1. 1 Preliminary Ozone Profile and Tropospheric Ozone Retrievals From OMI Xiong Liu 1,2, Kelly Chance 2, Lin Zhang 3, Thomas P. Kurosu 2, John R. Worden 4, Kevin W. Bowman 4, Pawan K. Bhartia 5, Daniel J. Jacob 3 1 GEST/UMBC 2 Harvard-Smithsonian Center for Astrophysics 3 Harvard University 4 Jet Propulsion Laboratory 5 NASA Goddard Space Flight Center OMI International Science Team Meeting June 5, 2007

2. 2 Outline nAlgorithm Description nDerivation of Soft Calibration nPreliminary Results (Collection-2) nCross-Evaluation with TES and GEOS-CHEM nComparison between Collection 2 and OPF40 nSummary and Future Outlook

3. 3 Algorithm Description Spectral fitting+Optimal estimation+LIDORT [Liu et al., 2005] Fitting Windows: 270-310 nm (UV-1), 310-330 nm (UV-2) Ozone State Vector: 24 layers (each layer is ~2.5 km) from surface to ~60 km, with the NCEP tropopause as stratospheric/tropospheric boundary, 4-6 tropospheric layers A Priori: ozone profile climatology by McPeters et al. [2007] Measurement error: OMI random-noise error Slit function: Assume Gaussian shape and derive slit widths Undersampling correction (UV-1) Fit wavelength shifts among radiance, irradiance, and ozone cross section (3-order)

4. 4 Algorithm Description LIDORT (pseudo-spherical) with additional corrections Polarization correction Ring effect: directly model the 1 st -oder RRS of the direct beam Clouds: Mixed LER, V8 TOMS/OMI CTP, f c from 368-372 nm Aerosols: SAGE stratospheric and GOCART tropospheric aerosols Surface albedo: varying with, partly taking residual aerosol and calibrations into account NCEP surface, tropopause pressure, and temperature Directly fit VCDs of other interfering trace gases: SO 2, NO 2, BrO, HCHO NO 2 : PRATMO (stratosphere) + GEOS-CHEM (troposphere) BrO: PRATMO (stratosphere) + well mixed in the troposphere SO 2 /HCHO: no stratospheric + GEOS-CHEM (troposphere)

5. 5 (a) Example of Retrievals May 8, 2006 Overpass US Partial Column Ozone (DU) (a) Retrieval (b) A priori (b)

6. 6 Soft Calibration: Motivation nOMI retrievals without additional calibration (Collection-2): u Across-track position dependent biases u Negative biases for edge pixels and positive biases for most positions

7. 7 Soft Calibration: Motivation nAcross-track stripes: u ~30-40 DU in TCO u ~10 DU in SCO u ~20-30 in Total Ozone

8. 8 Soft Calibration: Methodology nSimulate OMI radiances with McPeters et al. (2007) and Logan (1999) tropospheric ozone climatology. nParameters other than ozone were fitted in the retrievals. nDerive correction vs. and  from mean differences (1 day) nAssumption: Climatology represents ozone fields on global average

9. 9 Soft Calibration: Methodology nApplying soft calibration removes most stripes except for edge pixels. nEdge pixels: Neglect the sphericity in the line of sight. nRemove remaining systematic stripes based on one day’s retrieval nRetrieval artifacts due to absorbing aerosols and clouds. (c)

10. 10 OMI TCO (North Pacific on May 05-10, 2006) OMI tropospheric column ozone f c < 0.3 Gridded to 2.5°×2°

11. 11 OMI TCO (May 04-15, 2006) OMI tropospheric column ozone ( f c < 0.3)

12. 12 OMI/TES/GEOS-CHEM Comparison: Methodology nOMI/TES retrievals: Different retrieval grid and a priori u Relatively coarser vertical resolution (vs. ozonesonde) u TES: More tropospheric ozone info u OMI: More stratospheric ozone info, sensitive to ozone through the troposphere Clear-sky Averaging Kernels (AKs) (a) TES (67 levels) (b) OMI (24 layers) 15°N40°N60°N

13. 13 Comparison Methodology nTES: V 2.0 u Compares well with ozonesonde and LIDAR observations: generally biased higher by ~10% [Ray et al., 2007, Richard et al., 2007]. nGEOS-Chem simulation: V7-04-09 with GEOS-4 u Lightning NOx: 6 Tg/yr, rescaled with OTD/LIS climatology u Increase Chinese NOx emission by ~70% (2006) nUse GEOS-Chem as an intermediate, also evaluate GEOS-Chem: uInterpolate GEOS-Chem/TES to OMI grid (coarsest) uAppend GEOS-Chem with TES stratospheric ozone uCompare GEOS-Chem with TES (TES AKs + OMI a priori) uCompare GEOS-Chem with OMI (OMI AKs + OMI a priori) nPresent the comparison on May 08, 2006 (similar on other days) uRemove poor retrievals (i.e., TES master flag, emission layer flag, OMI fitting residuals) and cloudy pixels (OMI f c > 0.3) u 550 coincidences

14. 14 OMI/TES/GEOS-Chem TCO on May 8, 2006 AK: Averaging Kernels IG: A Priori Generally consistent spatial distribution despite systematic biases

15. 15 OMI/TES/GEOS-Chem TCO on May 8, 2006 MB = -8%MB = -6% MB = -7%MB = 4%

16. 16 OMI/TES/GEOS-Chem Comparison (a)Difference due to OMI/TES AKs can be up to 10-15% especially in UT (b)Large negative (10°N-20°N, high sun) and positive (40°S-25°S, low sun) biases may be caused by non-linearity of the OMI calibration. (a) (b) Mainly systematic OMI/TES differences

17. 17 Collection 2 vs. OPF40 (~Collection 3)

18. 18 Collection 2 vs. OPF40 (~Collection 3)

19. 19 Collection 2 vs. OPF40 (~Collection 3)

20. 20 Summary and Future Outlook nApply our GOME ozone profile retrieval algorithm to OMI. nSoft calibration: and  dependent correction of up to ±8%. nOMI TCO seems to be able to capture large spatiotemporal variability on the daily basis. nThe spatial distribution of OMI, TES, and GEOS-Chem tropospheric ozone is similar on the global scale. nOMI shows a negative bias of ~15% relative to TES except for 10°N-20°N (~ -30%) and 45°S-25°S (~20%), which may be related to the non-linearity calibration of OMI. nImprove soft calibration with VLIDORT+MLS+Clouds nImplement new correction for neglecting polarization and spherical geometry in the line of sight (with VLIDORT). nUse MLS retrievals to reduce the stratospheric influence nImprove aerosols, cloud, and surface albedo treatments Acknowledgements OMI and TES science team, GEOS-Chem community NASA and Smithsonian Institution