Japan Geoscience Union Meeting 2015

Presentation information

International Session (Poster)

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

[P-EM07] Space Weather, Space Climate, and VarSITI

Tue. May 26, 2015 6:15 PM - 7:30 PM Convention Hall (2F)

Convener:*Ryuho Kataoka(National Institute of Polar Research), Yusuke Ebihara(Research Institute for Sustainable Humanosphere, Kyoto University), Yoshizumi Miyoshi(Solar-Terrestrial Environement Laboratory, Nagoya University), Toshifumi Shimizu(Institute of Space and Astronautical Science, JAXA), Ayumi Asai(Unit for Synergetic Studies of Space, Kyoto University), Hidekatsu Jin(National Institude of Information and Communications Technology), Tatsuhiko Sato(Japan Atomic Energy Agency), Kanya Kusano(Solar-Terrestrial Environment Laboratory, Nagoya University), Hiroko Miyahara(College of Art and Design, Musashino Art University), Takuji Nakamura(National Institute of Polar Research), Kazuo Shiokawa(Solar-Terrestrial Environment Laboratory, Nagoya University), Kiminori Itoh(Graduate School of Engineering, Yokohama National University)

6:15 PM - 7:30 PM

[PEM07-P06] Prediction of the Auroral Electro jet index from the solar wind

*Ryota YAMAMOTO1, Yoshizumi MIYOSHI1, Yukinaga MIYASHITA1, Shinobu MACHIDA1 (1.STEL)

Keywords:AU index, AE index, AL index, Space weather

The auroral electrojet indices (AU, AL, AE) are a proxy of substorm as well as the auroral activity. The prediction of the these indices is important for the space weather forecast, because we can understand the basic mechanisms of the development of space environment, which may reduce possible space hazards. In this study, we develop a code to calculate the time variations of the AU and AL indices using the solar wind parameters based on the algorithm proposed by Goertz et al.(1993). Using the ACE measured solar wind data, we calculate the long-time variations of the AU index from 2000 to 2008. In order to evaluate the performance of the model, we calculate the skill score for each year. The largest skill score is found to be about 0.8. In this presentation, we report details of our code and how to improve the performance of the model, which has a strong dependence on the solar wind structure.