1 / 27

Alexander Marshak (GSFC) Robert Cahalan (GSFC) Támas Várnai (UMBC) Guoyong Wen (UMBC)

Learn how 3D science improves the accuracy of MODIS data interpretation for studying aerosol-cloud interactions. Discover the challenges in cloud and aerosol retrievals, the impact of 3D corrections, and the importance of understanding satellite analysis to enhance research in this field.

hmilton
Download Presentation

Alexander Marshak (GSFC) Robert Cahalan (GSFC) Támas Várnai (UMBC) Guoyong Wen (UMBC)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. How 3D Science Can Help to Correctly Interpret MODIS Data for Better Understanding of Aerosol-Cloud Interactions Alexander Marshak (GSFC) Robert Cahalan (GSFC) Támas Várnai (UMBC) Guoyong Wen (UMBC) Brian Vant-Hull (UMD) Alexander Marshak

  2. Instead of Intro • clouds are complex and “satellite analysis may be affected by potential cloud artifacts” (Kaufman and Koren, 2006); • for aerosol retrievals: • cloud contamination • adjacency effect may significantly overestimate AOT • for cloud retrievals: • clear sky contamination (with dark surface) • cloud 3D structure (e.g., shadowing) may significantly overestimate droplet size • To study cloud-aerosol interaction we need to be sure that retrieved correlations between AOT and re reflect real physics rather than remote sensing problems. Alexander Marshak

  3. A striking example Cloud  ASTER image Aerosol re Brazil, Jan. 25, 2003 centered at 0o, 53.78oW Alexander Marshak

  4. A striking example: const AOT Modeled (with const AOT but MODIS 3D cloud structure) vs. Observed Reflectance. Cor. coef. = 0.77 Alexander Marshak

  5. A striking example: 3D corrected vs. 3D uncorrected ‘before’ means 3D-uncorrected re vs. AOT Alexander Marshak

  6. A striking example: 3D corrected vs. 3D uncorrected ‘after’ means 3D-corrected re vs. AOT Alexander Marshak

  7. sensor Cloud in backscatter (illuminated) orientation Cloud in side-scatter (shadowed) orientation MODIS data granule Satellite ground track Relationship between sun and sensor locations The division of the data set into backscatter and side-scatter geometries. Alexander Marshak

  8. Cumulus clouds(MODIS, Aug.-Oct. 2002) Retrieved re for back (illum)- and side (shad)- scattered geometries (col. 4 and “5”) from Vant-Hull et al. (2006) Alexander Marshak

  9. Collection 4 vs. Collection 5 Cloud optical depth, Droplet effective radius, re Alexander Marshak

  10. Thin clouds Thick clouds Aerosol-cloud photon interaction Alexander Marshak

  11. D~ 0.004 D~0.04 or ~40% D~ 0.014 D~0.14 or ~140% Aerosol-cloud radiative interaction Thick clouds, <>=15 Thin clouds, <>=7 AOT0.66=0.1 enhancement: D 3D-1D Alexander Marshak

  12. Contributors to 3D cloud effects • Rayleigh scattering • Aerosol amount • Surface reflectance What is the relationship between these three factors? • and, of course, • Distance from cloudy pixels • Cloud optical thickness Alexander Marshak

  13. For dark surfaces, the enhancement only weakly depends on the AOT Enhancement of reflectance vs. AOT enhancement: D 3D-1D AOT=0.1 AOT=0.5 AOT=1.0 Alexander Marshak

  14. Enhancement of reflectance vs. Rayleigh scattering • 3% from surface • 15% from aerosol • 82% from molecule Cloud-molecule scattering is the key process Alexander Marshak

  15. Conceptual model to account for 3D cloud effects MODIS sensor molecule + aerosol aerosol or molecule Alexander Marshak surface

  16. Effect of distance to a cloudy pixel 3D enhancement (red) Cumulative distr. (blue) 0.66 m Nearest cloud dist. (km) Alexander Marshak

  17. 3D radiative effect in Cu clouds.Viewing angle dependence Rel. azimuth < 90o Rel. azimuth > 90o Alexander Marshak

  18. MODIS data used • The whole year: Sept. 2004 - Aug. 2005 • Collection 4 • Liquid phase; high confidence;  ≥ 2 • SZA between 55o and 80o • Based on local temp. gradient T, all cloudy pixels are divided into 3 equally populated categories with thresholds: • most homogeneous: 0.3-0.5 C/km (50-80 m) • most inhomogeneous: 1.1-1.5 C/km (180-250 m) Alexander Marshak

  19. Cloud optical thickness =c22+c1+c0 The most homogeneous (0.3-0.5 C/km) and the most inhomogeneous (1.1-1.5 C/km) cloudy pixels Alexander Marshak

  20. Cloud optical thickness: col. 4 vs col. 5 Collection 4 “Collection 5” “Col. 5” shows some improvements for inhomog. clouds; no change for homog. clouds =c22+c1+c0 c2=1.8 10-3 (col.4) c2=1.2 10-3 (“col.5”) Alexander Marshak

  21. Cloud effective radius, re B F The most homogeneous (0.3-0.5 C/km) and the most inhomogeneous (1.1-1.5 C/km) cloudy pixels Alexander Marshak

  22. Cloud effective radius: col. 4 vs col. 5 Collection 4 “Collection 5” The homog. clouds show no changes (mean±1.5 m) while there are some improvements for the inhomog. clouds Alexander Marshak

  23. Conclusions • For study aerosol-cloud interaction in Cu environment, 3D cloud effects cannot be ignored; • 3D cloud enhancement only weakly depends on AOT; molecular scattering is the key source for the enhancement; • Retrieved AOT can be corrected for the 3D rad. effects; • Both cloud  and re show strong dependence on VZA; • For study aerosol-cloud interaction, a difference of 2-3 m in re doesn’t necessarily mean the effect of aerosols on clouds but rather can be a remote sensing problem. Alexander Marshak

  24. How 3D Science Can Help to Correctly Interpret MODIS Data for Better Understanding of Aerosol-Cloud Interactions Alexander Marshak (GSFC) Robert Cahalan (GSFC) Támas Várnai (UMBC) Guoyong Wen (UMBC) Brian Vant-Hull (UMD) Alexander Marshak

  25. Example of corrected AOT Thin clouds Thick clouds Alexander Marshak

  26. Cloud optical thickness: land and ocean =c22+c1+c0 c2=2.4 10-3 (land) c2=1.4 10-3 (ocean) The most inhomog. (1/3) fraction of cloudy pixels over land and ocean; Liquid clouds over ocean are more homogeneous Alexander Marshak

  27. Cloud effective radius: land and ocean Droplet sizes retrieved over ocean are less sensitive to VZA (for all clouds) Alexander Marshak

More Related