Algorithm Theoretical Basis Document GlobAlbedo Aerosol Retrieval Peter North, Andreas Heckel Swansea University L. Guanter, R. Preusker, J. Fischer Free University of Berlin

Aims AOT retrieval for all sensors based on common set of aerosol models and RTM Avoid a priori assumptions on surface albedo for AOT retrieval Development of separate inversion constraints to exploit the characteristics of the instruments Per pixel uncertainty estimates for error propagation

Spatial interpolation Algorithm Scheme (A)ATSR RTOA (8 channels) MERIS RTOA (13 channels) VGT RTOA Iterative inversion (image subsets) Atmospheric correction using LUT: F(RTOA, Ma , t550 , O3, H2O, H,S, V, R, ) -> RSURF(,S, V, R, ) Optimise t550 and Ma to minimise EMOD Test fit with surface model: N Y RTOA (Land, cloud-free) Initial estimate: Ma , t550 Auxiliary data: O3,H2O, DEM, Ma, t550 and Dt550 (Sparse grid) Water/cloud masking RTOA (Land, cloud-free) Initial estimate: Ma , t550 Auxiliary data: O3,H2O, DEM, Aerosol Retrieval Ma, t550 and Dt550 (Sparse grid) Spatial interpolation (A)ATSR Ma, t550 Dt550 MERIS Ma, t550 Dt550 VGT Ma, t550 Dt550

TOA - SDR Inversion LUT Approximation of the radiative transfer problem Inversion of TOA reflectance to SDRs based on lookup tables Ratm, gl and rl tabulated in lookup tables

Aerosol Models LUTs provide atmospheric parameters for a set of 7 different aerosol models Potential to retrieve aerosol type Option to set fixed aerosol model to increase performance and consistency

Trace Gas Absorption Fitting of surface models requires correction of trace gas absorptions Additional LUT provide trace gas transmittance tg(AMFgeo)

Surface Reflectance Models Angular Model (ATSR-2,AATSR) Spectral Model (MERIS, VGT) Mixed Model (AATSR) Apply spectral model to AATSR channels Linear combination of angular and spectral models

Optimal AOT and Uncertainty Minimum of error metric determines optimal AOT Aim: tuning of constraints to retrieve same AOT with the different instruments Per-pixel uncertainty is defined as: Emin : Minimum value of error metric a : Curvature at minimum k : scaling (to be defined validation)

Tomsk

TOA Reflectance

Pixel classification Removal of cloudy and non-land pixel

Spatial Binning Assumption of smooth AOT field 9x9 pixel bins “Improves” AATSR co-registration Enables better surface model fits

Retrieved AOT Low resolution AOT field with gaps Gaps larger than in original resolution due to binning Needs gap filling

Spatial Interpolation Gaps interpolated and rescaled to original input resolution

Final AOT product Final AOT map with high resolution valid pixel mask applied

Preliminary AOT Test Data-Set AATSR TOA RGB AATSR AOT AATSR DAOT VGT TOA RGB VGT AOT VGT DAOT

AATSR Tomsk 15.5.2003 False colour RGB AOT AOT uncertainty Improved AOT AOT uncertainty

Tomsk: Synergy, MERIS Lv2, MODIS

Correlation TOA Reflectance VGT - AATSR

AOT Validation Quantitative comparison vs. Aeronet measurements Spatial average 15x15km Temporal overlap 30min Qualitative comparison vs. other satellite products (MERIS L2, MODIS)

Summary Draft processor for AOT retrieval of MERIS, VGT and AATSR implemented in BEAM 1 year test data-set of AOT and per-pixel uncertainty of AATSR and VGT processed Known issues / Next steps Refinement of constraints necessary to retrieve homogenous aerosol for all three sensors Single aerosol model only Optimisations of implementation to increase performance