Japan Geoscience Union Meeting 2016

Presentation information

International Session (Poster)

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

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

Sun. May 22, 2016 5:15 PM - 6:30 PM Poster Hall (International Exhibition Hall HALL6)

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)

5:15 PM - 6:30 PM

[PEM04-P21] Operational forecast of foF2 above Tokyo using solar wind input to a neural network

*Herbert Akihito Uchida1,4, Wataru Miyake2, Maho Nakamura3, Ryuho Kataoka4,1 (1.SOKENDAI (The Graduate University for Advanced Studies), 2.Tokai University, 3.Tokyo Gakugei University, 4.National Institute of Polar Research)

Keywords:Forecast, foF2, Neural network, Solar wind

A new empirical prediction model of foF2 above Tokyo, Japan (Uchida et al., 2016, submitted), has started its forecast operation at National Institute of Polar Research. Solar wind parameters are used for the first time to the input of a neural network (NN) to predict foF2 in that study. The model showed better forecast results compared to an existing operational NN model (Nakamura et al., 2009) which forecasts foF2 using K-index to the input. The results support our expectation that the NN can represent the physics between the ionospheric variations and the solar wind better. The forecast is operated every day at 0 UT for next 24 hours. The model uses day of year, sunspot number, F10.7 solar proxies, solar wind proton velocity, IMF By and Bz to the input. Prior 24 hour values to the forecast are lined to the input at once. To represent the time dependences, 24 of individual NNs are constructed for each hour and concatenated at forecast. We introduce the operational model and report the summary of current operation, and discuss several possibilities to improve the forecast.