Aerosol retrievals from AERONET sun/sky radiometers: Overview of - inversion principles - aerosol retrieval products - advances and perspectives Aerosol retrievals from AERONET sun/sky radiometers: Overview of - inversion principles - aerosol retrieval products - advances and perspectives The Second International Conference of Aerosol Science and Global Change August, 18-21, 2009, Hangzhou, China O. Dubovik, A. Sinuyk B.N. Holben O. Dubovik 1,2, A. Sinuyk 2, B.N. Holben 2 and AERONET team 1 - University of Lille, CNRS, France 2 - NASA/GSFC, Greebelt, USA

 ( ), I(  ),P(  ) Optimized Numerical inversion: (F 11 ; -F 12 /F 11 !!!) - Accounting for uncertainty (F 11 ; -F 12 /F 11 !!!) - Setting a priori constraints aerosol particle sizes, complex refractive index (SSA), Non-spherical fraction AERONET Inversion Forward Model: Single Scat: Multiple Scat: (scalar) Nakajima and Tanaka, 1988, or (polarized) Lenouble et al., JQSRT, 2007 ensemble of polydisperse randomly oriented spheroids (mixture of spherical and non-spherical aerosol components)

Accounting for multiple scattering effects - cloud-free atmosphere; - horizontal homogeneous atmosphere; - assumed gaseous absorption and molecular scattering; - vertically homogenous atmosphere (assumed profile of concentration !?) - bi-directional surface reflectance assumed from MODIS observations - accounting for polarization effects !?! ASSUMPTIONS in the retrievals:

AERONET model of aerosol spherical: Randomly oriented spheroids : (Mishchenko et al., 1997) AERONET model of aerosol Dubovik et al., 2006

Aerosol single particle scattering: EACH AEROSOL PARTICLE - sphere or spheroid (!!!); - homogeneous; ≤ n ≤ 1.6 (1.7- ???) (0 - ???) ≤ k ≤ 0.5 -n and k spectrally dependent (but smooth) ASSUMPTIONS in the retrievals:

Aerosol particle size distribution ASSUMPTIONS : - dV/dlnr - volume size distribution of aerosol in total atmospheric column; - size distribution is modeled using 22 triangle size bins (0.05 ≤ R ≤ 15  m); - size distribution is smooth (Twomey 1977) Trapezoidal approximation

Mixing of particle shapes ASSUMPTIONS: - dV/dlnr - volume size distribution is the same for both components; - non-spherical - mixture of randomly oriented polydisperse spheroids; - aspect ratio distribution N(  is fixed to the retrieved by Dubovik et al retrieved C  + (1-C)  Aspect ratio distr.

spheroid kernels data base for operational modeling !!! Basic Model by Mishchenko et al. 1997:  randomly oriented homogeneous spheroids   (  ) - size independent shape distribution Single Scattering using spheroids K - pre-computed kernel matrices: Input: n and k Input:  p (N p =11), V(r i ) (N i =22 -26) Output:  ( ),  0 ( ), F 11 (  ), F 12 (  ),F 22 (  ), F 33 (  ),F 34 (  ),F 44 (  ) Time: < one sec. Accuracy: < 1-3 % Range of applicability: ≤ 2  r/  ≤ 625 (41 bins) 0.3 ≤  ≤ 3.0 (25 bins) 1.3 ≤ n ≤ ≤ k ≤ 0.5

Particle Size Distribution: 0.05  m ≤ R (22 bins) ≤ 15  m Complex Refractive Index at = 0.44; 0.67; 0.87; 1.02  m INPUT INPUT of Forward Model Smoke Desert DustMaritime AERONET retrievals are driven by 31 variables : dV/lnr - size distribution (22 values); n( ) and k( ) - ref. index (4 +4 values) C spher (%) - spherical fraction (1 value)

Statistically Optimized Minimization - Fitting  (Dubovik and King, 2000) Measurements: i=1 - optical thickness i=2 - sky radiances -their covariances (should depend on and  ) -lognormal error distributions a priori restrictions on norms of derivatives of: i=3 -size distr. variability; i=4 -n spectral variability; i=5 -k spectral variability; i=6 - limiting dV/dlnr for R min Lagrange parameters consistency Indicator weighting Inversion

A priori restrictions on smoothness  (Dubovik and King, 2000) norms of derivatives Meaning : m=1 -constant straight line: V(lnr)= C; m=2 -constant straight line: V(lnr)= B lnr +C; m=3 -parabola: V(lnr)= A(lnr) 2 + B lnr +C; Most unsmooth KNOWN size distribution Strength of constraint A priori restirctions

AERONET retrieval products: Directly retrieved parameters: - dV/dlnR - size distribution; (- dynamic errors ) - C(t,f,c), R v (t,f,c),  (t,f,c), R eff (t,f,c) - integral parameters of dV/dlnR - n( ) and k ( ) at 0.44, 0.67, 0.8, 1.02  m; (- dynamic errors ) - C spherical - fraction of spherical particles (- dynamic errors ) - V1 - V2 - V3 Indirectly retrieved/estimated parameters:  popular:   - at 0.44, 0.67, 0.8, 1.02  m; (- dynamic errors ) - P 11 (  ) (- dynamic errors ) and ; - P 12 (  ) and P 22 (  ) - ??? (- dynamic errors ) - F  TOA ( ) and F  BOA ( ) - down ward spectral fluxes - F  TOA ( ) and F  BOA ( ) - upward spectral fluxes  not well-known / under-developed: - S( ) - lidar backscattering-to-extinction ratio; (- dynamic errors ) -  ( ) - lidar depolarization ratio ; (- dynamic errors ) - F  TOA and F  BOA - down ward broad-band (visible) fluxes; - F  TOA and F  BOA - upward broad-band (visible) fluxes; - ∆F TOA and ∆ F BOA - radiative forcing - ∆F Eff TOA and ∆F Eff BOA - radiative forcing efficiency

