Predicting typhoon intensity accurately across space and time is crucial for
issuing timely disaster warnings and facilitating emergency response. This has
vast potential for minimizing life losses and property damages as well as
reducing economic and environmental impacts. Leveraging satellite imagery for
scenario analysis is effective but also introduces additional challenges due to
the complex relations among clouds and the highly dynamic context. Existing
deep learning methods in this domain rely on convolutional neural networks
(CNNs), which suffer from limited per-layer receptive fields. This limitation
hinders their ability to capture long-range dependencies and global contextual
knowledge during inference. In response, we introduce a novel approach, namely
"Typhoon Intensity Transformer" (Tint), which leverages self-attention
mechanisms with global receptive fields per layer. Tint adopts a
sequence-to-sequence feature representation learning perspective. It begins by
cutting a given satellite image into a sequence of patches and recursively
employs self-attention operations to extract both local and global contextual
relations between all patch pairs simultaneously, thereby enhancing per-patch
feature representation learning. Extensive experiments on a publicly available
typhoon benchmark validate the efficacy of Tint in comparison with both
state-of-the-art deep learning and conventional meteorological methods. Our
code is available at this https URL

利用卫星图像进行场景分析以准确预测台风强度，引入自注意力机制的 “Typhoon Intensity Transformer”（Tint）通过每层具有全局感受野的自注意力操作，提升了局部和全局上下文关系的提取能力，并在公开的台风基准测试中验证了其在深度学习和常规气象方法方面的有效性。