# Presentation information

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

Sun. May 22, 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)

9:45 AM - 10:00 AM

# [PEM04-04] Solar Flare Prediction Studies Using Universal Time Series Predictor UFCORIN

Keywords:Space Weather Forecast, Flare Forecast

We have been studying space weather forecast using time-series prediction engine UFCORIN(Universal Forecast Constructor by Optimized Regression of INputs.) In our studies (Muranushi et al. 2015), we have compared 6'160 different prediction strategies that uses subset of wavelet features of SDO/HMI images as well as GOES past light curves.
Use of TSS (True Skill Statistics) as the indicator of flare forecast performance has been widespread since it is proposed by Bloomfield et. al(2012). However, we found that variation of bare TSS values over different cross-validation (CV) data sets is too large, so that we cannot measure significant difference between different forecast strategies. We found by using the $z$-value, or standard deviation of TSS, we can distinguish such strategies that show forecast performance consistently better than the average. We suggest the use of $z$-value as a method of finding good forecast strategies from thousands of candidates.
In our studies, the laregest TSS for X,M, and C class flare forecast, were $0.75\pm0.07$, $0.48\pm0.02$, and $0.56\pm0.04$, respectively.
Based on (Muranushi et al. 2015), we have been operating real-time flare forecast server since August 2015. The system have been making forecast every 12 minutes, except for some down times. We would also like to report on the latest state of this experience.