[P-EM10] Space Weather and Space Climate

[PEM10-P19] Ionospheric space weather forecast by the data assimilation of SuperDARN data with AI emulator

*Ryuho Kataoka¹, Shin ya Nakano², Shigeru Fujita², Aoi Nakamizo³ (1.National Institute of Polar Research, 2.The Institute of Statistical Mathematics, 3.National Institute of Information and Communications Techonology)

Keywords:Machine learning, Space weather forecast, Data assimilation

Physics-based auroral simulations, such as Japanese REProduce Plasma Universe (REPPU) code, are not practically fast enough for the purpose of real-time space weather forecast, even using the designated super computers. Here we developed a million-times-faster “emulator” to surrogate the outputs of the physics-based simulation, using the machine-learning technique called Echo State Network. The newly developed emulator, the surrogate model for REPPU auroral Ionosphere version 2 (SMRAI2), enables us to realize the real-time space weather forecast of the auroral current system as well as emsemble forecast and data assimilation forecast. In this talk we show several examples rather than technical details.