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S5P Cloud Verification Meeting: Cloud Model Adaptation for Accurate Gas Retrieval

This presentation at the S5P Verification Meeting outlines the adaptation of the OCRA cloud model for accurate trace gas retrieval with an emphasis on cloud effects in gas retrieval. It covers 3D cloud influences, OCRA adaptation to S5P input data, cloud-free composites, cloud fraction comparison, cloud model parameters retrieval, cloud heterogeneity effects, and conclusions. The study compares various cloud products and emphasizes the importance of precise cloud information for gas retrieval accuracy in S5P/TROPOMI missions.

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S5P Cloud Verification Meeting: Cloud Model Adaptation for Accurate Gas Retrieval

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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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