A CNN system for segmenting tropical cyclones neighborhoods in geostationary images

We present progress towards an automated system that can segment tropical cyclones (TCs) and their neighboring regions from geostationary infrared brightness temperature images. The purpose of these segmentation maps is to provide an area that can be used to compute the contribution of TCs to the upwelling radiation budget. Our previous work has identified regions of TC clouds, but it is known that TCs impact a larger area than just those covered by their clouds. Hence it is necessary to properly label both the TC clouds and the associated clear-sky regions in the vicinity of the TCs. Here we present a convolutional neural network method that can be used to reproduce cloud masks generated with our earlier, first-principles algorithm. We also discuss our efforts to create an extended training set of TC masks that include both clouds and clear sky.