Tropical Cyclone Intensity Estimation Using Two-Branch Convolutional Neural Network From Infrared and Water Vapor Images

This article proposes a two-branch convolutional neural network model (TCIENet) to estimate the intensity of tropical cyclone (TC) from infrared and water vapor images in the northwest Pacific basin. Three different sizes of input images are explored to train the TCIENet model, and the size of 60 × 60 pixels (radius 450 km) achieves the best performance with an overall root mean square error (RMSE) of 5.13 m/s and mean absolute error (MAE) of 4.03 m/s. TCs are divided into six categories whose RMSEs range from 4.07 to 6.05 m/s. In addition, the TCs in the year 2017 are used to analyze the correlation between the rainfall intensity from the global precipitation measurement (GPM) mission and the estimation errors of the TCIENet model. Preliminary results suggest that the model performs the best at the categories of tropical storm and super typhoon, but it degrades in performance for moderate intense categories and the weakest category of the tropical depression. The correlation coefficient between the estimation error and the rainfall intensity is 0.19. It is far from certain that the rainfall intensity accounts for the error achieved by the TCIENet model.