Cyclone Identify using Two-Branch Convolutional Neural Network from Global Forecasting System Analysis

Cyclone, especially tropical cyclones, are one of the most significant meteorological disasters in the world, which seriously threaten the safety of life and property. The ability to accurately identify the type and intensity of cyclones is crucial for disaster prevention. This article proposes the use of dual branches Convolutional Neural Network (CNN) model, based on Global Forecast System Analysis (GFS) to identify cyclones, including tropical cyclones, extratropical cyclones and subtropical cyclones, a total of 11 types of cyclone-related phenomena. The model can learn spatial information and extract crucial features, and merge at the end to achieve end-to-end prediction output. The results indicate that the model's identify accuracy of tropical cyclones and extratropical cyclones exceeds 90%, and the identify accuracy of cyclones disturbances and subtropical cyclones is also more than 75%, the model does not require expert knowledge, and the speed of operation fast.