{"slug": "efficient-neural-network-based-high-resolution-radiative-transfer-for-co-and-to", "title": "Efficient Neural-Network-Based High-Resolution Radiative Transfer for CO___ Retrieval, and Application to Interferometric Sensing", "summary": "Researchers proposed a feedforward multilayer perceptron (MLP) surrogate to predict top-of-atmosphere radiances in the CO2 weak band, achieving efficient and precise forward modeling for the NanoCarb imaging interferometer. The study, part of the Horizon Europe SCARBOn project, aims to reduce uncertainties in CO2 and CH4 emission estimates by enabling low-cost satellite constellations with improved revisit frequency and spatial coverage. The MLP-based radiative transfer surrogate, trained with a combined mean absolute error loss on radiances and Jacobians, shows promising results for CO2 concentration retrieval.", "body_md": "arXiv:2608.14645v1 Announce Type: new\nAbstract: Studying climate change requires reducing uncertainties in CO2 and CH4 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 CO2 and CH4 emissions in the atmosphere. However, estimating CO2 and CH4 concentrations with high revisit and spatial coverage 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 CO2 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 CO2 concentration retrieval.", "url": "https://wpnews.pro/news/efficient-neural-network-based-high-resolution-radiative-transfer-for-co-and-to", "canonical_source": "https://arxiv.org/abs/2608.14645", "published_at": "2026-08-18 04:00:00+00:00", "updated_at": "2026-08-18 04:13:04.901539+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["Horizon Europe SCARBOn", "NanoCarb"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/efficient-neural-network-based-high-resolution-radiative-transfer-for-co-and-to", "markdown": "https://wpnews.pro/news/efficient-neural-network-based-high-resolution-radiative-transfer-for-co-and-to.md", "text": "https://wpnews.pro/news/efficient-neural-network-based-high-resolution-radiative-transfer-for-co-and-to.txt", "jsonld": "https://wpnews.pro/news/efficient-neural-network-based-high-resolution-radiative-transfer-for-co-and-to.jsonld"}}