The prediction of the intensity, location and time of the landfall of a tropical cyclone well advance in time and with high accuracy can reduce human and material loss immensely. In this article, we develop a Long Short-Term memory based Recurrent Neural network model to predict intensity (in terms of maximum sustained surface wind speed), location (latitude and longitude), and time (in hours after the observation period) of the landfall of a tropical cyclone which originates in the North Indian ocean. The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy. For example, using 24 hours data of a cyclone anytime during its course, the model provides state-of-the-art results by predicting landfall intensity, time, latitude, and longitude with a mean absolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degree respectively, which resulted in a distance error of 51.7 kilometers from the landfall location. We further check the efficacy of the model on three recent devastating cyclones Bulbul, Fani, and Gaja, and achieved better results than the test dataset.
CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
School of Computer Science, School of Software（计算机科学学院、软件学院）；Nanjing University of Information Science and Technology（南京信息工程大学）；School of Atmospheric Sciences（大气科学学院）；Shanghai Typhoon Institute（上海台风研究所）；China Meteorological Administration（中国气象局）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
DAMO Academy, Alibaba Group（阿里集团达摩院）；State Key Laboratory of Severe Weather Meteorological Science and Technology, Nanjing University（南京大学 severe weather meteorological science and technology 国家重点实验室）
ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking
CMCC Foundation - Euro-Mediterranean Center on Climate Change（CMCC基金会 - 欧洲-地中海气候变化中心）；University of Salento, Department of Engineering for Innovation（萨伦托大学创新工程系）；Department of Earth and Environmental Engineering, Columbia University（哥伦比亚大学地球与环境工程系）
VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Hong Kong University of Science and Technology（香港科技大学）
TCR-GAN: Predicting tropical cyclone passive microwave rainfall using infrared imagery via generative adversarial networks
School of Geosciences, China University of Petroleum（中国石油大学地球科学学院）；College of Computer Science and Technology, China University of Petroleum（中国石油大学计算机科学与技术学院）
IDOL: Meeting Diverse Distribution Shifts with Prior Physics for Tropical Cyclone Multi-Task Estimation
Zhejiang University of Technology（浙江工业大学）；Shandong University（山东大学）；Zhejiang Key Laboratory of Visual Information Intelligent Processing（浙江省可视信息智能处理重点实验室）
Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Center for Ocean Mega-Science, Chinese Academy of Sciences（中国科学院海洋大科学研究中心）
Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance
Department of Statistics and Data Science（统计与数据科学系）；Carnegie Mellon University（卡内基梅隆大学）；Department of Atmospheric Sciences and Hydrology（大气科学与水文学系）；University of Arizona（亚利桑那大学）
Tianmu-TC: Physics-constraints Generative Artificial Intelligence for Global Tropical Cyclone Forecasting
College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）；School of Earth Sciences, Zhejiang University（浙江大学地球科学学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification
School of Meteorology, University of Oklahoma（俄克拉荷马大学气象学院）；Faculty of Geosciences and Environment, University of Lausanne（洛桑大学地球科学与环境学院）；Expertise Center for Climate Extremes, University of Lausanne（洛桑大学气候极端事件专业中心）
Revealing the Potential of Learnable Perturbation Ensemble Forecast Model for Tropical Cyclone Prediction
Artificial Intelligence Innovation and Incubation Institute（人工智能创新与孵化院）；Department of Atmospheric and Oceanic Sciences（大气科学系）；Institute of Atmospheric Sciences（大气科学研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）
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）
Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data
University of Colorado（科罗拉多大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Univ. Savoie Mont Blanc（萨瓦大学）；CNRS（法国国家科学研究中心）；IRD（法国发展研究院）；IFSTTAR（法国交通发展规划与网络研究院）；ISTerre（地球科学研究所）；Linear Accelerator Laboratory（线性加速器实验室）；Université Paris-Sud（巴黎第十一大学）；Inria Saclay–Ile-de-France（法国国家信息与自动化研究所萨克莱-法兰西岛分院）；LRI（信息学实验室）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Institute for Research in Environmental Sciences（环境科学合作研究所）；NOAA/OAR/ESRL/Global Systems Division（美国国家海洋和大气管理局海洋与大气研究办公室地球系统研究实验室全球系统部）
A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting
Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Fudan University（复旦大学）；Henan Meteorological Observatory（河南省气象台）；Tianjin Meteorological Information Center（天津市气象信息中心）
Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network
Faculty of Electronics and Telecommunications University of Engineering and Technology, VNU Hanoi, Vietnam（电子电信学院 工程技术大学 巴黎越南河内分校）；Faculty of Information Technology University of Engineering and Technology, VNU Hanoi, Vietnam（信息技术学院 工程技术大学 巴黎越南河内分校）
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）
Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones
National Institute of Informatics（国立情报学研究所）；Yokohama National University（横滨国立大学）；University of Southern California（南加州大学）；Grenoble-INP, Ensimag（格勒诺布尔综合理工学院，恩西马格）；Université Clermont Auvergne, ISIMA（克莱蒙奥弗涅大学，ISIMA）；Google DeepMind（谷歌DeepMind）
Potential Paradigm Shift in Hazard Risk Management: AI-Based Weather Forecast for Tropical Cyclone Hazards
Tongji University（同济大学）；Princeton University（普林斯顿大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Wollongong（伍伦贡大学）；Nanjing University（南京大学）；Shanghai Jiao Tong University（上海交通大学）
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）
Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks
Department of Astronomy, University of Wisconsin–Madison（天文学系，威斯康星大学麦迪逊分校）；Department of Chemistry and Biochemistry, University of California, Los Angeles（化学与生物化学系，加州大学洛杉矶分校）；School of Health Sciences, Guangzhou Xinhua University（健康科学学院，广州新华大学）；Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison（大气与海洋科学系，威斯康星大学麦迪逊分校）
