1. S5P cloud products Sebastián Gimeno García, Ronny Lutz, Diego Loyola German S5P Verification Meeting 1 Bremen, 28-29 November 2013 www.DLR.de Chart 1> Vortrag > Autor Dokumentname > Datum

2. Chart 2 Outline  General overview  OCRA adaptation to S5P  Cloud model – OCRA/ROCINN-CAL,-CRB  ROCINN CRB: CA variable vs. CA fixed  Cloud inhomogeneity effects  Conclusions  Outlook

3. Chart 3  S5P cloud information primarily needed for accurate trace gas retrieval  Influence of clouds on gas retrieval (1D): albedo effect shielding effect Multiple scattering effect Overview  + others: e.g. multiple cloud layering system, …

4. Chart 4  S5P cloud information primarily needed for accurate trace gas retrieval  Influence of clouds on gas retrieval (3D): neighbouring pixel effect in-pixel inhomogeneity effects Overview  + others: e.g. effect of scene variability on spectral calibration, …

5. Chart 5 OCRA adaptation to S5P – Input Data OCRA for GOME, SCIAMACHY and GOME-2 uses the PMD UVN data with a resolution of ~10x40km2 OCRA for TROPOMI will use the UVN radiance data with a resolution of 7x7km2 The initial S5P cloud-free composites will be based on OMI data with a resolution of 13x24km2 at nadir

6. Chart 6 OCRA adaptation – OMI cloud-free composite UV cloud-free for July VIS cloud-free for July UV cloud-free for January VIS cloud-free for January  Monthly composite of cloud-free reflectances in UV-2 and VIS OMI channels

7. Chart 7 OCRA adaptation – OMI cloud fraction results  CF comparison:  OCRA  OMTO3  OMDOAO3  Global pattern good represented by all products  Scan angle dependency  Comparison with OMI official cloud products:  OMCLDO2  OMCLDRR ongoing …

8. Chart 8 OCRA adaptation – OMI cloud fraction results (2)  Clear correlation between all CF products  OCRA shows slope in mean differences  OMDOAO3 delivers larger CFs than the other two products

9. Chart 9  Cloud fraction (CF) is retrieved using a RGB color space approach → OCRA  Cloud parameters (CTH, COT) are retrieved in the Oxygen A-band using regularization theory → ROCINN  CRB: Clouds are treated as Reflecting Boundary (Lambertian equivalent reflectors)  CAL: Clouds are treated As homogeneous Layers  Photon cloud penetration is allowed  Multiple scattering is accounted for  Modeled radiance contains information below the cloud layer  Retrieved CTH expected to be closer to the geometrical CTH Cloud Model – OCRA/ROCINN-CAL/CRB

10. Chart 10 Cloud Model – OCRA/ROCINN-CAL/CRB (2) Intra-cloud correction Loyola et al., JGR 2011 Surface Lambertian Cloud  CAL: Cloud As scattering Layer | CRB: Cloud as Reflecting Boundary

11. Chart 11 Cloud Model – OCRA/ROCINN-CAL/CRB (3)  Comment from a reviewer of the S5P Cloud ATBD:  „To treat clouds as simple reflectors … is far to simple and might work for large pixels averaging over more than 2000 Km, but is very likely not working for the interpretation of much finer spatial resolution TROPOMI measurements.“

12. Chart 12 Cloud Model – OCRA/ROCINN-CAL/CRB (4)  100000 independent spectra were simulated using the ROCINN CAL forward model (VLIDORT) covering the whole ROCINN CAL state space (1% noise added):  SH in [0, 2] km  SA in [0, 1]  CTH in [0, 15] km  COT in [0, 125]  CGT in [0.5, 14.5] km  SZA in [0, 85] °  VZA in [0, 75] °  CF in [0, 1]  CRB retrievals of CAL spectra: effects due to different cloud models  Relative difference:

13. Chart 13 Cloud Model – OCRA/ROCINN-CAL/CRB (5) -CRB retrieved cloud “top” height is systematically smaller than the geometrical cloud top height. -Discrepancy increases as cloud optical depth decreases. Global Mean Lambertian Model

