The e-CALLISTO is a network of CALLISTO (Compact Astronomical Low-frequency, Low-cost Instrument for Spectroscopy in Transportable Observatories) spectrometers which detect solar radio bursts 24 hours a day in frequency range 45-870 MHz. The number of channels per spectrum is 200 and the time resolution of whole spectrum is 0.25 second. The Korean e-CALLISTO station was developed by Korea Astronomy and Space Science Institute (KASI) collaborating with Swiss Federal Institute of Technology Zurich (ETH Zurich) since 2007. In this paper, we report replacement of the tracking mount and development of the control program using Visual C++/MFC. The program can make the tracking mount track the Sun and schedule CALLISTO to start and to finish its observation automatically using the Solar Position Algorithm (SPA). Daily tracking errors (RMSE) are 0.0028 degree in azimuthal axis and 0.0019 degree in elevational axis between 2014 January and 2015 July. We expect that the program can save time and labor to make the observations of solar activity for space weather monitoring, and improve CALLISTO data quality due to the stable and precise tracking methods.

We have developed a data integration system for ground-based space weather facilities in Korea Astronomy and Space Science Institute (KASI). The data integration system is necessary to analyze and use ground-based space weather data efficiently, and consists of a server system and data monitoring systems. The server system consists of servers such as data acquisition server or web server, and storage. The data monitoring systems include data collecting and processing applications and data display monitors. With the data integration system we operate the Space Weather Monitoring Lab (SWML) where real-time space weather data are displayed and our ground-based observing facilities are monitored. We expect that this data integration system will be used for the highly efficient processing and analysis of the current and future space weather data at KASI.

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.