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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.

The retrieval of aerosol and cloud properties such as their optical thickness and/or layer/top height requires the selection of a model that describes their microphysical properties. We demonstrate that, if there is not enough information for an appropriate microphysical model selection, the solution’s accuracy can be improved if the model uncertainty is taken into account and appropriately quantified. For this purpose, we design a retrieval algorithm accounting for the uncertainty in model selection. The algorithm is based on (i) the computation of each model solution using the iteratively regularized Gauss–Newton method, (ii) the linearization of the forward model around the solution, and (iii) the maximum marginal likelihood estimation and the generalized cross-validation to estimate the optimal model. The algorithm is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements corresponding to the Earth Polychromatic Imaging Camera (EPIC) instrument onboard the Deep Space Climate Observatory (DSCOVR) satellite. Our numerical simulations show that the heuristic approach based on the thesolution minimizing the residual, which is frequently used in literature, is completely unrealistic when both the aerosol model and surface albedo are unknown.

In this work, we address the image geolocation issue that is present in the imagery of EPIC/DSCOVR (Earth Polychromatic Imaging Camera/Deep Space Climate Observatory) Level 1B version 2. To solve it, we develop an algorithm that automatically computes a registration correction consisting of a motion (translation plus rotation) and a radial distortion. The correction parameters are retrieved for every image by means of a regularised non-linear optimisation process, in which the spatial distances between the theoretical and actual locations of chosen features are minimised. The actual features are found along the coastlines automatically by using computer vision techniques. The retrieved correction parameters show a behaviour that is related to the period of DSCOVR orbiting around the Lagrangian point L 1 . With this procedure, the EPIC coastlines are collocated with an accuracy of about 1.5 pixels, thus significantly improving the original registration of about 5 pixels from the imagery of EPIC/DSCOVR Level 1B version 2.