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, 13–17August2016, https://doi.org/10.1145/2939672.2939785, 2016. a
Cogan, A., Boesch, H., Parker, R., Feng, L., Palmer, P., Blavier, J.-F., Deutscher, N. M., Macatangay, R., Notholt, J., Roehl, C., Warneke, T., and Wunch, D.: Atmospheric carbon dioxide retrieved from the Greenhouse gases Observing SATellite (GOSAT): comparison with ground-based TCCON observations and GEOS-Chem model calculations, J. Geophys. Res.-Atmos., 117, D21301, https://doi.org/10.1029/2012JD018087, 2012. a
Crisp, D., Fisher, B. M., O'Dell, C., Frankenberg, C., Basilio, R., Bösch, H., Brown, L. R., Castano, R., Connor, B., Deutscher, N. M., Eldering, A., Griffith, D., Gunson, M., Kuze, A., Mandrake, L., McDuffie, J., Messerschmidt, J., Miller, C. E., Morino, I., Natraj, V., Notholt, J., O'Brien, D. M., Oyafuso, F., Polonsky, I., Robinson, J., Salawitch, R., Sherlock, V., Smyth, M., Suto, H., Taylor, T. E., Thompson, D. R., Wennberg, P. O., Wunch, D., and Yung, Y. L.: The ACOS CO2 retrieval algorithm – Part II: Global data characterization, Atmos. Meas. Tech., 5, 687–707, https://doi.org/10.5194/amt-5-687-2012, 2012. a
Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A. M., Oyafuso, F. A., Frankenberg, C., O'Dell, C. W., Bruegge, C. J., Doran, G. B., Eldering, A., Fisher, B. M., Fu, D., Gunson, M. R., Mandrake, L., Osterman, G. B., Schwandner, F. M., Sun, K., Taylor, T. E., Wennberg, P. O., and Wunch, D.: The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products, Atmos. Meas. Tech., 10, 59–81, https://doi.org/10.5194/amt-10-59-2017, 2017. a
Crisp, D., O'Dell, C., Eldering, A., Fisher, B., Oyafuso, F., Payne, V., Drouin, B., Toon, G., Laughner, J., Somkuti, P., McGarragh, G., Merrelli, A., Nelson, R., Gunson, M., Frankenberg, C., Osterman, G., Boesch, H., Brown, L., Castano, R., Christi, M., Connor, B., McDuffie, J., Miller, C., Natraj, V., O’Brien, D., Polonski, I., Smyth, M., Thompson, D., and Granat, R.: Orbiting carbon observatory (OCO)-2 level 2 full physics algorithm theoretical basis document Version 3.0 – Rev 1, https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO_L2_ATBD.pdf (last access: 25 October 2023), 2021. a, b
David, L., Bréon, F.-M., and Chevallier, F.: XCO2 estimates from the OCO-2 measurements using a neural network approach, Atmos. Meas. Tech., 14, 117–132, https://doi.org/10.5194/amt-14-117-2021, 2021. a, b
Eldering, A., Taylor, T. E., O'Dell, C. W., and Pavlick, R.: The OCO-3 mission: measurement objectives and expected performance based on 1year of simulated data, Atmos. Meas. Tech., 12, 2341–2370, https://doi.org/10.5194/amt-12-2341-2019, 2019. a
Gribanov, K., Imasu, R., and Zakharov, V.: Neural networks for CO2 profile retrieval from the data of GOSAT/TANSO-FTS, Atmospheric and Oceanic Optics, 23, 42–47, https://doi.org/10.1134/S1024856010010094, 2010. a
Hamazaki, T., Kaneko, Y., Kuze, A., and Kondo, K.: Fourier transform spectrometer for greenhouse gases observing satellite (GOSAT), in: Enabling sensor and platform technologies for spaceborne remote sensing, Honolulu, Hawai'i, United States, 8–12November 2004, SPIE, 73–80, 5659, https://doi.org/10.1117/12.581198, 2005. a
