It has recently been shown that an interesting inverse relationship exists between the strength of the overturning in the regional boreal Hadley circulation and tropical cyclone genesis and lifetime maximum intensity latitudes in the eastern North Pacific. One of the particularly curious aspects of this association is its uniqueness to this ocean basin. We show here that this result can be understood as the outcome of an equatorward shift in the local Intertropical Convergence Zone (ITCZ) and a moderate reduction of tropical vertical shear. These two factors are not necessarily concomitant. The magnitude of the vertical shear change is low enough to suggest that the primary physical mechanism behind this inverse relationship is the equatorward shift in the ITCZ. Since a significant proportion of tropical cyclone genesis results from aggregated convective cells forming into a coherent convective vortex and being shed from the ITCZ, this shift is apparently sufficient to explain the observed association. The equatorward shift in the ITCZ is potentially the result of a relative warming of the Southern Hemisphere in the boreal tropical cyclone season. This more equatorward ITCZ, located over warmer surface waters, presumably explains the more intense Hadley circulation, despite a reduction of the large-scale boreal meridional temperature gradient. Although, these links require further research.
Poleward trends in seasonal-mean latitudes of tropical cyclones (TCs) have been identified in direct observations from 1980 to present. Paleoclimate reconstructions also indicate poleward-equatorward migrations over centennial to millennial timescales. Hadley circulation (HC) is often both implicitly and explicitly invoked to provide dynamical linkages to these shifts, although no direct analysis of concurrent changes in the recent period has been presented. Here the observational TC record (1981-2016) and the ERA-Interim, JRA55 and MERRA2 reanalyses are studied to examine potential relationsh…
Unexpected limitation of tropical cyclone genesis by subsurface tropical central-north Pacific during El Niño
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)（南方海洋科学与工程广东省实验室（珠海））；State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences（中国科学院南海海洋研究所热带海洋环境国家重点实验室）；Department of Atmospheric Sciences, National Taiwan University（台湾大学大气科学系）；Department of Atmospheric and Oceanic Science, University of Maryland（马里兰大学大气与海洋科学系）；Indian Institute of Technology, Bombay（印度孟买理工学院）
Bayesian statistical models were developed for the number of tropical cyclones and the rate at which these cyclones became hurricanes in the North Atlantic, North and South Indian, and East and West Pacific Oceans. We find that there is small probability that the number of cyclones has increased in the past thirty years. The rate at which these storms become hurricanes appears to be constant. The rate at which hurricanes evolve into category 4 and higher major storms does appear to have increased. We also investigate storm intensity by measuring the distribution of individual storm lifetime in…
Insights from Ex-Typhoon Halong (2025) -- An Arctic Cyclone of Tropical Origin
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Alaska Fairbanks（阿拉斯加大学费尔班克斯分校）；Naval Research Laboratory（海军研究实验室）；National Science Foundation National Center for Atmospheric Research（美国国家科学基金会国家大气研究中心）
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）
Characterizing the Evolution of Tropical Cyclone Thermal Structure: A Tropical Cyclone Thermal Phase Space
Lanzhou University（兰州大学）；Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院西北生态环境资源研究院）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Defense Technology（国防科技大学）；Institute of Tibetan Plateau Research, Chinese Academy of Sciences（中国科学院青藏高原研究所）；Chinese Academy of Sciences（中国科学院）；China Meteorological Administration（中国气象局）
When studying tropical cyclones using the $f$-plane, axisymmetric, gradient balanced model, there arises a second-order elliptic equation for the transverse circulation. Similarly, when studying zonally symmetric meridional circulations near the equator (the tropical Hadley cells) or the katabatically forced meridional circulation over Antarctica, there also arises a second order elliptic equation. These elliptic equations are usually derived in the pressure coordinate or the potential temperature coordinate, since the thermal wind equation has simple non-Jacobian forms in these two vertical c…
Steering Tropical Cyclones Using Small Perturbations in an AI Weather Model
School of Complex Adaptive Systems and Water Institute, Arizona State University（复杂适应系统学院与水研究所，亚利桑那州立大学）；Department of Civil and Environmental Engineering, Seoul National University（土木与环境工程学院，首尔国立大学）；Department of Earth and Environmental Engineering and Columbia Water Center, Columbia University（地球与环境工程学院与哥伦比亚水中心，哥伦比亚大学）
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）
Mid-Tropospheric Cyclones (MTCs) are moist synoptic systems with distinct mid tropospheric vorticity maxima and weak signatures in the lower troposphere. Composites and statistics of MTCs over the tropics are constructed and compared with monsoon lows and depressions (together, lower troposphere cyclones; LTCs). We begin with South Asia, where tracking reveals that MTCs change character during their life, i.e., their track is composed of MTC and LTC phases. The highest MTC-phase density and least motion is over the Arabian Sea, followed by the Bay of Bengal and South China Sea. A MTC-phase com…
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）
A Data-Driven Regional Model for Skillful Medium-Range Typhoon Prediction
Shanghai Typhoon Institute, and Key Laboratory of Numerical Modeling for Tropical Cyclone of the China Meteorological Administration（上海台风研究所，中国气象局台风数值模式重点实验室）；Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University（复旦大学大气与海洋科学系和大气科学研究院）；Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校大气科学系）
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）
This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial-temporal data with statistical data by extracting features with deep-learning encoder-decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time track and intensity forecasts and show they achieve comparable mean absolute error a…
