Automated Segmentation of Tropical Cyclone Clouds in Geostationary Infrared Images

We demonstrate that a convolutional neural network (CNN) based on the U-Net architecture can be used to create a cloud mask dataset that accurately identifies the clouds associated with tropical cyclones (TCs). The CNN can be trained using a single year of cloud masks produced by an earlier first-principles algorithm, and the results are insensitive to the specific year of training data used. These masks were originally created to compute the upwelling radiation due to TC clouds, and we show that the predicted masks result in both pixel areas and radiation calculations that are nearly identical to those computed using the earlier masks.