Potential improvement of XCO₂ retrieval of the OCO-2 by having aerosol information from the A-train satellites

ABSTRACT

Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO₂ retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO₂ (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information.

KEYWORDS:

• Carbon dioxide
• satellite observations
• OCO-2
• retrieval
• aerosols

Acknowledgments

This work was supported by the National Institute of Meteorological Research [Meteorological work support technology development research] “Climate change prediction support and application research(KMA2018-00321)” and the Samsung PM2.5 Strategic Research Program. OCO-2 L1b and L2 data in this study were produced by the OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology, and obtained from the OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center. The CALIOP data are available from https://subset.larc.nasa.gov/calipso/. TCCON data were obtained from the TCCON data archive hosted by CaltechDATA, and are available from https://tccondata.org/ (Anmeyondo: 10.14291/tccon.ggg2014.anmeyondo01.R0/1149284, Darwin: 10.14291/tccon.ggg2014.darwin01.R0/1149290, Hefei: 10.14291/tccon.ggg2014.hefei01.R0, Lamont: 10.14291/tccon.ggg2014.lamont01.R1/1255070, Tsukuba: 10.14291/tccon.ggg2014.tsukuba02.R2).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The retrieval results of YCAR algorithm in this research can be found in https://figshare.com/s/c5fcdc5a253d6e23b42b.

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2023.2209968.

Additional information

Funding

The work was supported by the Samsung Advanced Institute of Technology [2021-11-2232].