Tropical Cyclones (TCs) have devastating effects on several coastal regions worldwide. Precautionary knowledge about TC characteristics such as wind direction, wind speed, epicenter position, condensed vapor pressure measure, and radius of maximum can be highly valuable in disaster management and economic planning. Existing literature has focused on TC wind direction, intensity, cloud shape, and epicenter position, but there has been limited research on estimation of the Radius of Maximum Wind (RMW). Accurate estimation of RMW is crucial as errors can significantly impact wind and storm surge assessments and forecasts. In this study, our objective is to determine the RMW over the North Indian Ocean (NIO). We chose this region due to its location surrounded by the Bay of Bengal and Arabian Sea, making it one of the six globally prominent areas prone to TCs. Our study is on the relationship between the center of the TC, the estimated pressure drop at the center, and the RMW, using historical observations and mathematical correlations. To address missing parameters in the best track database of the Indian Meteorological Department, we employ a local regression model. We validate the accuracy of our developed method using two statistical measures: error percentage and T-test. Numerous TC cases are discussed in the paper over the NIO. Our findings indicates that the suggested method exhibits an error percentage ranging from approximately $-63\%$ to $50\%$ when compared to the best track data provided by the Indian Meteorological Department (IMD). In contrast, the error percentages for two other references \cite{bib21, bib22} with the same best track data range from approximately $-26\%$ to $200\%$. Moreover, the T-test results demonstrate that our method outperforms than the other approaches in terms of statistical significance.
Advancing Seasonal Prediction of Tropical Cyclone Activity with a Hybrid AI-Physics Climate Model
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Google Research（谷歌研究院）；Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration（美国国家海洋和大气管理局地球流体动力学实验室）
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
University of Chicago, Department of the Geophysical Sciences（芝加哥大学地质科学系）；University of Chicago, Committee on Computational and Applied Mathematics（芝加哥大学计算与应用数学委员会）；University of Chicago, Research Computing Center（芝加哥大学研究计算中心）；University of California, Department of Applied Mathematics（加州大学应用数学系）；New York University, Courant Institute of Mathematical Sciences（纽约大学Courant数学科学研究所）
Global kilometre-scale tropical cyclone inner-core vector winds from sparse scalar CYGNSS observations
Shanghai Typhoon Institute, and Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration（上海台风研究所，中国气象局热带气旋数值模拟重点实验室）；State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources（自然资源部第二海洋研究所，卫星海洋环境动力学国家重点实验室）；School of Oceanography, Shanghai Jiao Tong University（上海交通大学海洋学院）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（广东海洋工程与科技实验室（珠海））；Fisheries and Oceans Canada, Institute of Ocean Sciences（加拿大渔业和海洋部，海洋科学研究所）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；Department of Marine and Oceanography（海洋与海洋学系）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；State Key Laboratory of Chemo and Biosensing, Hunan University（湖南大学化学与生物传感技术重点实验室）
Variability of North Atlantic hurricanes: seasonal versus individual-event features
热带气旋受到大量气候因子的影响，这些因素转化为复杂的发生模式。热带气旋活动的年度指标的变化已经被深入研究，特别是自20世纪90年代中期北大西洋突然激活以来。我们首先提供了一个快速的概述不同作者以前的工作，这些年度指标的NAtl盆地，在自然变化的现象，存在的趋势，记录的缺点，以及全球变暖的影响一直是有趣的辩论的主题。接下来，我们提出了一种替代方法，该方法不关注季节特征，而是关注单个事件的特征[Corral et al Nature Phys 6，693，2010]。有人认为，个别风暴功率耗散指数（PDI）构成了一个自然的方式来描述每个事件，并进一步，PDI统计产生一个强大的规律，热带气旋的发生在幂律。在这方面，这些分布的拟合方法进行了讨论。作为这项工作的一个重要扩展，我们引入了一个分布函数，模型的整个范围内的PDI密度（不包括在最小值的不完整性的影响），伽玛分布，包括在一个幂律与指数衰减的尾巴。这种衰减的特征尺度，表示的截止参数，提供了非常有价值的信息的有限大小的盆地，通过最大值的PDIs，盆地可以维持。我们使用伽马拟合来评估海表面温度（SST）对极端PDI值发生的影响，我们发现，对于0.49 degC的SST平均差异，这些全流域事件的值增加了约50%。...
A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)
Lamont-Doherty Earth Observatory, Columbia University（哥伦比亚大学拉蒙特-多赫蒂地球观测站）；School of Complex Adaptive Systems, Arizona State University（亚利桑那州立大学复杂适应系统学院）；Earth and Environmental Engineering, Columbia University（哥伦比亚大学地球与环境工程系）
Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery
Cooperative Institute for Research in the Atmosphere, Colorado State University（大气研究合作研究所，科罗拉多州立大学）；National Oceanic and Atmospheric Administration (NOAA) Global Systems Laboratory (GSL)（美国国家海洋和大气管理局全球系统实验室）；Colorado State University（科罗拉多州立大学）
