日本地球惑星科学連合2018年大会

講演情報

[EE] ポスター発表

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

[A-CG36] 衛星による地球環境観測

2018年5月24日(木) 10:45 〜 12:15 ポスター会場 (幕張メッセ国際展示場 7ホール)

コンビーナ:沖 理子(宇宙航空研究開発機構)、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、共同)、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)、Allen HL Huang (University of Wisconsin Madison)

[ACG36-P03] Influence of the aerosol under the cloud layer on calculation of the shortwave radiation flux in China from Himawari-8/AHI satellite measurement

*フス リート1中島 孝2 (1.中国科学院リモートセンシング研究所、2.東海大学情報技術センター)

キーワード:Himawari-8/AHI 衛星データ、地表面放射フラックス

In this study, shortwave radiation flux simulated from Himawari-8 satellite products is compared to ground-based observations in Xianghe site of China. In clear and cloudy sky with clean atmospheric conditions, the shortwave radiation fluxes using satellite products agree well with ground-based measurements. However, in cloudy sky with polluted atmospheric conditions, the fluxes using satellite products are overestimated by 17.5% as compared to the ground-based measurements. Aerosols below the cloud layer can bias the retrieval of the cloud optical and microphysical properties and lead to the overestimation of the shortwave radiation at ground level.
To quantitatively investigate the influence of the heavy aerosol in retrieval of the cloud properties, the RSTAR radiative transfer model is employed to simulate the retrieval error of the cloud parameters caused by aerosols in the boundary layer. The results indicate that when the aerosol optical thickness (AOT) is 0.1, the error of the surface shortwave radiation is small; whereas with the increasing AOT, the error of the shortwave radiation increases obviously. When AOT is 1.2, the relative error reaches 18.38%. For the heavily-polluted areas of North China, it is important to investigate the influence of aerosol on the retrieval of the cloud parameters and solar shortwave radiation in cloudy conditions, which is critical to the assessment of the energy budget in North China suffered from heavy aerosols below the cloud layer.