1. Mark.Weber 1 / Mark.Weber@uni-bremen.de GOME2 Error Study: Column Retrieval Overview of Final Presentation (IUP Contribution): Main contribution from IUP  Radiative Transfer Modelling and Data Simulation  Error analysis for GOME2 trace gas column retrieval Contributors  Rüdiger de Beek (retrieval, error analysis, RTM database)  Vladimir and Alexei Rozanov (RTM development)  Andreas Richter (DOAS settings, GOME1 error assessment)  Marco Vountas (Ring effect)  Mark Weber (project management, error analysis)  John Burrows (PI University of Bremen, GOME1 lead scientist)

2. Mark.Weber 2 / Mark.Weber@uni-bremen.de GOME2 Error Study: Column Retrieval Topics:  Task Report 1: Tool Adaptation and Definition of Data  DOAS trace gas column retrieval (WP120, M. Weber)  RTM and spectral simulation of Input Datasets (WP130, M. Weber)  Overview of error sources (WP150, M. Weber)  Basic SNR error (WP 150, R. de Beek)  Task 2: Analysis of Error Sources  Spatial Aliassing (WP 210, M. Weber)  Spectral Resolution and Undersampling (WP 230, R. de Beek)  RTM Assumptions and Earth Curvature (WP250, R. de Beek)  BRDF (WP 260, M. Weber)  Pointing and Geolocation (WP 270, R. de Beek)  Task 3: Optimal Operational Settings and Error Mitigation (M. Weber)  Overall Error Budget  Recommendations  Future Work

3. Mark.Weber 3 / Mark.Weber@uni-bremen.de WP120: Column Retrieval Technique WP 120: Tools adaptation A) Trace gas column retrieval Retrieval Techniques:  DOAS = Differential Optical Absorption spectroscopy (Platt and Perner 1994)  Basic assumptions:  weak trace gas absorption (10 -4 -10 -2 )  negligible T-dependence of x-sections  slow variation in Rayleigh- and aerosol scattering contribution with  condition fulfilled for NO2, OClO, and BrO, but O3 is a strong absorber! Standard DOAS (Two step retrieval)  slant column fit: linear fit to match superpositions of X-sections to observed differential optical depth  conversion to vertical columns via AMF calculation by RTM  Although modified DOAS (Diebel et al. 1996) or weighting function DOAS (Buchwitz et al., 2000) may be more appropriate for ozone, standard DOAS was used in this study for all trace gases as done in the current operational GOME1 retrieval  AMF error is dominated by a-priori assumptions (beyond scope of this assessment), here focus on slant column retrieval

4. Mark.Weber 4 / Mark.Weber@uni-bremen.de WP 130: Acquisition of Input Data Spectral fitting windows  Recommendation based upon GOME1 experience  O3 VIS as option investigated in selected cases Analysis of data  DOAS Algorithm: KVANT (Fortran 90, M. Eisinger)  Linear mapping of errors (see WP150)

5. Mark.Weber 5 / Mark.Weber@uni-bremen.de WP 130: Acquisition of Input Data B) Simulation of GOME2 Spectra Radiative Transfer Model  Full spherical model CDIPI (Combined Differential Integration with Picard Iteration) (Rozanov et al. 2001)  Arbitrary viewing geometries (SZA<98°)  Limb  Nadir  Spectral range: 240-2380 nm (SCIAMACHY range)  IR: line-by-line, correlated-k  Accuracy (UV/VIS): 90°)  Approximate spherical model CDI (Rozanov et al. 2000)  Pseudo-spherical source functions, i.e. CDIPI w/o PI  Accuracy: <2% in limb geometry above ~35 km tangent height  Pseudo-spherical version as option (SCIATRAN/GOMETRAN compatible)  CDI sufficient for non-limb geometry as is the case for GOME2

6. Mark.Weber 6 / Mark.Weber@uni-bremen.de WP 130: Acquisition of Input Data Comparison between CDI and CDIPI  CDI sufficient for non-limb geometry as is the case for GOME2

7. Mark.Weber 7 / Mark.Weber@uni-bremen.de Viewing and solar angles in a topocentric coordinate system (tcs)  Satellite (SAT)  Top of atmosphere (TOA)  surface (GRD)  Plan-parallel atmospheres use only one fixed set of angles Modifications to CDI during this study:  BDRF (RPV formalism, ocean glint)  Refraction  Weighting function WP 130: Acquisition of Input Data Choices of origins of tcs: SAT, TOA, GRD Snow BDRF 300-330nm SZA=40°

