Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows

In three linesDeep learning framework for retrieving atmospheric CO2 from NASA's OCO-2 satellite spectra. Uses Laplace approximations and normalizing flows for uncertainty quantification. Inference orders of magnitude faster than operational algorithms, with better-calibrated non-Gaussian posterior estimates.

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