Aerosol retrievals from AERONET sun/sky radiometers: Overview of

29
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 O. Dubovik 1,2 , A. Sinuyk , A. Sinuyk 2 , B.N. Holben B.N. Holben 2 and AERONET team 1 - University of Lille, CNRS, France 2 - NASA/GSFC, Greebelt, USA

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Aerosol retrievals from AERONET sun/sky radiometers: Overview of - inversion principles - aerosol retrieval products - advances and perspectives. - PowerPoint PPT Presentation

Transcript of Aerosol retrievals from AERONET sun/sky radiometers: Overview of

Page 1: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

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 ChangeAugust, 18-21, 2009, Hangzhou, China

O. DubovikO. Dubovik1,2, A. Sinuyk, A. Sinuyk2, B.N. Holben B.N. Holben2 and AERONET team1 - University of Lille, CNRS, France

2 - NASA/GSFC, Greebelt, USA

Page 2: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

((), I(), I(),P(),P()) Optimized Numerical inversion:Optimized Numerical inversion:- Accounting for uncertainty (F(F1111; -F; -F1212/F/F11 11 !!!)!!!) - Setting a priori constraints

aerosol particle sizes,aerosol particle sizes, complex refractive index (complex refractive index (SSASSA)), ,

Non-spherical fractionNon-spherical fraction

AERONET InversionAERONET InversionForward Model:Forward Model:

Single Scat:Single Scat:

Multiple Scat:Multiple Scat: (scalar) Nakajima and Tanaka, 1988, or (polarized) Lenouble et al., JQSRT, 2007

ensemble of polydisperse randomly oriented spheroidsensemble of polydisperse randomly oriented spheroids(mixture of spherical and non-spherical aerosol components)

Page 3: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

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:

Page 4: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

AERONETAERONET model of aerosol model of aerosolspherical:spherical:

Randomly orientedRandomly orientedspheroids :spheroids :

(Mishchenko et al., 1997)(Mishchenko et al., 1997)

Dubovik et al., 2006

Page 5: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Aerosol single particle scattering:

EACH AEROSOL PARTICLE

- sphere or spheroid (!!!);

- homogeneous;

- 1.33 ≤ n ≤ 1.6 (1.7- ???)

- 0.0005 (0 - ???) ≤ k ≤ 0.5

-n and k spectrally dependent (but smooth)

ASSUMPTIONS in the retrievals:

Page 6: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

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

0

0.05

0.1

0.15

0.2

0.25

0.3

0.1 1 10

Size Distribtuion Approximation

Particle Radius (m)

Vtotal

(r) = (i=1,...,22)

aiV

i(r)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.1 1 10

Size Distribtuion

dV

/dln

(r)

(m3

/m2

)

Particle Radius (m)

Voriginal(r)

(Twomey 1977)

Trapezoidal approximation

Page 7: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

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. 2006

C Kspherical (

rmin

rmax

k;n;r )V(r )dr (1 C) K (k;n;r ,)

min

max

N()d

rmin

rmax

V(r )dr

retrieved

C + (1-C)

Aspect ratio distr.

0.1

1

10

100

0 40 80 120 160

Spheres Spheroids

Pha

se

Fun

ctio

n (0

.532

m

)

Scattering Angle (degrees)

Page 8: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

spheroidspheroid kernels data basekernels data basefor for operational modeling !!!operational modeling !!!

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

,F11,...,F44 K ip ...; n;k i;p p V ri

K - pre-computed kernel matrices:Input: n and k

Input: p (Np =11), V(ri) (Ni =22 -26)

Output: (), 0(),

F11(), F12(),F22(),

F33(),F34(),F44()

Time:Time: < < one sec.one sec.Accuracy:Accuracy: < < 1-3 %1-3 %

Range of applicability:Range of applicability:0.012 ≤ 20.012 ≤ 2r/r/≤ ≤ 625 625 (41 bins)(41 bins)

0.3 ≤ 0.3 ≤ ≤ 3.0 ≤ 3.0 (25 bins)(25 bins)1.3 ≤ n ≤ 1.7 1.3 ≤ n ≤ 1.7

0.0005 ≤ k ≤ 0.50.0005 ≤ k ≤ 0.50.1

1

10

100

0 40 80 120 160

Phase Functions (0.67 m)