Imasu, R., Matsunaga, T., Nakajima, M., Yoshida, Y., Shiomi, K., Morino, I., Saitoh, N., Niwa, Y., Someya, Y., Oishi, Y., Hashimoto, M., Noda, H., Hikosaka, K., Uchino, O., Maksyutov, S., Takagi, H., Ishida, H., Nakajima, T. Y., Nakajima, T., and Shi, C.:Greenhouse gases Observing SATellite 2 (GOSAT-2): mission overview, Progress in Earth and Planetary Science, 10, 33, https://doi.org/10.1186/s40645-023-00562-2, 2023. a
Iwasaki, C., Imasu, R., Bril, A., Oshchepkov, S., Yoshida, Y., Yokota, T., Zakharov, V., Gribanov, K., and Rokotyan, N.: Optimization of the Photon Path Length Probability Density Function-Simultaneous (PPDF-S) Method and Evaluation of CO2 Retrieval Performance Under Dense Aerosol Conditions, Sensors, 19, 1262, https://doi.org/10.3390/s19051262, 2019. a
Jin, Z., Tian, X., Han, R., Fu, Y., Li, X., Mao, H., Chen, C., and GAO, J.: Tan-Tracker global daily NEE and ocean carbon fluxes for 2015–2019 (TT2021 dataset), https://doi.org/10.11888/Meteoro.tpdc.271317, 2021. a
Keely, W. R., Mauceri, S., Crowell, S., and O'Dell, C. W.: A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10, Atmos. Meas. Tech., 16, 5725–5748, https://doi.org/10.5194/amt-16-5725-2023, 2023. a
Kuze, A., Suto, H., Nakajima, M., and Hamazaki, T.: Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring, Appl. Optics, 48, 6716–6733, https://doi.org/10.1364/AO.48.006716, 2009. a
Li, Y., Jiang, F., Jia, M., Feng, S., Lai, Y., Ding, J., He, W., Wang, H., Wu, M., Wang, J., Shen, F., and Zhang, L.: Improved estimation of CO2 emissions from thermal power plants based on OCO-2 XCO2 retrieval using inline plume simulation, Sci. Total Environ., 913, 169586, https://doi.org/10.1016/j.scitotenv.2023.169586, 2023. a, b, c
Liang, A., Gong, W., Han, G., and Xiang, C.: Comparison of satellite-observed XCO2 from GOSAT, OCO-2, and ground-based TCCON, Remote Sens.-Basel, 9, 1033, https://doi.org/10.3390/rs9101033, 2017. a
Liu, C., Wang, W., Sun, Y., and Shan, C.: TCCON data from Hefei, China, Release GGG2020R0, CaltechDATA [data set], https://doi.org/10.14291/tccon.ggg2020.hefei01.R0, 2022. a, b
Liu, Y., Wang, J., Yao, L., Chen, X., Cai, Z., Yang, D., Yin, Z., Gu, S., Tian, L., Lu, N., and Lyu, D.: The TanSat mission: preliminary global observations, Sci. Bull., 63, 1200–1207, https://doi.org/10.1016/j.scib.2018.08.004, 2018. a
Marchetti, Y., Rosenberg, R., and Crisp, D.: Classification of anomalous pixels in the focal plane arrays of Orbiting Carbon Observatory-2 and-3 via machine learning, Remote Sens.-Basel, 11, 2901, https://doi.org/10.3390/rs11242901, 2019. a
Matsunaga, T. and Tanimoto, H.: Greenhouse gas observation by TANSO-3 onboard GOSAT-GW, in: Sensors, Systems, and Next-Generation Satellites XXVI, SPIE, 12264, 86–90, 5–8 September 2022, Berlin, Germany, https://doi.org/10.1117/12.2639221, 2022. a
McDuffie, J., Bowman, K., Hobbs, Jo., Natraj, V., Sarkissian, E., Mike, M. T., and Val, S.: Reusable Framework for Retrieval of Atmospheric Composition (ReFRACtor) (Version 1.09), Zenodo [code], https://doi.org/10.5281/zenodo.4019567, 2020. a, b
Meng, G., Wen, Y., Zhang, M., Gu, Y., Xiong, W., Wang, Z., and Niu, S.: The status and development proposal of carbon sources and sinks monitoring satellite system, Carbon Neutrality, 1, 32, https://doi.org/10.1007/s43979-022-00033-5, 2022. a
