日本地球惑星科学連合2022年大会

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[E] ポスター発表

セッション記号 A (大気水圏科学) » A-CG 大気海洋・環境科学複合領域・一般

[A-CG38] 衛星による地球環境観測

2022年5月31日(火) 11:00 〜 13:00 オンラインポスターZoom会場 (11) (Ch.11)

コンビーナ:沖 理子(宇宙航空研究開発機構)、コンビーナ:本多 嘉明(千葉大学環境リモートセンシング研究センター)、高薮 縁(東京大学 大気海洋研究所)、コンビーナ:松永 恒雄(国立環境研究所地球環境研究センター/衛星観測センター)、座長:本多 嘉明(千葉大学環境リモートセンシング研究センター)、高薮 縁(東京大学 大気海洋研究所)、松永 恒雄(国立環境研究所地球環境研究センター/衛星観測センター)

11:00 〜 13:00

[ACG38-P04] MWPMで導出したGOSAT-2/TANSO-CAI-2エアロゾル特性の他衛星と地上観測との比較および誤差調査

*橋本 真喜子1、石 崇3中島 映至2 (1.宇宙航空研究開発機構、2.国立環境研究所、3.Aerospace Information Research Institute, Chinese Academy of Sciences)

キーワード:エアロゾル、衛星リモートセンシング

The Greenhouse Gases Observing Satellite-2 (GOSAT-2) launched in 2019 has Cloud and Aerosol Imager 2 (TANSO-CAI-2). CAI-2 has ten bands composed of seven wavelengths at 340, 380, 443, 550, 674, 869 and 1630 nm with spatial resolution of 460 m (and 920 m at 1630nm), and performs two-directional observation in forward and backward directions. CAI-2 is characterized by having two ultraviolet (UV) bands that dust and black carbon (BC) particles are sensitive for light absorption.
We have developed aerosol retrieval algorithm called MWPM (Multiple wavelengths and pixels method) (Hashimoto and Nakajima, 2017). The method simultaneously retrieves fine and coarse mode AOT and single scattering albedo (SSA) by using several wavelengths and pixels where surface reflectance is difference spatially. The method is useful for aerosol retrieval over spatially inhomogeneous surface region like an urban area where is a source region of anthropogenic aerosols.
We have applied the algorithm to CAI-2 data and retrieve aerosol properties, such as fine and coarse mode AOT, SSA, BC volume fraction in fine mode particles and Ångström exponent (AE). Furthermore, equivalent value of PM2.5 (ePM2.5) is calculated by derived aerosol properties. It is important to derive accurate aerosol optical properties to calculate and evaluate ePM2.5.
We have compared retrieved aerosol optical properties to ground-based observation, SKYNET and AERONET. In the case that Ag is less than 0.1, root mean square deviation (RMSD) of AOT at 550nm is around 0.1, and correlation coefficient is over 0.7. SSAs at 340 and 380nm are correlated between CAI-2 and SKYNET. CAI-2 AE is basically underestimated compared with AETRONET and SKYENT. We also have compared the results with other satellite data such as VIIRS, MODIS and Himawari-8/AHI aerosol products. CAI-2 AOT is a little overestimate, but in good agreement with those of the other satellites with correlation coefficient over 0.8. We will show the comparison results and discuss the results for modification of the algorithm in the future.