Tropical cyclones (TCs) are among the most deadly and damaging natural disasters in coastal areas worldwide. Traditional forecasting methods face challenges as they neglect crucial spatial information related to intensity changes and require substantial human and material resources. Moreover, current deep learning approaches often rely on reanalysis of data from observations far from land, making them challenging to acquire and operationalize. In response to these issues, the article introduces the TC intensity prediction network (TCIP-Net), which, while maintaining interpretability, successfully extracts rich convective structural information from the infrared (IR) channel of satellite imagery. We present the spatio-temporal evolution trajectory of TC radial structural information through Hovmöller diagrams. In addition, we construct a subnetwork with one backbone convolution and four branch convolution operations to extract asymmetric information of TC structure. The convective core (CC) reveals the distribution of convective systems around the eye, aiding in targeted attention to convective information in IR imagery. The model aims to quantitatively explain the contributions of satellite imagery (IR and microwave), convective structure, and key physical factors to the TC intensity prediction task. We utilize multiple TC cases to assess and validate the applicability and effectiveness of the model. The results indicate that TCIP-Net achieved good performance. This approach provides practical guidance for predicting TC intensity using advanced artificial intelligence-based methods and is expected to complement operational models.

中文翻译：

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TCIP-Net：量化热带气旋强度预测的径向结构演化


热带气旋 (TC) 是全球沿海地区最致命、最具破坏性的自然灾害之一。传统的预测方法面临挑战，因为它们忽略了与强度变化相关的关键空间信息，并且需要大量的人力和物力资源。此外，当前的深度学习方法通​​常依赖于对远离陆地的观测数据进行重新分析，这使得它们的获取和操作具有挑战性。针对这些问题，文章介绍了TC强度预测网络（TCIP-Net），该网络在保持可解释性的同时，成功地从卫星图像红外（IR）通道中提取了丰富的对流结构信息。我们通过 Hovmöller 图呈现了 TC 径向结构信息的时空演化轨迹。此外，我们构建了一个具有一个主干卷积和四个分支卷积运算的子网络来提取TC结构的不对称信息。对流核心 (CC) 揭示了眼睛周围对流系统的分布，有助于有针对性地关注红外图像中的对流信息。该模型旨在定量解释卫星图像（红外和微波）、对流结构和关键物理因素对TC强度预测任务的贡献。我们利用多个TC案例来评估和验证模型的适用性和有效性。结果表明TCIP-Net取得了良好的性能。该方法为使用先进的基于人工智能的方法预测热带气旋强度提供了实用指导，并有望补充业务模型。