Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument

1. 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 • XCH4, XCO and its error is computed from retrieved state after iterative retrieval has converged

4. Typical State Vector Retrieved properties

5. Instrumental setup

6. Instrumental errors (ECHAM Scenarios) Geophysical scenario described in Butzet al. 2010, Butz et al. 2012 • Aerosol profiles originating from ECHAM 5 model simulations (Stier et al 2005) • Global coverage for one day (April 15th, 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

7. Simulated ECHAM scenarios ECHAM Desaster

8. Simulated ECHAM scenarios 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

9. Simulated ECHAM scenarios additive ARA NIR 1 NIR 2

10. Simulated ECHAM scenarios multiplicative ARA NIR 1 NIR 2

11. Simulated ECHAM scenarios Sensitivity to ISRF • Linear mapping of errors has been used to determine sensitivity to ISRF • 11 different slit functions are studied asymmetry Scene inhom Spectral offset width

12. Simulated ECHAM scenarios Sensitivity to ISRF R. Siddans

13. Sensitivity to ISRF

14. Simulated ECHAM scenarios Sensitivity to ISRF • 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

15. Simulated ECHAM scenarios 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%.

16. Instrumental errors (ECHAM) Conclusions 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 14th 2010 • Use atmospheric data from the previous scenarios in combination with ECMWF aerosols to increase number of successful retrievals

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

19. Representation errors (MACC scenarios) 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`

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. Representation errors (MACC scenarios) without fluorescence with fluorescence NIR 1&2 NIR 2 NIR 1&2 NIR 2 without offset with offset • 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. Representation errors (MACC scenarios) CH4 bias [%] without fluorescence with fluorescence NIR 1&2 NIR 2 NIR 1&2 NIR 2 without offset with offset Blue: converged retrievals over ice-free land Green: a-posteriori filter is applied

23. Representation errors (MACC scenarios) CH4 retrieval error [%] without fluorescence with fluorescence NIR 1&2 NIR 2 NIR 1&2 NIR 2 without offset with offset Blue: converged retrievals over ice-free land Green: a-posteriori filter is applied

24. MACC scenarios • 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 • Systematic is described here by the mean bias • Pseudo random is described as the standard deviation of the mean bias • Random is given by the mean of the retrieval error where applicable. • 50% of user requirement • Minimum and maximum values from the ILS variations. Min. variations are taken from asy1pc, max. values from idisp-3 • 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.