1. www.le.ac.uk Requirements Consolidation of the Near- Infrared Channel of the GMES-Sentinel-5 UVNS Instrument Scattering profile characterisation for SWIR Leif Vogel, Hartmut Boesch University Leicester

2. Approach for Retrieval Simulations Spectra are simulated using the forward model of UoL FP retrieval algorithm for a range of geophysical scenarios Sensitivity tests for retrievals w.r.t. scattering profiles, i.e. the retrieval applies the same a priori trace gas profiles, temperature profile, surface albedo different setup for aerosol and cirrus a priori Maximal sensitivity to scattering induced errors Bias given by difference between true and retrieved XCH4

3. The UoL Retrieval Algorithm Measured radiance spectra are non- linear function of atmospheric parameters retrieval is performed iteratively by alternating calls to FW and IM 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

4. 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 2 AerosolsAOD, height and widthGauss profile Cirrus cloudsAOD, height and widthGauss profile Surface Albedo#bands x 2paraAlbedo at band centre and slope Typical State Vector Retrieved properties

5. 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 Instrumental setup

6. Instrumental errors (ECHAM Scenarios) Aerosol profiles originating from ECHAM 5 model simulations (Stier et al 2005) Global coverage for one day (April 15 th, 2015) Atmosphere: 18-level profile SZA: noon local time (27º- 87º) Total AOD given by MODIS measurements Surface albedo determined by MODIS and Sciamacy data Geophysical scenario described in Butz et al. 2010, Butz et al. 2012 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 Cirrus clouds Calipso data, Gaussian profile, optical properties from Baum et al. (2005) for r eff = 60µm

7. ECHAM Desaster Simulated ECHAM scenarios

8. Sensitivity to radiometric accuracy Linear mapping of errors has been used to determine additive and multiplicative ARA errors –Additive gain: 3% of trop dark scenario in respective band –Multiplicative gain: 3% for NIR1 and 2 RSRA/ESRA errors determined by SWIR study Simulated ECHAM scenarios

9. additive ARA Error source ECHAM Scenarios; Concept A Instrume ntal Std devmean ARA- additive NIR 10.703-0.207 NIR 21.0720.881 NIR 1 NIR 2 Simulated ECHAM scenarios

10. multiplicative ARA Error source ECHAM Scenarios; Concept A Instrume ntal Std devmean ARA- multiplica tive NIR 10.750-0.258 NIR 20.9460.494 NIR 1 NIR 2 Simulated ECHAM scenarios

11. Sensitivity to ISRF ISF nominal Gaussian function convolved with a box-car, fitted to the ESA supplied function asy1pc Independently scaling the width of the nominal function on either side of the peak. Asymmetry is introduced with a maximum impact of 1% of the peak value of the nominal function homm Scene inhomogeneity Gradient in along-slit illumination (10% of the mean illumination, direction of assumed gradient “…m” or “…p”) homp idisp-1 Spectrally offset versions of the nominal slit function are added to the slit function itself; ISRF with the same centre of mass and FWHM as the original pattern. (error falls with then 1% enveloped of the requirement) idisp1 idisp-2 idisp2 idisp-3 idisp3 wid1pc The effect of perturbing the width of the nominal function by 1% Linear mapping of errors has been used to determine sensitivity to ISRF 11 different slit functions are studied asymmetry Scene inhom Spectral offset width Simulated ECHAM scenarios

12. Sensitivity to ISRF R. Siddans Simulated ECHAM scenarios

13. Sensitivity to ISRF

14. Greatest CH4 error via idisp-3 NIR 1 channel much less sensitive Allowing for the retrieval algorithm to spectrally shift and squeeze may mitigate (or mask) effects ISF CH4 bias NIR1 mean [%] NIR1 std [%] NIR2 mean [%] NIR2 std [%] Total mean [%] Total std [%] asy1pc -0.0060.007-0.0070.024 0.0100.025 idisp-1 -0.0030.0840.1630.298 0.1630.309 idisp1 -0.0130.0820.1210.232 0.1220.246 idisp-2 0.0050.0960.2070.358 0.2070.371 idisp2 -0.0090.0920.1200.234 0.1200.251 idisp-3 0.0140.1070.2360.402 0.2370.416 idisp3 -0.0020.1060.1200.264 0.1200.285 wid1pc -0.0180.0990.1720.342 0.1730.356 Total range (asy1pc – idisp-3) -0.006 – 0.14 0.007 – 0.107 -0.007 – 0.120 0.024 – 0.402 0.010 – 0.237 0.025 – 0.416 Simulated ECHAM scenarios

