Weather systems such as tropical cyclones, fronts, troughs and ridges affect our daily lives. Yet, they are often manually located and drawn on weather charts based on forecasters? experience. To identify them, multiple atmospheric elements need to be considered, and the results may vary among forecasters. In this paper, we contribute to the fields of pattern recognition and meteorological computing by designing a generic model of weather systems, along with a genetic algorithm-based framework for finding them from multidimensional numerical weather prediction data. It was found that our method not only can locate weather systems with 80% to 100% precision, but also discover features that could indicate the genesis or dissipation of such systems that could be ignored by forecasters.