[A-CG41] Satellite Earth Environment Observation

[ACG41-26] Advancement and AI integration of satellite data assimilation of clouds, precipitation, and the ocean

*Takemasa Miyoshi¹, Shun Ohishi¹, Jianyu Liang¹, Michael Goodliff¹, Shigenori Otsuka¹, Hirofumi Tomita¹, Kaya Kanemaru², Hisashi Yashiro³, Koji Terasaki⁴, Kozo OKAMOTO⁴, Shunji Kotsuki⁵, Shuhei Matsugishi⁶, Masaki Satoh⁶, Craig Bishop⁷, Takuji Kubota⁸, Misako Kachi⁸ (1.RIKEN, 2.NICT, 3.NIES, 4.JMA/MRI, 5.Chiba University, 6.University of Tokyo, 7.University of Melbourne, 8.JAXA)

Keywords:Data Assimilation, Artificial Intelligence, Satellite, Precipitation, Cloud, Ocean

This presentation outlines the plan of a research project started in FY2025. This research aims to advance data assimilation, analysis, and prediction of clouds, precipitation, and the ocean, based on the achievements from the previous projects since 2013: “ensemble data assimilation of TRMM/GPM precipitation observations” (2013-2016); “advancing data assimilation of GPM observations” (2016-2019); “advancing precipitation prediction algorithm by data assimilation of GPM observations” (2019-2022); “development of a satellite ocean data assimilation system with the JAXA Supercomputer System Generation 2” (2017-2020); “satellite data assimilation using an ocean model” (2020-2022); and “advances and applications of satellite data assimilation of clouds, precipitation, and the ocean” (2022-2025). We developed the global atmospheric ensemble data assimilation system NICAM-LETKF with the local ensemble transform Kalman filter (LETKF) and the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). We also developed a precipitation nowcasting system GSMaP RIKEN Nowcast (GSMaP_RNC) using the satellite-analyzed Global Mapping of Precipitation (GSMaP) dataset. By seamlessly merging data from the NICAM-LETKF numerical weather prediction and GSMaP_RNC, we established a real-time precipitation prediction system and have been continuously operating it for public data dissemination. Moreover, we have been deeply involved in the development of JAXA’s operational real-time atmospheric analysis NEXRA (NICAM-LETKF JAXA Research Analysis) which provides Level-4 products with proven data quality through validation for extreme weather cases such as typhoons and heavy rainfalls. For ocean data assimilation, we implemented the LETKF with an ocean model sbPOM and developed a daily-update ensemble analysis system using dense and frequent SST data from the Himawari-8 and -9 geostationary satellites. In the project starting in FY2025, we plan to enhance our data assimilation systems by integrating AI techniques. Through this research, we aim to deepen our integrated understanding of the earth system on clouds, precipitation, and the ocean and to advance the analysis, prediction, and real-life applications.