Data for "Constructing Deep Learning Datasets to Reveal Climate Trends of Tropical Cyclone Intensity and Structure Extremes"

Climate change has been linked to tropical cyclone (TC) poleward migration, TCextreme precipitation, and an increased proportion of major hurricanes. Understandingpast TC trends and variability is critical for projecting future TC impacts on humansociety. However, past trends in TC structure and energy remain uncertain due tolimited observations; subjection analyses and inherent spatiotemporal heterogeneity ofbest-track data have led to reduced confidence in the assessed TC responses to thechanging climate.The DSAT uses deep learning to reconstruct past "observations" andyield an objective global TC wind profile dataset during 1981‒2020, facilitating acomprehensive examination of TC intensity (Vmax), structure, and integrated kineticenergy (IKE). The proposed model converts multichannel satellite imagery to a 0-750-km wind profile of axisymmetric surface winds; it is trained based on labeled dataintegrating best tracks and numerical model reanalysis of 2004–2018 TCs.The DSAT model (Chen et al. 2025) features a hybrid GAN-CNN architecture (Chen B et al., 2021a), incorporating a generative adversarial network (GAN, Goodfellow et al., 2014) module and a polar convolutional neural network (CNN, Krizhevsky et al., 2012; Chen B et al., 2021b) regressor. The DSAT GAN module is trained to generate synthetic VIS images (VIS*) based on all-time available infrared (IR) and water vapor (WV) images, enabling the indirect take advantage of original VIS images to train the CNN regressor. On the other hand, the CNN regressor, employing a "polar-convolution filter" design (Chen B et al., 2021b), extracts essential features from satellite images and regresses these features to labeled wind profiles of azimuthally averaged winds.Chen, B. F., Chen, B., Hsiao, C. M., Teng, H. F., Lee, C. S., & Kuo. H. C. (2025). Constructing Deep Learning Datasets to Reveal Climate Trends of Tropical Cyclone Intensity and Structure Extremes. Artificial Intelligence for the Earth Systems (accepted with revision).