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
IGARSS Conference 2011, Vancouver, Canada
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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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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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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Principles and methodology
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
Top of Layer reflectance
Surface reflectance
Aerosol scattering
Gaseous absorptionMolecular scattering
Top of Atmosphere reflectance
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Principles and methodology
Classical radiative transfer equation [Lenoble, 1985]
– One scattering layer
– Surface reflectance as a boundary condition
Aerosol Scattering
Surface Reflectance
DownwardTransmission
UpwardTransmission
Aerosol Reflectance
SphericalAlbedo
ToL s ,v , =T a
s , T a v ,
1−S a s
s s ,v a s ,v ,
Top of Layer Reflectance
Aerosol Reflectance
Surface Reflectance
AOT
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Model parametrization
),,(),,(),,( φϑϑφϑϑφϑϑ vsvolvolvsgeogeoisovs fkfkkR ++=
isotropic geometric volumique
(Roujean et al., 1992)
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
( ) ( ) ( )τφθθρφθθρρ
τθτθφθθρ ;,,,,1
1);();(,, vsaervss
evsvsTOL S
TT +−
= ↑↓
( ) ( )∑=
=2
0
,,.,,i
vsiivss fk φθθφθθρ
( ) ( ) ( ) ( )[ ][ ] 1111
4;,, ητµµξ
ηµµωτφθθρ −−−+= eHHP vs
vsvsaer
( )( )
( )
−+−+
=
−+++−+−=
=
3
1]sincos)
2[(
1
3
4,,
)costantan2tantantan(tan1
tantan]sincos)[(2
1,,
1,,
2
221
0
ξξξπµµπ
φθθ
φθθθθθθπ
θθφφφππ
φθθ
φθθ
vsvs
vsvsvsvsvs
vs
f
f
f
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Model parametrization
Surface contribution
ToL s ,v , , =∑i=0
3
k i f i' s ,v , ,
f 0 s ,v , =1
f 1 s ,v ,=12
[ − cossin ]−1
tanstan v tans2tan v
2−2 tan s tanvcos
f 2 s ,v , =43
1sv
[2 − cossin]−13Roujean et al. ,1992
Direct aerosols contribution
Aerosols and surface reflectance form a single BRDF model decomposed into a series of angular kernels representing elementary photometric processes
➢Pseudo linear theory (surface/aerosol coupling is non-linear)➢All components are analytical (the model is differentiable)
f 3' s ,v , , =
0P
4sv
1−e−m
mf ms
f ms=17−
5Rozanov and Kokhanovsky ,2006
f i=0,2' s ,v , , =
T a s ,T av ,
1−S a⟨ s⟩f i s ,v ,
T a ,=e−/e−u−v −w 2
S a =a e−/b e−/c
u , v ,w depend onand ga , b , c , , are constants parameterized by gKokhanovsky et al. , 2005
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Mathematical design
Kalman filter approach
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
Z=FK
Z=[ToL1 s
1,v1,1 , ... ,ToL
N sN ,v
N ,N ] vector of N observationsK=[ k 0, k 1, k 2,] vector of parameters
F=[ f ' 0, f ' 1, f ' 2, f ' 3] matrix of angular kernel functions
{K=AT BC ap
−1K apC reg−1 K reg
C k−1
C k= AT AC ap−1C reg
−1 −1
covariancematrix
A , B scaled matrices for Z , F , normalized by the standard errorToL
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Two steps process
All clear data are used at full resolution
SAF-NWC CMa product is used here
Atmospherecharacterisation
ECMWF forecasts
TOA SEVIRI
radiances
Cloudmask
Partial atmospheric
correction
TOLradiancesscreened
DEMLSM
Surfacereflectance
TOLradiances
Inversion process:unmixing aerosol/surface
Aerosolproduct
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Validation against AERONET data sets
Location of the AERONET stations investigated in the
present study
Daily MSG AOT values are compared to AERONET ground measurements.
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Validation against AERONET data sets
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Validation with AERONET stations in Europe
Daily AODDaily AOD
AERONET
SEVIRI
bias=-0.026stdev=0.104R=0.54
bias=-0.027stdev=0.112R=0.56
bias=-0.022stdev=0.089R=0.69
False cloud detection ?
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Validation with AERONET stations in Africa
AERONET
SEVIRI
bias=-0.011stdev=0.233R=0.90
bias=-0.122stdev=0.277R=0.75
bias=-0.028stdev=0.092R=0.83
Daily AODDaily AOD
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Monitoring an aerosol event
AOD estimated for SEVIRI visible band
AOD from MODIS product superimposed over ocean (0.5°)
Good consistency is noticed withAOD up to 3 and beyond...
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Monitoring an aerosol event
SEVIRI AOD in blackAERONET AOD in greenover 6 Western African sites, March 1st-21th, 2006
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Intercomparison with MODIS product
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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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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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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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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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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
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.
This link can be used for 200 accesses - login ID and password: 80387941
http://www.agu.org/journals/jd/jd1010/2009JD012272/
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