Estimation of Tropical Cyclone Size by Combining Sequential Infrared Satellite Images With Multitask Deep Learning

Currently, the extraction of the size characteristics of a tropical cyclone (TC) from satellite data is mainly based on subjective human experience and archived satellite data. Few objective methods have been developed to estimate the size of a TC. To overcome this problem, this study introduces a new deep learning (DL) model, called a TC multitask estimation (TCMSE) model. The goal of using the model is to accurately extract the spatiotemporal characteristics of the size of a TC from infrared satellite images acquired during the current and previous three moments, combined with a multitask learning (MTL) approach and physically assisted task learning, to produce more reliable estimates of the wind radii of a TC. We used the TCMSE model to estimate the TC wind radius on the infrared satellite image of the Northwest Pacific Ocean to verify the performance of the model. The experimental results show that the proposed deep spatiotemporal feature extraction network, which combines MTL and physically assisted tasks, is superior to existing methods for estimating the wind radii of a TC. More specifically, the TCMSE model reduces the MAE error in estimating the TC wind radii by 6%, compared to the MTL approach, and 14%, compared to separately learning a single wind radii task.