1. Steve Platnick 1 & M. D. King 1, J. Riedi 2, G. T. Arnold 1,3, P. Hubanks 1,3, G. Wind 1,3, R. Pincus 4, L. Oreopoulos 1,5 2 Laboratoire d’Optique Atmosphérique, USTL, Lille, France, 3 SSAI, Inc., Seabrook MD, 4 NOAA/ESRL, Boulder CO, 5 UMBC/JCET, Baltimore MD Yoram J. Kaufman Symposium on Aerosols, Clouds, and Climate NASA GSFC, Greenbelt, MD 30 May 2007 Spectral Signatures for the Remote Sensing of Clouds

2. Satellite Cloud Spectral Signature Animation MODIS Cloud Optical & Microphysical Retrieval Capability and Issues – Fundamental algorithm issues: cloud detection and phase – Level-3 (1° aggregation) sensitivity studies MODIS Level-3 Cloud Retrieval Applications: Cloud Susceptibility and Grid-scale Inhomogeneity Biases Outline

3. MODIS Data Granule Spectral Animation Example Canadian Fires, MODIS Terra, 7 July 2002, 1630 UTC water cloud sea ice ice cloud smoke

4. MODIS Data Granule Spectral Animation Example, cont. in spectral order, excluding ocean bands (thanks to L. Gonzales, R. Simmons)

5. sea ice sea ice smoke ice cloud true color SWIR composite (RGB = VIS, 1.6, 2.1 µm) MODIS Data Granule Spectral Animation Example, cont.

6. Kaufman, Y. J. and Holben, B. N., “Calibration of the AVHRR visible and near-IR bands by atmospheric scattering, ocean glint and desert reflection”, Int. J. Remote Sens., 1993. Fraser and Kaufman, “Calibration of satellite sensors after launch”, Appl. Opt., 1986. Kaufman and Mekler, “Possible causes of calibration degradation of the AVHRR visible and near-IR channels”, Appl. Opt., 1995. Vermonte and Kaufman, “Absolute calibration of AVHRR visible and near-IR channels using ocean and cloud views”, Int. J. Remote Sens., 1995.

7. MODIS Aqua Solar Bands Response Trending (3.5 yrs) (J. Xiong et al., MODIS Characterization & Support Team) VIS SWIR NIR

8. Some MOD06 Optical/Microphysical Collection 5 Changes (King, Platnick, Riedi, Wind, Arnold, Hubanks, Pincus; modis-atmos.gsfc.nasa.gov/products_C005update.html) To retrieve or not to retrieve? New “Clear Sky Restoral” algorithm implemented after cloud mask to further discriminate cloudy pixels and remove cloud edges. Cloud thermodynamic phase: liquid water or ice libraries? Updated cloud retrieval phase algorithm. A difficult problem (incomplete spectral coverage in SWIR)! Multilayer/multiphase scenes: detectable? New “research-level” multilayer cloud flag. Level-3 code separately aggregates single layer and multilayer cloud fraction, as well as single layer retrievals. Ice cloud models. New ice cloud models (Baum et al. 2005). Surface spectral albedo, including ancillary information regarding snow/ice extent. New MODIS-derived global snow-free land surface spectral albedo maps; snow/ice spectral albedo maps for Antarctica, Greenland; hemispheric average ecosystem-based snow/ice albedo over land and sea ice; new IGBP ecosystem map. Surface albedo mitigation w/new 1.6-2.1 µm retrievals over ocean and snow/ice surfaces. Retrieval uncertainty. New pixel-level , r e, WP retrieval uncertainties from 3 fundamental error sources (baseline) & estimates for uncertainty in L3 means.

9. SWIR compositeCloud Mask overall conf.“Clear Sky Restoral” cloudy probably cloudy spatial/spectral tests edge detection 250m cloud mask probably clear clear

10. SWIR compositeCloud Mask overall conf. Retrieval process. phase cloudy probably cloudy liquid water ice undetermined probably clear clear Cloud_Phase_Optical_Properties

11. Optical Thickness, Effective Radius Retrievals optical thicknesseffective radius (µm) 100 101 1 icewater 402552010 30 101520 icewater Cloud_Optical_ThicknessCloud_Effective_Radius Partial retrievals not aggregated to Level-3

12. Collection 5 (C5) Algorithm Sensitivity Studies C5-C4 global mean comparisons are ambiguous. C5-C4 global mean comparisons are ambiguous. – What is the relative effect of the Clear Sky Restoral algorithm (removal of pixels)? What is the effect of changes in the phase algorithm (redistribution of water and ice PDFs)? Effect of changes in surface albedo maps, reflectance look up tables, …? Submitted two research runs to the MODIS Atmosphere Team production system (MODAPS) Submitted two research runs to the MODIS Atmosphere Team production system (MODAPS) – C5 operational code with the Clear Sky Restoral algorithm bypassed – C5 operational code with a “C4-like” phase algorithm Run on April 2005 MODIS Terra and Aqua data Run on April 2005 MODIS Terra and Aqua data Resulting changes in cloud properties from changes in fundamental detection and phase discrimination provide: Resulting changes in cloud properties from changes in fundamental detection and phase discrimination provide: – quantitative assessment of “improvements” or measure of the inherent “noise” in retrieval algorithms?