Spheres

Spheroids - 1()

Spheroids - 1()

Scattring Angle (degree)

Page 9: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Particle Size Distribution:0.05 m ≤ R (22 bins) ≤ 15 m

Complex Refractive Index at = 0.44; 0.67; 0.87; 1.02 m

0.01

0.02

0.04

0.05

0.07

0.1 1 10

dV

/dln

R(

m3 /

m2)

Radius (m)0.00

0.01

0.10

Wavelength (m)0.44 0.67 0.87 1.02

Imarinary PartImaginary Part

Smoke Desert Dust Maritime

AERONET retrievals are driven by 31 variables :

1.35

1.40

1.45

1.50

1.55

1.60

Wavelength (m)0.44 0.67 0.87 1.02

Real Part

dV/lnr - size distribution (22 values); n() and k() - ref. index (4 +4 values) Cspher (%) - spherical fraction (1 value)

Page 10: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Statistically Optimized Minimization - Fitting (Dubovik and King, 2000)

Measurements:i=1 - optical thicknessi=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 Rmin

Lagrange parameters

consistencyIndicator

weighting

0

2

i2 fi

fi x 2, i

02

i2 fi

a fi x 2i (N total - Nx ) ˆ 0

2

Page 11: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

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

i

2 amax dmV (lnr)

dm ln r

r

2

d ln r

Page 12: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

AERONET retrieval products:

Directly retrieved parameters:- dV/dlnR - size distribution; (- dynamic errors )- C(t,f,c), Rv(t,f,c), (t,f,c), Reff (t,f,c) - integral parameters of dV/dlnR - n() and k () at 0.44, 0.67, 0.8, 1.02 m; (- dynamic errors )- Cspherical - 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 ) - P11() (- dynamic errors ) and <cos()> ; - P12() and P22() - ??? (- dynamic errors )- F

TOA() and FBOA() - down ward spectral fluxes

- FTOA() 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 FBOA - down ward broad-band (visible) fluxes;

- FTOA and F

BOA - upward broad-band (visible) fluxes; - ∆FTOA and ∆ FBOA - radiative forcing - ∆FEff

TOA and ∆FEffBOA - radiative forcing efficiency

Page 13: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Fine / Coarse modes parameters:

0.1

1

10

100

0 45 90 135 180

totalfine modecoarse mode

Pha

se

Fun

ctio

n

Particle Radius (micron)

02:05:2003,09:27:51,PolarPP,Beijing,14

0

0.3

0.6

0.9

0 45 90 135 180

totalfine modecoarse mode

Line

ar

Pol

ariz

atio

n (

-F12

/F11

)Particle Radius (micron)

02:05:2003,09:27:51,PolarPP,Beijing,14

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.1 1 10

dV/d

lnR

(m

3 /m

2 )

Particle Radius (micron)

Coarse

Fine

Coarse

02:05:2003,09:27:51,PolarPP,Beijing,14

Beijing Aerosol

Flexible separation: minimum between: 0.194 and 0.576 m

0.45m

Integral parameters of dV/dlnR: t - total; f - fine ; c - coarseC(t,f,c) - Volume ConcentrationRv(t,f,c) - Mean Radius(t,f,c) - Standard DeviationReff (t,f,c) - Effective Radius

Page 14: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Retrieval accuracy and limitationsSensitivity tests by Dubovik et al. 2000

Real Part Imaginary PartSSA

≤ 0.05

80-100%0.05-0.07

≥ 0.02550%0.03

Size Distribution:

0.00

20.00

40.00

60.00

80.00

0.1 1 10

Err

ors

(%

)

Radius (m)

bias ∆ = ± 0.01Effective

Random errors Nonsphericitybiases

0

0.05

0.1

0.15

0.1 1 10

aureolefull almucantar

dV

/dln

R(

m3/

m2 )

Radius (m)

1.30

1.35

1.40

1.45

1.50

1.55

1.60

Wavelength (m)0.44 0.67 0.87 1.02

Real Part

wide angularcoverage

Page 15: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Error estimates: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.

