| 12 Aug 2026
Accelerating greenhouse gas retrievals with neural network-based forward models
Abstract. Greenhouse gas (GHG) retrievals rely on repeated evaluations of computationally expensive physics-based forward models, which limit the feasibility of near-real-time retrievals and timely detection of emission hotspots. A promising alternative is to replace these forward models with fast machine learning emulators trained to approximate their input-output mapping, while retaining the overall retrieval algorithm.
Here, we assess the feasibility of this approach in the context of the Sentinel-5 mission, systematically comparing two different emulation strategies: an end-to-end approach, which directly approximates the full forward model with neural networks, and a hybrid approach, which combines fast non-scattering simulations with a neural network-based correction for atmospheric scattering effects. We comprehensively validate each emulator in the full retrieval chain, evaluating their impact on the accuracy of retrieved XCO2 and XCH4.
Our results show that a hybrid approach is needed to meet the stringent accuracy requirements on XCH4 and XCO2. While the end-to-end emulator achieves large speed-ups exceeding a factor of 300, it introduces considerable errors of 7.22 ppb for XCH4 and 4.25 ppm for XCO2 compared to full-physics retrievals, and fails to generalize to high-emission scenarios beyond the training range. In contrast, the hybrid approach can effectively leverage the information provided by the non-scattering approximation, reducing emulator-induced retrieval errors to less than 1.5 ppb for XCH4 and 0.5 ppm for XCO2, while still being an order of magnitude faster than full-physics retrievals and maintaining robust performance for high-emission scenarios.
Together, these results pave the way for operational deployment of neural network-based forward models in GHG retrievals from Sentinel-5, and more broadly demonstrate the potential of hybrid machine learning emulators to facilitate timely and accurate processing of the rapidly growing data volumes from modern satellite missions.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
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