Ozone Profile Retrieval from MetOp

1. Ozone Profile Retrieval from MetOp NCEO Atmospheric Composition Theme Acknowledgements: NERC/NCEO for funding this work EUMETSAT & ECMWF for provision of data Dr N Richards (U. Leeds) TOMCAT data R. Siddans, G. Miles, B. Latter A. Waterfall, B. Kerridge

2. Developments Ozone ECV Project • Delivered sonde-matched pixels for 2008 test year to round robin exercise • Additionally 3 days per month (all orbits) for more detailed spatial comparison • Begun comparisons with models and official Ozone SAF product (KNMI) • Identified opportunities to improve aspects of our retrieval scheme • Additionally • Contribution to NCEO-theme partner N. Richards (U. Leeds) • Through visiting scientist project delivered prototype modules from RAL scheme for experimental use at KNMI • Ongoing comparisons to models (TOMCAT, MACC)

3. Performance against sondes in troposphere and stratosphere

4. Sonde Comparisons with time

5. GOME-1 24-26th February 1997 average GOME-1 (1995-2011) aboard ERS-2 platform as compared to TOMCAT TOMCAT TOMCAT GOME TOMCAT +G1AK TOMCAT + G1AK GOME

6. GOME-2 24-26th July 2008 average Model data sampled as GOME-2 would see it, with averaging kernels applied TOMCAT + G2AK TOMCAT + G2AK GOME-2 MACC + G2AK MACC +G2AK GOME-2

7. GOME-2 Orbit model cross-sections A Priori August 24th 2008 TOMCAT MACC MACC G2AK TOMCAT G2AK

8. In Development 0-6km • Comparisons to both sondes and models indicate some aspects of the scheme can be improved upon • Some spurious high tropospheric ozone values in NH spring • This might be improved by implementing a polarisation correction in the radiative transfer G2-sonde mean bias G2-sonde(AK) mean bias 6-12km

9. Next steps – Chappuis Bands Broad absorption bands 420-650 nm • large continuum overlapped with diffuse vibrational structure • First steps to analyse the information content for GOME-2 in this spectral region

10. Next steps – Chappuis Bands - Potentially more information about ozone closer to the surface over land as higher reflectance in the visible - Requires accurately characterised surface properties to fit the measured reflectance due to broad nature of Chappuis bands

11. Next steps – Polarisation Correction Neglecting polarisation in radiative transfer calculation can: • Cause inaccuracies in surface albedo and scattering parameter estimates • This can lead to overestimate in ozone absorption • Implement polarisation correction in Huggins Bands 0-6km Ozone Albedo

12. Next steps – Joint Scheme • Further develop Joint IASI-GOME-2 retrieval scheme • Compare to existing individual schemes GOME-2 Only GOME-2 + IASI AK 6km AK 6km AK 12km AK 12km

14. Previous talk/Supplementary Slides

15. Jan Feb Mar Apr May Jun Aug Sept Jul Oct Nov Dec

16. In Development • Comparisons to both sondes and models indicate some aspects of the scheme can be improved upon • High tropospheric ozone values in NH spring suggesting need to implement a polarisation correction in the radiative transfer Scheme with basic QC: With extended QC and filtering: 0-6km Ozone 24th April 2008

17. Why do we retrieve the slit function? Not retrieving the slit function: 2008 2009 2007 With slit function retrieval: 1st June 0-6km Sub-column

18. Global monthly mean sonde bias Without retrieving instrument slit function width • Bias changing with time due to instrument degradation G2-sonde G2-sonde+G2AK Retrieving instrument slit function width • Small positive bias • larger in 2010 after last decontamination test

19. Through-put degradation • Dashed lines indicate instrument decontamination tests. • Final test in September 2009. Mean absolute Wavelength shift (band 2) Retrieved slit function width

20. Band 2 fit cost Mean residual fitting only Fitting of the Eigenvectors

21. RAL GOME-2 Ozone Scheme Overview cloudy cloud-free Fit residuals < 0.1% Measured spectra in Huggins bands Ozone absorption 3-step retrieval: band 1a, surface albedo, band 2b. Use sun-normalised radiance in Hartley and Huggins bands to measure ozone in Earth’s atmosphere Forward model inc. Rayleigh + cloud scattering, surface Huggins band reveals information on tropospheric ozone, requires precision of fit >0.1%. For band 1, absolute calibration is important, especially for stratospheric ozone. For band 2, a good estimate of noise is important for precision of the fitting for tropospheric ozone

