Articles | Volume 17, issue 13
https://doi.org/10.5194/amt-17-3949-2024
https://doi.org/10.5194/amt-17-3949-2024
Research article
 | 
03 Jul 2024
Research article |  | 03 Jul 2024

Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements

Fengxin Xie, Tao Ren, Changying Zhao, Yuan Wen, Yilei Gu, Minqiang Zhou, Pucai Wang, Kei Shiomi, and Isamu Morino

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Cited articles

Bacour, C., Bréon, F.-M., and Chevallier, F.: On the challenge posed by the estimation of XCO2 from OCO-2 observations in near-real time based on artificial neural network, IWGGMS-19, Paris, France, 4–6 July 2023, https://iwggms19.com/wp-content/uploads/2023/05/ID_097_cedric_bacour.pdf (last access: 25 October 2023), 2023. a, b
Bréon, F.-M., David, L., Chatelanaz, P., and Chevallier, F.: On the potential of a neural-network-based approach for estimating XCO2 from OCO-2 measurements, Atmos. Meas. Tech., 15, 5219–5234, https://doi.org/10.5194/amt-15-5219-2022, 2022. a, b
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Carvalho, A. R., Ramos, F. M., and Carvalho, J. C.: Retrieval of carbon dioxide vertical concentration profiles from satellite data using artificial neural networks, Trends in Computational and Applied Mathematics, 11, 205–216, https://tcam.sbmac.org.br/tema/article/view/90 (last access: 25 October 2023), 2010. a
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Short summary
This study demonstrates a new machine learning approach to efficiently and accurately estimate atmospheric carbon dioxide levels from satellite data. Rather than using traditional complex physics-based retrieval methods, neural network models are trained on simulated data to rapidly predict CO2 concentrations directly from satellite spectral measurements.