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  • 1. Aerosol Optical Depth based on a temporal and directional analysis of SEVIRI observationsDominique 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 IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 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 conditionTransmission 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 IGARSS Conference 2011, Vancouver, Canada
  • 7. Model parametrizationMethod:-discriminate directional signatures of the surface and aerosols by isolating at high solarangles the higher sensitivity to atmospheric properties.-use Kalman Filter with different characteristic time scale for land and atmosphericvariations 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 2ρ 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 [ ] IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 11. Validation against AERONET data setsDaily MSG AOT values are compared to AERONET ground measurements. Location of the AERONET stations investigated in the present study IGARSS Conference 2011, Vancouver, Canada
  • 12. Validation against AERONET data setsIGARSS Conference 2011, Vancouver, Canada
  • 13. Validation with AERONET stations in Europe bias=-0.026 stdev=0.104 AERONET R=0.54 SEVIRIFalse cloud bias=-0.027detection ? stdev=0.112 R=0.56 Daily AOD bias=-0.022 stdev=0.089 R=0.69 IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 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... IGARSS Conference 2011, Vancouver, Canada
  • 16. Monitoring an aerosol event SEVIRI AOD in black AERONET AOD in green over 6 Western African sites, March 1st-21th, 2006IGARSS Conference 2011, Vancouver, Canada
  • 17. Intercomparison with MODIS productIGARSS Conference 2011, Vancouver, Canada
  • 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. IGARSS Conference 2011, Vancouver, Canada
  • 19. Method ApproximationsMie 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 IGARSS Conference 2011, Vancouver, Canada
  • 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.IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 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 IGARSS Conference 2011, Vancouver, Canada
  • 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.frThis link can be used for 200 accesses - login ID and password: 80387941http://www.agu.org/journals/jd/jd1010/2009JD012272/