Presentation Information
[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.