1. Aerosol Optical Depth based on a temporal and
directional analysis of SEVIRI observations
Dominique Carrer, Olivier Hautecoeur, and Jean-Louis Roujean
CNRM-GAME
Météo-France / CNRS
Toulouse, France

2. Introduction
 Determination of the aerosol load is at the core of many
applications: epidemiologic risk, food security, air quality,
health, weather forecasting, climate change detection and the
hydrological cycle.
 Aerosols essentially originate from human activities, dust
storms, biomass burning, vegetation, sea, volcanoes, and also
from the gas-to-particule conversion mechanism.
 Aerosols: fine solid particles or liquid droplets in suspension
in the atmosphere
– Sea salt (SS), dust (DU), sulphate (SU), particle organic matter
(OM), black carbon (BC)
➢ A mixing of aerosol classes from different sources of emission is generally
observed and the aerosols interact rapidly with trace gases and water.
The type and amount of aerosols in the atmosphere vary greatly from day
to day and place to place
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3. Principles and methodology
 Main difficulty of aerosol detection is the separation of the contributions to the
measured signal arising from atmospheric scattering and surface reflectance.
 Quantitative assessment of the aerosol load from a retrieval of Aerosol Optical Depth
 Optimum exploitation of the 4 dimensions of the signal to characterize aerosols:
– Spatial (contrast reduction, aerosol layer more homogeneous than clouds)
– Spectral (Angström coefficient → aerosol type)
– Temporal (aerosol components evolute more quickly than surface components)
– Directional (aerosols and surface exhibit different angular signature)
➔
Proposed method
➢
Separates aerosol signal from the surface (vegetation,
desert, snow) under clear sky conditions
➢
Simultaneous inversion of surface and aerosol properties
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4. Principles and methodology
 Daily collection of « apparent » surface reflectance describes the directionality of the
ground surface reflectance
– Since aerosol and surface reflectance have different directional behaviour and different
temporal evolution, it is possible to discriminate the aerosol signal from the signal measured by
satellite.
 Joint retrieval of aerosol optical thickness and surface bidirectional reflectance
distribution function (BRDF)
– Derived from the operational surface albedo processing chain
– Daily estimate of AOT over land
– No spectral information is used, only VIS06 is used
– No a priori information on aerosol load nor on aerosol type
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5. Principles and methodology
Top of Atmosphere reflectance
Gaseous absorption
Molecular scattering
Top of Layer reflectance
Aerosol scattering
Surface reflectance
 Scattering and absorption properties of the atmosphere are treated separately for
aerosols and molecules
– Removal of gas absorption and Rayleigh scattering on “apparent” reflectance
– Joint retrieval of AOD and surface BRDF
➢ Coupling molecular / H2O absorption and aerosols scattering are neglected
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6. Principles and methodology
Top of Layer Reflectance
Aerosol
 Classical radiative transfer equation
Reflectance [Lenoble, 1985]
– One scattering layer
Aerosol Scattering – Surface reflectance as a boundary
Downward Upward condition
Transmission Transmission
Spherical
Albedo
AOT
 
T   ,   T a   , 
s v
ToL   ,  ,   = a
s v s   ,   a   ,  , 
s v s v
1−S a     s
Surface Reflectance
Aerosol Reflectance
Surface Reflectance
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7. Model parametrization
Method:
-discriminate directional signatures of the surface and aerosols by isolating at high solar
angles the higher sensitivity to atmospheric properties.
-use Kalman Filter with different characteristic time scale for land and atmospheric
variations
1
ρ TOL (θ s ,θ v , φ ) = T ↓ (θ s ;τ )T ↑ (θ v ;τ ) ρ s (θ s , θ v , φ ) + ρ aer (θ s , θ v , φ ;τ )
1 − Sρ e
2
ρ s (θ s , θ v , φ ) = ∑k
i =0
i . f i (θ s ,θ v , φ )

 f (θ , θ , φ ) = 1
 0 s v
(Roujean et al., 1992)  1 1
 f 1 (θ s , θ v , φ ) = [(π − φ ) cos φ + sin φ ] tan θ s tan θ v − (tan θ s + tan θ v + tan θ s 2 + tan θ v 2 − 2 tan θ s tan θ v cos φ )
 2π π
R (ϑ, ϑ, φ
s v )  2 (θ s
k ,φ ) 4 ϑϑφ
=k iso f+, θ vgeo= f geo1( [(s , ξ ) cos,ξ + sin ξ+ vol f vol ( s , v , )
π
− v ) ] − 1k ϑϑφ
 3π µ s + µ v 2 3
isotropic geometric volumique
ρ aer (θ s , θ v , φ ;τ ) =
ω 1 1
4 µ s µv η
[ P ( ξ ) + H ( µ s ) H ( µ v ) − 1] 1 − e −ητ [ ]
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8. Model parametrization
Aerosols and surface reflectance form a single BRDF model decomposed into a series of
angular kernels representing elementary photometric processes
3
'
ToL   s ,v ,  ,  =∑ k i f i  s , v , ,  
i=0
➢Pseudo linear theory (surface/aerosol coupling is non-linear)
➢All components are analytical (the model is differentiable)
 Surface contribution  Direct aerosols contribution
' T s ,  T a  ,   0 P  1−e−m
f i=0,2   s ,v ,  ,  = a 1−S 〈 v 〉 f i   s , v ,  '
f  s , v ,  ,   =
3
4 s  v m
f ms 
a s
T a  ,=e−/  e−u −v −w 
2
7−
f ms =1
− / −/ 5
S a = a e b e c
Rozanov and Kokhanovsky , 2006
u , v , w depend on and g
a , b , c ,  , are constants parameterized by g
Kokhanovsky et al. , 2005
f 0  s , v ,   =1
1 1
f 1   s , v ,   =
2
[  −  cos sin  ]−   tan  stan v  tan  s 2tan v 2−2 tan s tan  v cos  
f 2   s , v ,  =
4 1
3  s  v 2[ 

