Aerosol Parameters Retrieval from TROPOMI/S5P Using Physics-Based Neural Networks

Abstract

inn this paper, we present three algorithms for aerosol parameters retrieval from TROPOMI measurements in the O2 A-band. These algorithms use neural networks (i) to emulate the radiative transfer model and a Bayesian approach to solve the inverse problem, (ii) to learn the inverse model from the synthetic radiances, and (iii) to learn the inverse model from the principal-component transform of synthetic radiances. The training process is based on full-physics radiative transfer simulations. The accuracy and efficiency of the neural network based retrieval algorithms are analyzed with synthetic and real data.