14. Chart 14 ROCINN-CRB: CA variable versus CA fixed  100000 independent spectra were simulated using the ROCINN CRB forward model (VLIDORT) covering the whole ROCINN CRB state space (1% noise added):  SH in [0, 2] km  SA in [0, 1]  CH in [0, 15] km  CA in [0, 1]  SZA in [0, 85] °  VZA in [0, 75] °  CF in [0, 1]  Cloud albedo (CA) was set to 0.8 in the cloud property retrieval  Results show the impact of fixing CA to 0.8 in CRB in comparison with a variable CA (not CRB vs. CAL!)  Relative difference:

15. Chart 15 ROCINN-CRB: CA variable versus CA fixed (2)  ROCINN CRB with fixed CA (=0.8):  underestimates CF if actual CA is lower than 0.8  overestimates CF if actual CA is higher than 0.8  overestimates CTH if actual CA is lower than 0.8  underestimates CTH if actual CA is higher than 0.8  the larger the SA, the larger the CTH underestimation CF rel. diff. vs. cloud albedo CTH rel. diff. vs. cloud albedo CTH rel. diff. vs. surface albedo

16. Chart 16  MoCaRT (Monte Carlo Radiative Transfer) Model reflectivities Cloud inhomogeneity effects

17. Chart 17 Conclusions  OCRA CF algorithm has been adapted for S5P/TROPOMI  preliminary results for OMI look very promising  OCRA algorithm is computationally very efficient  good agreement with existing algorithms (OMTO3, OMDOAO3):  OCRA CFs correlate with both  ROCINN CRB (LER) evaluation:  ROCINN CRB underestimates CTH (as expected)  CTH discrepancies increase with decreasing CA/COT  Setting CA to a fixed value (CA_ref=0.8) leads to a complex two- regime (below and above CA_ref) dependency of {CTH, CF} on cloud albedo (cloud optical thickness) and surface albedo

18. Chart 18 Outlook  Comparisons of OCRA with official OMI cloud products (OMCLDO2, OMCLDRR) ongoing  Case studies with synthetic spectra  OCRA  ROCINN-CRB/CAL  3D effects

19. Chart 19 Thank you for your attention!

20. Chart 20 Information theory analysis Degree of freedom of the signal (DFS) ~ 2 Only two independent parameters can be retrieved in the O2 A-band CTH and COT are retrieved with ROCINN in the O2 A-band OCRA/ROCINN --- CAL

21. Chart 21 ROCINN CRB verification  100000 independent spectra were simulated using the ROCINN CRB forward model (VLIDORT) covering the whole ROCINN CRB state space (1% noise added):  SH in [0, 2] km  SA in [0, 1]  CH in [0, 15] km  CA in [0, 1]  SZA in [0, 85] °  VZA in [0, 75] °  CF in [0, 1]  Test retrieval performance with respect to {CF, CTH}  Relative difference:

22. Chart 22 ROCINN_CRB --- CTH, CA --- verification (1)  The relative differences between the reference CF‘s and CTH‘s and corresponding retrieved values, X_rel := 100 * (X_out – X_ref) / X_ref, show good overall perfonmance of the algorithm  Median of the distributions close to zero  Most differences within few percent

23. Chart 23  Very good overall CF retrieval performance  Almost perfect correlation between reference and retrieved CFs  Relative differences show higher spread for large SZA (small cosines: CSZA)  CF retrieval does not show dependency on cloud (CA) and surface albedo (SA) ROCINN_CRB --- CTH, CA --- verification (2) CF_out vs. CF_ref CF_rel vs. CSZA CF_rel vs. CA CF_rel vs. SA

24. Chart 24  Good overall CTH retrieval performance  CTH slightly understimated and higher spread of CTH_rel for large SZA  CTH relative differences show higher spread for small „cloud albedo fractions“ CAF=CA*CF  CTH retrieval does not show dependency on surface albedo (SA) ROCINN_CRB --- CTH, CA --- verification (2) CTH_out vs. CF_ref CTH_rel vs. CSZA CTH_rel vs. CAF CTH_rel vs. SA

25. Chart 25 CA --- COT --- SZA relationship