− cos sin  −
] 1
3
Roujean et al. ,1992
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9. Mathematical design
 Kalman filter approach
Z= FK 
1
Z =[ ToL    1 ,... , N  N , v , N ]
1,
s
1,
v ToL s
N
vector of N observations
K=[ k 0, k 1, k 2, ] vector of parameters
F =[ f ' 0, f ' 1, f ' 2, f ' 3 ] matrix of angular kernel functions
{
T −1 −1
A BC ap K ap C reg K reg
K=
C −1
k
−1 −1
C k =  A AC ap C reg 
T −1
covariance matrix
A , B scaled matrices for Z  , F  , normalized by the standard error   ToL 
 Our semiphysical approach aims to derive an algorithm that performs efficiently
 Ill-conditioning is avoid using regulation terms Kreg and Creg
 A persistent algorithm using prior information Kap and Cap
 State variable K is estimated in adopting a recursive procedure
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10. Two steps process
DEM
Atmosphere LSM
characterisation
ECMWF forecasts
TOA Partial TOL
Cloud TOL
SEVIRI atmospheric radiances
mask radiances
radiances correction screened
Surface
reflectance
 All clear data are used at full
Inversion process: Aerosol
resolution unmixing aerosol/surface product
 SAF-NWC CMa product is used here
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11. Validation against AERONET data sets
Daily MSG AOT values are compared to AERONET ground measurements.
Location of the AERONET
stations investigated in the
present study
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12. Validation against AERONET data sets
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13. Validation with AERONET stations in Europe
bias=-0.026
stdev=0.104 AERONET
R=0.54
SEVIRI
False cloud
bias=-0.027
detection ? stdev=0.112
R=0.56
Daily AOD
bias=-0.022
stdev=0.089
R=0.69
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14. Validation with AERONET stations in Africa
bias=-0.028
stdev=0.092
R=0.83 AERONET
SEVIRI
bias=-0.011
stdev=0.233
R=0.90
Daily AOD
bias=-0.122
stdev=0.277
R=0.75
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15. Monitoring an aerosol event
 AOD estimated for SEVIRI visible band
 AOD from MODIS product
superimposed over ocean (0.5°)
 Good consistency is noticed with
AOD up to 3 and beyond...
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16. Monitoring an aerosol event
SEVIRI AOD in black
AERONET AOD in green
over 6 Western African sites, March 1st-21th, 2006
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17. Intercomparison with MODIS product
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18. AOT vs density of urbanization
 Mean AOT from Monday 20060529 to Sunday 20060702 (5 complete weeks) versus day of the
week and town density in a region including Europe and North Africa.
 Three categories were established using the GLC2000 land cover classification: MSG/SEVIRI
pixels containing less than 30%, between 30% and 90%, and more than 90% of the class 'artificial
surfaces'.
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19. Method Approximations
Mie phase fonction (colour) for representative aerosol types.
Henyey-Greenstein (black) for g=0.6 (solid) and g=0.75 (dash)
Some aerosol types are particular sensitive to the particule size (DU,SS) while other (OM,SU)
present characteristics depending on relative humidity.
g=0.3
g=0.6
g=0.75
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20. SEVIRI angular sampling
 Min/Max of scattering
angle
– Varies in place and
time
➢ Aerosol type could not be
discriminated everywhere
on the disk
➢ Our physical assumptions
seem adapted to the
angular capabilities that
are offered by
MSG/SEVIRI.
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21. Conclusion
 A method was presented to retrieve the aerosol optical depth
– Based on a joint retrieval of AOD and surface reflectance. The angular shape of BRDF is
particularly sensitive to the presence of aerosols and allows aerosol and surface signals to be
separated.
– Working for any surface type (including bright targets)
– Validated against AERONET and MODIS data (bias < 0,03)
– Relied on simple model (only analytical formulas not a “black box”)
– Hypothesis and limits well identified
 Compact code
– Framework in C++, ~ 2200 LOC
– Easy to maintain and upgrade
 Low computational resources required
– One day of data: 96 slots full disk
– Run time : ~ 3h on a PC workstation
• 2h for preprocess and partial atmospheric correction
• 1h for joint aerosol/surface inversion
➢ Suitable to be integrated in an operational centre
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22. On-going developments
 Introduction of a simplified water BRDF reflectance model
– To adapt the method for ocean in designing a BRDF adapted to sea surface
 Use of the three solar channels for aerosol type discrimination
– To exploit the spectral and angular information to derive the aerosol class. Angström
coefficient determination
 Continuous work to increase the grid resolution and extend the geographical
coverage
– To include data from different instruments (does not require further methodological
developments).
 Analysis of the input signal
– For error/uncertainty determination
 Cloud mask
– To recover strong aerosol episodes and filter residual clouds or thin cirrus
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23. Carrer, D., J.-L. Roujean, O. Hautecoeur, and T. Elias (2010),
Daily estimates of aerosol optical thickness over land surface based on a
directional and temporal analysis of SEVIRI MSG visible observations,
J. Geophys. Res., 115, D10208, doi:10.1029/2009JD012272.
dominique.carrer@meteo.fr
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http://www.agu.org/journals/jd/jd1010/2009JD012272/