Algorithm Status:

1. Described in Dubovik et al., AMT, 2011 Core Algorithm is developed and performs well: - uses very elaborated aerosol and RT models; - based on rigorous statistical optimization; - performs well in numerical test (Dubovik et al. 2011, Kokhanovsky et al. 2010); - has a lot of flexibility for constraining retrieval: both for single-pixel and/or multi-pixel scenarios) 2. Issues: - too long - 10 sec per 1 pixel!!! - needs to be optimally set for operational processing - cloud – screening – need to be improved !!! Algorithm Status: Main Objective: to make algorithm practical Réunion Parasol-Calcul-Tosca au CNES, PARIS , 10 février, Paris

2. Algorithm developments Pressing needs: Development of the FRAMEWORK !!! 2. Accelerating the performance of the core inversion algorithm Réunion Parasol-Calcul-Tosca au CNES, PARIS , 10 février, Paris

3. “Science” Algorithm BLOCK of PARASOL level 1 cloud-screened DATA Nx ✕ Nx✕ Nt Strength of a priori constraints: single-pixel and multi-pixel CORE INVERSION Results in pixels – “neighbours” (if available) RESULTS INITIAL GUESS Climatology of surface reflectance ESA , Frascati, Italy, June 26, 2012

4. Parallel processing of PARASOL data zone of Nx× Ny× Nt pixels Decomposition of the processing area • The zones can be treated independently; • A special treatment will be needed for the edges of zones. • The zones should have the same number of pixels; • The zones should be coherently arranged in time and space; • The climatology and retrieval data storage should have similar zone structure ESA , Frascati, Italy, June 26, 2012

5. Sub-zones Nx× Ny are treated independently using “core inversion” CORE INVERSION Nx× Ny = 100 x 100 ? t Nt >>1 (for simplicity) Nt = 1? (for simplicity) ESA , Frascati, Italy, June 26, 2012

6. Nx x Ny zone Sequential processing block-by-block of Nx x Ny x Nt zones Storage of the retrieval results block p+1 block p Climatology

7. Accelerating the performance of core inversion algorithm: • Optimizing the initial conditions(FRAMEWORK); • Optimizing sparse matrix operation and inversion; • Benefitting from parallel programming; • Using common memory arrays, etc. (FRAMEWORK ?) • Optimizing the performance of radiative transfer (OS), that includes: • - finding trade-off between speed and accuracy; • - avoiding re-calculation of the same variables; • - optimizing the land surface reflectance modeling; • - parallelizing some integrations in the code ESA , Frascati, Italy, June 26, 2012

8. « AERONET like » statistically optimized « no look-up tables » inversion Dubovik et al., AMT, 2011 optimized to presence of noise a priori constraints

9. Vector of retrieved parameters : aaer - aerosol properties asurf - surface properties to parallelize ? to parallelize ? aaer asurf t(l), w0(l), P(l,Q) ? BRDF BPDF Nl= 1,…,6 Nl= 1,…,6 Forward Model to parallelize ? to parallelize ? Full Radiative Transfer: F(l,Q,…) ? Nl= 1,…,6 Simulated satellite observations: f(ap) ESA , Frascati, Italy, June 26, 2012

10. Vectors of parameters and observations : apf p to parallelize ? ai = ai + Δai to memorize ? Calculation of derivatives Forward Model f(ai+Δai) Nn= 1,…,61 Jacobian : Kp ESA , Frascati, Italy, June 26, 2012

11. « PARASOL » statistically optimized « no look-up tables » multi-pixel inversion Dubovik et al., AMT, 2011 single - pixel a priori multi-pixel constraints

12. to parallelize ? Calculation of derivatives Kp Forward Model Calculation of Kp and f pin multi-pixel retrieval Npixel= 1,…,100 Kp, f Sparse ESA , Frascati, Italy, June 26, 2012

13. 100 X 100 extremely sparse 100 1 2 3 4 .. sparse 1 Dealing sparse matrices 2 3 .. 100 Matrix size: (100 X 100) X (100 X 100) ESA , Frascati, Italy, June 26, 2012

14. Full Radiative Transfer model OUTCOME: Radiative Transfer (RT)Package • OS code- Radiative Transfer Code of Successive Orders of Scattering [Lenoble et al. 2007] (developed by M. Herman) • The RT package allows rigorous RT calculations : • Accounts for surface bi-directional reflection using RPV model; • Accounts for polarization effects of both aerosol and surface; • Accounts for vertical variability of aerosol properties; • Allows correction for the forward peak using “truncation” approach (that allows improve speed of calculation without loosing accuracy); • allows a number of “trade off” between computation time and accuracy: • - one can choose the number of moments in expansion of angular functions (e.g. 7); • - the number of vertical layers can be changed; • - set of angles in Phase Matrix can be arbitrary • - several aerosol modes can be used, etc. ESA , Frascati, Italy, June 26, 2012

15. Synergy GEOSTATIONARY and POLAR(multi-pixel approach) 3MI/EPSSG FCI/MTG ( t3+iΔt; x ; y ) ( t3; x ; y ) ( t3+iΔt; x ; y ) multi-days observations ( t3+iΔt; x ; y ) ( t2; x ; y ) t ( t1; x ; y ) Time-Variability Constraints ∆z X Y Additionally the retrieval can use the data from: - CALIPSO; - MODIS, - AERONET, etc. Y-Variability Constraints ∆y ∆x X X-Variability Constraints