SPAR@MEP Project’s objectives and approach Yves Govaerts and Marta Luffarelli Rayference Proba-V QWG#9, Brussels 17 – 18 April 2019 SPAR@MEP Project’s objectives and approach Yves Govaerts and Marta Luffarelli Rayference Proba-V QWG#9, Brussels 17 – 18 April 2019 SPAR@MEP Project’s objectives and approach Yves Govaerts and Marta Luffarelli Rayference Proba-V QWG#9, Brussels 17 – 18 April 2019 SPAR : Spot-PROBA-V Surface Aerosol Retrieval
Transcript
SPAR@MEP
Project’s objectives and approach
Yves Govaerts and Marta LuffarelliRayference
Proba-V QWG#9, Brussels 17 – 18 April 2019
SPAR@MEP
Project’s objectives and approach
Yves Govaerts and Marta LuffarelliRayference
Proba-V QWG#9, Brussels 17 – 18 April 2019
SPAR@MEP
Project’s objectives and approach
Yves Govaerts and Marta LuffarelliRayference
Proba-V QWG#9, Brussels 17 – 18 April 2019
SPAR : Spot-PROBA-V Surface Aerosol Retrieval
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Overview
• The CISAR algorithm;
• CISAR past and current projects;
• Data processing within SPAR@MEP;
• Risks;
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The CISAR algorithm
The CISAR algorithm is an innovative aerosol retrieval algorithm based the continuous variations of the state variables in the solution space to secure consistency within an Optimal Estimation retrieval framework.
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The CISAR algorithm
• An Optimal Estimation method seeks the best balance between information derived from the observations and the prior information.
• An aerosol class represents a strong prior information of the aerosol single scattering property spectral variations thought no uncertainties are associated to this prior information.
• The use of predefined aerosol classes is not compatible with an Optimal Estimation Approach as described in Govaerts et al. 2010.
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The CISAR algorithm
The radiative transfer equation requires the aerosol single scattering properties:
• Single scattering albedo;
• Phase function (asymmetry parameter);
• Optical thickness;
Aerosol classes to sample the solution
space (after Govaerts et al., 2010);
These classes represent prior
information on aerosol single
scattering properties.
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The CISAR algorithm
The radiative transfer equation requires the aerosol single scattering properties:
• Single scattering albedo;
• Phase function (asymmetry parameter);
• Optical thickness;
4/4/4
Luffarelli, Marta, and Yves Govaerts. 2019. “Joint
Retrieval of Surface Reflectance and Aerosol Properties
with Continuous Variation of the State Variables in the
Solution Space – Part 2: Application to Geostationary
and Polar-Orbiting Satellite Observations.” Atmospheric
Measurement Techniques 12 (2): 791–809.
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The CISAR algorithm
4/4/4
Definition of mono-modal mode vertices that bounds the solution space
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The CISAR algorithm
4/4/4
Definition of mono-modal mode that bounds the solution space
Solution space in the red spectral region
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The CISAR algorithm
4/4/4
Definition of mono-modal mode that bounds the solution space
Solution space in the red spectral region
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The CISAR algorithm
Noise-free simulation in the
principal plane with a dual-
mode aerosol model.
Retrieval of the single
scattering properties from the
combination of two fine
mono-mode and one coarse
mono-mode.
Govaerts and Luffarelli,
(2018).
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The CISAR algorithm
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TEST ENVIRONMENT
OPERATIONAL
ENVIRONMENT
GEDAP: GEneric Data Processing Chain
CISAR data
processing
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CISAR past and current projects
Projects Timeframe Purpose
QA4ECV (FP7) 2014 - 2018 Derive surface albedo from SEVIRI
FIDUCEO (H2020) 2015 - 2019 Derive aerosol CDR from MVIRI/VIS
aerosol_cci (ESA) 2015 - 2017 Derive hourly AOT from SEVIRI
PV-LAC (ESA) 2016 - 2018 Feasibility study to derive AOT from PROBA-V
aerosol_cc+ (ESA) 2019 - 2022 Derive aerosol from SLSTR with full uncertainty
(non-diagonal) terms
SPAR@MEP (ESA) 2019 - 2021 Derive aerosol and surface reflectance from
SPTO-VGT and PROBA-V (1998 – now)
Explore GPU technology
Rayference is also involved into the InDust cost action
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SPAR@MEP : Objectives
Overall objective : Derive a consistent Spot-PROBA-V Aerosol and surface reflectance long-term data record in the MEP with the CISAR algorithm.
Deliverables:
• A long-term (1998 – 2018) data record (LTDR) of AOT and BRF at 1km resolution over key macro-regions around selected AERONET stations;
• A global processing for few key years (e.g., 5 years) is required at a spatial resolution suitable for climate studies, i.e., 5km. (aerosol_cci+ 10km)
aerosol_cci+ format will be used for the aerosol product.
18/04/2019
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SPAR@MEP : LTDR key regions
18/04/2019
1999 AERONET stations
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SPAR@MEP : LTDR key regions
18/04/2019
2017 AERONET stations
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SPAR@MEP : Main Work Packages
• WP2 : Data radiometric quality verification of VGT-1, -2 and PROBA-V over Libya-4;
• WP3 : GEDAP-CISAR MEP update (Scheduler, Input Tile maker, final product format, [GPU]);
• WP4 : LTDR Product generation V1 (5x5 pixels around AERONET stations) and V2 (full key areas);
• WP5 : LTDR Product evaluation against AERONET and MODIS/GlobAlbedo products;
• WP6 : Global Product generation (5 years) probably pre-MODIS era and PROBA-V;
• WP7 : Global Product evaluation
18/04/2019
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SPAR@MEP : Work packages
18/04/2019
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SPAR@MEP : Risks
• L1b data temporal consistency: The 20+ year MEP archive has been acquired by 3 different radiometers with different performances.
• Cloud mask: The PV-LAC study has demonstrated some possible omission and commission errors in the PROBA-V cloud mask that might affect the retrieval of aerosol properties.
• Radiometric uncertainty: The inversion of FASTRE, the CISAR forward RTM, relies on an Optimal Estimation scheme. The accurate estimation of the uncertainties associated to all input values is of critical importance, in particular for the satellite observations where these uncertainties should be ideally provided at the pixel-level.
18/04/2019
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SPAR@MEP : Risks
• Processing Speed: CISAR algorithm is very CPU intensive and has been designed to run on dedicated multicore CPUs. GPU technology will be explored.
