日本地球惑星科学連合2023年大会

講演情報

[E] 口頭発表

セッション記号 P (宇宙惑星科学) » P-EM 太陽地球系科学・宇宙電磁気学・宇宙環境

[P-EM09] Space Weather and Space Climate

2023年5月25日(木) 15:30 〜 16:30 101 (幕張メッセ国際会議場)

コンビーナ:片岡 龍峰(国立極地研究所)、Antti A Pulkkinen(NASA Goddard Space Flight Center)、Mary Aronne中村 紗都子(名古屋大学宇宙地球環境研究所)、座長:中村 紗都子(名古屋大学宇宙地球環境研究所)、片岡 龍峰(国立極地研究所)

16:15 〜 16:30

[PEM09-21] Mapping of ionospheric electric potential with data assimilation into an emulator of global MHD simulation

*中野 慎也1,2,4片岡 龍峰3,4藤田 茂1,2 (1.情報・システム研究機構 統計数理研究所、2.情報・システム研究機構 データサイエンス共同利用基盤施設、3.情報・システム研究機構 国立極地研究所、4.総合研究大学院大学)

キーワード:SuperDARN、電離圏対流、エミュレータ、データ同化

The SuperDARN (Super Dual Auroral Radar Network) provides the valuable information on global plasma flow in the polar ionosphere. However, since there are some wide gaps in the spatial coverage of the SuperDARN, it is not an easy task to retrieve a global convection map from the SuperDARN data. One useful way to obtain the global convection map is to use a numerical simulation, Indeed, recent global magneto-hydrodynamic (MHD) models of the magnetosphere successfully simulate the ionospheric potential pattern. If ionospheric observation data could be assimilated into the MHD model, we could obtain a reliable global potential map.

The problem with the simulation approach is its computational cost. The realistic MHD model is too computationally expensive to apply data assimilation. In this study, we employ a machine learning-based emulator of the global MHD model to resolve the problem of the computational cost. This emulator is based on an echo state network model, and it efficiently mimics the MHD model to reproduce an ionospheric potential pattern under a give solar wind condition. We can therefore assimilate the SuperDARN data into this emulator to obtain the global potential map. We will demonstrate the electric potential maps as a result of data assimilation into the emulator.