Fine / Coarse modes parameters: Flexible separation: minimum between: and  m 0.45  m Integral parameters of dV/dlnR: t - total; f - fine ; c - coarse C(t,f,c) - Volume Concentration R v (t,f,c) - Mean Radius  (t,f,c) - Standard Deviation R eff (t,f,c) - Effective Radius

Retrieval accuracy and limitations Sensitivity tests by Dubovik et al Real Part Imaginary Part SSA  ≤  %  ≥  % 0.03 Size Distribution: bias ∆  = ± 0.01 Effective Random errors Nonsphericity biases Accuracy summary wide angular coverage

Error estimates: New strategy: Errors are to be provided in each single retrievals for all retrieved parameters Important Error Factors: - Aerosol Loading - Scattering Angle Range - Number of Angles (homogeneity) - Number of spectral channels - Aerosol Type etc.

Rigorous ERRORS estimates: General case : large number of unknowns and redundant measurements U - matrix of partial derivatives in the vicinity of solution Above is valid: - in linear approximation - for Normal Noise - strongly dependent on a priori constraints - very challenging in most interesting cases Dubovik 2004

Input ERRORS and biases Random (normally distributed with 0 means): - optical thickness:     COS(SZA) - sky-radiances:  sky  3% - sky-radiances:  sky  3% - a priori:  sky /  i  % (Dubovik and King, 2000) - a priori:  sky /  i  % (Dubovik and King, 2000) Biases (constant): - optical thickness:   COS(SZA) - sky-radiances:  3% + obtained misfit - sky-radiances:  3% + obtained misfit - a priori: % - a priori: % The error estimates are calculated twice with + and - bias. Size distribution

Examples of error estimates high loading low loading

- vector of partial derivatives in the vicinity of solution Above is valid: - in linear approximation - for Normal Noise - strongly dependent on a priori constraints Dubovik 2004 ERRORS estimates for the functions of the retrieved parameters:  0, P ii (  ), etc.

Statistical variability of SSA errors A. Sinyuk The Second International Conference of Aerosol Science and Global Change August, 18-21, 2009, Hangzhou, China

Statistical variability of errors for sphericity parameter A. Sinyuk The Second International Conference of Aerosol Science and Global Change August, 18-21, 2009, Hangzhou, China

Lidar Ratio S=19 S=50 CALIOP Data: Extinction Lidars are sensitive to:

Optics Microphysics Volten et al. Volten et al. 2001

Lidar Ratio from AERONET climatology Cattrall et al., 2005

Size Dependence of Depolarization for Randomly Oriented Spheroids Log-normal monomodal dV(r)/dlnr :  v = 0.5,  = 0.44  m, n = 1.4, k = F 22 / F 11 F 22 (  )/ F 11 (  ) Lidar signal depolarization

AERONET estimated broad-band fluxes in solar spectrum Size distribution F  TOA and F  BOA F  TOA and F  BOA Integrations details:  min = 0.2  m,  max = 4.0  m; more than 200 points of integration between; Aerosol: dV/dlnR - retrieved n( ) and k( ) are interpolated/extrapolated; from n( i ) and k( i ) retrieved; Radiative transfer code uses 12 moments for P 11 (  ) Surface: Surface reflection is Lambertian; Values of surface refelctance are interpolated/ extrapolated from MODIS data values Gases: Gaseous absorption is calculated using correlated k- distributions implemented by P. Dubuisson Validation studies: Derimian et al Garcia et al ( F  BOA ~ 10% agreement )

AERONET estimated aerosol forcing in solar spectrum Size distribution Radiative forcing: ∆F TOA = F  0 TOA - F  TOA ∆F BOA = F  0 BOA - F  BOA Radiative forcing efficiency: ∆F Eff TOA = ∆F TOA /  0.55  ∆F Eff BOA = ∆F BOA /  0.55  Finding by Derimian et al : importance of non-sphericity: up to 10% overestimation of ∆F TOA/BOA ; Suggested improvements by Derimian and others: Use net fluxes: ∆F BOA = (F  0 BOA - F  0 BOA ) - (F  BOA - F  BOA ) Estimate daily forcing Estimates of IR fluxes/forcing Sol. Radiance,mWcm -2 str -1 m -1 Terr. Radiance,mWcm -2 str -1 m -1 size Aerosol

Water+ Soluble+ Insoluble+ +BC n( ) k( ) Shuster, et al. 2005, 2009 m( )= m( a 1 m 1 ( ); a 2 m 2 ( ); a 3 m 3 ( )) ?

Perspectives: 1.Improving retrieval products: - releasing dynamic errors; - polishing Flux and Forcing products (ref: Y. Derimian talk) - providing lidar ratios; - providing depolarizations ratios; 2. Updating scattering model: - including surface roughness for spheroids - expanding ranges of n and k 3. New Inversion developments: - inversion of polarized data (ref: Z. Li talk) - AERONET/MODIS/PARASOL (ref: A. Sinuyk talk) - AERONET/CALIPSO (ref: A. Sinuyk work ) - inversion of daily data, combining with PARASOL (ref: O. Dubovik talk ) - deriving composition information (ref: G. Shuster work) The Second International Conference of Aerosol Science and Global Change August, 18-21, 2009, Hangzhou, China