Algorithm Status: 1.Core Algorithm is developed and performs well: - uses very elaborated aerosol and RT models; - based on rigorous statistical optimization;

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

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

CORE INVERSION

BLOCK ofPARASOL level 1 cloud-screened DATA Nx ✕ Nx ✕ Nt

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

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 Nx× Ny× Nt pixels

o The zones can be treated independently; o A special treatment will be needed for the edges of zones.


Sub-zones Nx× Ny are treated independently using “core

inversion”

t

CORE INVERSION

Nx× Ny = 100 x 100 ?

Nt >>1 (for simplicity)

Nt = 1? (for simplicity)


Sequential processing block-by-block of Nx x Ny x Nt zones

Climatology

Storage of the retrieval resultsblock p block p+1

Nx x Ny zone

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


« AERONET like » statistically optimized « no look-up tables » inversion

a prioriconstraints

optimized to presence of

noise

Dubovik et al., AMT, 2011

Forward Model

Full Radiative Transfer:F(,…)

(), 0(), P(,)

Simulated satellite observations: f(ap)

BRDF

Vector of retrieved parameters :aaer - aerosol properties asurf - surface properties

€

aaer asurf

N… BPDF? N…

? N…

to parallelize ?to parallelize ?

to parallelize ?

to parallelize ?


Calculation of derivatives

Forward Model f(ai+Δai)

Vectors of parameters and observations :

ap f p

€

to parallelize ?

ai = ai + Δai

∂fj∂ai

≈fj ai +Δai( )−fj ai( )

Δai

Jacobian : Kp

to memorize ?

Nn…


« PARASOL » statistically optimized « no look-up tables » multi-pixel inversion

Dubovik et al., AMT, 2011

single - pixel

a priori multi-pixel constraints

Calculation of Kp and f p

in multi-pixel retrieval

Forward Model

€

to parallelize ?

Kp

Kp 0 0

0 Kp2 0

0 0 Kp3

⎛

⎝

⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟

Calculation of derivatives Kp

fp ap( )

fpfpfp

⎛

⎝

⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟

SparseKp, f

Npixel…00


Dealing sparse matrices

Ap,1 0 0 ...

0 Ap,2 0 ...

0 0 Ap,3 ...

... ... ... ...

⎛

⎝

⎜⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟⎟

+Ωiner

KpT (f

p −f*)

Kp2T (f2

p −f2*)

Kp3T (f3

p −f3*)

...

⎛

⎝

⎜⎜⎜

⎞

⎠

⎟⎟⎟+Ω

inerap

100 X00

1

2

3

..

4

100

1 2 3 .. 100

Matrix size: (100 X 100) X (100 X 100)

sparse

extremely sparse


Full Radiative Transfer model 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


Synergy GEOSTATIONARY and POLAR(multi-pixel approach)

X

∆z

X∆y

∆x

Y

t

( t( t22; ; x x ; ; y )y )

multi-days

observations

X-Variability Constraints

Y-Variability

Constraints

Tim

e-Va

riabi

lity

Con

stra

ints

FCI/MTG 3MI/EPSSG( t( t33+iΔt+iΔt; ; x x ; ; y )y )

( t( t33+iΔt+iΔt; ; x x ; ; y )y )

( t( t33+iΔt+iΔt; ; x x ; ; y )y )

( t( t33; ; x x ; ; y )y )

( t( t11; ; x x ; ; y )y )

Additionally the retrieval can use the data from: - CALIPSO; - MODIS, - AERONET, etc.

Documents

Algorithm Status: 1.Core Algorithm is developed and performs well: - uses very elaborated aerosol and RT models; - based on rigorous statistical optimization;