The CISAR algorithm
The Combined Inversion of Surface and Atmosphere pRoperties (CISAR) algorithm is an advanced mathematical method developed by Rayference for the joint retrieval of surface reflectance and aerosol or cloud properties from observations acquired by space-based imagers.
The CISAR algorithm relies on the FASTRE radiative transfer model that describes surface reflectance and atmospheric absorption/scattering processes. The lowest level represents the surface. The lower layer hosts the scattering particles (aerosols and clouds). Molecular scattering and absorption are also taking place in that layer which is radiatively coupled with the surface for both the single and the multiple scattering. The upper layer is only subject to molecular absorption (Govaerts and Luffarelli, 2018).
The inversion of the FASTRE model within the CISAR algorithm against satellite observations provides accurate estimates of the surface reflectance field, aerosol concentration and single scattering properties in each processed spectral band (Luffarelli and Govaerts, 2019).
The inversion relies on the Optimal Estimation approach based on Bayes' theorem. This one-dimensional variational retrieval scheme seeks an optimal balance between information that can be derived from the observations, and the one that is derived from prior knowledge of the system. An estimate of the retrieval uncertainty is also provided following a formal mathematical framework to propagate the uncertainty (A. Sayer et al., 2020).
The CISAR algorithm has been first applied on MSG/SEVIRI observations in the framework of the ESA Aerosol_cci project to retrieve hourly aerosol properties over land and sea surfaces. It has next been applied on PROBA-V data in the context of the ESA PV-LAC study for the retrieval of surface reflectance. Following this successful feasibility study, the entire Vegetation-1, -2 and PROBA-V archive has been processed in the framework of the SPAR@MEP ESA project. The original CISAR concept has been generalised to also include the retrieval of cloud micro-physical properties within the context of the ESA CIRCAS project. This development represents a major milestone as it allows the retrieval of aerosol properties without the need of an external cloud mask. Finally, MSG/SEVIRI observations acquired simulataneous by Meteosat-8 and -11 have been processed over the commonly observed area in the context of the DUST2MSG ESA project.
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