Japan Geoscience Union Meeting 2016

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Symbol P (Space and Planetary Sciences) » P-EM Solar-Terrestrial Sciences, Space Electromagnetism & Space Environment

[P-EM18] Dynamics in magnetosphere and ionosphere

Wed. May 25, 2016 10:45 AM - 12:15 PM 103 (1F)

Convener:*Tomoaki Hori(Institute for Space-Earth Environmental Research, Nagoya University), Yoshimasa Tanaka(National Institute of Polar Research), Aoi Nakamizo(Applied Electromagnetic Research Institute, National Institute of Information and Communications Technology), Mitsunori Ozaki(Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University), Shin'ya Nakano(The Institute of Statistical Mathematics), Yoshizumi Miyoshi(Institute for Space-Earth Environmental Research, Nagoya University), Chair:Yukinaga Miyashita(Institute for Space-Earth Environmental Research, Nagoya University), Keisuke Hosokawa(Department of Communication Engineering and Informatics, University of Electro-Communications), Yoshimasa Tanaka(National Institute of Polar Research), Shin'ya Nakano(The Institute of Statistical Mathematics)

11:45 AM - 12:00 PM

[PEM18-05] Forecast of AU/AL index with real time data assimilation

*Ryota Yamamoto1, Yoshizumi Miyoshi1, Shinobu Machida1, Genta Ueno2, Yukinaga Miyashita1, Masahito Nose3 (1.The Solar-Terrestrial Environment Laboratory, 2.The institute of Statistical Mathematics, 3.Kyoto-University)

Keywords:data assimilation, AU index

The AU index is a proxy of substorm as well as auroral activity, so that the forecast of the index is important for the space weather research and forecast. In this study, we have developed a data-assimilation code to estimate variations of the AU index based on Goertz’s model. In the Goertz’s model, there are several parameters, and these parameters are related to the ionospheric conductivity. From the estimation of the developed data-assimilation code, we found a seasonal dependence of these parameters in the model. It is expected that these seasonal variations are caused by the seasonal variations of ionospheric conductivity as indicated by Goertz et al. The original Goerts model assumed the constant amplitude for these parameters, and seasonal dependence derived from our data assimilation may contribute the improve the forecast score.