Page 16: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Rigorous ERRORS estimates:Rigorous ERRORS estimates: General caseGeneral case: : large number of unknownslarge number of unknowns and and

redundant measurementsredundant measurements

U - matrix of partial derivatives in the vicinity of solution

ˆ x i 2 ˆ x irandom 2 ˆ x i

bias 2

ˆ x

Above is valid: - in linear approximation

- for Normal Noise - strongly dependent on a priori constraints

- very challenging in most interesting cases

C ˆ x random UTC-1UU a

T Ca 1Ua -1

ˆ x bias UTC-1UU aT Ca

1Ua 1

UTC-1Ibias U aT Ca

1Iabias

Dubovik 2004

Page 17: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Input ERRORS and biasesInput ERRORS and biases

Random (normally distributed with 0 means):

- optical thickness: - optical thickness: 0.015 0.015 COS(SZA) COS(SZA)

- sky-radiances: - sky-radiances: skysky3% 3%

- a priori: - a priori: skysky/ / ii100 - 300 % 100 - 300 % (Dubovik and King, 2000)(Dubovik and King, 2000)

Biases (constant): - optical thickness: - optical thickness: 0.015 0.015 COS(SZA) COS(SZA)

- sky-radiances: - sky-radiances: 3% + obtained misfit3% + obtained misfit

- a priori: 100 - 300 %- a priori: 100 - 300 %

The error estimates are calculated The error estimates are calculated twice with + and - biastwice with + and - bias..

Size distribution

0

0.05

0.1

0.15

0.1 1 10

dV/d

lnR

(m

3 /m

2 )

Radius(m)

GSFC, (0.44) = 0.7

Page 18: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Examples of error estimates

0

0.05

0.1

0.15

0.1 1 10

dV/d

lnR

(m

3 /m

2 )

Radius(m)

GSFC, (0.44) = 0.7

1.25

1.3

1.35

1.4

1.45

0.4 0.6 0.8 1

GSFC, (0.44) = 0.7

Re

al

Pa

rt o

f R

efr

ac

tiv

e I

nde

x

Wavelength (m)

0.6

0.7

0.8

0.9

1

0.4 0.6 0.8 1

GSFC, (0.44) = 0.07

Sin

gle

Sc

att

eri

ng A

lbe

do

Wavelength (m)

0.8

0.9

1

0.4 0.6 0.8 1

GSFC, (0.44) = 0.7

Sin

gle

Sca

tter

ing

Alb

edo

Wavelength (m)

high loading

low loading

Page 19: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

- vector of partial derivatives in the vicinity of solution

Above is valid: - in linear approximation

- for Normal Noise - strongly dependent on a priori constraints

C ˆ x random UTC-1UU a

T Ca 1Ua -1

ˆ x bias UTC-1UU aT Ca

1Ua 1

UTC-1Ibias U aT Ca

1Iabias

Dubovik 2004

0 2 u0

T Cx random (

x bias)(

x bias)T u0

ERRORS estimates for theERRORS estimates for the functions of the retrieved parameters:functions of the retrieved parameters:

00, , PPiiii((), etc.), etc.

ˆ x

u0

Page 20: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Statistical variability of SSA errors

A. Sinyuk

The Second International Conference of Aerosol Science and Global ChangeAugust, 18-21, 2009, Hangzhou, China

Page 21: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Statistical variability of errors for sphericity parameter

A. Sinyuk

The Second International Conference of Aerosol Science and Global ChangeAugust, 18-21, 2009, Hangzhou, China

Page 22: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Lidar Ratio

0

0.05

0.1

0.15

0.2

0.1 1 10

5 channels (+ 1.637 m)

dV/d

lnR

(m

3 /m

2 )

Particle Radius (micron)

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

0.1

1

10

100

0 40 80 120 160

Spheres Spheroids

Pha

se

Fun

ctio

n (0

.532

m

)

Scattering Angle (degrees)

S() 4

0 P ,1800

S=19

S=50

0

5

10

15

20

25

30

35

40

-0.005 0 0.005 0.01 0.015

CALIOP single laser pulse

Alt

itu

de,

(km

)

Attenuated backscattering coefficient, (km)-1 (sr)-1

532 nm

0

5

10

15

20

25

30

35

40

0 0.001 0.002 0.003 0.004

Along track and vertically averegedCALIOP data

Alt

itu

de,

(km

)

Attenuated backscattering coefficient, (km)-1 (sr)-1

532 nm

CALIOP Data:

() Extinction

Lidars are sensitive to:

Page 23: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Optics Optics Microphysics Microphysics

Volten et al.

