1. Retrieval of Ozone Profiles from GOME (and SCIAMACHY, and OMI, and GOME2 ) Roeland van Oss Ronald van der A and Johan de Haan, Robert Voors, Robert Spurr

2. Content Algorithm outline Current issues Validation Application: -Data assimilation -Tropospheric ozone column

3. Algorithm outline Basic elements On-line radiative transfer model: LIDORTA Inversion: Optimal Estimation Level 1 improvements 4 instruments: -GOME (ESA CHEOPS) -GOME2 (EUMETSAT O3MSAF) -OMI (National funding) -SCIAMACHY (…)

4. LIDORTA LIDORT, Linearized discrete ordinates model (Spurr et al, 2000): radiances & derivatives > LIDORTA: tailored for ozone profile retrieval Task for development: as fast as possible, but within required accuracy

5. LIDORTA …simulating radiance Geophysical Pressure Temperature Trace gasses Aerosol Clouds Surface Optical absorption scattering I

6. LIDORTA …and derivatives Geophysical Pressure Temperature Trace gasses (X) Aerosol Clouds Surface Optical d(absorption)/dX d(scattering)/dX dI/dX

7. LIDORTA: improving speed Minimum number of streams (angular resolution) -allowing analytic solutions Minimum number of atmospheric layers -40 layers required -Separate single scattering with 40 layers -LIDORTA only for multiple scattered: 20 layers enough Minimum number of calls to LIDORTA (number of wavelengths) -Sparse sampling for slow varying parts of spectrum (< 310 nm)

8. Optimal Estimation.. Following Rodgers (2000)

9. Level 1 improvements Wavelength recalibration (van Geffen & van Oss, 2003) Improved correction for polarization sensitivity of GOME (Schutgens & Stammes, 2003) Radiometric recalibration (van der A et al. 2002) fixed in time Varying in time (degradation)

10. Current issues LIDORT(A) does not treat POLARIZATION -Up to 10% error -Solved with look-up table LIDORT(A) does not treat Raman scattering -“de Haan” approach (similar to TOGOMI/total ozone) LIDORT(A) does treat sphericity of atmosphere -Accounting for high solar angles -Accounting for high viewing angles (GOME-2, OMI)

11. Validation Ozone profile Working Group BIRA & RIVM contributions

13. Applications of ozone profile retrieval at KNMI Data assimilation (Arjo Segers) Retrieval of global tropospheric ozone fields (Ronald van der A en Michiel van Weele)

14. Data assimilation of ozone profiles using averaging kernels Low-res retrieved profile Averaging kernel GOME measurement O3O3 Data assimilation High-res model profile estimate Low-res model profile

15. Data assimilation of profiles Intrusion of ozone rich air from the Arctic over Europe at a height of 150 hPa. April 12, 2000, 11h00

16. Comparison at Payerne with sonde

17. Retrieving tropospheric ozone columns Processing of ozone profiles, averaging kernels and error covariance matrices Comparison O 3 profiles and sondes to determine realistic measurement errors Data assimilation of ozone profiles, averaging kernels and error covariances provides accurate stratospheric ozone column. Total column derived with TOGOMI Cloud filtering using FRESCO Tropospheric column = (Total column) – (stratospheric ozone column)

18. Advantages of this method GOME profiles are very accurate (nadir gives high spatial resolution) Use of averaging kernels and data assimilation gives a realistic split-up between troposphere and stratosphere Several definitions of tropopause possible Global coverage ( southern hemisphere is sparsely covered with ground observations)

19. Example global tropospheric ozone product September 2000 Mean tropospheric ozone vmr (ppb) below 300 hPa