Messerschmidt, J., Geibel, M. C., Blumenstock, T., Chen, H., Deutscher, N. M., Engel, A., Feist, D. G., Gerbig, C., Gisi, M., Hase, F., Katrynski, K., Kolle, O., Lavrič, J. V., Notholt, J., Palm, M., Ramonet, M., Rettinger, M., Schmidt, M., Sussmann, R., Toon, G. C., Truong, F., Warneke, T., Wennberg, P. O., Wunch, D., and Xueref-Remy, I.: Calibration of TCCON column-averaged CO2: the first aircraft campaign over European TCCON sites, Atmos. Chem. Phys., 11, 10765–10777, https://doi.org/10.5194/acp-11-10765-2011, 2011. a
Modest, M. F. and Mazumder, S.: Radiative heat transfer, Academic Press, https://doi.org/10.1016/C2018-0-03206-5, 2021. a
Morino, I., Ohyama, H., Hori, A., and Ikegami, H.: TCCON data from Rikubetsu, Hokkaido, Japan, Release GGG2020R0, CaltechDATA [data set], https://doi.org/10.14291/tccon.ggg2020.rikubetsu01.R0, 2022a. a, b
Morino, I., Ohyama, H., Hori, A., and Ikegami, H.: TCCON data from Tsukuba, Ibaraki, Japan, 125HR, Release GGG2020R0, CaltechDATA [data set], https://doi.org/10.14291/tccon.ggg2020.tsukuba02.R0, 2022b. a, b
EarthData: GES DISC, Data Collections, NASA, https://disc.gsfc.nasa.gov/datasets/, last access: 25 October 2023. a
Natraj, V. and Spurr, R. J.: A fast linearized pseudo-spherical two orders of scattering model to account for polarization in vertically inhomogeneous scattering–absorbing media, J. Quant. Spectrosc. Ra., 107, 263–293, https://doi.org/10.1016/j.jqsrt.2007.02.011, 2007. a
Nguyen, H., Katzfuss, M., Cressie, N., and Braverman, A.: Spatio-temporal data fusion for very large remote sensing datasets, Technometrics, Taylor & Francis, 56, 174–185, https://doi.org/10.1080/00401706.2013.831774, 2015. a
OCO-2 Science Team, Gunson, M., and Eldering, A.: OCO-2 Level 1B calibrated, geolocated science spectra, Retrospective Processing V10r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/6O3GEUK7U2JG, 2019. a
OCO-2 Science Team, Gunson, M., and Eldering, A.: OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V10r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/6SBROTA57TFH, 2020a. a
OCO-2 Science Team, Gunson, M., and Eldering, A.: OCO-2 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V10r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/E4E140XDMPO2, 2020b. a
O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C., Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J., Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval algorithm – Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5, 99–121, https://doi.org/10.5194/amt-5-99-2012, 2012. a, b, c
Payne, V. H., Drouin, B. J., Oyafuso, F., Kuai, L., Fisher, B. M., Sung, K., Nemchick, D., Crawford, T. J., Smyth, M., Crisp, D., Adkins, E., Hodges, J. T., Long, D. A., Mlawer, E. J., Merrelli, A., Lunny, E., and O’Dell, C. W.: Absorption coefficient (ABSCO) tables for the Orbiting Carbon Observatories: version 5.1, J. Quant. Spectrosc. Ra., 255, 107217, https://doi.org/10.1016/j.jqsrt.2020.107217, 2020. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, vol. 2, World Scientific, https://doi.org/10.1142/3171, 2000. a
Shiomi, K., Kawakami, S., Ohyama, H., Arai, K., Okumura, H., Ikegami, H., and Usami, M.: TCCON data from Saga, Japan, Release GGG2020R0, CaltechDATA [data set], https://doi.org/10.14291/tccon.ggg2020.saga01.R0, 2022. a, b
