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This study presents an efficient neural-network-based surrogate model for high-resolution radiative transfer simulations, aimed at improving CO2 concentration retrieval from satellite measurements for climate monitoring.
A deep learning framework for probabilistic CO2 column retrieval from OCO-2 spectra using Laplace approximations and normalizing flows, achieving faster inference and better uncertainty quantification than traditional methods.