Accounting for particle non-sphericity in photopolarimetric remote sensing of desert dust.

(1)

ÓPTICA PURA Y APLICADA – Vol. 37, núm. 3 - 2004

Recibido: 15 – may - 2004

3077

-Accounting for particle non-sphericity in photopolarimetric

remote sensing of desert dust

Oleg Dubovik

(1,2)

, Alexander Sinyuk

(1,3)

, Tatyana Lapyonok

(1,3)

, Brent N. Holben

(1)

,

Michael Mishchenko

(4)

_{, Ping Yang}

(5)

_{, Hester Volten}

(6)

_{, Ben Veihelmann}

(7)

_,

Anne Vermeulen

(1,3)

, Tom F. Eck

(1,3)

, and Ilya Slutsker

(1,3)

(1) Laboratory for Terrestrial Physics, NASA Goddard Spaceflight Center, Greenbelt, MD (2) GEST Center, University of Maryland, Baltimore County, Baltimore, MD

(3) Science Systems and Applications, Inc., Lanham, Maryland (4) NASA Goddard Institute for Space Studies, New York, USA

(5) Texas A&M University, College Station, TX, USA (6) Free University, Amsterdam, Netherlands

(7) SRON Space Research Organization Netherlands, Utrecht, Netherlands

REFERENCES AND WEB LINKS.

[1] Arimoto, R., B. J. Ray, N. F. Lewis et al., Mass-particle size distributions of atmospheric dust and the dry deposition of dust to the remote ocean. J. Geophys. Res., 102, 15867-15874, (1997).

[2] Dubovik, O., and M. D. King,, A flexible inversion algorithm for retrieval of aerosol optical properties from sun and sky radiance measurements, J. Geophys. Res., 105, 20,673-20,696, (2000).

[3] Dubovik, O., A. Smirnov, B.N.Holben et al., Accuracy assessments of aerosol optical properties retrieved from AERONET sun and sky radiance measurements, J. Geophys. Res., 105, 9791-9806, (2000).

[4] Dubovik, O., B. N. Holben, T. F. Eck, et al., Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590-608, (2002).

[5] Hansen, J., M. Sato, R. Ruedy, A. Lacis and V. Oinas, Global warming in the twenty-first century: An alternative scenario. Proc. Natl. Acad. Sci. USA, 97, 9875- 9880, (2000).

ABSTRACT:

A possibility of using spheroids, for modeling light scattering of desert dust

aerosol is discussed. For fast and accurate simulations of optical properties of

polydisperse randomly oriented spheroids, the look-up tables of phase

matrices, extinction and absorption were computed for 35 narrow size bins

covering aerosol size parameters from ~ 0.3 to ~ 280. This numerical tool has

been integrated into an algorithm fitting the polarimentric laboratory

measurements. The fitting of the phase matrices measured by Volten et al.

2001 has shown that spheroids can closely reproduce the desert dust light

scattering. Then a model of spheroids was successfully tested in the retrievals

of aerosol from intensity and polarization measured by AERONET

ground-based sun/sky radiometers.

(2)

Opt. Pur. y Apl., Vol. 37, núm. 3 - 2004 _{- 3078 -} Autor: O. Dubovik et alt.

[6] Heintzenberg, J., Particle size distributions from scattering measurements of nonspherical particles via Mie-theory, Beitr. Phys. Atmos., 51, 91-99, (1998).

[7] Holben, B. N., T. F. Eck, I. Slutsker, et al., AERONET - A federated instrument network and data ar-chive for aerosol characterization, Rem. Sens. Environ, 66, 1-16, (1998).

[8] Kahn, R., R. West, D. McDonald, et al., Sensitivity of multiangle remote sensing observations to aerosol sphericity. J. Geophys. Res.,102,16861-16870, (1997).

[9] Kaufman, Y. J., A. Gitelson, A. Karnieli, et al., Size distribution and phase function of aerosol particles retrieved from sky brightness measurements. J. Geophys. Res., 99,10341-10356, (1994).

[10]Kaufman, Y. J., D. Tanré, L. A. Remer, et al., Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J. Geophys. Res.,102, 17051-17067, (1997).

[11]King, M. D., Y. J. Kaufman, D. Tanré, et al., Remote sensing of tropospheric aerosols from space: Past, present, and future. Bull. Amer. Meteor. Soc., 80, 2229-2259, (1999).