Sierk, B., Fernandez, V., Bézy, J.-L., Meijer, Y., Durand, Y., Courrèges-Lacoste, G. B., Pachot, C., Löscher, A., Nett, H., Minoglou, K., Boucher, L., Windpassinger, R., Pasquet, A., Serre, D., and te Hennepe, F.: The Copernicus CO2M mission for monitoring anthropogenic carbon dioxide emissions from space, in: International Conference on Space Optics–ICSO 2020, Vol. 11852, SPIE, 1563–1580, 30 March–2 April 2021, Antibes, France, https://doi.org/10.1117/12.2599613, 2021. a
Spurr, R.: LIDORT and VLIDORT: Linearized pseudo-spherical scalar and vector discrete ordinate radiative transfer models for use in remote sensing retrieval problems, Light scattering reviews 3: Light scattering and reflection, Springer, Berlin, Heidelberg, 229–275, https://doi.org/10.1007/978-3-540-48546-9_7, 2008. a
TCCON DATA ACHIEVE: Total Carbon Column Observing Network (TCCON), Caltech Library Research Data Repository, https://tccondata.org/ (last access: 25 October 2023), 2023. a
Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J., Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The Total Carbon Column Observing Network, Philos. T. Roy. Soc. A, 369, 2087–2112, https://doi.org/10.1098/rsta.2010.0240, 2011. a
Wunch, D., Toon, G. C., Sherlock, V., Deutscher, N. M., Liu, C., Feist, D. G., and Wennberg, P. O.: The Total Carbon Column Observing Network's GGG2014 Data Version, CaltechDATA, https://doi.org/10.14291/tccon.ggg2014.documentation.r0/1221662, 2015. a
Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, C. M., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson, J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold, S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L., Podolske, J., Hillyard, P. W., Kawakami, S., Dubey, M. K., Parker, H. A., Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp, D., and Eldering, A.: Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) measurements with TCCON, Atmos. Meas. Tech., 10, 2209–2238, https://doi.org/10.5194/amt-10-2209-2017, 2017. a
Xie, F. and Ren, T.: MLP-based XCO2 Retrieval Model for East Asian OCO-2 Nadir Observation (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.12598972, 2024. a
Yoshida, Y., Kikuchi, N., Morino, I., Uchino, O., Oshchepkov, S., Bril, A., Saeki, T., Schutgens, N., Toon, G. C., Wunch, D., Roehl, C. M., Wennberg, P. O., Griffith, D. W. T., Deutscher, N. M., Warneke, T., Notholt, J., Robinson, J., Sherlock, V., Connor, B., Rettinger, M., Sussmann, R., Ahonen, P., Heikkinen, P., Kyrö, E., Mendonca, J., Strong, K., Hase, F., Dohe, S., and Yokota, T.: Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data, Atmos. Meas. Tech., 6, 1533–1547, https://doi.org/10.5194/amt-6-1533-2013, 2013. a
Zehr, S.: The sociology of global climate change, WIREs Clim. Change, 6, 129–150, https://doi.org/10.1002/wcc.328, 2015. a
Zhao, Z., Xie, F., Ren, T., and Zhao, C.: Atmospheric CO2 retrieval from satellite spectral measurements by a two-step machine learning approach, J. Quant. Spectrosc. Ra., 278, 108006, https://doi.org/10.1016/j.jqsrt.2021.108006, 2022. a, b
Zhou, M., Wang, P., Nan, W., Yang, Y., Kumps, N., Hermans, C., and De Mazière, M.: TCCON data from Xianghe, https://doi.org/10.14291/tccon.ggg2020.xianghe01.R0, 2022. a, b