Automatic identification of weather systems from numerical weather prediction data using genetic algorithm

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 propose an automatic weather system identification method. A generic model of weather systems is designed, along with a genetic algorithm-based framework for finding them automatically from multidimensional numerical weather prediction data. The framework allows multiple weather elements to be analyzed. It is found that our method not only can locate weather systems with 80–100% precision, but can also discover features that could indicate the genesis or dissipation of such systems that forecasters may overlook. The method provides an independent and objective source of information to assist forecasters in identifying and positioning weather systems.