Using Video Recognition to Identify Tropical Cyclone Positions

Abstract

Tropical cyclone (TC) center fixing is a challenge for improving forecasting and establishing TC climatologies. We propose a novel objective solution through the use of video recognition algorithms. The videos of tropical cyclones in the Western North Pacific are of sequential, hourly, geostationary satellite infrared (IR) images. A variety of convolutional neural network architectures are tested. The best performing network implements convolutional layers, a convolutional long short-term memory layer, and fully connected layers. Cloud features rotating around a center are effectively captured in this video-based technique. Networks trained with long-wave IR channels outperform a water vapor channel-based network. The average position across the two IR networks has a 19.3 km median error across all intensities. This equates to a 42% lower error over a baseline technique. This video-based method combined with the high geostationary satellite sampling rate can provide rapid and accurate automated updates of TC centers.