A Convolutional Neural Network Approach for Estimating Tropical Cyclone Intensity Using Satellite-based Infrared Images

摘要

Existing techniques for satellite-based tropical cyclone (TC) intensity estimation involve an explicit feature extraction step to model TC intensity on a set of relevant TC features or patterns such as eye formation and cloud organization. However, crafting such a feature set is often time-consuming and requires expert knowledge. In this paper, a convolutional neural network (CNN) approach, which eliminates explicit feature extraction, for estimating the intensity of tropical cyclones is proposed. Utilizing a Visual Geometry Group 19-1ayer CNN (VGG19) model pre-trained on ImageNet, transfer learning experiments were performed using grayscale IR images of TCs obtained from various geostationary satellites in the Western North Pacific region (1996 - 2016) to estimate TC intensity. The model re-trained on TC images achieved a root-mean-square error (RMSE) of 13.23 knots - a performance comparable to existing feature-based approaches (RMSE ranging from 12 to 20 knots). Moreover, the model was able to learn generic TC features that were previously identified in feature-based approaches as important indicators of TC intensity.