Articles | Volume 16, issue 23
https://doi.org/10.5194/amt-16-5725-2023
https://doi.org/10.5194/amt-16-5725-2023
Research article
 | 
29 Nov 2023
Research article |  | 29 Nov 2023

A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10

William R. Keely, Steffen Mauceri, Sean Crowell, and Christopher W. O'Dell

Related authors

A global perspective on CO2 satellite observations in high AOD conditions
Timo H. Virtanen, Anu-Maija Sundström, Elli Suhonen, Antti Lipponen, Antti Arola, Christopher O'Dell, Robert R. Nelson, and Hannakaisa Lindqvist
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-77,https://doi.org/10.5194/amt-2024-77, 2024
Revised manuscript under review for AMT
Short summary
A novel data-driven global model of photosynthesis using solar-induced chlorophyll fluorescence
Russell Doughty, Yujie Wang, Jennifer Johnson, Nicholas Parazoo, Troy Magney, Zoe Pierrat, Xiangming Xiao, Luis Guanter, Philipp Köhler, Christian Frankenberg, Peter Somkuti, Shuang Ma, Yuanwei Qin, Sean Crowell, and Berrien Moore III
EGUsphere, https://doi.org/10.22541/essoar.168167172.20799710/v1,https://doi.org/10.22541/essoar.168167172.20799710/v1, 2024
Short summary
Synchrony of African rainforest solar induced chlorophyll fluorescence and environmental factors
Russell Doughty, Michael C. Wimberly, Dan Wanyama, Helene Peiro, Nicholas Parazoo, Sean Crowell, and Moses Azong Cho
EGUsphere, https://doi.org/10.5194/egusphere-2023-3022,https://doi.org/10.5194/egusphere-2023-3022, 2024
Short summary
The importance of digital elevation model accuracy in XCO2 retrievals: improving the Orbiting Carbon Observatory 2 Atmospheric Carbon Observations from Space version 11 retrieval product
Nicole Jacobs, Christopher W. O'Dell, Thomas E. Taylor, Thomas L. Logan, Brendan Byrne, Matthäus Kiel, Rigel Kivi, Pauli Heikkinen, Aronne Merrelli, Vivienne H. Payne, and Abhishek Chatterjee
Atmos. Meas. Tech., 17, 1375–1401, https://doi.org/10.5194/amt-17-1375-2024,https://doi.org/10.5194/amt-17-1375-2024, 2024
Short summary
The GeoCarb greenhouse gas retrieval algorithm: simulations and sensitivity to sources of uncertainty
Gregory R. McGarragh, Christopher W. O'Dell, Sean M. R. Crowell, Peter Somkuti, Eric B. Burgh, and Berrien Moore III
Atmos. Meas. Tech., 17, 1091–1121, https://doi.org/10.5194/amt-17-1091-2024,https://doi.org/10.5194/amt-17-1091-2024, 2024
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Estimation of biogenic volatile organic compound (BVOC) emissions in forest ecosystems using drone-based lidar, photogrammetry, and image recognition technologies
Xianzhong Duan, Ming Chang, Guotong Wu, Suping Situ, Shengjie Zhu, Qi Zhang, Yibo Huangfu, Weiwen Wang, Weihua Chen, Bin Yuan, and Xuemei Wang
Atmos. Meas. Tech., 17, 4065–4079, https://doi.org/10.5194/amt-17-4065-2024,https://doi.org/10.5194/amt-17-4065-2024, 2024
Short summary
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
Atmos. Meas. Tech., 17, 3949–3967, https://doi.org/10.5194/amt-17-3949-2024,https://doi.org/10.5194/amt-17-3949-2024, 2024
Short summary
A new method for estimating megacity NOx emissions and lifetimes from satellite observations
Steffen Beirle and Thomas Wagner
Atmos. Meas. Tech., 17, 3439–3453, https://doi.org/10.5194/amt-17-3439-2024,https://doi.org/10.5194/amt-17-3439-2024, 2024
Short summary
Accounting for the effect of aerosols in GHGSat methane retrieval
Qiurun Yu, Dylan Jervis, and Yi Huang
Atmos. Meas. Tech., 17, 3347–3366, https://doi.org/10.5194/amt-17-3347-2024,https://doi.org/10.5194/amt-17-3347-2024, 2024
Short summary
Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS
Sora Seo, Pieter Valks, Ronny Lutz, Klaus-Peter Heue, Pascal Hedelt, Diego Loyola, Hanlim Lee, and Jhoon Kim
EGUsphere, https://doi.org/10.5194/egusphere-2024-1137,https://doi.org/10.5194/egusphere-2024-1137, 2024
Short summary

Cited articles

Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E., McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., Staelin, D. H., Strow, L. L., and Susskind, J.: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems, IEEE T. Geosci. Remote, 41, 253–264, https://doi.org/10.1109/tgrs.2002.808356, 2003. 
Blumenstock, T., Hase, F., Schneider, M., Garcia, O. E., and Sepulveda, E.: TCCON data from Iza na (ES), Release GGG2014R1, TCCON data archive, CaltechDATA [data set], https://doi.org/10.14291/TCCON.GGG2014.IZANA01.R1, 2017. 
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Nöel, S., Rozanov, V. V., Chance, K. V., and Goede, A.: SCIAMACHY—Mission objectives and measurement modes, J. Atmos. Sci., 56, 127–150, https://doi.org/10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2, 1999. 
Breiman, L.: Classification and Regression Trees, 1st edn., Routledge, New York, https://doi.org/10.1201/9781315139470, 1984. 
CAMS (Copernicus Atmosphere Monitoring Service): Validation report for the CO2 fluxes estimated by atmospheric inversion, v18r2, Version 1.0, https://atmosphere.copernicus.eu/sites/default/files/2019-08/CAMS73_2018SC1_D73.1.4.1-2018-v1_201907_v1.pdf (last access: 10 January 2022), 2021. 
Download
Short summary
Measurement errors in satellite observations of CO2 attributed to co-estimated atmospheric variables are corrected using a linear regression on quality-filtered data. We propose a nonlinear method that improves correction against a set of ground truth proxies and allows for high throughput of well-corrected data.