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S5P cloud products

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. Outline. General overview OCRA adaptation to S5P Cloud model – OCRA/ROCINN-CAL,-CRB

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S5P cloud products

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

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

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

  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. 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. Cloud Model – OCRA/ROCINN-CAL/CRB • 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

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

  11. Cloud Model – OCRA/ROCINN-CAL/CRB (3) • Comment from areviewerof 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. Cloud Model – OCRA/ROCINN-CAL/CRB (4) • 100000 independent spectra were simulated using the ROCINN CAL forward model (VLIDORT) covering the whole ROCINN CAL statespace (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. Cloud Model – OCRA/ROCINN-CAL/CRB (5) Global Mean Lambertian Model • CRB retrieved cloud “top” height is systematically smaller than the geometrical cloud top height. • Discrepancy increases as cloud optical depth decreases.

  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 statespace (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. ROCINN-CRB: CA variable versus CA fixed (2) CF rel. diff. vs. cloud albedo CTH rel. diff. vs. cloud albedo CTH rel. diff. vs. surface albedo • 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

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

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

  19. Thank you for your attention!

  20. OCRA/ROCINN --- CAL 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

  21. ROCINN CRB verification • 100000 independent spectra were simulated using the ROCINN CRB forward model (VLIDORT) covering the whole ROCINN CRB statespace (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. 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. ROCINN_CRB --- CTH, CA --- verification (2) CF_out vs. CF_ref CF_rel vs. CSZA • 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) CF_rel vs. CA CF_rel vs. SA

  24. ROCINN_CRB --- CTH, CA --- verification (2) CTH_out vs. CF_ref CTH_rel vs. CSZA • 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) CTH_rel vs. CAF CTH_rel vs. SA

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

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