UFCORIN: A Fully Automated Predictor of Solar Flares in GOES X-Ray Flux

Takayuki Muranushi, Takuya Shibayama, Yuko Hada Muranushi, Hiroaki Isobe, Shigeru Nemoto, Kenji Komazaki, Kazunari Shibata

Published 2015-07-29Version 1

We have developed UFCORIN, a platform for studying and automating space weather prediction. Using our system we have tested 6,160 different combinations of SDO/HMI data as input data, and simulated the prediction of GOES X-ray flux for 2 years (2011-2012) with one-hour cadence. We have found that direct comparison of the true skill statistics (TSS) is ill-posed, and used the standard scores ($z$) of the TSS to compare the performance of the various prediction strategies. The best strategies we have found for predicting X, $\geq$M and $\geq$C class flares are better than the average of the 6,160 strategies by 2.3$\sigma$, 2.1$\sigma$, 3.8$\sigma$ confidence levels, respectively. The best three's TSS values were $0.745\pm0.072$, $0.481\pm0.017$, and $0.557\pm0.043$, respectively.