0

0.2

0.4

0.6

0.8

1

1.2

0.1 1 10

determined by granulometryretrieved (0.44 m)retrieved (0.63 m)

dV

/dln

r (

m3/

m3)

(no

mal

ized

to

max

imu

m)

Particle Radius (m)

a

0

0.05

0.1

0.15

0.2

0.25

0.3

0.5 1

Mixture 1 (ret. 0.44m)Mixture 3 (ret. 0.63m)Mixture 3 (from modeling) ~Mishchenko et al. 1997

dn

()/

dln

(n

orm

alized

to 1

)

Axis Ratio

b

Volten et al. 2001

0

0.05

0.1

0.15

0.2

0.25

0.3

1 2

Mixture 1 (ret. 0.44 m)Mixture 3 (ret. 0.63 m)Mixture 3 (from modeling) ~Mishchenko et al. 1997

dn

()/

dln

Aspect Ratio

Page 24: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Lidar Ratio from AERONET climatology

S()4

0 P ,1800

Cattrall et al., 2005

Page 25: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Size Dependence of Depolarization for Randomly Oriented Spheroids

Log-normal monomodal dV(r)/dlnr : v = 0.5, = 0.44 m, n = 1.4, k = 0.005

F22/ F11

0

0.2

0.4

0.6

0.8

1

0 45 90 135 180

SPHEROIDS

Rv = 0.1Rv = 0.12Rv = 0.14Rv = 0.2Rv = 0.4Rv = 0.6Rv = 1.0Rv = 2.0Rv = 3.0Rv = 5.0

F2

2/F11

Scattering Angle (degrees)

F22()/ F11()

() 1F22 1800, F11 1800,

1F22 1800, F11 1800,

Lidar signal depolarization

0

0.1

0.2

0.3

0.4

0.5

0.1 1 10

1 10 100

Dep

ola

riza

tio

n R

atio

Volume Median Radius in m (for =0.532 m)

Effective Size Parameter

Page 26: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

AERONET estimated broad-band AERONET estimated broad-band fluxes in fluxes in solar spectrumsolar spectrum

Size distribution

FTOA and F

BOA

FTOA and F

BOA

Fbroadband F()dmin

max

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 P11()

Surface: Surface reflection is Lambertian; Values of surface refelctance are interpolated/ extrapolated from MODIS data valuesGases: Gaseous absorption is calculated using correlated k-distributions implemented by P. Dubuisson

Validation studies:Derimian et al. 2008Garcia et al. 2008( F

BOA ~ 10% agreement )

Page 27: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

AERONET estimated aerosol AERONET estimated aerosol forcing in forcing in solar spectrumsolar spectrum

Size distribution

Radiative forcing: ∆FTOA = F0

TOA - FTOA

∆FBOA = F0BOA - F

BOA

Radiative forcing efficiency: ∆FEff

TOA = ∆FTOA/0.55 ∆FEff

BOA = ∆FBOA/0.55

Finding by Derimian et al. 2008: importance of non-sphericity: up to 10% overestimation of ∆FTOA/BOA;

Suggested improvements by Derimian and others: Use net fluxes: ∆FBOA = (F0

BOA- F0BOA) - (F

BOA- FBOA)

Estimate daily forcing Estimates of IR fluxes/forcing

0

50

100

150

200

0

0.5

1

1.5

2

1 10Wavelength, m

Gas absorption

Aerosolextinction

C440nm

=1.0

C440nm

=0.5

Sol. R

adia

nce

,mW

cm-2st

r-1m

-1

Terr. R

adia

nce

,mW

cm-2str

-1m

-1

size

Aerosol

Page 28: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

QuickTime™ and aTIFF (LZW) decompressor

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Water+Soluble+Insoluble++BC

n()

k()

Shuster, et al. 2005, 2009

m()= m( a1 m1(); a2 m2(); a3 m3()) ?

Page 29: Aerosol retrievals from AERONET sun/sky radiometers:  Overview of

Perspectives: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)

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The Second International Conference of Aerosol Science and Global ChangeAugust, 18-21, 2009, Hangzhou, China