13. Difference in Cloud Fraction: C5 – C5 run w/out Clear Sky Restoral 6 Apr 2005

14. Difference in Cloud Fraction: C5 – C5 run w/C4 Phase Algorithm

15. Monthly Mean Cloud Effective Radius water cloud “standard” r e retrieval & cloud fraction Cloud_Fraction_Liquid_FMean Cloud_Effective_Radius_Liquid _QA_Mean_Mean April 2005 Aqua C5 (QA mean) Liquid cloud fraction 1.0 0 0.5

16. Liquid clouds, April 2005 Difference in : C5 – C5 run w/out Clear Sky Restoral Liquid clouds, April 2005 Clear Sky Restoral has little effect on r e !

17. Liquid clouds, April 2005 Difference in : C5 – C5 run w/out Clear Sky Restoral Liquid clouds, April 2005 Clear Sky Restoral increases the mean  as expected (e.g., eliminates broken cloud or aerosol portion of PDF)

18. Water cloud , Terra 8-Day Aggregation, S. Atlantic, 30 March - 6 April 2005 Difference: C5 run w/out Clear Sky Restoral vs. C5 Water cloud , Terra 8-Day Aggregation, S. Atlantic, 30 March - 6 April 2005 reduction of counts for “broken” cloud fields “Means” don’t mean a thing!? = 8.6 = 7.3 (-15%)

19. Optical Thickness, MODIS Aqua, April 2005 Differences Between Modes and Means? Optical Thickness, MODIS Aqua, April 2005

20. Baseline/minimum expected uncertainty for water clouds, MODIS Aqua C5 Uncertainty in Mean  : Daily & Monthly Example Baseline/minimum expected uncertainty for water clouds, MODIS Aqua C5 Assumption: pixel-level error sources correlated Assumption: daily errors uncorrelated (optimistic)

21. July 2002 to May 2004, linearly-weighted means (Pincus, Batstone, Platnick) MODIS – ISCCP (D2) Optical Thickness Differences July 2002 to May 2004, linearly-weighted means (Pincus, Batstone, Platnick) Data set differences include sampling (spatial and temporal) in addition to instruments and algorithms. Temporal re-sampling of ISCCP to match MODIS sun synchronous observations doesn’t improve agreement substantially (not shown).

22. Global Oceans 50N-50S, August 2001, MODIS Terra MODIS and ISCCP-like  vs. p c Joint Histograms Global Oceans 50N-50S, August 2001, MODIS Terra

23. An observation-only, instantaneous approach to assessing the radiative cloud sensitivity to microphysics (i.e., cloud susceptibility): – Uses operational Collection 5 MODIS global daily Level-3 joint histograms of liquid water cloud optical thickness (  c ) and effective radius (r e ). – Individual  c, r e combinations from joint histograms and, T(z), q(z), A sfc,  input to broadband code for each grid. – Ancillary data sets same as used in MODIS cloud algorithm (NCEP GDAS, surface spectral albedo maps from Moody et al., 2005) – Unperturbed and perturbed (due to  r e changes) TOA albedo differences gives susceptibility. – Daily susceptibilities aggregated to provide monthly means. – Calculations assume no change in water amount with microphysical changes Does not address: – Current day vs. pre-industrial changes (not a sensitivity but a climate change question that includes feedbacks) – Cloud amount and precipitation sensitivities (requires statistical and/or modeling studies to eliminate dynamic/thermodynamic sensitivities) MODIS Level-3 Application: Cloud Susceptibility (Oreopoulos, Platnick)

24. January 2005April 2005 July 2005October 2005 Susceptibility (S),  N=1 cm -3, LWC=0.3 gm -3, MODIS Terra October 2005 S has ≈ r e 3 dependence smaller S over bright surfaces

25. Relative Susceptibility (S rel ),  N/N=10%, MODIS Terra April 2005 July 2005October 2005 January 2005 smaller S rel over bright surfaces S rel correlates with A cloud October 2005

26. Susceptibility-cloud fraction relations are important! Global Susceptibility Forcing Examples Global TOA flux change for  N=1cm -3, LWC=0.3 gm -3 Global TOA flux change for  N/N=10% 0.4 0.6 0.8 1.0 1.2 0.2 0 0.5 1.0 1.5 2.0 0

27. Sensitivity to Grid Horizontal Inhomogeneity for MODIS Terra Water Clouds: Differences in Monthly Cloud Radiative Forcing from Daily L3 CRF( grid, grid ) – CRF(  vs. r e histograms for the grid) April 2005 Oct 2005 smaller biases over bright surfaces Jan 2005 July 2005  CRF= 10.0 Wm -2  CRF= 9.7 Wm -2  CRF= 9.0 Wm -2  CRF= 10.2 Wm -2

