講演情報
[PEM10-P19]Ionospheric space weather forecast by the data assimilation of SuperDARN data with AI emulator
*片岡 龍峰1、中野 慎也2、藤田 茂2、中溝 葵3 (1.国立極地研究所、2.統計数理研究所、3.情報通信研究機構)
キーワード：
機械学習、宇宙天気予報、データ同化
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.