  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.
