ACCELERATED HIGH-RESOLUTION RADIATIVE TRANSFER SIMULATION FOR CO2 CONCENTRATION ESTIMATION FROM NANOCARB MEASUREMENTS

Jordan Lontsi Tedongmo, Pablo Musé, Andres Almansa, Gabriele Facciolo

TL;DR: We propose a feedforward MLP surrogate for efficient radiative transfer modeling in the CO₂ weak band, trained with combined radiance and Jacobian losses, enabling precise and fast CO$_2$ concentration retrievals using the NanoCarb concept.

Abstract: Studying climate change requires reducing uncertainties in CO\(_2\) and CH\(_4\) emission estimates to better distinguish anthropogenic from natural sources, which motivates spaceborne measurements with improved revisit frequency and spatial coverage. In this context, the Horizon Europe SCARBOn project assesses a low-cost satellite constellation featuring the NanoCarb imaging interferometer as its core sensor for monitoring CO\(_2\) and CH\(_4\) emissions in the atmosphere. However, estimating CO\(_2\) and CH\(_4\) concentrations from NanoCarb measurements poses significant challenges: full-physics retrieval algorithms commonly used rely on repeated high-resolution radiative transfer (RT) simulations, which are computationally expensive when using line-by-line RT models. As an alternative, we propose in this study a feedforward multilayer perceptron (MLP) surrogate designed to accurately and efficiently predict top-of-atmosphere radiances in the CO\(_2\) weak band, using a combined mean absolute error (MAE) loss on radiances and RT Jacobians to preserve both spectral accuracy and sensitivity to geophysical parameters. Coupling the MLP-based RT surrogate with the NanoCarb instrumental response yields an efficient and precise forward model for NanoCarb measurements, which shows promising results for CO\(_2\) concentration retrievals.

Submission Number: 38