1. Algorithm Status: 1.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 !!! Described in Dubovik et al., AMT, 2011 Main Objective: to make algorithm practical Réunion Parasol-Calcul-Tosca au CNES, PARIS, 10 février, Paris

2. Pressing needs: 1.Development of the FRAMEWORK 2. Accelerating the performance of the core inversion algorithm !!! Algorithm developments Réunion Parasol-Calcul-Tosca au CNES, PARIS, 10 février, Paris

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

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

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

6. Sequential processing block-by- block of N x x N y x N t zones Climatology Storage of the retrieval results block p block p+1 N x x N y zone

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 a priori constraints optimized to presence of noise Dubovik et al., AMT, 2011

9. Forward Model Full Radiative Transfer: F(,  ) Full Radiative Transfer: F(,  )  ( ),  0 ( ), P(,  ) Simulated satellite observations: f(a p ) BRDF Vector of retrieved parameters : a aer - aerosol properties a surf - surface properties Vector of retrieved parameters : a aer - aerosol properties a surf - surface properties a aer a surf N  BPDF ? N  ? to parallelize ? ESA, Frascati, Italy, June 26, 2012

10. Calculation of derivatives Forward Model f(a i +Δa i ) Forward Model f(a i +Δa i ) Vectors of parameters and observations : a p f p Vectors of parameters and observations : a p f p to parallelize ? a i = a i + Δa i Jacobian : K p to memorize ? N n  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. Calculation of K p and f p in multi-pixel retrieval Forward Model to parallelize ? Calculation of derivatives K p Sparse K p, f N pixel  ESA, Frascati, Italy, June 26, 2012

13. Dealing sparse matrices 100  X  1 2 3.. 4 100 123.. 100 Matrix size: (100 X 100) X (100 X 100) sparse extremely sparse ESA, Frascati, Italy, June 26, 2012

14. Full Radiative Transfer model 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. OUTCOME: Radiative Transfer (RT)Package ESA, Frascati, Italy, June 26, 2012

15. Synergy GEOSTATIONARY and POLAR (multi-pixel approach) X ∆z X ∆y ∆x Y t ( t 2 ; x ; y ) multi-days observations X-Variability Constraints Y-Variability Constraints Time-Variability Constraints FCI/MTG 3MI/EPSSG ( t 3 +iΔt ; x ; y ) ( t 3 ; x ; y ) ( t 1 ; x ; y ) Additionally the retrieval can use the data from: - CALIPSO; - MODIS, - AERONET, etc.