A Multitropical Cyclone Trajectory Prediction Method Based on Density Maps With Memory and Data Fusion

Dongfang Ma, Zhaoyang Ma, Chengying Wu, Jianmin Lin

Abstract: Tropical cyclones (TCs) are destructive weather systems, and the accurate prediction of the trajectory of TCs is crucial. Previous studies have focused mainly on trajectory prediction for individual TCs, which cannot take into account the interaction between different TCs, affecting the prediction performance. To address this problem, this study proposed an innovative method for multi-TC trajectory prediction based on a density map. Instead of predicting the location of a TC directly, the article first predicts the density map of a sea area, and then obtain TC centers from the predicted density maps. In the first step, a relation extraction module (REM) is proposed in order to analyze the interaction between multiple TCs. Further, a 3-D cloud feature extraction module was designed to enhance the ability to use 3-D cloud structural information on TCs via feature extraction and the fusion of density maps, satellite images, and environmental data. In addition, a long short-term memory (LSTM) fusion module was designed to adaptively select important historical information, which improves the ability to extract long-term spatiotemporal dependencies. In the second step, those density map pixels with extreme values are identified as TC centers. The proposed method was verified by experiments using Gridsat, IBTrACS, and ERA5 datasets. The results show that the mean distance error of TC trajectory prediction is reduced by 10.0%, 10.7%, 10.5%, and 11.7% for overall performance, and 21.5%, 18.0%, 19.1%, and 19.8% for multi-TC scenario in the 6-, 12-, 18-, and 24-h predictions compared with state-of-the-art prediction models.

External IDs:dblp:journals/tai/MaMWL25