Li describes the performance of the model.

Traditional models directly predict this tropical cyclone intensity change over time from the input data.

Our model asks a different question.

What makes one storm intensify while another, seemingly similar, does not? By comparing these cases, the model learns to isolate the key physical signals behind the rapid intensification.

This makes it more robust, especially for rare and extreme events where traditional models often struggle.

We use three main categories of data.

The first one is atmospheric and oceanic environment data.

Li's model achieved around 92% detection accuracy for rapid intensification events while keeping the false alarm rate below 10%.