[12]Krotkov, N. A., D. E. Flittner, A. J. Krueger, et al., Effect of particle nonsphericity on satellite monitoring of drifting volcanic ash clouds. J. Quant. Spectrosc. Radiat. Transfer, 63, 613-630, (1999).

[13]Li-Jones, X., and J. M. Prospero, Variations in the size distribution of non-sea-salt sulfate aerosol in the marine boundary layer at Barbados: Impact of African dust. J. Geophys. Res.,103, 16073-16084, (1998).

[14]Liu, Y. G., W. P. Arnott and J. Hallett, Particle size distribution retrieval from multispectral optical depth: Influences of particle nonsphericity and refractive index, J. Geophys. Res., 104, 31753-31762, (1999).

[15]Mishchenko, M. I. and L. D. Travis, T-matrix computations of light-scattering by large spheroidal particles, Opt. Commun., 109, 16-21, (1994).

[16]Mishchenko, M. I., L. D. Travis, R. A. Kahn et al., Modeling phase functions for dustlike tropospheric aerosols using a shape mixture of randomly oriented polydisperse spheroids. J. Geophys. Res., 102, 16831-16847, (1997).

[17]Mishchenko, M. I., J. W. Hovenir and L. D. Travis, Light Scattering by Nonspherical Particles, Academic Press, San-Diego, 690p, (2000).

[18]Nakajima, T., G. Tonna, R. Rao, et al., Use of sky brightness measurements from ground for remote sensing of particulate polydispersions, Appl. Opt., 35, 2672-2686, (1996).

[19]Tanré D., Y. J. Kaufman, M. Herman, et al., Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. J. Geophys. Res., 102, 16971-16988, (1997).

[20]Tanré, D., Y. J. Kaufman, B. N. Holben, et al., Climatology of dust aerosol size distribution and optical properties derived from remotely sensed data in the solar spectrum, J. Geophys. Res., 106, 18,205-18,218, (2001).

[21]Torres O., P. K. Bhartia, J. R. Herman, et al., Derivation of aerosol properties from satellite measurements of back scattered ultraviolet radiation: Theoretical basis.J.Geophys.Res.,103,17099-17110, (1998).

[22]Volten H., Munoz O., Rol E., de Haan J. F., Vassen W., Hovenier J. W., Muinonen K., Nousiainen T., Scattering matrices of mineral aerosol particles at 441.6 nm and 632.8 nm, J. Geophys. Res., 106, 17375-17401, (2001).

[23]Yang P., and K. N. Liou, Geometric-optics-integral-equation method for light scattering by nonspherical ice crystals. Appl. Opt., 35, 6568-6584, (1996).

(3)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3079

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 A&M University)

Anne Vermeulen

(SSAI, Code 923)

Tom Eck (UMBC/GSFC, Code 923)

Ilya Slutsker

(SSAI, Code 923)

Hester Volten

(Free University,Netherlands)

(4)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3080

Outlines:

!

Simulating non

_{Simulating non}

-

spherical dust scattering

in remote sensing retrievals

!

Fitting laboratory

_{Fitting laboratory}

polarimetric

measurements of dust light scattering

!

Sensitivity of

_{Sensitivity of}

polarimetric

measurements to aerosol parameters

!

Applications to AERONET

_{Applications to AERONET}

polarimetric

retrievals

(5)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3081

Difficulties of accounting for particle

non

-

sphericity in aerosol retrievals:

1. 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

(6)

Simplest

model of non

-

spherical

aerosol

Randomly oriented

spheroids :

(

Mishchenko

et al., 1997)

(7)

0

0.02

0.04

0.06

0.08

0.1

1

10 dV/dlnR (

µ

m

3

µ

/

m

2

)

Radius (

µ

m)

Modeling Polydispersions

τ λ

( )

=

K

_τ

(

r

_min

r

_max

∫

k

;

n

;

r

)

V

(

r

)

dr

≈

V

(

r

_i

)

K

_τ

(

r

_i

−∆

/ 2

r

_i

+∆

/ 2

∫

k

;

n

;

r

)

dr

∑

K

(

k

;

n

;

r

_i

)

- Kernel look-up table for fixed r

_i

(22 points)

(1.33

≤

n

≤

1.6; 0.0005

≤

k

≤

0.5)

0

0.02

0.04

0.06

0.08

0.1

1

10 dV/dlnR (

µ

m

3

µ

/

m

2

)

Radius (

µ

m)

(8)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3084

Single Scattering using

spheroids:

τ λ

( )

=

K

_τ

(

λ

;

n;

k;

r;

ε

)

V

(r)

ω ε

( )

d

ε

_min

ε

_max

∫













r

_min

r

_max

∫

dr

Model by

Model by Mishchenko

Mishchenko

et al. 1997:

!

particles are randomly oriented homogeneous spheroids

!

