[A-CG37] 衛星による地球環境観測

[ACG37-12] Advances and applications of satellite data assimilation of clouds, precipitation, and the ocean

*三好 建正¹、大石 俊¹、Liang Jianyu¹、Konduru Rakesh Teja¹、大塚 成徳¹、小槻 峻司²、寺崎 康児³、岡崎 淳史⁴、富田 浩文¹、Chen Ying-Wen⁵、金丸 佳矢⁶、佐藤 正樹⁵、八代 尚⁷、岡本 幸三³、Kalnay Eugenia⁸、久保田 拓志⁹、可知 美佐子⁹ (1.理化学研究所、2.千葉大学、3.気象研究所、4.弘前大学、5.東京大学、6.情報通信研究機構、7.国立環境研究所、8.メリーランド大学、9.宇宙航空研究開発機構)

キーワード：数値天気予報、衛星データ同化、海洋データ同化

This presentation summarizes progress of a research project started in FY2022. 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, i.e., “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), and “satellite data assimilation using an ocean model” (2020-2022). We developed the global atmospheric ensemble data assimilation system called NICAM-LETKF, where NICAM stands for the Nonhydrostatic ICosahedral Atmospheric Model and the LETKF for the Local Ensemble Transform Kalman Filter. We also developed a precipitation nowcasting system called GSMaP RIKEN Nowcast (GSMaP_RNC) using the satellite-analyzed Global Mapping of Precipitation (GSMaP) dataset. We developed real-time precipitation prediction system by seamlessly merging data from the NICAM-LETKF numerical weather prediction and GSMaP_RNC and have been operating it continuously for public data dissemination. In addition, we have been operating JAXA’s real-time atmospheric analysis system called NEXRA (NICAM-LETKF JAXA Research Analysis) and have been disseminating real-time level-4 analysis products using satellite data, with proven data quality by analyzing past high-impact weather events such as typhoons and heavy rainfalls. Moreover, we implemented the LETKF with an ocean model called sbPOM and developed daily-update ocean data assimilation system using dense and frequent SST data from the Himawari-8 geostationary satellite. This research will integrate these atmospheric and oceanic data assimilation projects with significant extension to the following research items. Through the research, we aim to deepen our integrated understanding of the earth system on clouds, precipitation and the ocean and to advance analysis and prediction, and their real-life applications.
1) NEXRA
2) GSMaP_RNC and Seamless Precipitation Prediction System
3) Ocean Data Assimilation & Daily Analysis Product and Verification
4) GPM DPR Data Assimilation
5) Use of EarthCARE Cloud Radar Data
6) Incorporating Observation Error Correlation in Satellite Data Assimilation
7) Application of Machine Learning (ML) to Satellite Observation Operator
8) Non-linear and non-Gaussian Data Assimilation with Local Particle Filter (LPF)
9) Atmosphere-Ocean Coupled Data Assimilation (ensemble for the atmosphere-ocean interface)