DMANet_KF: Tropical Cyclone Intensity Estimation Based on Deep Learning and Kalman Filter From Multispectral Infrared Images
Wenjun Jiang, Gang Hu, Tiantian Wu, Lingbo Liu, Bubryur Kim, Yiqing Xiao, Zhongdong Duan
Abstract
It is very crucial to identify the intensity of tropical cyclone (TC) accurately. In this article, a novel TC intensity estimation method is proposed to estimate the TC intensity from multispectral infrared images in the Northwest Pacific Basin. A deep multisource attention network (DMANet) is proposed to model the dynamics of multispectral infrared images along the spatial dimension. We first introduce a message-passing enhancement module based on the conditional random fields to process multispectral infrared images. Multispectral data transfer the complementary information to refine the features of TC. Second, we utilize a local global attention module to make the model focus on local key features (i.e., the typhoon eye) and obtain deeper global semantic information of TC. The ablation experiment is set up in the same dataset and computing environment to verify the effectiveness of each module. Finally, we use a Kalman filter to correct the error of TC intensity during its lifetime estimated by the DMANet model. After using Kalman filter, the evolution of TC intensity becomes smooth and corresponding root-mean-square error (RMSE) decreases from 9.79 to 7.82 knots. Compared with the best result of the existing TC intensity estimation method, the RMSE of our method is reduced by 9.07%. Therefore, the proposed TC intensity estimation method shows a great potential for accurately estimating the TC intensity.