Analysis of Different Modern Methods and Technologies in Typhoon Track Prediction

Abstract

Typhoon track forecasting is of great significance for disaster prevention and mitigation in coastal areas. In recent decades, due to advances in methods like numerical weather prediction, ensemble forecasting, or data-driven modeling. In this paper, an overview will be provided on the current state-of-the-art typhoon track forecasting methods such as traditional NWP system, novel DL or PDE-based hybrid methods. In particular, the review gives an overview on basic concepts, key publications and performance in applications for both direct methods and for ensemble methods. The benefits and drawbacks of data-driven deep learning models will also be reviewed and compared, such as single-modal, multimodal, and physics-informed structures, in terms of prediction accuracy, uncertainty expression, physical consistency, and commercialization relevance. Finally, key challenges and outlooks are presented. These challenges include probabilistic calibration, model generalizability, physical model interpretability and incorporating AI into operation forecasting system. It is hoped that this review will provide an organized guide for readers who are interested in advancing current methods on typhoon tracking forecasts.