CNN Based Tropical Cyclone Intensity Estimation Using Satellite Images Around Indian Subcontinent

In this work, we have used deep learning models for estimating tropical cyclone (TC) intensity using satellite images. This is an image to regression problem, where an image is given as input and intensity value is estimated as output. In the literature, various deep learning methods have been proposed for TC intensity estimation but their focus on cyclones around the Indian subcontinent is limited. We have implemented three models: regression model, classification model, and a multitask model having regression and classification output as two tasks. We have worked with two sets of input data. One set of data contains single channel input containing infrared (IR) brightness temperature satellite image. Another set of data contains two channel inputs having infrared (IR) brightness temperature satellite image as one of the channels, and rain rate derived from passive microwave (PMW) satellite image as another channel. We have used satellite images for cyclones occurring in the Atlantic, Northeast Pacific, and North Central Pacific regions from 2006 through 2016. For cyclones around the Indian subcontinent, we have used satellite images from 2005- 2016.