## 1 Introduction  
The concentration of carbon dioxide (CO\({}_{2}\)) in the atmosphere has been rapidly increasing since the 1750s, and CO\({}_{2}\) has been recognized as one of the most significant greenhouse gases responsible for global warming [1]. To understand and mitigate anthropogenic CO\({}_{2}\) emissions, regional carbon flux estimation is required for identifying CO\({}_{2}\) sources and sinks. State-of-the-art data assimilation methods, coupled with modern atmospheric transport modeling, can provide reliable estimates of CO\({}_{2}\) surface flux when using a high-quality measurement dataset [2]. Unfortunately, limits on our understanding of CO\({}_{2}\) emissions and uptake lead to large uncertainties in climate change research due to a lack of accurate and continual measurements with global coverage. The cutting-edge technologies utilized in the manufacturing of spectrometers onboard satellites provide an opportunity to improve the global measurement coverage. The path finder instrument, Scanning Imaging Absorption Spectrometer for Atmospheric Chatography (SCIAMACHY), which is onboard the European Space Agency's (ESA) ENVISAT, first detected CO\({}_{2}\) signals in the atmosphere using near infrared (NIR) and shortwave infrared (SWIR) bands which are sensitive to near surface CO\({}_{2}\) concentration, providing reliable observations of global CO\({}_{2}\) column-averaged dry-air mole fractions (XCO\({}_{2}\)). The Japanese satellite, Greenhouse Gases Observing Satellite (GOSAT), was successfully launched in 2009 and showed a good performance in-orbit [3; 4]. The XCO\({}_{2}\) retrieved from GOSAT measurements indicated precision better than 1% [5], which significantly enhanced the knowledge of regional CO\({}_{2}\) surface fluxes [6; 7]. In July 2014, NASA launched the Orbiting Carbon Observatory-2 (OCO-2), which started to provide XCO\({}_{2}\) data products with high quality to the public [8; 9]. The Chinese carbon dioxide observation satellite mission (TanSat) began in 2010 and was launched on 22 December 2016 [10; 11; 12]. Recent studies have released preliminary XCO\({}_{2}\) maps produced from TanSat measurements [13]. Improvements in the accuracy and precision of XCO\({}_{2}\) retrievals will significantly contribute to reducing uncertainty in the estimation of CO\({}_{2}\) fluxes. Ideally, the accuracy of XCO\({}_{2}\) should be better than 0.3% (~1 ppm) to guarantee the accuracy of the regional surface carbon flux calculations [14]. Scattering in the atmosphere from aerosols and clouds could introduce serious errors in retrievals because the scattering changes the light path and results in different absorption by the gases. [PERSON], et al. [15] concluded that uncertainty in the aerosol optical depth (AOD) should be controlled to within 0.05 to maintain the total error in the CO\({}_{2}\) column at less 0.5%. The simulations of OCO-2 observations and the corresponding linear error analysis indicated that the uncertainties in the aerosols were among the dominant error sources encountered in CO\({}_{2}\) retrievals [16; 17]. Additionally, according to error analyses of simulated GOSAT observations, imperfectly characterized clouds and aerosols are the dominant sources of error encountered during XCO\({}_{2}\) retrievals [18; 19], meaning that to approach high-precision CO\({}_{2}\) retrieval, a method of eliminating uncertainty in aerosol property calculations is required, especially in heavily air-polluted areas. Synchronous aerosol measurements have been highly recommended for several satellite missions to reduce the retrieval errors from aerosol and cloud scattering. For instance, TanSat carries an ancillary instrument, the Clouds and Aerosol Polarimetric Imager (CAPI), which aims to improve the knowledge of aerosol and cloud contamination by obtaining simultaneous measurements with the Atmospheric Carbon Dioxide Grating Spectrometer (ACGS).