Accounting for non-sphericity of aerosol particles in photopolarimetric remote sensing of desert dust

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Accounting for non-sphericity of aerosol
particles in photopolarimetric remote sensing of
desert dust
Oleg Dubovik (UMBC / GSFC, Code 923)
Alexander Sinyuk (SSAI, Code 923) Tatyana
Lapyonok ( GSFC, Code 923) Brent Holben
( GSFC, Code 923) Michael Mishchenko
(NASA/GISS) Ping Yang (Texas AM
University) Anne Vermeulen (SSAI, Code
923) Tom Eck (UMBC/GSFC, Code
923) Ilya Slutsker (SSAI, Code
923) Hester Volten (Free
University,Netherlands) Ben Veihelmann
(SRON Space Res., Netherlands)
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• Outlines
• Simulating non-spherical dust scattering in
  remote sensing retrievals
• Fitting laboratory polarimetric measurements of
  dust light scattering
• Sensitivity of polarimetric measurements to
  aerosol parameters
• Applications to AERONET polarimetric retrievals

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Difficulties of accounting for particle
non-sphericity
Difficulties of accounting for particle
non-sphericity in aerosol retrievals

• many limitations in simulating light scattering
  by non-spherical particles (on particle size,
  shape, refractive index, etc.)
• 2. Simulation are too slow for operational
  retrievals (much slower than Mie scattering by
  spherical particle)
• 3. Concept of choosing particle shape is unclear
• 4. Validation of models is ambigious

• Main limitations of T-Matrix code (Mishchenko et
  al.)
• - only spheroid shape (?)
• size parameter 60
• aspect ratio 2.4
• speed (for large aspect raitos) 100 times
  slower than Mie

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AERONET model of aerosol
Simplest model of non-spherical aerosol
How to implement operationally ??? Is this
correct???
Randomly oriented spheroids (Mishchenko et al.,
1997)
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Modeling Polydispersions
Modeling Polydispersions
V(ri)
V(ri)

• Kernel look-up table for fixed ri (22 points)
• (1.33 n 1.6 0.0005 k 0.5)

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Single Scat. By spheroids
Single Scattering using spheroids
Model by Mishchenko et al. 1997

• particles are randomly oriented homogeneous
  spheroids
• w(e) - size independent aspect ratio
  distribution

K - kernel matrix 0.05 r 15 (mm) 1.33 n
1.6 0.0005 k 0.5 0.4 e 2.4
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Single Scattering using spheroids
spheroid kernels data basefor operational
modeling !!!
Input wp (Np 11), V(ri) (Ni 22 -30)
K - pre-computed kernel matrices Input n and k

• Basic Model by Mishchenko et al. 1997
• randomly oriented homogeneous spheroids
• w(e) - size independent shape distribution

Time lt one sec. Accuracy lt 1-3 Range of
applicability 0.15 2pr/l 280 (26 bins) 0.4
e 2.4 (11 bins) 1.33 n 1.6 0.0005 k 0.5
Output t(l), w0(l), F11(Q), F12(Q),F22(Q), F33(Q
),F34(Q),F44(Q)
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Modeling of dust light scattering by mixture of
spheroids

n(l) k (l)

• w(e) - size independent shape
• distribution

Averaging with w(e)
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Modeling of dust light scattering by mixture of
spheroids

n(l) k (l)

• w(e) - size independent shape
• distribution

Averaging with w(e)
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Computational challenge
Computational challenge of using spheroids
(phase function)
Contribution of different sizes to scattering at
1200
Mishchenko and Travis, 1994
Yang and Liou, 1996
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Computational challenge
Computational challenge of using spheroids
(polarization)
Contribution of different sizes to scattering at
1200
Mishchenko and Travis, 1994
Yang and Liou, 1996
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http//www.astro.uva.nl/scatter
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Inversion of Scattering Matrices
Forward Model
F11(l ,Q), -F12 (l ,Q)/F11 (l ,Q) F22/F11 ,
F33/F11, F34/F11, F44/F11
Numerical inversion -Accounting for uncertainty
(F11 -F12/F11 !!!) - Setting a priori
constraints
aerosol particle sizes, refractive index,
single scattering albedo, aspect ratio
distribution
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Fitting of Measured Scattering Matrix by
spheroids model
Feldspar 0.441 mm
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Role of total reflectance
Accounting for polarization in radiation transmitt
ed through the atmospheric
L1 L2 - rotation matrices
Total
phase matrix !!!
I
- Stokes vector
F(Ql)
- Intensity
-Linear Polarization
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Fitting of Measured Scattering Matrix by
spheroids model
Fitting of Measured Scattering Matrix by
spheroids model
Feldspar 0.633 mm
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Fitting of Measured Scattering Matrix by spheres

Feldspar 0.441 mm
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Size and shape distributions retrieved from
Scattering Matrix
Spheroids
Aspect ratio distribution
dV(r)/dlnr
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Sensitivity of Linear Polarization of fine mode
aerosol to real part of refractive index
Log-normal monomodal dV(r)/dlnr sv 0.5, m
0.44 mm, k 0.005
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Sensitivity of Linear Polarization of coarse mode
aerosol to real part of refractive index
Log-normal monomodal dV(r)/dlnr sv 0.5, m
0.44 mm, k 0.005
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Shape effect in presence of Multiple
Scattering(Radiance)
Log-normal monomodal dV(r)/dlnr rv 2mm, sv
0.5, m 0.44 mm, n 1.45, k 0.005
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Shape effect in presence of Multiple
Scattering(Polarization)
Log-normal monomodal dV(r)/dlnr rv 2mm, sv
0.5, m 0.44 mm, n 1.45, k 0.005
t
t 1.0
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AERONET Polarized Inversion
Forward Model
Single Scat
Multiple Scat
DEUZE JL, HERMAN M, SANTER R, JQSRT, 1989
Successive Orders of Scattering Code
t(l), I(l,Q),P(l,Q)
Numerical inversion -Accounting for uncertainty
(F11 -F12/F11 !!!) - Setting a priori
constraints
aerosol particle sizes, refractive index,
single scattering albedo
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Inversions of intensity and polarization measured
by AERONET
Banizombu (Africa) Sept. 26, 2003 t(0.87) 0.5
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Inversions of intensity and polarization measured
by AERONET
Cape Verde July 12,2001 t(0.87) 0.6
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Inversions TESTS of intensity and polarization
measured at 4 wavelengths
Solar Vilage t(1.02) 0.4
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Modeling Desert Dust Lidar Ratio
Muller, et al., 2003 S(0.532mm) 5080sr
Dhabi Aerosol
S19
S50
S80
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• Conclusions
• Kernel look-up tables seems to be promising for
  remote sensing retrievals
• Spheroids may closely reproduce laboratory
  polarimetric measurements of dust scattering
• Spheroid model is successfully employed in both
  intensity and polarized AERONET retrievals
• Sensitivity to particle shape is a challenge for
  utilizing polarization for aerosol retrievals