Japan Geoscience Union Meeting 2016

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International Session (Oral)

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

[P-EM04] Space Weather, Space Climate, and VarSITI

Mon. May 23, 2016 9:00 AM - 10:30 AM 103 (1F)

Convener:*Ryuho Kataoka(National Institute of Polar Research), Antti Pulkkinen(NASA GSFC), Yusuke Ebihara(Research Institute for Sustainable Humanosphere, Kyoto University), Yoshizumi Miyoshi(Institute for Space-Earth Environmental Research, 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(Institute for Space-Earth Environmental Research, Nagoya University), Hiroko Miyahara(College of Art and Design, Musashino Art University), Kiminori Itoh(Graduate School of Engineering, Yokohama National University), Kazuo Shiokawa(Institute for Space-Earth Environmental Research, Nagoya University), Takuji Nakamura(National Institute of Polar Research), Shigeo Yoden(Division of Earth and Planetary Sciences, Graduate School of Science, Kyoto University), Kiyoshi Ichimoto(Kwasan and Hida Observatories, Kyoto University), Mamoru Ishii(National Institute of Information and Communications Technology), Chair:Ryuho Kataoka(National Institute of Polar Research)

9:15 AM - 9:30 AM

[PEM04-23] Total electron content forecast model over Japan using a machine
learning technique

*Michi Nishioka1, Takashi Maruyama1, Takuya Tsugawa1, Hidekatsu Jin1, Mamoru Ishii1 (1.National Institute of Information and Communications Technology)

Keywords:machine learning, total electron content, TEC forecast

Forecasting ionospheric condition is important for space weather operation, especially for predicting propagation delay of the radio waves in the ionosphere. National Institute of Information and Communications Technology (NICT), Japan, develops an ionospheric forecasting system of total electron content (TEC) in addition to a TEC monitoring system. Although several empirical and theoretical models have been developed in a decade, no model is available for forecasting TEC over Japan. Our purpose is to accomplish an operational TEC model over Japan using an artificial neural network technique which is developed by Maruyama [2007]. In our model, absolute TEC values for each day over Japan were projected on a two-dimension TEC map, that is, a local-time and latitudinal map. Then the time-latitudinal variation was fitted by using the surface harmonic function. The coefficients of the expansions were modeled by using a neural network technique. For the learning process, we used absolute TEC value from 1997 to 2014. The input parameters are proxies of the season, the solar activity, and the geomagnetic activity. Thus, daily two-dimensional TEC maps can be obtained for any days when the input parameters are available. We used input parameters which are provided in real-time by some institutes and achieved one-day TEC prediction over Japan.