Synergy of MODIS Deep Blue and Operational Aerosol
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 NASAs Vision: To improve life here, To extend life to there, To find life beyond. Solar radiation is the sole largescale 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. Understanding the role of aerosols means Understanding how the properties of those aerosols refractive index afffect various aspects of species solar and terrestrial mixture radiation hygroscopicity spectral () size distribution spatial (x, y, z) shape angular (q, f) temporal (t) for each type of aerosol that occurs in the atmosphere Dust Particles Biomass Burning Smoke Air Pollutants Sea Salts
Aerosol Aerosol Remote Remote Sensing Sensing & & Retrieval Retrieval The early days of AVHRR, since 1983: Geogdzhayev, Mishchenko, et al., J. Atmos. Sci., 20 = 0.55 mm Optical Thickness July 1988 Monthly Mean ngstrom ExponentJuly 1988 Monthly Mean The current days of MODIS, since 2000: Remer, Kaufman, Tanr, et al., J. Atmos. Sci., 2004. = 0.55 mm August 2003 Monthly Mean Fine-mode Fraction August 2003 Monthly Mean Optical Thickness
Visible Visible && NIR NIR Bands: Bands: superimposed superimposed on on the the GOME GOME spectral spectral reflectance reflectance taken taken over over the the Sahara Sahara MODIS MODIS 645 nm 412 nm 470 nm Viewing Viewing Geometry
90 principal plane 0 View = 30 deg 60 deg 90 deg Surface Reflectance Data Base - Sep 2004 SeaWiFS 412 nm (nadir) MODIS/Aqua 412 nm (nadir) SeaWiFS 670 nm (nadir) MODIS/Aqua 650 nm (nadir) Flowchart for Deep Blue Algorithm Radiances Radiances
3x3 Pixels Spatial 412/490 Absorbing Cloud Screening or Cloud Mask Algorithm Variance at 412 nm Aerosol Index 412, 490, 670 nm 412, 490, 670 nm Yes NO RETRIEVAL Cloudy? No 412 412nm nmSurface Surface Reflectivity Reflectivity(0.1x0.1) (0.1x0.1) Surface Reflectance 490, 670... nm Surface
490, 670... nm Surface Determination Reflectivity Reflectivity(0.1x0.1) (0.1x0.1) Aerosol Type Dust DustModel Model Dust Dominant Smoke SmokeModel Model Mixed Aerosols Maximum Likelihood Method Aerosol Optical Single-Scattering Aerosol
Optical+ ngstrm ngstrm Single-Scattering Aerosol Optical + Thickness Exponent Albedo Thickness Exponent Albedo Thickness Phase Phase Function Function for for Dust Dust Model Model The aerosol characteristics used to generate the simulated radiances in these two figures are shown below Aerosol Model
0.91 0.90 0.96 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, = aRdust + (1-a)Rsmoke Rsmoke Gaussian distribution with a peak at 3 km and a width of 1 km was assumed Deep Deep Blue Blue Algorithm Algorithm for for SeaWiFS/MODIS SeaWiFS/MODIS
a 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. a 0(dust670nm)=1.0 Works well on retrieving aerosol properties over various types of surfaces, including very bright desert. Aerosol AerosolOptical OpticalThickness ThicknessRetrieved
Retrievedfrom fromDeep DeepBlue BlueAlgorithm: Algorithm: Dust Dustplumes plumesin inAfrica Africa a Validation: Validation: Comparisons Comparisons with with AERONET AERONET Aerosol Aerosol Optical Optical Thickness Thickness North Africa February 2000 Arabian Peninsula
June - July 2000 September 12, 2004 1.04 1.01 0.66 0.88 0.61 0.36 AERONET (500nm) = 0.66 0.56 Deep DeepBlue BlueAlgorithm 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 0.43 AERONET = 0.44 0.71 Validation Validation During During UAE2 UAE2 Experiment Experiment August August September September 2004 2004 Harmim Mezaira
Aerosol Observation Strategy Satellite Observation s A Satellite Observations B Synergy Aerosol Product Intercomparisons Reducing Uncertainty Of Climate Forcing Improvements of Retrieval Products Constraining Models 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 0.5 1.0 1.5 2.0 0.0 1.0 2.0 6 April 2001 MODIS
SeaWiFS AOT 0.0 1.0 2.0 2.0 MISR AOT MODIS AOT 0.0 1.0 0.0 1.0 2.0 2 April 2001 Collection 4
MODIS SeaWiFS MODIS OpAOT AOT 0.00.0 MODIS AOT 0.0 1.0 2.0 1.0 1.0 2.0 2.0 MISR AOT 0.0 1.0
2.0 Tracking Movements and Evolutions of Aerosol Plumes 0.0 1.0 2.0 9/12/04 12 September 2004 MODIS/Aqua Terra AOT 10:30 AM LST 0.0 1.0 2.0 0.0
SeaWiFS AOT Noon LST 1.0 2.0 9/12/04 Aqua AOT 1:30 PM Intercomparisons of April 2001 Monthly Mean AOT Over East Asia Large Daily Variability in AOT Deep Blue MODIS AOT Frequent Presences of Clouds 0.0 Operational
MODIS AOT 0.0 1.0 2.0 1.0 2.0 MISR AOT 0.0 1.0 2.0 Summary Summary Deep Blue algorithm provides aerosol optical thickness, Angstrom exponent and single scattering albedo for both land and water.
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. Summary Summary (continued) (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.
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