## 1 Introduction
Previous studies have reported that aerosol microphysical property information, e.g., aerosol volume concentration and size parameters, can be obtained from CAPI measurements [20; 21]. The _a priori_ of aerosol model parameters in TanSat CO\({}_{2}\) retrieval will be improved by using CAPI measurements. However, this simultaneous aerosol measurement depends on the mission budget and instrument design and is not commonly achieved for each satellite. Under this circumstance, following the retrieval by the optimal estimation method (OEM), the a priori assumption of aerosol parameters becomes important when there is a lack of information in the measurements. A time- and location-specific 3D aerosol status provided by the chemical transport model (CTM) is an alternative option to obtain first estimates of aerosol parameters that are close to reality to reduce retrieval errors from the aerosol parameter uncertainty. For example, in the Japanese National Institute for Environmental Studies fullphysical (NIES-FP) retrieval algorithm of GOSAT, for every observed day, AOD computed through the Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) is used as a priori value. Additionally, during the XCO\({}_{2}\) retrieval process, the AODs of both fine and coarse-mode aerosols are retrieved synchronously, while aerosol type and size distribution data are provided by external sources [5,22]. In the NASA Atmospheric CO\({}_{2}\) Observations from Space (ACOS) retrieval algorithm for OCO-2 [19], three aerosol and profile parameters (AOD at 755 nm, peak height, and width of profile) for five types of scattering particles, including liquid water and ice clouds, two dominant tropospheric aerosols, and one stratospheric aerosol, have been corrected synchronously as the state vector. Both tropospheric aerosols were selected from those available in the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis system, i.e., dust, smoke, sea salt, sulfate aerosol, organic carbon, and black carbon, which constitute the two largest fractions of AOD based on monthly averaged MERRA data. In general, there are two types of aerosol solutions which can be used in CO\({}_{2}\) retrieval algorithms. Both of them have advantages and disadvantages. One type uses a combination of multiple types of aerosols and retrieves the profile and loading of each type, such as in ACOS, NIES-FP and University of Leicester full physical (UoL-FP) algorithms [18,19,22,23], which are more related to the CTM model simulation. The other aerosol combination is depicted by an empirical aerosol model, namely, the wavelength dependence of aerosol optical properties (RemoTeC algorithm from institutes in Netherland and Germany and Institute of Atmospheric Physics, Chinese Academy of Sciences (IAPCAS)-TanSat algorithm from China), which show more dynamic ranges of aerosol microphysical parameters (e.g., size distribution) [10,24,25]. Although Gamma distribution is used as the aerosol size distribution in some CO\({}_{2}\) algorithms (such as RemoTeC), the bimodal size distribution aerosol model, which has been widely used in aerosol retrievals, has also been proven to describe a realistic empirical aerosol model well [26,27]. Broad band measurements in NIR/SWIR, which are always used for aerosol remote sensing, contain limited independent information on aerosol parameters for several reasons. Fundamentally, aerosol parameters have no specific spectral structure that could be easily characterized from the measured spectrum of interference of several gas absorption lines, e.g., CO\({}_{2}\) and H\({}_{2}\)O. The continual scattering of aerosols in a broad band shows a similar effect to surface reflectance, especially multiple scatterings between aerosols and the surface and is the main issue in satellite remote sensing of aerosols. However, hyperspectral measurements that describes the lines in NIR/SWIR are different.