1. Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard Space Flight Center Greenbelt, Maryland USA

2. NASA’s Vision: To improve life here, To extend life to there, To find life beyond. Solar radiation is the sole large-scale source of diabatic heating that drives the weather & climate system on planet Earth. Terrestrial radiation keeps the planet in balance to make Earth habitable for all forms of life. Aerosols may play an important role in modifying solar and terrestrial radiation. Understanding that role is critical to understanding the energy balance that shapes our weather/climate.

3. Understanding how the properties of those aerosols… –refractive index –species –mixture –hygroscopicity –size distribution –shape …afffect various aspects of solar and terrestrial radiation… –spectral ( ) –spatial (x, y, z) –angular (q, f) –temporal (t) …for each type of aerosol that occurs in the atmosphere –Dust Particles –Biomass Burning Smoke –Air Pollutants –Sea Salts Understanding the role of aerosols means

4. Aerosol Remote Sensing & Retrieval Optical Thickness The early days of AVHRR, since 1983: Geogdzhayev, Mishchenko, et al., J. Atmos. Sci., 2002. Ångstrom ExponentJuly 1988 Monthly Mean = 0.55 µm Optical ThicknessFine-mode Fraction August 2003 Monthly Mean The current days of MODIS, since 2000: Remer, Kaufman, Tanré, et al., J. Atmos. Sci., 2004. = 0.55 µm

5. Visible & NIR Bands: superimposed on the GOME spectral reflectance taken over the Sahara MODIS Visible & NIR Bands: superimposed on the GOME spectral reflectance taken over the Sahara MODIS 412 nm 470 nm 645 nm

6. Viewing Geometry Differences View Angle vs. Relative Azimuth Angle Viewing Geometry Differences View Angle vs. Relative Azimuth Angle Aqua SeaWiFS Forward Backward principal plane View = 30 deg 60 deg 90 deg 0 90 180 0

7. MODIS/Aqua 412 nm (nadir) MODIS/Aqua 650 nm (nadir) SeaWiFS 412 nm (nadir) SeaWiFS 670 nm (nadir) Surface Reflectance Data Base - Sep 2004

8. Maximum Likelihood Method Dust Dominant NO RETRIEVAL Yes No Surface Reflectance Determination Smoke Model Dust Model 490, 670... nm Surface Reflectivity (0.1ºx0.1º) 412 nm Surface Reflectivity (0.1ºx0.1º) 412 nm Surface Reflectivity (0.1ºx0.1º) Flowchart for Deep Blue Algorithm Radiances 412, 490, 670 nm Cloudy? Cloud Screening or Cloud Mask Algorithm 3x3 Pixels Spatial Variance at 412 nm 412/490 Absorbing Aerosol Index Single-Scattering Albedo Single-Scattering Albedo Aerosol Optical Thickness Aerosol Optical Thickness + Aerosol Type Ångström Exponent Ångström Exponent + Aerosol Optical Thickness Aerosol Optical Thickness Mixed Aerosols

9. Phase Function for Dust Model

10. Aerosol Mode l  412  470  490  470 Refractive Index 412 nm Refractive Index 490 nm  0 412 nm  0 490 nm Dust Smoke 1.00 1.55 – 0.020i 1.55 – 0.022i 1.55 – 0.008i 1.55 – 0.026i 0.910.96 0.890.901.300.92 The aerosol characteristics used to generate the simulated radiances in these two figures are shown below In areas of mixed aerosol types, we linearly mix radiances from the dust aerosol model, R dust, with those from the smoke aerosol model, R smoke = aR dust + (1-a)R smoke Gaussian distribution with a peak at 3 km and a width of 1 km was assumed

11. Deep Blue Algorithm for SeaWiFS/MODIS Utilize solar reflectance at  = 412, 490, and 670 nm to retrieve aerosol optical thickness (  a ) and single scattering albedo (  o ). Less sensitive to aerosol height, compared to UV methods. Works well on retrieving aerosol properties over various types of surfaces, including very bright desert.  0 (dust 670nm )=1.0   aa aa

12. Aerosol Optical Thickness Retrieved from Deep Blue Algorithm: Dust plumes in Africa Aerosol Optical Thickness Retrieved from Deep Blue Algorithm: Dust plumes in Africa aa

13. Validation: Comparisons with AERONET Aerosol Optical Thickness Validation: Comparisons with AERONET Aerosol Optical Thickness North Africa February 2000 Arabian Peninsula June - July 2000

14. AERONET (500nm) = 0.66 0.56 0.36 0.61 1.01 0.88 1.04 0.66 0.43 AERONET = 0.44 0.71 0.61 1.13 1.30 0.23 September 12, 2004 SeaWiFS retrieved aerosol optical thickness and Angstrom exponent showing a dust front pushing the air mass with small particle air pollution over both water and land on this day. Deep Blue Algorithm

15. Harmim Mezaira Validation During UAE2 Experiment August – September 2004 Validation During UAE2 Experiment August – September 2004

16. Satellite Observations A Aerosol Product Intercomparisons Improvements of Retrieval Products Satellite Observations B Synergy Constraining Models Reducing Uncertainty Of Climate Forcing Aerosol Observation Strategy

17. 0 0.51.01.52.0 Current MODIS retrievals: Aerosol Optical Thickness 6 April 2001 MODIS Red-Green-Blue with Rayleigh scattering removed New MODIS Deep Blue: Aerosol Optical Thickness 0.0 1.0 2.0

18. 0.0 1.0 2.0 SeaWiFS AOT 6 April 2001 0.0 1.0 2.0 MODIS AOT MODIS MISR AOT 0.0 1.0 2.0

19. 0.0 1.0 2.0 SeaWiFS AOT 2 April 2001 0.0 1.0 2.0 MODIS AOT MODIS 0.01.0 2.0 MISR AOT 0.0 1.02.0 MODIS Op AOT Collection 4

20. 0.0 1.0 2.0 Terra AOT 10:30 AM LST 9/12/04 Tracking Movements and Evolutions of Aerosol Plumes SeaWiFS AOT Noon LST 0.0 1.0 2.0 0.0 1.0 2.0 Aqua AOT 1:30 PM LST 9/12/04 MODIS/Aqua 12 September 2004

21. Intercomparisons of April 2001 Monthly Mean AOT Over East Asia 0.0 1.0 2.0 MODIS AOT 0.0 1.0 2.0 MISR AOT Operational Large Daily Variability in AOT Frequent Presences of Clouds 0.0 1.0 2.0 MODIS AOT Deep Blue

22. Summary Deep Blue algorithm provides aerosol optical thickness, Angstrom exponent and single scattering albedo for both land and water. Deep Blue algorithm successfully applied to SeaWiFS and MODIS: evolution (spatial & temporal) of aerosols can be studied for the first time over deserts using one consistent algorithm. Compared well with AERONET aerosol products: –Separate dust well from other anthropogenic sources –Aerosol optical thickness agree with AERONET values within 10-20% over water and 20-30% over deserts

23. Summary (continued) Current Issues in Aerosol Product Synergy: Presences of Clouds: requires careful selection in geolocation to conduct intercomparisons; Variability of Aerosol Loading: requires spatial coverage; Accurate and Consistent Calibration across each individual sensors.