Algorithm for Retrieval of Aerosol Optical Properties over East Asia from TANSO-Cloud and Aerosol Imager

Friday, 19 December 2014
Sanghee Lee1, Jhoon Kim1, Mijin KIM1, Myungje Choi1, Sujung Go1, HyunKwang Lim1, Mi-Lim Ou2 and Tae-Young Goo2, (1)Yonsei University, Seoul, South Korea, (2)National Institute of Meteorological Research, Jeju, South Korea
An aerosol retrieval algorithm for East Asia was developed from Thermal And Near infrared Sensor for carbon Observation-Cloud and Aerosol Imager (TANSO-CAI) launched in January 2009 onboard the Greenhouse Gases Observing Satellite (GOSAT). To investigate aerosol optical properties over the East Asia, inversion products from AERONET sun-photometer observation were analyzed and look-up table (LUT) approach to retrieve optical properties of aerosol was adopted using the method from Kim et al. (2007). To define fine and coarse aerosols in calculating LUT, optical properties of aerosol such as refractive index and volume peak ratio (VPR) were validated by AERONET products with AOD values of 0.15, 0.45, 0.8, 1.2, 1.8, and 2.6, respectively. For the aerosol absorptivity, single scattering albedo (SSA) of 0.95 was used as a threshold, i.e. SSA greater than the threshold as non-absorbing aerosol and SSA less than the values as absorbing with VPR, respectively. The algorithm retrieves aerosol optical depth (AOD), size distribution of aerosol, and aerosol type in 0.015 degree x 0.015 degree resolution and surface reflectance was estimated using the clear sky composite method. To distinguish aerosol absorptivity, the reflectance difference method was considered using 4 channels of TANSO-CAI. In this study, the retrieved AOD was compared with those of AERONET and MODIS dataset from January 2012 and August 2014. The retrieved AOD shows reasonable correlation coefficient, however, the largest error sources in aerosol retrieval has been surface reflectance. The retrieved aerosol properties were provided to improve data availability and minimize the errors due to aerosol in carbon dioxide retrieval using TANSO-FTS onboard the same platform. Based on the results with CAI algorithm developed, we are continuously improving the algorithm for better performance.