8. Mark.Weber 8 / Mark.Weber@uni-bremen.de WP 130: Acquisition of Input Data GOME2 spectral simulation  Viewing geometries (ERS Propagator)  Jan, April, July, October  Latitudes: 5N, 55N, 75N, 75S  High (0.8) and low (0.05) albedo  24 trace gas scenarios  Scan Simulation  zenith line-of-sight angles - 46.5° to 46.5°  Scan time  = 4.5 sec.  Sampling time  = 0.01875 sec  # of line-of-sights  /  = 240  IT=0.1875s  10 LOS (24 ground pixels per forward scan)

9. Mark.Weber 9 / Mark.Weber@uni-bremen.de WP 130: Acquisition of Input Data Realistic tracegas scenarios  SLIMCAT 3D CTM stratospheric profiles  Tropospheric modifications  Constant tropospheric O 3 number density profile (all)  Biomass burning/ biogenic emission (5N, July)  H2CO 2ppb < 3 km (may affect BrO fit)  NO2 taken from MPI-2D CTM  O3 doubled < 5 km  Free tropospheric BrO (55N, April)  BrO 1ppt < 10 km (Fitzenberger et al., 2001)  PBL BrO explosion (75S, October, „ozone hole“)  BrO 50ppt < 2 km  O3 0ppm < 2 km MPI 2D Albedo dependent photochemical activity BrO, April, 55°N

10. Mark.Weber 10 / Mark.Weber@uni-bremen.de WP 130: Acquisition of Input Data Comparison between MPI 2D (GOME1 tracegas climatology) to modified SLIMCAT  Ozone hole, 75S, Oct  PBL low ozone event  high OClO, 75S, Oct  chlorine activation MPI 2D  MPI 2D as used in GOME1 V2.7 LV2 retrieval is outdated  GOME1 V3 climatology based upon TOMS V7 (O3) and US standard (NO2)

11. Mark.Weber 11 / Mark.Weber@uni-bremen.de WP150: Overview of column errors Overview of potential error sources in trace gas column retrieval diffuser plate spectral signature diffuser plate spectral signature  Spectral interference pattern from sanded Al surface in solar irradiance  GOME2 currently uses the same diffuser plate  Errors of 50% and 70% in NO2 and BrO VC density, respectively  Without improving diffuser in GOME2 minor tracegas column retrieval not possible  O3 UV retrieval errors are ~0.3% Richter and Wagner, 2001

12. Mark.Weber 12 / Mark.Weber@uni-bremen.de WP150: Overview of column errors Dichroic features in Channel 3 Dichroic features in Channel 3  No specific investigation for GOME2  SCIAMACHY investigation on combined polarisation correction and dichroics effect on O3 VIS retrieval reported in Appendix of Final Report  GOME1: no reliable O3 VIS retrieval & shift of NO2 fitting window to 425-450nm  Different polarisation state measurements and polarisation correction scheme in SCIAMACHY and GOME2 make a translation of the error to GOME2 difficult  Dichroics are reduced in GOME2 as compared to GOME1 Error in GOME2 polarisation correction Error in GOME2 polarisation correction  Direct O3UV fitting of simulated error spectra provided by SRON  O3 UV column error on the order of 0.3% Richter and Wagner

13. Mark.Weber 13 / Mark.Weber@uni-bremen.de WP150: Overview of column errors Scan mirror degradation Scan mirror degradation  Different UV degradation rate for irradiance and nadir spectra after 1999  No systematic trend in GOME1 V2.7 total ozone observed after 1999  Decrease in SNR due to blackening of the mirror effect column retrieval  DOAS retrieval is robust against instrumental degradation, indirect effect due to SNR changes, however, increases retrieval error Bramstedt et al., 2002 Comparison of GOME1 total ozone with NH Dobson stations

14. Mark.Weber 14 / Mark.Weber@uni-bremen.de WP150: Overview of column errors Wavelength calibration error Wavelength calibration error  Air-vacuum effect and outgassing  see dichroics  radiometric calibration error (wavelength shift in key parameters)  Doppler shift in solar irradiance (0.008 nm)  See undersampling error in WP230  DOAS retrieval does not require absolute radiometric calibration, however steep gradients in key parameter (dichroics) and noise due to interpolation (undersampling) error can introduce unwanted spectral artefacts  Wavelength shifts without secondary effects (see above) can be corrected using shift and squeeze to align radiances and x-section spectra  Dichroics are much reduced in GOME2 as compared to GOME1

15. Mark.Weber 15 / Mark.Weber@uni-bremen.de WP150: Overview of column errors Error sources investigated in Task 1 and Task 2 Error sources investigated in Task 1 and Task 2  SNR (WP 150)  baseline error/reference case  Spatial Aliassing (WP 210)  Spectral Resolution (WP 230)  Open Slit  Defocussing  Undersampling (interpolation) error  RTM assumption (WP 250)  Refraction  Pseudo-spherical approximations (SAT, TOA, GRD)  BRDF and ocean glint (WP 260)  Geolocation and pointing error (WP 270)