Tropical Cyclone Size Estimation Using Deep Convolutional Neural Network.

The accurate estimation of tropical cyclone (TC) size is one of the key steps in TC forecasting and disaster warninglmanagement. In this study, we proposed the use of deep convolutional neural networks (CNN) to estimate the size of tropical cyclone. To the best of our knowledge, this is the first study to estimate the size of tropical cyclones using deep learning methods; we use about 1,000 tropical cyclones which contain about 30,000 infrared remote sensing images as the data set. Compared with the best track archives, the mean error of our proposed model is 24nmi, the error is even smaller than the Multiplatform Tropical Cyclone Surface Winds Analysis (MTCSWA) operated by National Oceanic and Atmospheric Administration(NOAA), which shows the great potential of deep learning in estimating the size of tropical cyclones.