1148_daily_estimates_of_the_tropospheric_aerosol_optical_thickness_over_land_surface_from_msg_geostrationary_observation.pdf...

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


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



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



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



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


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


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


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


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


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.


Validation against AERONET data sets


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 ?


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


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...


Monitoring an aerosol event

SEVIRI AOD in blackAERONET AOD in greenover 6 Western African sites, March 1st-21th, 2006


Intercomparison with MODIS product


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'.


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


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.


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


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.

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1148_daily_estimates_of_the_tropospheric_aerosol_optical_thickness_over_land_surface_from_msg_geostrationary_observation.pdf...

Technology

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tan s tan v cos f