Validation of Retrieved Aerosol Optical Properties over Northeast Asia for Five Years from GOSAT TANSO-Cloud and Aerosol Imager

An aerosol retrieval algorithm was developed from Thermal And Near infrared Sensor for carbon Observation-Cloud and Aerosol Imager (TANSO-CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT). The algorithm retrieves aerosol optical depth (AOD), size distribution of aerosol, and aerosol type in 0.1 degree grid resolution by look-up tables, which is used in retrieving optical properties of aerosol using inversion products from Aerosol Robotic NETwork (AERONET) sun-photometer observation. To improve the accuracy of aerosol algorithm, first, this algorithm considered the annually estimated radiometric degradation factor of TANSO-CAI suggested by Kuze et al. (2014). Second, surface reflectance was determined by two methods: one using the clear sky composite method from CAI measurements and the other the database from MODerate resolution Imaging Sensor (MODIS) surface reflectance data. At a given pixel, the surface reflectance is selected by using normalized difference vegetation index (NDVI) depending on season (Hsu et al., 2013). In this study, the retrieved AODs were compared with those of AERONET and MODIS dataset for different season over five years. Comparisons of AODs between AERONET and CAI show reasonable agreement with correlation coefficients of 0.65 ~ 0.97 and regression slopes between 0.7 and 1.2 for the whole period, depending on season and sites. Moreover, those between MODIS and CAI for the same period show agreements with correlation coefficients of 0.7 ~ 0.9 and regression slopes between 0.7 and 1.0, depending on season and regions. The results show reasonably good correlation, however, the largest error source in aerosol retrieval has been surface reflectance of TANSO-CAI due to its 3-days revisit orbit characteristics.