Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks
Lanlan Rao, Jian Xu, Dmitry Efremenko, Diego Loyola, Adrian Doicu
Abstract
In this paper, we present three algorithms for aerosol parameters retrieval from TROPOMI measurements in the <inline-formula><tex-math notation="LaTeX">$\textrm {O}_{2}$</tex-math></inline-formula> 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.