Anthropogenic influences have been linked to tropical cyclone (TC) poleward migration, TC extreme precipitation, and an increased proportion of major hurricanes [1, 2, 3, 4]. Understanding past TC trends and variability is critical for projecting future TC impacts on human society considering the changing climate [5]. However, past trends of TC structure/energy remain uncertain due to limited observations; subjective-analyzed and spatiotemporal-heterogeneous "best-track" datasets lead to reduced confidence in the assessed TC repose to climate change [6, 7]. Here, we use deep learning to reconstruct past "observations" and yield an objective global TC wind profile dataset during 1981 to 2020, facilitating a comprehensive examination of TC structure/energy. By training with uniquely labeled data integrating best tracks and numerical model analysis of 2004 to 2018 TCs, our model converts multichannel satellite imagery to a 0-750-km wind profile of axisymmetric surface winds. The model performance is verified to be sufficient for climate studies by comparing it to independent satellite-radar surface winds. Based on the new homogenized dataset, the major TC proportion has increased by ~13% in the past four decades. Moreover, the proportion of extremely high-energy TCs has increased by ~25%, along with an increasing trend (> one standard deviation of the 40-y variability) of the mean total energy of high-energy TCs. Although the warming ocean favors TC intensification, the TC track migration to higher latitudes and altered environments further affect TC structure/energy. This new deep learning method/dataset reveals novel trends regarding TC structure extremes and may help verify simulations/studies regarding TCs in the changing climate.

翻译：人为影响已被认为与热带气旋（TC）向极地迁移、TC极端降水以及强飓风比例增加有关[1,2,3,4]。理解过去TC的变化趋势与变异性，对于在气候变化背景下预测TC未来对社会的影响至关重要[5]。然而，由于观测数据有限，过去TC结构/能量的变化趋势仍存在不确定性；基于主观分析且存在时空异质性的"最佳路径"数据集，降低了评估TC对气候变化响应的置信度[6,7]。本研究利用深度学习重建过去的"观测数据"，生成了1981年至2020年间全球客观的TC风廓线数据集，从而实现了对TC结构/能量的全面分析。通过使用融合了最佳路径数据与2004-2018年TC数值模式分析的独特标记数据集进行训练，我们的模型可将多通道卫星图像转换为0-750千米范围内的轴对称表面风廓线。通过与独立卫星雷达表面风数据对比验证，该模型性能足以支撑气候研究。基于新创建的均质化数据集，过去四十年间强TC比例增加了约13%。此外，超高能量TC的比例增加了约25%，同时高能量TC的平均总能量呈现上升趋势（超过40年变率的一个标准差）。尽管海洋变暖有利于TC增强，但TC路径向高纬度迁移及环境变化进一步影响了TC结构/能量。这种新的深度学习方法和数据集揭示了TC极端结构的新趋势，有助于验证气候变化背景下TC的相关模拟与研究。