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

Related authors

Spectral Signatures of Zenith Sky Radiances from Surface-Based Sky Radiometers: Implications for Clear- and Cloudy-Sky Detection
Pradeep Khatri, Hitoshi Irie, Hiroshi Kobayashi, Isamu Morino, Yoshitaka Jin, Tadahiro Hayasaka, Hironobu Iwabuchi, Tamio Takamura, and Yoshiki Takayama
EGUsphere, https://doi.org/10.5194/egusphere-2026-2943,https://doi.org/10.5194/egusphere-2026-2943, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Ammonia variability and trends from globally distributed FTIR measurements and model simulations
Beatriz Herrera, Enrico Dammers, Martine De Maziere, Omaira Garcia, Michel Grutter, James W. Hannigan, Dylan B. A. Jones, Nicholas Jones, Emmanuel Mahieu, Maria Makarova, Kazuyuki Miyazaki, Isamu Morino, Isao Murata, Ivan Ortega, Mathias Palm, Anatoly Poverovskii, Takashi Sekiya, Dan Smale, Hannah Sill, Wolfgang Stremme, Ralf Sussmann, Geoffrey Toon, Corinne Vigouroux, Wei Wang, Tyler Wizenberg, and Kimberly Strong
EGUsphere, https://doi.org/10.5194/egusphere-2026-3138,https://doi.org/10.5194/egusphere-2026-3138, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Towards routine shipborne measurements of columnar CO2, CH4, CO, and NO2: a case study for tracking regional-scale emission patterns
Vincent Enders, Astrid Müller, Matthias Max Frey, Frank Hase, Ralph Kleinschek, Marvin Knapp, Benedikt Löw, Isamu Morino, Shin-Ichiro Nakaoka, Hideki Nara, Hiroshi Tanimoto, Sanam N. Vardag, Karolin Voss, and André Butz
Atmos. Meas. Tech., 19, 2633–2655, https://doi.org/10.5194/amt-19-2633-2026,https://doi.org/10.5194/amt-19-2633-2026, 2026
Short summary
Characterization and correction of detector nonlinearity in Fourier-transform interferograms
Bavo Langerock, Minqiang Zhou, Martine De Mazière, Mahesh Kumar Sha, Filip Desmet, Bart Dils, Corinne Vigouroux, Rigel Kivi, Isamu Morino, Mathias Palm, Gopala Krishna Darbha, Soumik Banerjee, Sujata Ray, and Mohmmed Talib
EGUsphere, https://doi.org/10.5194/egusphere-2026-1890,https://doi.org/10.5194/egusphere-2026-1890, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
TROPOMI/WFMD v2.0: Improved retrievals of XCH4 and XCO with XGBoost-based quality filtering
Oliver Schneising, Heinrich Bovensmann, Michael Buchwitz, Matthias Buschmann, Nicholas M. Deutscher, David W. T. Griffith, Jonas Hachmeister, Frank Hase, Laura T. Iraci, Rigel Kivi, Isamu Morino, Hirofumi Ohyama, Christof Petri, Maximilian Reuter, John Robinson, Coleen Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Wei Wang, Thorsten Warneke, Damien Weidmann, Debra Wunch, Minqiang Zhou, and Hartmut Bösch
Atmos. Meas. Tech., 19, 2407–2435, https://doi.org/10.5194/amt-19-2407-2026,https://doi.org/10.5194/amt-19-2407-2026, 2026
Short summary

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
Download
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.
Share