Tropical Cyclone Maximum Wind Estimation from Infrared Satellite Data with Integrated Convolutional Neural Networks

Abstract

Tropical cyclone (TC) maximum wind is an important parameter for estimating TC risks such as wind potential damage and storm surge. Previous work has shown that the estimation of TC maximum wind through a series of empirical rules based on the cloud characteristics shown in the satellite cloud image. Deep learning like convolutional neural networks (CNNs) has this ability of extracting and understanding these cloud features like the eye, the spiral rainbands that closely associated with its maximum wind. However, CNNs are used for object recognition and classification, CNS has less application in regression. We proposed an integrated architecture based on Convolutional Neural Network for the estimation of the TC maximum wind with higher accuracy. More specifically, it includes input layer, convolutional layers, activation functions and pooling layers for training and capturing non-linear relationships between cloud image and its wind, and a fully connection for the estimation task. We evaluate the state of the art for regression between infrared image and its TC maximum wind, discussing the necessity of different components. It demonstrates an improvement on the ability to estimate the TC intensity.