In this algorithm, the inversion method is a combination of an optimal method and smoothing constraint for the state vector. Furthermore, this method has been combined with the direct radiation transfer calculation (RTM) accelerated by neural network-based method, EXAM (Takenaka et al., 2011), using Rster code.
We applyed MWPM combined with EXAM to GOSAT/TANSO-CAI (Cloud and Aerosol Imager). CAI is a supplement sensor of TANSO-FTS, dedicated to measure cloud and aerosol properties. CAI has four bands, 380, 674, 870 and 1600 nm, and observes in 500 meters resolution for band1, band2 and band3, and 1.5 km for band4. Retrieved parameters are aerosol optical properties, such as aerosol optical thickness (AOT) of fine and coarse mode particles at a wavelenth of 500nm, a volume soot fraction in fine mode particles, and ground surface albedo of each observed wavelength by combining a minimum reflectance method and Fukuda et al. (2013). We will show the results of global aerosol properties from CAI and discuss the accuracy of the algorithm for various surface types.
We will extend the algorithm for analysis of GOSAT-2/TANSO-CAI-2 that will be launched in FY2018, and also GCOM/C-SGLI and Himawari-8/AHI data.
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