ω(ε)

- size independent aspect ratio distribution

τ λ

( )

≈

V

_i

ω

_p

K

_τ

(

...;

r

;

ε

)

drd

ε

∆

r

_i

∫

∆

ε

∫

_p













i

;

p

( )

∑

=

K

_ip

(

...;

n

;

k

)

i

;

p

( )

∑

ω

p

V

i

K

- kernel matrix:

0.05 ≤

r

≤

15 (

µ

m)

1.33 ≤

n

≤

1.6 0.0005

≤

k

≤

0.5

(9)

spheroid

kernels data base

for

operational modeling !!!

Basic Model by

Mishchenko

et al.

1997:

!

randomly oriented

homogeneous spheroids

!

ω

(

ε

)

- size independent shape

distribution

τ λ

( )

,

F

₁₁

,...,

F

₄₄

≈

K

_ip

(

...;

n

;

k

)

i

;

p

( )

∑

ω

p

V r

( )

i

K

-

pre-computed

kernel matrices:

Input: n and k

Input:

ω

_p

(N

_p

=11),

V(r

_i

)

(N

_i

=22 -30)

Output:

τ

(

λ

),

ω

₀

(

λ

),

F

₁₁

(

Θ

), F

₁₂

(

Θ

),F

₂₂

(

Θ

),

F

₃₃

(

Θ

),F

₃₄

(

Θ

),F

₄₄

(

Θ

)

Time:

<

one sec.

Accuracy:

<

1

1 -

-

3 %

Range of applicability:

0.15 ≤

2

0.15 ≤

2 π

π

r/

λ

≤

280

280 (26 bins)

(26 bins)

0.4 ≤

ε

≤

2.4

2.4 (11 bins)

(11 bins)

1.33 ≤

n

≤

1.6

1.33 ≤

n

≤

1.6 0.0005

≤

k

≤

0.5 0.0005

≤

k

≤

0.5

0.1 1 10 100

0 40 80 120 160

Phase Functions (0.67 µm)

Spheres

Spheroids - ω

1(ε)

Spheroids - ω₁(ε)

(10)

Modeling of dust light scattering

by mixture of spheroids

0.1 1 10 100

0 40 80 120 160

Aspect Ratios: 0.40 0.48 0.58 0.69 0.83 1.0 1.44 1.20 1.73 2.07 2.49

Phase Function (0.34

µ

m)

Scattering anlge (degree)

Single aspect raitios spheroids

0.1 1 10 100

0 40 80 120 160

Spheres

Spheroid mixture

µ

m)

Mixture of spheroids

0 0.05 0.1 0.15 0.2 0.25

0. 1 10

Retireved size distribution

Radius (microns)

µ

m

3/µ

m

2)

!

ω

(

ε

)

- size independent shape

distribution

n(

λ

)

k (

λ

)

Averaging with

ω

(

ε

)

0 0.05 0.1 0.15 0.2 0.25

0.5 1 1.5 2 2.5 3

Pr

obabilit

y

(11)

Modeling of dust light scattering

by mixture of spheroids

0 0.05 0.1 0.15 0.2 0.25

0. 1 10

Retireved size distribution

Radius (microns)

µ

m

3/µ

m

2)

!

ω

(

ε

)

- size independent shape

distribution

n(

λ

)

k (

λ

)

Averaging with

ω

(

ε

)

-0.2 0 0.2 0.4 0.6

0 40 80 120 160

Spheres

Spheroid mixture

D

egr

ee of Li

near Pol ar iz ati on (0. 44 µ m)

Mixture of spheroids

-0.2 0 0.2 0.4 0.6

0 40 80 120 160

Aspect Ratios: 0.40 0.48 0.58 0.69 0.83 1.0 1.44 1.20 1.73 2.07 2.49

Degree of Linear Polarization (0.44

µ

m)

Single aspect raitios spheroids

0 0.05 0.1 0.15 0.2 0.25

0.5 1 1.5 2 2.5 3

Pr

obabilit

y

(12)

0

0.05

0.1

0.15

1

10

100 K

_scat

(120

0

, x)