28. Collection 5 enhancements to the MOD06/MYD06 optical and microphysical product include: Clear Sky Restoral, updated phase algorithm, new ice models, L2 and L3 uncertainty estimates (for a subset of error sources), multilayer flag research product. Collection 5 enhancements to the MOD06/MYD06 optical and microphysical product include: Clear Sky Restoral, updated phase algorithm, new ice models, L2 and L3 uncertainty estimates (for a subset of error sources), multilayer flag research product. Completed initial Level-3 test runs of C5 with C4 algorithm modules to assist in understanding algorithm changes. Completed initial Level-3 test runs of C5 with C4 algorithm modules to assist in understanding algorithm changes. Histograms (1D and 2D) necessary to help understand/use retrieval statistics. Comparing “means” between different algorithms/instruments sensitive to different parts of the PDF (by design or observations) is apples and oranges. Histograms (1D and 2D) necessary to help understand/use retrieval statistics. Comparing “means” between different algorithms/instruments sensitive to different parts of the PDF (by design or observations) is apples and oranges. MODIS vs. ISCCP comparison tools completed and initial analysis begun. MODIS vs. ISCCP comparison tools completed and initial analysis begun. Cloud susceptibility tools/analysis (using 2D  vs. r e histograms) begun. Cloud susceptibility tools/analysis (using 2D  vs. r e histograms) begun. Begun initial efforts to compare phase and multilayer detection with CALIPSO (w/Bob Holz, Steve Ackerman), and tools for L3 geometric sensitivities (w/Robert Pincus, Paul Hubanks, Steve Ackerman, Brent Maddux). Begun initial efforts to compare phase and multilayer detection with CALIPSO (w/Bob Holz, Steve Ackerman), and tools for L3 geometric sensitivities (w/Robert Pincus, Paul Hubanks, Steve Ackerman, Brent Maddux). MODIS Atmosphere Team Collection 5 reprocessing completed in early April 2006 for Aqua, in Feb 2007 for Terra. All products now archived and distributed via the MODIS LAADS system (disk storage archive w/ftp access, search capability, subsetting, etc.). Distribution from Goddard DAAC discontinued. MODIS Atmosphere Team Collection 5 reprocessing completed in early April 2006 for Aqua, in Feb 2007 for Terra. All products now archived and distributed via the MODIS LAADS system (disk storage archive w/ftp access, search capability, subsetting, etc.). Distribution from Goddard DAAC discontinued. Summary

29. Extras

30. Some Solar Reflectance Optical/Microphysical Retrieval Issues Critical issues (esp. for global processing): To retrieve or not to retrieve? Cloud thermodynamic phase: liquid water or ice libraries? Multilayer/multiphase scenes: detectable? Ice cloud models Surface spectral albedo, including ancillary information regarding snow/ice extent Other ancillary information: Atmospheric corrections require moisture & temperature profiles, p c ; 3.7 µm retrievals require T c, T sfc (band contains solar and emissive radiance) 3-D cloud effects Retrieval uncertainty (pixel-level and aggregated)

31. Retrieval Uncertainty Estimates opt. thickness: effective radius: 5002550025 0 0 icewater  c  /  c (%)  r e  / r e (%) Error sources: cal./fwd. model (5%), sfc. albedo(15%), atmo. correction (20% PW c ) Cloud_Optical_Thickness_UncertaintyCloud_Effective_Radius_Uncertainty

32. Multilayer Flag Multilayers w/different phases: disagreement between IR-phase retrieval and phase derived for optical/microphysical retrieval (SWIR bands, cloud mask tests, …). Multilayers w/different phases: disagreement between IR-phase retrieval and phase derived for optical/microphysical retrieval (SWIR bands, cloud mask tests, …). General multilayer: 0.94 µm water vapor absorption band. General multilayer: 0.94 µm water vapor absorption band. liquid water ice undetermined ML, retr’d as water ML, retr’d as ice ML undetermined SWIR composite from Quality_Assurance_1km

33. MODIS Aqua Granule Example 20 Aug 2006, Central Am./NW SA, true color composite

34. Error sources: calibration/forward model, surface albedo, atmospheric correction WP (gm -2 )  WP/WP (%) 10 3 10 2 1010 2 10 icewater icewater 50250 50250 icewater MODIS Aqua Granule Example, cont. IWP, LWP, and Baseline Uncertainty Estimate Cloud_Water_Path Cloud_Water_Path_Uncertainty

35. MODIS Aqua Granule Example, cont. Uncertainty vs. IWP: Ocean Pixels Only

38. Optical Thickness, Aqua, April 2005 Differences Between Modes and Means? Optical Thickness, Aqua, April 2005