Convective Cells Algorithm For Storm Data Tracking

Atmospheric conditions, such as thunderstorms, are significant factors that influence human activity. Harsh weather may severely impact both daily life and professional activities. Severe thunderstorms are a considerable hazard -- they can generate heavy rainfall, high winds, large hail and tornadoes. Tracking of thunderstorms is necessary to gain situational awareness - knowledge of present and future storm-related threats and their significance. Thunderstorms are weather phenomena associated with cumulonimbus clouds. Those clouds are formed in deep, moist convection and are composed of liquid and solid water particles. Weather radars can detect those particles. Cumulonimbus-related particle concentration areas are represented in weather radar data as convective cells, making that measurement technique useful for storm-tracking applications. This paper proposes a new algorithm for storm data tracking in the data fusion process. The algorithm has been tested with real data from the POLRAD weather radar network and upper-air observations. The efficiency of the proposed algorithm has been justified in the empirical analysis. The algorithm projections can be useful in generating weather warnings due to accurate projections of storm movement.