V(x)

K

_scat

(120

0

**, x) * V(x)**

K

scat

(120

0

,x), V(x), K

scat

(120

0

**,x) * V(x),**

Size Parameter

0.0001 0.001 0.01 0.1 1

1

10

100

3

0

5

0

30

0

60

0

120

0

3

0

5

0

30

0

60

0

120

0

K

scat

(

Θ

; ... )

Size Parameter

Mishchenko

and

Travis, 1994

Yang and

Liou

, 1996

Contribution of different

sizes to scattering at 120

0

0 Computational challenge of using

Computational challenge of using

(13)

Mishchenko

and

Travis, 1994

Yang and

Liou

, 1996

Contribution of different

sizes to scattering at 120

0

0 Computational challenge of using

Computational challenge of using

spheroids (polarization)

0 3.4 10-5

6.8 10-5 0.000102 0.000136

0. 1 10 100

120

0

140

0

120

0

140

0

-K

F12

(

Θ

,...)

Size Parameter

0 0.08 0.16 0.24 0.32

0. 1 10 100

-K

12

(

Θ

,...)

V(r)

-K

12

(

Θ

,...) V(r)

-K

F12

(

Θ

,...), V(r), -K

F12

(

Θ

,...)V(r)

(14)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3090

name of sample feldspar

origin crushed piece of Feldspar rock from Finland

main constituents K-feldspar, plagioclase, quartz

particle size distributions measured with laser diffraction

ref f=1.0 micrometer, vef f =1.0

particle shape irregular (SEM image)

refractive index estimated to be in the range:

1.5-1.6 - i0.001-0.00001

color light pink to white powder

scattering matrix from 5-173 degrees scattering angle

wavelength 441.6 nm (figure)

632.8 nm (figure)

article Scattering matrices of mineral particles at 441.6 nm and 632.8 nm.

Volten H, Muñoz O, Rol E, de Haan JF, Vassen W, Hovenier JW, Muinonen K, Nousiainen T. Journal of Geophysical Research, 106, 17375-17401,2001

Facts and Figures

(15)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3091

F

₁₁

(λ ,Θ), −F

₁₂

(λ ,Θ)

_/

F

₁₁

(λ ,Θ)

F

_22/

F

₁₁

, F

_33/

F

₁₁

, F

_34/

F

₁₁

, F

_44/

F

₁₁

Numerical inversion:

-Accounting for uncertainty

(F

₁₁

;

-

F

₁₂

/F

₁₁

!!!)

- Setting a priori constraints

aerosol particle sizes,

refractive index,

single scattering

albedo

,

aspect ratio distribution

Inversion of Scattering Matrices

F

11 ,

F

12 ,

F

22 ,

F

33 ,

,

F

34 ,

,

F

44 ,

≈

K

ip

(

λ

;θ;

n

;

k

)

i

;

p

( )

∑

ω

p

V r

( )

i

Forward Model:

(16)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3092

Fitting of Measured Scattering Matrix

by spheroids model

(17)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3093

I

( )

Θ

;

λ

=

µ

0 (

exp

(

−

µ

0 τ

)

−

exp

(

−

µ

1 τ

)

µ

₀

+

µ

₁

(

ω

0 τ

L

2 P

( )

Θ

;

λ

L

1 I

0 +

mult

.

scat

.

)

Accounting for polarization in radiation

transmitted through the atmospheric

L

₁

;

L

₂

-

rotation matrices

Total:

I

Q

U

V













I

=

_-

_{Stokes vector}

F

(

Θ;λ

)

=

F

₁₁

F

₁₂

0

0 F

₁₂

F

₂₂

0

0 F

₃₃

F

₃₄

0

0 −

F

₃₄

F

₄₄













phase

matrix !!!

I

₀

=

1

0

0 











I

( )

Θ

;

λ

~

(

ω

₀

τ

F

₁₁

( )

Θ

;

λ

+

mult

.

scat

.

)

P

( )

Θ

;

λ

~ -

(

F

₁₂

( )

Θ

;

λ

/

F

₁₁

( )

Θ

;

λ

+

mult

.

scat

.