15. ISF CH4 bias NIR1 mean [%] NIR1 std [%] NIR2 mean [%] NIR2 std [%] Total mean [%] Total std [%] homm 0.0350.071-0.0330.464 0.0480.469 homp -0.0350.0710.0340.465 0.0480.470 Scene Inhomogeneity homm; homp 0.0350.0710.0340.465 0.0480.469 Sensitivity to ISRF, Scene inhomogeniety NIR 1 channel much less sensitive Homm; homp slit function errors are not independent –Error of scene inhomogeneity is given as absolute mean Introduced bias is very low with 0.048% A greater variability in the NIR2 channel leads to total standard deviation of 0.469%. Simulated ECHAM scenarios

16. Instrumental errors (ECHAM) Conclusions Error sourceECHAM Scenarios; Concept A InstrumentalStd devmean ARA-multiplicative 1) NIR 10.750-0.258 NIR 20.9460.494 ARA-additive 2) NIR 10.703-0.207 NIR 21.0720.881 RSRA or ESRA 3) Done by SWIR study group 10 ISRF variations 4) NIR 10.007 - 0.107-0.006 - 0.014 NIR 20.024 - 0.402-0.007 - 0.237 Scene inhomogeneity 5) NIR 10.0710.035 NIR 20.4650.034 Total Instrumental 6) NIR1 and NIR22.0791 - 2.1201.064 - 1.090 Only NIR 21.780 - 1.7870.814 - 0.816 ARA requirements: Mean CH4 accuracy meets requirements, but standard deviation is rather high. Reduction would be beneficial

17. Simulated MACC scenarios  Simulations with ECHAM 5 model simulations as described in Stier et al 2005, Butz et al 2010, Butz et al 2012  Description of atmospheric parameters and aerosol optical properties not directly transferable to the UoL algorithm.  Calculated aerosol optical properties are either dust or sulphate dominated  ECHAM 5 aerosols replaced with aerosols from MACC, ECWMF integrated forecasting system (IFS), 12h GMT April 14 th 2010  Use atmospheric data from the previous scenarios in combination with ECMWF aerosols to increase number of successful retrievals

18. ECHAM Desaster MACC vs. ECHAM Scenarios  Replace only aerosols  All other scenario information remains unchanged  Cirrus clouds, atmosphere, pressure levels, surface albedo, etc.

19. Different retrievals for MACC simulations Aerosol parameterization: Use two linear combination of aerosol types to approximate true type 2 generic Gaussian aerosol extinction profiles (altitude =2km agl, width = 1.5km, aod = 0.1) Cirrus (altitude 10km agl, width =1km, cod = 0.05 In total 8 global retrievals to study representation errors` Representation errors (MACC scenarios) Forward simulations Retrieval Without fluorescenceWith fluorescence 2 aerosolsNIR1 and NIR2Only NIR2NIR1 and NIR2Only NIR2 2 aerosls with zero level offset (fluorescence mitigation) NIR1 and NIR2Only NIR2NIR1 and NIR2Only NIR2

20. Simulation of fluorescene Fluorescence data supplied by L. Guanter FS Spectra added to simulated Spectra taking into account respective aerosol load and viewing direction

21. NIR 1&2 NIR 2 without fluorescencewith fluorescence without offset with offset Representation errors (MACC scenarios) Some regional differences can be observed: effect of fluorescence (without offset correction) Indication that zero level offset may couple unfavourably with cirrus clouds Similar coverage of NIR 1&2 and NIR 2 only retrievals

22. CH 4 bias [%] NIR 1&2 NIR 2 without fluorescencewith fluorescence without offset with offset Representation errors (MACC scenarios) Blue: converged retrievals over ice-free land Green: a-posteriori filter is applied

23. NIR 1&2 NIR 2 without fluorescencewith fluorescence without offset with offset CH 4 retrieval error [%] Blue: converged retrievals over ice-free land Green: a-posteriori filter is applied Representation errors (MACC scenarios)

