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
Cansot, E., Pistre, L., Castelnau, M., Landiech, P., Georges, L., Gaeremynck, Y., and Bernard, P.: MicroCarb instrument, overview and first results, in: International Conference on Space Optics – ICSO 2022, edited by: Minoglou, K., Karafolas, N., and Cugny, B., International Society for Optics and Photonics, Dubrovnik, Croatia, 3–7 October 2022, SPIE, 12777, 1277734, https://doi.org/10.1117/12.2690330, 2023. a
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
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794, San Francisco, CA, USA, 13–17 August 2016, https://doi.org/10.1145/2939672.2939785, 2016. 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.
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