在天气模式中对强热带气旋的直接计算机模拟受到计算费用的限制。强烈的TC是罕见的，并且具有小规模的结构，因此很难在高分辨率下产生大的风暴集合。此外，模型往往无法捕捉到快速强化的过程，这是许多强烈TC的一个显著特征。在全球变暖的背景下，理解快速强化尤其重要，这可能会增加强烈TC的频率。为了更好地利用计算资源的快速集约化的研究，我们介绍了一个行动最小化算法应用于WRF和WRFPLUS模型。行动最小化推动模型形成更强烈的TC比它否则会;它这样做是通过极大似然路径的随机制定的模型，从而允许有针对性的研究强化机制。我们将动作最小化应用于6 km分辨率的飓风Danny（2015）和Fred（2009）的模拟，以证明该算法始终通过物理上合理的路径增强TC。我们显示了大约十倍的计算节省使用行动最小化研究的TC强度分布的尾部。此外，对于飓风丹尼和弗雷德，行动最小化产生的扰动，优先减少低层切变相比，上层切变，至少在大约$4 \mathrm{\ m \ s^{-1}}$的阈值以上。我们还表明，不对称的，随时间变化的加热模式可以导致显着的TC强化超出对称，方位平均加热，并找到一个制度的非线性响应不对称加热，在以前的工作中没有得到广泛的研究。
英文摘要：
Direct computer simulation of intense tropical cyclones (TCs) in weather models is limited by computational expense. Intense TCs are rare and have small-scale structures, making it difficult to produce large ensembles of storms at high resolution. Further, models often fail to capture the process of rapid intensification, which is a distinguishing feature of many intense TCs. Understanding rapid intensification is especially important in the context of global warming, which may increase the frequency of intense TCs. To better leverage computational resources for the study of rapid intensification, we introduce an action minimization algorithm applied to the WRF and WRFPLUS models. Action minimization nudges the model into forming more intense TCs than it otherwise would; it does so via the maximum likelihood path in a stochastic formulation of the model, thereby allowing targeted study of intensification mechanisms.
We apply action minimization to simulations of Hurricanes Danny (2015) and Fred (2009) at 6 km resolution to demonstrate that the algorithm consistently intensifies TCs via physically plausible pathways. We show an approximately ten-fold computational savings using action minimization to study the tail of the TC intensification distribution. Further, for Hurricanes Danny and Fred, action minimization produces perturbations that preferentially reduce low-level shear as compared to upper-level shear, at least above a threshold of approximately $4 \mathrm{\ m \ s^{-1}}$. We also demonstrate that asymmetric, time-dependent patterns of heating can cause significant TC intensification beyond symmetric, azimuthally-averaged heating and find a regime of non-linear response to asymmetric heating that has not been extensively studied in previous work.
Disaggregating the Increase in Tropical Cyclone Intensity in Kossin et al. (PNAS 2020)
Kossin et al.（2020）（在过去四十年中）成功地测试了气候变化模型的预测，即有条件的热带气旋强度（主要[3-5类]气旋的频率除以所有气旋的频率）应该增加。然而，发生率相对比例增加最高的是3级气旋。最高的相对比例下降发生在5类气旋。这种推论不是由于对测量误差的不同处理。这可能是由于相对罕见的5级气旋的功率较低。
WRF model parameter calibration to improve the prediction of tropical cyclones over the Bay of Bengal using Machine Learning-based Multiobjective Optimization
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）
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）
Crucial role of subsurface ocean variability in tropical cyclone genesis
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））；Department of Atmospheric Sciences, National Taiwan University（台湾大学大气科学系）；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学国家重点实验室）；Global Ocean and Climate Research Center, South China Sea Institute of Oceanology, Chinese Academy of Sciences（中国科学院南海海洋研究所全球海洋与气候研究中心）；Guangdong Key Laboratory of Ocean Remote Sensing, South China Sea Institute of Oceanology, Chinese Academy of Sciences（中国科学院南海海洋研究所广东省海洋遥感重点实验室）；Frontier Science Centre for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China（中国海洋大学深海多圈层与地球系统前沿科学中心）；Physical Oceanography Laboratory, Ocean University of China（中国海洋大学物理海洋学实验室）；Department of Atmospheric Sciences, University of Hawaii（夏威夷大学大气科学系）；Department of Atmospheric and Oceanic Science, University of Maryland（马里兰大学大气与海洋科学系）；Inter-Disciplinary Program in Climate Studies, Indian Institute of Technology Bombay（印度孟买理工学院气候研究跨学科项目）；Department of Civil and Environmental Engineering, Princeton University（普林斯顿大学土木与环境工程系）
Watch an AI Weather Model Learn (and Overshoot) Tropical Cyclones
Colorado State University（科罗拉多州立大学）；Boston University（波士顿大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）
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）
