[A-CG36] 衛星による地球環境観測

[ACG36-19] Advancing GSMaP Precipitation by Land Data Assimilation and Data Science

*小槻 峻司¹、武藤 裕花¹、白石 健太¹、塩尻 大也¹、片岡 優衣奈²、舩冨 卓哉²、金丸 佳矢³ (1.千葉大学　環境リモートセンシング研究センター、2.奈良先端科学技術大学、3.情報通信研究機構)

キーワード：GSMaP、陸面同化、深層学習、超解像

This study aims to improve the Global Satellite Mapping of Precipitation (GSMaP) by incorporating multiple satellite data and leveraging advanced data science techniques. The GSMaP provides near-real-time global precipitation using Microwave Radiometers (MWR) mounted on polar-orbiting satellites, while referring to surface precipitation data from satellite-borne precipitation radars. Owing to the constellation of polar-orbiting satellites, GSMaP issues the global precipitation every hour at the spatial resolution of 0.10 degree. Despite these advancements, GSMaP confronts challenges in accurately estimating precipitation in regions lacking MWR observations. Here, our research aims to improve the GSMaP precipitation by integrating spatially sparse global gauge observations. We succeeded in improving gauge-based global precipitation estimates using the algorithm of the local ensemble transform Kalman filter, and are working to adjust the GSMaP precipitation against the gauge-based precipitation estimates. In addition, our research incorporates data science techniques such as deep learning to bridge the gap by geometrically extrapolating observable MWR-based precipitation based on the atmospheric motion vectors, and conduct super-resolution of precipitation. Preliminary experiments provide encouraging results, showcasing improved spatial precipitation estimates using deep learning models. This presentation will include the most recent progress by the time of the conference.