)

- Intensity

(18)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3094

Fitting of Measured Scattering Matrix

by spheroids model

Fitting of Measured Scattering Matrix

by spheroids model

(19)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3095

Fitting of Measured Scattering Matrix

by spheres

(20)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3096

dV(r)/dlnr

Aspect ratio

distribution

Size and shape distributions retrieved

from Scattering Matrix

(21)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3097

-0.5 0 0.5 1

0 45 90 135 180

SPHERES (Rv = 0.14µm)

n = 1.33

n = 1.4

n = 1.45

n = 1.5

n = 1.55

n = 1.6

Deg ree o f L in ear Plarizatio n (-F 12 /F11 )

Scattering Angle (degrees)

-0.5 0 0.5 1

0 45 90 135 180

SPHEROIDS (Rv = 0.14µm)

n = 1.33

n = 1.4

n = 1.45

n = 1.5

n = 1.55

n = 1.6

Deg ree o f L in ear Plarizatio n (-F 12 /F11 )

Sensitivity of Linear Polarization of fine mode

aerosol to real part of refractive index

Log-normal monomodal dV(r)/dlnr :

σ

_v

= 0.5,

(22)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3098

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

0 45 90 135 180

SPHERES (Rv = 2.0µm)

n = 1.33

n = 1.4

n = 1.45

n = 1.5

n = 1.55

n = 1.6

Degree of Linear Plarization (-F

12

/F11

)

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

0 45 90 135 180

SPHEROIDS (Rv = 2.0µm)

n = 1.33

n = 1.4

n = 1.45

n = 1.5

n = 1.55

n = 1.6

Degree of Linear Plarization (-F

12

/F11

)

Sensitivity of Linear Polarization of coarse mode

aerosol to real part of refractive index

Log-normal monomodal dV(r)/dlnr :

σ

_v

= 0.5,

(23)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3099

Shape effect in presence of Multiple Scattering

(Radiance)

Log-normal monomodal dV(r)/dlnr : r

_v

= 2

µ

m,

σ

_v

= 0.5,

(24)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3100

Shape effect in presence of Multiple Scattering

(Polarization)

Log-normal monomodal dV(r)/dlnr : r

_v

= 2

µ

m,

σ

_v

= 0.5,

µ

= 0.44

µ

m,

n = 1.45, k = 0.005

t

(25)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3101

τ

(

λ

_λ

), I(

λ,Θ

_λ,Θ

),P(

λ,Θ

_λ,Θ

)

Numerical inversion:

-Accounting for uncertainty

Numerical inversion:

(F

₁₁

;

-

F

₁₂

/F

₁₁

!!!)

- Setting a priori constraints

aerosol particle sizes,

refractive index,

single scattering

albedo

AERONET Polarized Inversion

P

11 ,

P

12 ,

P

22 ,

P

33 ,

,

P

34 ,

,

P

44 ,

≈

K

ip

(

λ

;

θ

;

n

;

k

)

i

;

p

( )

∑

ω

p

V r

( )

i

Forward Model:

Single Scat:

Multiple Scat:

DEUZE JL, HERMAN M, SANTER R, JQSRT, 1989

Successive Orders of Scattering Code

(26)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3102

Inversions of intensity and polarization

measured by AERONET

Banizombu

(Africa)

Sept. 26,

(27)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3103

Inversions of intensity and polarization

measured by AERONET

Cape Verde

(28)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3104

Inversions TESTS of intensity and polarization

measured at 4 wavelengths

Solar Vilage

(29)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3105

Modeling Desert Dust Lidar Ratio

0 0.05 0.1 0.15 0.2

0.1 1 10

5 channels (+ 1.637 µm)

dV/dlnR

(

µ

m

3 /µ

m

2 )

Particle Radius (micron)

21:02:2004, 05:21:34, Dhabi

0.1 1 10 100

0 40 80 120 160

Spheres

Spheroids

µ

m)

S

(

λ

)

=

4 π

ω

₀

( )

λ

P

(

λ,180

0 )

Muller, et al., 2003

:

S(0.532

µ

m)= 50~80sr

S=19

S=50

S=80

(30)

Opt. Pur. y Apl., V. 37, n3, 2004

Autor: O. Dubovik et alt.

3106

Conclusions:

!

Kernel look

_{Kernel look}

-

up tables seems to be

promising for remote sensing retrievals

!

Spheroids may closely reproduce

_{Spheroids may closely reproduce}

laboratory

polarimetric

measurements of

dust scattering

!

Spheroid

_Spheroid

model

is successfully

employed in

both intensity and polarized

AERONET retrievals

!

Sensitivity to particle

_{Sensitivity to particle}

shape is a

challenge for

utilizing

polarization

for

aerosol retrievals