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Benchmarking Tropical Cyclone Rapid Intensification with Satellite Images and Attention-based Deep Models
Ching-Yuan Bai, Buo-Fu Chen, Hsuan-Tien Lin
Rapid intensification (RI) of tropical cyclones often causes major destruction to human civilization due to short response time. It is an important yet challenging task to accurately predict this kind of extreme weather event in advance. Traditionally, meteorologists tackle the task with human-driven feature extraction and predictor correction procedures. Nevertheless, these procedures do not leverage the power of modern machine learning models and abundant sensor data, such as satellite images. In addition, the human-driven nature of such an approach makes it difficult to reproduce and benchmark prediction models. In this study, we build a benchmark for RI prediction using only satellite images, which are underutilized in traditional techniques. The benchmark follows conventional data science practices, making it easier for data scientists to contribute to RI prediction. We demonstrate the usefulness of the benchmark by designing a domain-inspired spatiotemporal deep learning model. The results showcase the promising performance of deep learning in solving complex meteorological problems such as RI prediction.
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