Oleg Dubovik (UMBC / GSFC, Code 923) Alexander Sinyuk (SSAI, Code 923)

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) Ben Veihelmann (SRON Space Res., Netherlands)

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

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

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

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)

Single Scattering using spheroids: Single Scat. By 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

Single Scattering using spheroids spheroidkernels 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: < one sec. Accuracy: < 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)

Modeling of dust light scattering by mixture of spheroids n(l) k (l) • w(e) - size independent shape distribution Averaging with w(e)

Computational challenge of using spheroids (phase function) Computational challenge Contribution of different sizes to scattering at 1200 Mishchenko and Travis, 1994 Yang and Liou, 1996

Computational challenge of using spheroids (polarization) Computational challenge Contribution of different sizes to scattering at 1200 Mishchenko and Travis, 1994 Yang and Liou, 1996

http://www.astro.uva.nl/scatter

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

Fitting of Measured Scattering Matrix by spheroids model Feldspar 0.441 mm

Role of total reflectance Accounting for polarization in radiation transmitted through the atmospheric L1; L2 - rotationmatrices Total: phase matrix !!! I = - Stokes vector F(Q;l) = - Intensity -Linear Polarization

Fitting of Measured Scattering Matrix by spheroids model Fitting of Measured Scattering Matrix by spheroids model Feldspar 0.633 mm

Fitting of Measured Scattering Matrix by spheres Feldspar 0.441 mm

Size and shape distributions retrieved from Scattering Matrix Spheroids Aspect ratio distribution dV(r)/dlnr

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

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

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

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

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

Inversions of intensity and polarization measured by AERONET Banizombu (Africa) Sept. 26, 2003 t(0.87)~ 0.5

Inversions of intensity and polarization measured by AERONET Cape Verde July 12,2001 t(0.87)~ 0.6

Inversions TESTS of intensity and polarization measured at 4 wavelengths Solar Vilage t(1.02)~ 0.4

Modeling Desert Dust Lidar Ratio Muller, et al., 2003: S(0.532mm)= 50~80sr Dhabi Aerosol S=19 S=50 S=80

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

Oleg Dubovik (UMBC / GSFC, Code 923) Alexander Sinyuk (SSAI, Code 923)