Title: Vision Transformer for Extracting Tropical Cyclone Intensity from Satellite Images
Author: YE TIAN, Wen Zhou, Paxson Cheung, Zhenchen LIU
Issue&Volume: 2024-07-03
Abstract: TC intensity estimation is an essential task in TC observation and forecasting. Deep learning models have recently been applied to estimate TC intensity from satellite images and produce accurate results. This work proposes the ViT-TC model based on the Vision Transformer (ViT) architecture built by the attention mechanism. Satellite images of TCs, including infrared (IR), water vapor (WV), and passive microwave (PMW), are used as inputs for TC intensity estimation. Experiments show that inputting a combination of IR, WV, and PMW can give a more accurate estimation than other combinations of input channels. The ensemble mean technique is applied and improves the model’s estimations to a root mean square error (RMSE) and mean absolute error (MAE) of 9.65 and 6.98 knots, which outperforms traditional methods and is comparable to existing deep learning models. The model assigns high attention weights to areas with high PMW, indicating that PMW magnitude is essential information for the model’s estimation. The model also gives high attention weights to non-cloud areas with high IR and WV, suggesting that the model detects the positive correlation between TC size and intensity and derives this feature from the non-cloud area over the edge of the sample.
