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• The SAF-Net is proposed to combine domain-knowledge and data-driven knowledge. • The spatial attention is used to automatically selected high-response wind speed area. • Extensive experiments is proceeded in the real-world dataset. A typhoon is a destructive weather system that can cause severe casualties and economic losses. Typhoon intensity (TI) is a measurement to evaluate its ruinous degree. Hence, typhoon intensity prediction is an important research problem and many methods have been proposed. However, most of the existing approaches have very limited capability to combine the 2D Typhoon Structure Domain-expert Knowledge (2D-TSDK) and the 3D Typhoon Structure Data-driven Knowledge (3D-TSDK) for the TI prediction. To address this issue, this paper proposes a spatio-temporal deep learning method named Spatial Attention Fusing Network (SAF-Net). The designed model aims to fuse the 2D-TSDK and the 3D-TSDK by developing a specific Wide & Deep framework. In the data-driven component, a special Spatial Attention (SA) module is designed to automatically select high-response wind speed areas and embedded into a three-branch CNN to exploit the 3D-TSDK. Then, the Wide & Deep framework integrates the 2D-TSDK and the 3D-TSDK for the TI prediction. Comprehensive experiments have been conducted on a real-world dataset, and the result shows that the proposed method outperforms state-of-the-art typhoon intensity prediction methods. The code is available in GitHub: https://github.com/xuguangning1218/TI_Prediction [ABSTRACT FROM AUTHOR]

Tropical Cyclone Intensity Estimation (TIE) is a fundamental study subject for tropical cyclone development, flood or landslide avoidance, etc. Despite considerable efforts, two main challenges remain unresolved in this critical endeavor. The first challenge is that the TIE task is frequently conducted as a coarse-grained recognition problem rather than a fine-grained one. The second challenge is that the prediction fails to consider general wind speed information. To conquer these two challenges, we offer a novel model, namely Tropical cyclone intensity estimation from a Fine-grained perspective with the Graph convolution neural Network (TFG-Net). It is composed of three key components, viz., the Backbone, the Fine-grained Tropical cyclone Features Extractor (FTFE), and the Wind Scale Transition Rule Generator (WTRG), which aim at extracting general spatial features, subtle spatial features, and general wind speed information, respectively. To validate the proposed method, extensive experiments on a well-known real-world tropical dataset named GridSat were carried out. Following the standard benchmark task setting that the model estimates the wind speed from a given satellite image, the proposed TFG-Net reaches 11.12 knots in the RMSE metric, which outperforms 33.33%, 2.54% to the traditional method and the state-of-the-art deep learning method, respectively. The code is available on GitHub: https://github.com/xuguangning1218/TI%5fEstimation and its reproductive result is available on Code Ocean: https://doi.org/10.24433/CO.6606867.v1. • We first proposed to conduct cyclone intensity estimated in fine-grained perspective. • We propose to divide predictive wind speed into general part and particular part. • Extensive experiments and analysis are conducted on the real-world dataset. [ABSTRACT FROM AUTHOR]