A Review on the Application of Machine Learning Methods in Tropical Cyclone Forecasting - Wang, Zhen; Zhao, Jun; Huang, Hong; Wang, Xuezhong (Frontiers in Earth Science, 2022)
There is still a bottleneck in today forecasting of tropical cyclones (TCs) due to their complex dynamic mechanisms and various impact factors. The significant advantages of machine learning (ML) methods in data processing and image recognition have been fully utilized, and the potential of satellite, radar and surface station data on TC forecasting has been deeply explored in recent studies, which provides a new strategy to solve the difficulties in TC forecasting. In this paper, by analyzing the existing problems of TC forecasting, we review the current application of ML methods in TC forecasting. In addition, we comprehensively summarize the various predictors and advanced algorithm models. Moreover, a preliminary discussion on the challenges of applying ML methods in TC forecasting is presented. Our review shows that, in the future, the ML methods with higher interpretation, intervention and precision are needed to improve the skill of TC prediction.