Enhancing tropical cyclone track and intensity predictions with the OWZP-Transformer model

Tropical cyclones (TCs) pose signif­icant risks, particularly in coastal regions, making accurate prediction of their track and intensity is crucial for effective disaster preparedness and response. Traditional numerical models struggle with balancing accuracy and computational efficiency, although TC track prediction has achieved substantial progress, challenges remain in forecasting TC intensity, especially rapid intensification (RI). This study aims to (1) develop a Transformer-based deep learning (DL) model to predict TC track, intensity, and 24-hour future intensity change simultaneously, and (2) investigate the relative importance of input variables to the contribution of improving model forecast ability. Based on 2001–2021 best track data and ERA5 reanalysis data over the western North Pacific (WNP), we develop an optimal model called OWZP-Transformer, which leverages the multi-head self-attention mechanism and incorporates the 13 input parameters categorized into four factors: basic, environmental, gradient, and structural. Specifically, our study incorporates structural parameters, which is represented by Okubo-Weiss-Zeta Parameter (OWZP). Our OWZP-Transformer model achieves competitive results for track prediction and shows excellent performance in intensity forecasting for the next 6 hour, with an overall root mean square error (RMSE) of 0.91 m/s. This result represents an improvement of 61.9% to 68.4% compared to existing DL models, which generally have RMSE values above 2 m/s. In addition, our model demonstrates superior performance in predicting 24-hour future intensity change, achieving an overall lower mean absolute error (MAE) of 1.57 m/s, which is 63.5% to 74.6% lower than existing DL models. Furthermore, our model successfully identifies all 11 RI events out of 30 TCs samples from WNP test dataset during 2020-2021. We further evaluate the contributions of each parameter for the first time using two explainable feature importance methods: DeepLIFT and DeepLiftShap. The results indicate that self-contributions and the basic factors play a dominant role in short-term forecasts, while the OWZ parameter plays a significant role following them. This study is the first attempt to comprehensively predict a broad range of TC forecasting tasks using a single DL model, highlighting the potential the OWZP-Transformer model as a reliable tool for enhancing both the accuracy and efficiency of TC predictions.