资源环境科技发展态势分析平台

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 infra‐red (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 vapour 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.