1. Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument MTR, 1 st October 2013 Task 2 Scattering profile characterisation for SWIR Leif Vogel, Hartmut Boesch University of Leicester

2.  Study Sentinel 5 instrument concepts (A and B) w.r.t. aerosol profile characterisation for SWIR species.  Link from aerosol information in NIR to trace gas retrievals in SWIR  Simulate global coverage for a single day (April) of realistic S5 sampling applying ECHAM 5 simulation supplied by Butz et al. 1)Instrument noise (based on recent input from ESA). 2)Effect of vegetation fluorescence 3)Error in spectral response function width (assuming 1% error) 4)Spectrally uniform offset in radiance units (assuming 1% of continuum radiance). 5)The ARA requirement  SWIR S5 Products UoL Task 2 Overview Target gasSpectral windows CH41.6μm, 2.3μm CO2.3μm Additional information used for aerosol profile O2-A0.76μm O2-B0.69μm CO21.6μm for proxy retrievals

3. Approach for Retrieval Simulations  Spectra are simulated using the forward modelling of UoL FP retrieval algorithm  two instrumental setups  range of geophysical scenarios  Retrieval sensitivity tests for retrievals w.r.t. scattering profiles, retrieval applies  the same a priori trace gas profiles, temperature profile, surface albedo  different setup for aerosol and cirrus a priori  Bias given by difference true and retrieved XCH4

4. The UoL Retrieval Algorithm  Measured radiance spectra are non-linear function of atmospheric parameters  retrieval is performed iteratively by alternating calls to:  Forward Model describes physics of measurement:  Multiple-scattering RT  Instrument Model  Solar Model  Inverse Method estimates state:  Rodger’s optimal estimation technique  X CH4, X CO and its error is computed from retrieved state after iterative retrieval has converged

5. NamesQuantityNotes CO and CH 4 1Multiplier to a priori profile H 2 O, HDO, CO21Multiplier to a priori profile Temperature1Additive offset to a priori profile AerosolsAOD, height and widthGauss profile CloudsAOD, height and widthGauss profile Surface Albedo#bands x 2paraAlbedo at band centre and slope Typical State Vector

6. Concept A BandsNIR (685 – 773 nm)*SWIR 1SWIR3 NIR 1NIR 2 Wavelengths [nm]685 - 700750 – 7731590 - 16752305 - 2385 Numbers of pixel116177850800 FWHM ISF0.39 0.25 *) Simulated retrievals do not use full range due to strongly changing surface albedo Concept B BandsNIRSWIR 1SWIR3 Wavelengths [nm]755 – 7731590 - 16752305 - 2385 Numbers of pixel450850870 FWHM ISF0.120.23 Instrumental setup

7. Concept A: NIR1 NIR2 SWIR1 SWIR3 Concept B: NIR SWIR1 SWIR3

8. Simulated scenarios  Atmosphere: 18-level profile  SZA: noon local time (27º- 87º)  Aerosol profiles: 7 different aerosol types with log normal size distribution Varying reff, mre, mim Spherical particles (Mie calculations) distributions varying in AOD and altitude Total AOD given by MODIS measurements  Cirrus clouds Gaussian profile with altitude, width and AOD given by CALIPSO measurements Optical properties are from Baum et al. (2005) for r eff = 60µm  Surface albedo determined by MODIS and Sciamacy data One day in April 2015 as described in Butz et al. 2010, Butz et al. 2012 applying ECHAM 5 model simulations (Stier et al 2005) Stier et al 2005

9. ECHAM Desaster

10. Simulated scenarios  Problems encountered:  Description of atmosperic parameters and aerosol optical properties not directly transferable to the UoL algorithm.  New calculations and mixing of all aerosol properties not feasible  Initial approach: Combining aerosol properties of respective types at all altitudes for each observation Mix aerosol opt. properties to one type at predefined profile ECHAM 5 model simulations as described in Stier et al 2005, Butz et al 2010, Butz et al 2012 (18 layers x 7 aerosol types x ~2700 Observations)

11. Simulated scenarios In most cases very large Aerosols particles with subsequent unrealistic low Angstroem coefficients. Very strong absorption in the SWIR3 band Erroneous relative signal to noise ratios for different wavelength channels

12. Simulated scenarios Alternative approach:  Replacing aerosol types described in Stier et al. 2005 with similar ones described in Kahn et al. 2001 based on aerosol type and radius  Creating a joint aerosol mix per observation with weights depending on respective ECHAM composition per observation  Applying original aerosol altitude profile ECHAMKahn et al. 2001 ModeAerosolsBase/Mixt.Aerosols NucleationSUBaseSU land AitkenSU, BC, POMMix 5aSU, acc.DU, BC, Carb AccumulationSU, BC, POM, SS, DUMix 3aSU, SS, BC, Carb CoarseSU, BC, POM, SS, DUMix 4aSU, acc.DU, coarse DU, Carb AitkenBC, POMMix 3bBC, Carb, SU, SS AccumulationDUBaseAcc. DU CoarseDUBaseCoarse DU

13. Simulated scenarios  Atmosphere: 18-20 level profile  SZA: noon local time (27º- 87º)  Aerosol profiles: Mixture of 7 “Kahn”-types Varying distributions per observation, AOD and altitude profile Total AOD given by MODIS measurements  Cirrus clouds Gaussian profile with altitude, width and AOD given by CALIPSO measurements Optical properties are from Baum et al. (2005) for r eff = 60µm  Surface albedo determined by MODIS and Sciamacy data Forward model:

