XCO2 retrieval using Yonsei CArbon Retrieval algorithm: impact of aerosol information on aerosol-loaded scenes

T he Orbiting Carbon Observatory-2 (OCO-2) satellite, launched in 2014, was designed to measure atmospheric carbon dioxide (CO₂) and has provided extensive observations with the accuracy and resolution needed to estimate regional scale CO₂ fluxes. Urban areas account for about 70 % of the overall contribution to global atmospheric CO₂ concentration, however, anthropogenic CO₂ emission in urban areas is usually accompanied by aerosols, which is a main reason of retrieval errors.

The OCO-2 satellite belongs to the Afternoon constellation so called A-train, along with the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) and the Aqua satellites. In this study, we developed a retrieval algorithm (Yonsei CArbon Retrieval; YCAR) based on the Optimal Estimation method, and used aerosol information at the location of OCO-2 measurement from collocated measurements of Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) onboard CALIPSO and MODerate-resolution Imaging Spectrometer (MODIS) onboard Aqua. We used aerosol optical depth and aerosol profile from MODIS and CALIOP data, respectively. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and compared its prognostic error statistic to diagnostic retrieval error. Also, the impact of realistic aerosol information on CO₂ retrieval was studied for aerosol-loaded scenes.