TCSE_BiLSTM: Spatial-Temporal Characteristics Extraction Using Deep Learning to Improve Tropical Cyclone Size Estimation from Infrared Satellite Imagery

Currently, size feature extraction for tropical cyclones (TCs) from satellite data is largely based on subjective human experience and archived satellite data. There are few objective methodologies for estimating the size of a TC. To overcome this, in this paper, we introduce a novel deep learning model, which we call TCSE_BiLSTM. The model is designed to fully extract the spatial-temporal characteristics of TC size from infrared satellite images using the current and previous three intervals. To verify the model’s performance, we conducted experiments on TC wind radii estimation using the TCSE_BiLSTM model on infrared satellite images of the Northwest Pacific region. Our experiments showed that the proposed deep spatial-temporal feature extraction network has advantages in estimating TC wind radii over current methods. Therefore, it can be used for fully automated TC wind radii estimation.