22. RAL MetOp Ozone GOME-2 Retrieval Scheme IASI Retrieval Scheme Optimal estimation scheme Uses RTTOV as forward model (with recomputed coefficients) State vector: Ozone, Surface Temperature, H2O and Surface Emissivity (using MODIS/Wisconsin data as prior) • UV/Vis spectrometer • Optimal estimation retrieval with sun-normalised radiance • Uses Huggins band to add information for tropospheric ozone • Requires fit precision < 0.1% for tropospheric ozone

23. We have experimented with co-adding pixels along and across track, to improve the quality of GOME-2 tropospheric ozone. RAL GOME-2 Ozone 0-6 km Ozone SAF OOP 0-6km Co-adding 4 pixels along track, cloud screened

24. AVHRR/3 Cloud Products for GOME-2 and IASI Optical Depth Effective Radius False colour (measured) • Oxford RAL Aerosol Cloud (ORAC) Scheme • Retrieve cloud properties for high resolution AVHRR imager pixels, combine for GOME-2 or IASI pixels • optical depth • effective radius • cloud top pressure • cloud fraction + • Products can be used for screening data for quality control or used directly in radiative transfer model

25. Use of AVHRR/3 imager data for GOME and IASI ozone Cloud height / km Optical Depth/ km ORAC Relative sensitivity of GOME to lower tropospheric ozone Relative sensitivity of GOME to ozone profile compared to cloud-free conditions (from RTM) Across-track pixel

26. GOME-2 vs AVHRR Derrived Ozone Sensitivity Factor (O3SF) • AVHRR and GOME-2 derived O3 factors are comparable • AVHRR O3SF more sensitive than GOME-2 cloud flag (contributing sub-pixel information) • Used for screening for the affects of cloud • Putting factors directly into RTM

27. IASI GOME-2 Pixel matching IASI pixel within GOME-2 pixel selected for lowest cloud factor, derived with AVHRR

28. 22-24th August 2008 GOME-2 and TES 0-6km RAL GOME-2 Ozone: Co-added 4 pixels along track TES 0-6 km Ozone: 1 month of observations gridded 2x2 degrees

29. Statistical comparison of GOME-2 vsSondes(2008, global) Prior vsSondes Retrieval vsSondes Retrievals vsSondes x Averaging kernels IASI + GOME dashed

30. Log averaging kernels Linear Averaging kernels

31. AVHRR Cloud products for GOME-2 and IASI Z* / km Optical Depth Effective Radius Oxford RAL Aerosol Cloud (ORAC) Scheme Retrieve cloud properties for high resolution AVHRR imager pixels, combine for GOME-2 or IASI pixels Retrieve optical depth, effective radius, cloud top pressure, cloud fraction

32. AVHRR Cloud and Ozone Sensitivity Factor (O3SF) Ozone factor derived from AVHRR or GOME-2 cloud properties Quantifies relative sensitivity to ozone in the troposphere AVHRR has sub-pixel cloud sensitivity AVHRR O3SF more sensitive to high cloud GOME-2 O3SF potentially better over multilayer cloud

33. Single orbit: GOME only retrieval + Avg. Kernels on 23 Aug 2008 1012Molec/cm3

34. Single orbit: GOME+IASI retrieval + Avg. Kernels on 23 Aug 2008

35. RAL IASI Ozone Retrieval Scheme • Optimal estimation scheme • Uses RTTOV as forward model (with recomputed coefficients) • State vector = Ozone, Surface Temperature, H2O and Surface Emissivity

36. MetOp Joint Ozone Scheme: • Directly fits GOME-2 and IASI spectra simultaneously in non-linear retrieval • IASI FM based on RTTOV • Uses AVHRR ORAC scheme to identify IASI pixel least affected by cloud • Fits down to noise in most scenes DAY IASI only Ozone 0-6km GOME-2 only Ozone 0-6km NIGHT