24. MACC scenarios Converged MACC Scenarios Error source NIR1 and NIR2 (O2-A and O2-B band) NIR2 (O2-A band) Error CH4 [%]std devmean bias Mean precision # retrievals 1) std devmean bias Mean precision # retrievals 1) No FS, no offset0.609-0.0670.127 1269 (65%) 0.864-0.1680.162 1419 (74%) No FS, offset0.838-0.0520.196 1429 (74%) 1.039-0.1160.224 1659 (86%) FS, no offset0.8740.0000.129 1069 (55%) 0.793-0.1080.162 1353 (70%) FS, offset0.831-0.0490.197 1395 (72%) 1.066-0.1290.226 1623 (84%) 1) Number of converged retrievals out of a total of 1933 simulated measurements over land and ice free surface. All retrievals fulfill requirements Less converged retrievals for NIR 1&2 than for only NIR2 Tighter boundary conditions due to O2-B band Retrievals with NIR 1&2 show better performance in random and systematic errors Fluorescence leads to higher errors, but its effect can be mitigated Indication that aerosol information in the O2-B band constrains the retrievals at cost of lesser coverage filtering effect

25. Summary Instrumental errors from ECHAM Scenarios InstrumentalSystematic 1) Pseudo random 2) Random 3) ARA-multiplicative NIR 1-0.2580.750 NIR 20.4940.946 ARA-additive NIR 1-0.2070.703 NIR 20.8811.072 RSRA or ESRA 4) Done by SWIR study group 01 ISRF variations 5) NIR 1-0.006 - 0.0140.007 - 0.107 NIR 2-0.007 - 0.2370.024 - 0.402 Scene inhomogeneity NIR 10.0350.071 NIR 20.0340.465 Total Instrumental 6) NIR1 and NIR21.064 - 1.0902.0791 - 2.120 Only NIR 20.814 - 0.8161.780 - 1.787 Representation errors from MACC scenarios Converged but not filtered results Systematic 1) Pseudo random 2) Random 3) Simulation with Fluorescence, retrieval with 2 aerosols and zero level offset NIR1 and NIR2 (1395, 72%) 0.0490.8310.197 Only NIR 2 (1623, 84%) 0.1291.0660.226 Total error 9) NIR1 and NIR21.065 - 1.0912.239 - 2.2770.197 Only NIR 20.824 - 0.8262.075 - 2.0810.226 1)Systematic is described here by the mean bias 2)Pseudo random is described as the standard deviation of the mean bias 3)Random is given by the mean of the retrieval error where applicable. 4)50% of user requirement 5)Minimum and maximum values from the ILS variations. Min. variations are taken from asy1pc, max. values from idisp-3 6)The two values result from the assumed minimum and maximum error of the ILS

26. Conclusion: Retrievals have been performed with two geophysical Scenarios based on ECHAM and MACC aerosol distributions for instrumental and representation errors. Most error sources lead to results inline with the requirements. However, additive and multiplicative ARA induced errors are high. Reduction of these error sources is desirable. Representation errors meet the requirements. Using a two NIR (O2-A & B) band retrieval increases accuracy at the cost of slightly diminished coverage, and its use is beneficial to prevent erroneous results. The effect of fluorescence can be mitigated using a zero level offset. Further potential lies in improved aerosol representations (regional dependencies, climatology, improved optical properties), which may also lead to increased coverage.

27. Aerosol properties show a larger range than original ECHAM simulations (total AOD normalized to ECHAM simulations) Although not erroneous, this lowers the amount of successful retrievals Dust dominated Sulphur dominated Angstroem Coefficient Original ECHAM simulation Simulated ECHAM scenarios

28. Simulated scenarios MACC aerosolsCalculated MACC aerosols

29. O2 A & B band: Extinction coefficients of aerosols Change in ext. coeff. from O2 B – O2 A does not necessarily reflect changes towards longer wavelengths  Dust 2 & 3  Sea salt 2 & 3

30. O2 A & B band: Extinction coefficients of aerosols NIR1NIR2SWIR1SWIR3

31. O2 A - 2.3 μm vs O2 A – 1.6μm O2 A – O2 B vs O2 A – 1.6μm O2 A – O2 B vs O2 A – 2.3μm Distribution of Angstroem coefficients for geophysical scenario  2 fixed aerosol types used in the retrieval  Aerosols O2 B band holds additional aerosol information, which may give raise to more complex situation 2 fixed aerosols in the retrieval