Development of Sumatera eArly warNing ConvectIve System (SANCIS) for Thunderstorm Prediction Model

Abstract

Since the activity of thunderstorm over Sumatera area – Indonesia increased during intermonsoon season in September, October, and November (SON) month, the thunderstorm as a natural disaster is influenced human activity. During the thunderstorm status increased may change an economy factors in this state due to natural hazard damage. Therefore, the development of Sumatera eArly warNing of ConvectIve System (SANCIS) for Thunderstorm Prediction System is necessary to avoid the natural hazard victims and helping meteorologist to predict thunderstorm event. To support the SANCIS development, we designed the thunderstorm model based on Adaptive Neuro Fuzzy Inference System (ANFIS). This system is equipped database meteorology and satellite imaging to update information and status thunderstorm event. In addition, to create the ANFIS model we use a two variable such as relative humidity (H) and PWV from radiosonde (RSPWV) from Weather Underground (WU) website and University of Wyoming (UW), respectively. Furthermore, the thunderstorm status prediction was updated in the SANCIS website. The two information per-day of status thunderstorm prediction were covered thunderstorm activity in this area.  Finally, the system was designed to monitor and giving the information of thunderstorm status during thunderstorm event.