[A-CG37] 衛星による地球環境観測

[ACG37-04] GOSATとGOSAT-2のターゲットモード観測より得られた東京付近のCO_２分布

*今須 良一¹、新井 豊¹、松永 恒雄²、近藤 裕昭³ (1.東京大学大気海洋研究所、2.国立環境研究所、3.産業技術総合研究所)

キーワード：GOSAT、GOSAT-2、二酸化炭素、大規模排出源

Greenhouse Gases Observing Satellite (GOSAT) can observe carbon dioxide (CO₂) concentration at 16 points during one orbital pass over a specified place. This mode of operation is called specific observation mode. GOSAT science team have conducted a target observation of CO₂ concentration around mega-city Tokyo using the specific observation mode from 2010 to obtain CO₂ concentration map around the city. The target points are located at 4 x 4 mesh-point with two patterns. The first is a pattern in which four points are arranged at equal intervals in the latitude and longitude directions. The other is based on the initial pattern with prioritizes to the validation points and large-emission sources such as electric power plants. Time-series data at each location have been obtained through 7-year operation. At all locations, there is a trend of monotonical increase with seasonal changes. As there is a gap of more than 30km between the observation points of the 4 x 4 point observation, the mode have switched to the one with 9 x 9 observation points from 2017. With this observation mode, CO₂ concentration map around Tokyo city have been obtained for each 54 days as far as the scenes are not covered with clouds. These maps show that there are several regions with locally high concentrations, unlike the general source inventory distribution, which shows a radial distribution centered at the city center. Furthermore, fine tuning of the observation point arrangement was carried out in 2019 to enable observations around lakes and coastlines where observation requests have been rejected. CO₂ concentration maps without missing areas have been obtained with the new operation mode. By analyzing the data, CO₂ maps characterizing each season and wind condition have been obtained. Especially in calm conditions, high CO₂ concentrations have been observed near large emission sources such as power plants. Using these observation data as input data to an ensemble transform Kalman filter (EnTKF) and Kalman smoother (KS) system based on a regional transport model, AIST meso-scale model (AIST-MM), CO₂ surface fluxes of the city is updated.