14. Simulated scenarios Dust dominated Sulphur dominated  Aerosol properties show more realistic optical properties (Angstrom) than in first approach  But range of properties is very large which is expected to be problematic for retrieval

15. Concept A Concept B Example of produced spectra from TN1

16. Retrieval Setup  State vector: Scaling factors for the CH 4, CO, H 2 O, HDO, CO 2 vmr profile; Temperature factors, surface albedo + tilt per band, parameters for Gauss profile for cirrus, parameters for Gauss profile for 2 aerosol types  A priori values:  Atmosphere as in simulations  Aerosol extinction profile: Gaussian-shaped at height of 2 km a.g.l., width (FWHM) of 1 km and AOD of 0.05  Cirrus extinction profile: Gaussian-shaped at height of 10 km, width (FWHM) of 1 km and optical depth of 0.05.  2 Aerosol types: Due to the wide range of simulated aerosols which are not captured by individual Kahn mixtures, two simulated aerosols were chosen: A) Large Angstroem Coefficients (high sulfate component) B) Small Angstroem Coefficients (high dust component)  Cirrus type: as in simulations  Aerosol + cirrus parameters differ from simulations (except cirrus type)

17. Quality-Filtering Retrievals Concept AConcept B CH 4 Converged Soundings594 (23%)881 (35%) Filtered Soundings405 (16%)639 (25%) Only converged retrievals are used:  Number of converging iteration steps ≤ 12  Number of diverging iteration steps ≤ 5 Additional post-processing quality filter:  Χ 2 < 1 per spectral band  CH4 error < 0.4%  Retrieved AOD < 0.2  Retrieved AOD+COD < 0.3  Surface albedo at O2 bands < 0.7 (removes snow and ice)

18. 0 0 0 10 All soundings converged soundings filtered soundings Concept A

19. Soundings Total number Converged Filtered Effect of the filter

20. Results: Concept A CH 4 Converged Filtered Bias (%)0.013 +/- 0.9550.036 +/- 0.342 Precision (%)0.112 +/- 0.0550.103 +/-0.052 Impact of scattering error on trace gas retrieval

21. unfiltered filtered CH4 Bias CH4 rand. err. Converged retrievals do not show obvious dependency on location Concept A

22. Results: Concept A  Degrees of freedom inferred from the diagonal elements of averaging kernel matrix (Rogers, 2001)  Maximum number of DoF for aerosol and cirrus = 3 (optical depth, altitude, width) per type  Aerosol type 1 DoF ~ 2  Aerosol type 2 DoF ~1 - 2  Cirrus clouds DoF ~ 1 - 2  Mean DoF (AOD+COD): 4.63 (4.83)  Aerosols poorly retrieved  Distribution of retrieved vs. true AOD mirrors the wide range of aerosol mixtures retrieved with two opposing types and high possibly aerosol load  Good correlation between retrieved and true COD

23. Results: Concept A Dependency on albedo

24. Results: Concept A Dependency of CH4 bias on aerosol

25. 0 0 0 10 All soundings converged soundings filtered soundings Concept B

26. Soundings Total number Converged Filtered Effect of the filter

27. Results: Concept B CH 4 Converged Filtered Bias (%)-0.307 +/- 1.263-0.171 +/- 0.683 Precision (%)0.140 +/- 0.083 0.124 +/- 0.068 Impact of scattering error on trace gas retrieval

28. unfiltered filtered CH4 Bias CH4 rand. err. Converged retrievals do not show obvious dependency on location Concept B

29. Results: Concept B  Aerosol type 1 DoF ~ 2  Aerosol type 2 DoF ~1 – 2  In comparison to Concept A, no obvious change in DoF and distribution for both aerosol types  Cirrus clouds DoF ~ 1 - 2  Total DoF ~ 4 – 5.5 − Distribution is slightly skewed to lower values in comparison to Concept A  Good information content for retrieving aerosol and cirrus parameters  Distribution of retrieved vs. true AOD mirrors the wide range of aerosol mixtures retrieved with two opposing types, although not as extrem as Concept A  Good correlation between retrieved and true COD

30. Results: Concept B Dependency of CH4 bias on aerosols

31. Results: Concept B Dependency on albedo

32. Concept AConcept B CH 4 Converged Soundings594 (23%)881 (35%) Bias (%)0.013 +/- 0.955-0.307 +/- 1.263 Precision (%)0.112 +/- 0.0550.140 +/- 0.083 Filtered Soundings405 (16%)639 (25%) Bias (%)0.036 +/- 0.342-0.171 +/- 0.683 Precision (%)0.103 +/-0.052 0.124 +/- 0.068 Comparison of concepts A & B Results obtained for concept A and B show that  The number of converged retrievals and retrievals that passed quality filter is higher for concept B  variability of simulated aerosols vs. the two opposing types used in the retrieval and the lesser constrain by the missing O2-B band.  Mean bias and standard deviations for CH 4 is larger for concept B  Both concepts have similar precision  These conclusions hold after applying the filter

33. Concept A Concept B

34. Main conclusions (so far):  Concept A may yield less biased results at higher precision, although the true AOD was resolved at lesser accuracy.  The additional O2-B band may therefore yield important aerosol information However, in total the differences between concepts are not very big.  However, additional errors may be introduced (see RAL study) Still to be assessed for these scenarios: 1)Effect of vegetation fluorescence, from which the O2-B band is more affected relatively 2)Error in spectral response function width (assuming 1% error) 3)Spectrally uniform offset in radiance units (assuming 1% of continuum radiance). 4)The ARA requirement Technical note will be provided ….