Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS

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

2. 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. NASA’s Vision: To improve life here, To extend life to there, To find life beyond. Altimetry Brightness Temp NPP • 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. Precip Irradiance 3-D Clouds Ozone

3. Understanding the role of aerosols means Understanding how the properties of those aerosols… • refractive index • species • mixture • hygroscopicity • size distribution • shape …afffect various aspects of solar and terrestrial radiation… • spectral (l) • 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

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

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

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

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

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

9. Phase Function for Dust Model

10. The aerosol characteristics used to generate the simulated radiances in these two figures are shown below 412 490 0 412 nm 0 490 nm Aerosol Model Refractive Index 412 nm Refractive Index 490 nm 470 470 Dust 1.00 1.00 1.55 – 0.020i 1.55 – 0.008i 0.91 0.96 Smoke 1.30 0.92 1.55 – 0.022i 1.55 – 0.026i 0.90 0.89 In areas of mixed aerosol types, we linearly mix radiances from the dust aerosol model, Rdust, with those from the smoke aerosol model, Rsmoke = aRdust + (1-a)Rsmoke 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 l = 412, 490, and 670 nm to retrieve aerosol optical thickness (ta) 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. wo ta wo ta w0(dust670nm)=1.0

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

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

14. September 12, 2004 1.04 1.01 0.66 0.88 0.36 0.61 AERONET(500nm) = 0.66 0.56 Deep Blue Algorithm 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. 1.30 0.23 1.13 0.61 0.71 0.43 AERONET = 0.44

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

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

17. 6 April 2001 MODIS Red-Green-Blue with Rayleigh scattering removed Current MODIS retrievals: Aerosol Optical Thickness New MODIS Deep Blue: Aerosol Optical Thickness 0.0 2.0 1.0 0.5 1.0 1.5 2.0 0

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

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

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

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

22. Deep Blue algorithm provides aerosol optical thickness, Angstrom exponent and single scattering albedo for both land and water. Summary • 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 • 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.

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