GOSAT-2/TANSO-CAI-2 Aerosol properties and sensitivity test of aerosol models in aerosol retrieval algorithm

Abstract:

Atmospheric aerosol particles scatter and absorb sunlight, affecting the earth's radiation budget, and it is still an uncertain factor in its estimation. Aerosol particles also affect human health. It is important to understand the optical properties of aerosols in urban areas, which is the main source of anthropogenic aerosols. The multi-wavelength and multi-pixel method (MWPM) is a method developed so that aerosol characteristics can be derived with high accuracy even in areas where the ground surface reflectance is non-uniform like urban areas (Hashimoto and Nakajima, 2017). The method simultaneously retrieves fine and coarse mode of aerosol optical thickness (AOT) and single scattering albedo (SSA) by using several wavelengths and spatial difference of surface reflectance. MWPM has been accelerated by neural network-based method called EXAM (Takenaka et al., 2011).

Greenhouse gases Observing Satellite-2 (GOSAT-2) that is a successor of GOSAT was launched on October 29th, 2018. GOSAT-2 has Cloud and Aerosol Imager called TANSO-CAI-2 (or CAI-2). CAI-2 is characterized by having two ultraviolet (UV) bands at 340 and 380 nm. The algorithm is applied to CAI-2 aerosol retrieval, and aerosol optical properties such as AOT of fine and coarse particles, Ångström exponent (AE), and BC volume fraction are derived. Then, equivalent value of PM2.5 (ePM2.5) is calculated.

In the algorithm, aerosol models are assumed for fine and coarse mode particles. However, aerosol particle size and these light absorption properties are different in different regions. It is important to know a bias occurred by assumed aerosol models when the aerosol products are validated by ground-based observations, and when the algorithm are modified, so we have investigated the impact on the products by assumed aerosol models, such as aerosol size distribution, complex refractive indices, aerosol height etc.. We show the result of the sensitivity test and discuss aerosol models in the algorithm using a comparison result between aerosol properties.

Acknowledgment:This research was supported by the Environment Research and Technology Development Fund (JPMEERF20192001) of the Environmental Restoration and Conservation Agency of Japan, and by JAXA/NIES/MOE GOSAT and GOSAT-2 project team