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<p>The retrieval of atmospheric aerosol properties from satellite remote sensing is a complex and under-determined inverse problem. Traditional retrieval algorithms, based on radiative transfer models, must make approximations and assumptions to reach a unique solution or repeatedly use the expensive forward models to be able to quantify uncertainty. The recently introduced Invertible Neural Networks (INNs), a machine learning method based on Normalizing Flows, appear particularly suited for tackling inverse problems. They simultaneously model both the forward and the inverse branches of the problem, and their generative aspect allows them to efficiently provide non-parametric posterior distributions for the retrieved parameters, which can be used to quantify the retrieval uncertainty. So far INNs have successfully been applied to low-dimensional idealised inverse problems and even to some simpler scientific retrieval problems. Still, satellite aerosol retrievals present particular challenges, such as the high variability of the surface reflectance signal and the often comparatively small aerosol signal in the top-of-the-atmosphere (TOA) measurements.</p> <p>In this study, we investigate the use of INNs for retrieving aerosol optical depth (AOD) and its uncertainty estimates at the pixel level from MODIS TOA reflectance measurements. The models are trained with custom synthetic datasets of TOA reflectance-AOD pairs made by combining the MODIS Dark Target algorithm’s atmospheric look-up tables and a MODIS surface reflectance product. The INNs are found to perform emulation and inversion of the look-up tables successfully. We initially train models adapted to different surface types by focusing our application on limited regional and seasonal contexts. The models are applied to real measurements from the MODIS sensor, and the generated AOD retrievals and posterior distributions are compared to the corresponding Dark Target and AERONET retrievals for evaluation and discussion.</p>

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