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

Remote Sensing Group 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)

Remote Sensing Group Performance against sondes in troposphere and stratosphere

Remote Sensing Group Sonde Comparisons with time © 2010 RalSpace

Remote Sensing Group GOME TOMCAT TOMCAT +G1AK GOME-1 ( ) aboard ERS-2 platform as compared to TOMCAT th February 1997 average TOMCAT + G1AK TOMCAT GOME-1

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

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

Remote Sensing Group In Development 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 0-6km G2-sonde mean bias G2-sonde(AK) mean bias 6-12km

Remote Sensing Group Broad absorption bands nm –large continuum overlapped with diffuse vibrational structure –First steps to analyse the information content for GOME-2 in this spectral region © 2010 RalSpace Next steps – Chappuis Bands

Remote Sensing Group © 2010 RalSpace 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

Remote Sensing Group 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 © 2010 RalSpace 0-6km Ozone Albedo

Remote Sensing Group Next steps – Joint Scheme Further develop Joint IASI-GOME-2 retrieval scheme Compare to existing individual schemes GOME-2 OnlyGOME-2 + IASI AK 6km AK 12km AK 6km AK 12km

Remote Sensing Group © 2010 RalSpace

Remote Sensing Group PREVIOUS TALK/SUPPLEMENTARY SLIDES © 2010 RalSpace

Remote Sensing Group JanFebMar AprMayJun Jul AugSep t OctNovDec

Remote Sensing Group 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 24 th April 2008 With extended QC and filtering: Scheme with basic QC: 0-6km Ozone

Remote Sensing Group 1 st June 0-6km Sub-column Why do we retrieve the slit function? Not retrieving the slit function: With slit function retrieval:

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

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

Remote Sensing Group © 2010 RalSpace Fitting of the Eigenvectors Mean residual fitting only Band 2 fit cost

Remote Sensing Group © 2010 RAL Space RAL GOME-2 Ozone Scheme Overview 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 Fit residuals < 0.1% cloud-free cloudy Measured spectra in Huggins bands Ozone absorption

Remote Sensing Group RAL MetOp Ozone GOME-2 Retrieval Scheme 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 IASI Retrieval Scheme Optimal estimation scheme Uses RTTOV as forward model (with recomputed coefficients) State vector: Ozone, Surface Temperature, H 2 O and Surface Emissivity (using MODIS/Wisconsin data as prior) © 2010 RalSpace

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

Remote Sensing Group AVHRR/3 Cloud Products for GOME-2 and IASI 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 Effective RadiusOptical Depth False colour (measured)

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

Remote Sensing Group 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 GOME-2 vs AVHRR Derrived Ozone Sensitivity Factor (O3SF)

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

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

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

Linear Averaging kernelsLog averaging kernels

Remote Sensing Group AVHRR Cloud products for GOME-2 and IASI 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 Z* / km Effective RadiusOptical Depth

Remote Sensing Group 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

Remote Sensing Group Single orbit: GOME only retrieval + Avg. Kernels on 23 Aug Molec/cm 3

Remote Sensing Group Single orbit: GOME+IASI retrieval + Avg. Kernels on 23 Aug 2008

Remote Sensing Group 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

Remote Sensing Group GOME-2 only Ozone 0-6km IASI only Ozone 0-6km NIGHT DAY 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