m) Retrieve partial columns in 24 layers 13x24 km 2 pixels (nadir), global daily coverage Convert TES averaging kernels to OMI grid and partial columns

3. Ozonesonde validation of TES and OMI Use global ensemble of coincident ozonesonde profles for 2005-2007: 528 for TES, 2568 for OMI Global mean biases at 500 hPa: +4 ± 7 ppbv for TES, +3 ± 6 ppbv for OMI …but data are very sparse : validation space is inadequately sampled

4. 500 hPa ozone from TES, OMI, and the GEOS-Chem CTM Year 2006 data reprocessed with fixed a priori; GEOS-Chem smoothed by the averaging kernels of each instrument GEOS-Chem simulation with TES vs. OMI averaging kernels shows that differences between the two instruments partly reflect differences in sensitivity; Can we use the residual as measure of the bias between the two instruments?

5. Intercomparing satellite instruments TES OMI O 3 sondes 1. In situ method: true validation but sparse 3. Averaging kernel smoothing method (Rodgers and Connor, 2003): smooth retrieval of instrument 1 with the averaging kernels of instrument 2 2. CTM method: Compare instruments independently to CTM GEOS-Chem

6. What does each method actually intercompare? 1, In situ method: directly measure x (ozonesondes) 2. CTM method: reference retrievals to local CTM values Start from the retrievals of ozone concentrations x : 3. Averaging kernel smoothing method: process TES retrieval through OMI avker noise! reduced noise

7. Intercomparison by the CTM and avker smoothing methods referenced to the in situ method Differences  at 500 and 800 hPa for 180 sonde/TES/OMI coincidences in 2006 The CTM method closely approximates the in situ method The avker smoothing method dampens differences and has large noise Averaging kernel smoothing method CTM method

8. Global intercomparison of TES and OMI by the CTM method Seasonal mean TES-OMI differences (  ) at 500 hPa for year 2006 Differences generally < 10 ppbv except for northern mid-latitudes in summer, some tropical continental regions

9. GEOS-Chem evaluation using TES and OMI Ozone at 500 hPa; TES and OMI have been corrected for their global mean biases Black areas are where TES and OMI are inconsistent (  > 10 ppbv) We find that GEOS-Chem is too low in tropics, too high at southern mid-latitudes

10. Application of the GEOS-Chem model adjoint to optimize CO sources using multi-sensor data Annual mean CO column May 2004- April2005 observed CO Earth surface 4-D Var sensitivity of observed concentrations to emissions upwind sensitivity time transport chemistry transport chemistry emission AIRS MOPITT TES SCIAMACHY (Bremen)

11. CONSISTENCY BETWEEN SATELLITE INSTRUMENTS FOR CO Results show good consistency between instruments and with in situ “truth” Global (2 o x2.5 o ) correlation of daily data with GEOS-Chem, May 2004 –April 2005; GEOS-Chem fields processed by averaging kernels of each instrument in situ

12. 1. Use AIRS, MOPITT, SCIAMACHY-Bremen in adjoint inversion; Best prior estimate from current inventories Annual CO emissions 2004-2005 Annual correction factors from adjoint inversion General underestimate of emissions, but with large seasonal variation 2. Use TES, NOAA/GMD, MOZAIC for evaluation of inversion results EMEP Streets GFED2 EDGAR NEI99 x0.4 INVERSE MODEL RESULTS

13. Best prior estimate (EPA NEI99 reduced by 60% on basis of ICARTT) is OK in summer when ICARTT was flown but not in other seasons Underestimate of emissions from cold vehicle starts in winter? MOZAIC data observed a priori a posteriori CORRECTION FACTOR IN US: SEASONAL VARIATION GMD data

15. Cross-instrument bias revealed by common reference to CTM Ozone retrievals at 500 hPa for year 2006 Residual