Abstract
To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.
Highlights
Aerosols affect the Earth’s climate directly by disturbing the Earth’s radiation budget and indirectly by altering cloud processes
The results of aerosol retrieval computed by means of a Bayesian algorithm that takes into account the uncertainty in aerosol model selection are presented
The solution corresponding to a specific aerosol model is characterized by a relative evidence which is used to construct (i) the maximum solution estimate, corresponding to the aerosol model with the highest evidence, and (ii) the mean solution estimate, representing a linear combination of solutions weighted by their evidences
Summary
Aerosols affect the Earth’s climate directly by disturbing the Earth’s radiation budget and indirectly by altering cloud processes. The information on the aerosol optical depth can be retrieved from the data provided by satellite sensors, such as the Advanced Very High Resolution Radiometer (AVHRR) [1], the Moderate Resolution Imaging Spectroradiometer (MODIS) [2], the Visible Infrared. Imaging Spectroradiometer (MODIS) characterizes a set of aerosol models and provides global distributions of aerosol types for different seasons based on a cluster analysis of the AERONET climatology [2,11]. For the first time, we use the Bayesian approach to jointly retrieve the aerosol optical depth and aerosol layer height from TROPOMI/S5P (Sentinel-5 Precursor) [26] measurements in the O2 A band (758–771 nm). The accuracy of the Bayesian model selection approach is analyzed in Section 4 for synthetic measurements and in Section 5 for real data over a wild fire scene in South Africa
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