Tropical and Extratropical Cyclones Research

Últimos artículos

• FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting

  Shijie Xiao, Jonathan Lin, Thomas Ehrmann, Ali Sarhadi · 23 de septiembre de 2026

  Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive,…

• From Manual Construction to AI-Driven Scenario Emergence: Rethinking Catastrophe Risk Modeling

  Hang Gao · 16 de septiembre de 2026

  Traditional catastrophe (CAT) risk models rely on costly manual construction to generate extreme weather scenarios, an approach largely unchanged since the 1990s. As climate extremes intensify, this creates mounting challenges to the entire risk transfer chain. This study proposes the TAISE framewor…

• The Towers Were Standing: A Cause Decomposition of Cellular Outages During Hurricane Helene

  Oluseyi Olukola, Oare Danielle Addeh, Esther Abiodun Konan, Nick Rahimi · 11 de septiembre de 2026

  Hurricane Helene produced the largest absolute cell-site outage in the public FCC record, peaking at 4562 sites. The conventional model is physical: towers destroyed. Helene did destroy over 1700 miles of fibre, but almost none of it was cell sites. We present the first cause-decomposed study of the…

• TC-Next: Zero-Shot Multimodal Cyclone Forecasting

  Zhe Wang, Sijie Chen, Yiming Luo, Daehyun Kim, Chien-Yi Chang · 9 de septiembre de 2026

  We present TropicalCycloneNext (TC-Next), a multimodal deep learning model that forecasts tropical cyclone track and intensity at $6$-$24$ h leads by leveraging a foundation model's forecast fields of atmospheric kinematic and thermodynamic fields and GridSat infrared satellite imagery. Trained only…

• Too Rare to Learn: Prescribed Cyclone Tracks Degrade a Bay of Bengal Ocean Emulator

  Sumaiya Islam · 7 de septiembre de 2026

  Neural ocean emulators are being proposed for regional forecasting in cyclone-exposed coastal seas, and a natural design choice is to hand the network the cyclone as a prescribed input. We test that choice in the Bay of Bengal and find it harmful. We withhold 15 whole cyclones spanning 65 to 150 kt …

• SurgeGen: A Hybrid Generative Diffusion Framework for Storm Surge Scenario Synthesis

  Shunan Zheng, John J. Hasenbein · 4 de septiembre de 2026

  Predicting storm surge induced by landfalling tropical cyclones is crucial for flood mitigation and coastal risk management. Traditionally, physics-based numerical models simulate storm surge by solving the Navier--Stokes equations using numerical methods, but these simulations are computationally e…

• Frequency-aware forecasting for short-term typhoon gust prediction

  Xuefei Wang, Tingyi Liu, Heng Zhang, Shengjun Zhang · 27 de agosto de 2026

  Accurate gust forecasting under typhoon conditions remains challenging due to the highly non-stationary and multi-scale characteristics of extreme wind fluctuations. Existing deep learning models often struggle to simultaneously capture long-term trends and rapid local variations, resulting in degra…

• Tianmu-TC: Physics-constraints Generative Artificial Intelligence for Global Tropical Cyclone Forecasting

  Shiqi Zhang, Pan Mu, Cheng Huang, Hanting Yan, Yuchao Zhu, Jinglin Zhang, Shengyong Chen, Shoujuan Shu, Cong Bai · 20 de agosto de 2026

  Tropical cyclones (TCs) pose severe risks from strong winds and heavy rainfall. However, forecasting their track and intensity remains challenging due to chaotic atmosphere and the rapid amplification of initial condition errors, leading to growing forecast uncertainty. While numerical weather predi…

• AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting

  Anna Allen, Wessel P. Bruinsma, Michael Maier-Gerber, Harrison Cook, Matthew Chantry, Richard E. Turner · 12 de agosto de 2026

  AI weather models are in the process of revolutionising weather forecasting. While these models have been shown to achieve superior performance to physics-based NWP in forecasting tropical cyclone (TC) tracks, they dramatically underestimate intensity. Here we present AIFS-TC, a simple correction to…

• Tropical Cyclone Forecasting via Latent Rectified Flow using Satellite Imagery and Atmospheric Fields

  Meheru Zannat, Sk. Md. Masudul Ahsan · 11 de agosto de 2026

  Tropical cyclones are growing more destructive in a changing climate, and efficient forecasting of their structure and track has become a necessity. Deep generative models promise an alternative to computationally expensive numerical weather prediction (NWP), yet current systems produce either satel…

• Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

  Swastik Agrawal, Nishkal Hundia, Ziyue Liu, Michelle Bensi · 11 de agosto de 2026

  Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses. This study evaluates data-driven approaches for Rmax…

• Real-time probabilistic tsunami forecasting via generative AI

  Yusuke Oishi, Takashi Furumura, Fumihiko Imamura · 6 de agosto de 2026

  Explicit onshore tsunami inundation forecasting can improve public risk awareness, but deterministically predicted inundation boundaries under highly uncertain conditions, such as near-field tsunamis generated by megathrust earthquakes, may falsely imply safety outside the boundaries. Consequently, …

• Conditional Tropical Cyclogenesis Rates via Rare-Event Sampling in a Neural Weather Emulator

  John S. Schreck, William Chapman, Charlie Becker, David John Gagne II · 1 de julio de 2026

  We couple Forward Flux Sampling (FFS), a non-equilibrium rare-event technique from statistical mechanics, to a neural weather emulator (SDL-WXFormer, 1{\deg} grid spacing) to estimate conditional tropical cyclogenesis rates, or how often a tropical cyclone achieves a hurricane-level central pressure…

• Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps

  Elizabeth Cucuzzella, Rafael Izbicki, Ann B. Lee · 29 de junio de 2026

  Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on complex inputs x. However, these distributions are often locally miscalibrated, especially for high-stakes tail events where accurate uncertainty quantification…

• MotifGen: Spatiotemporal interpolation of misaligned satellite images via multi-source generative modeling, in an application to tropical cyclones

  Cl\'ement Dauvilliers (Inria), Claire Monteleoni (Inria) · 24 de junio de 2026

  Microwave satellite imagery plays a crucial role in monitoring tropical cyclone precipitation and intensity worldwide, but suffers from long revisit times, potentially missing rapid storm evolution phases. While this raises the need for an interpolation method, it is made challenging by the high lev…

• Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels

  Jose Marie Antonio Mi\~noza, Rex Gregor Laylo, Sebastian C. Iba\~nez · 3 de junio de 2026

  Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estimates, a critical gap for high-stakes decisions during extreme weather events. This paper proposes Neural Tangent Kernel-…

• The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench

  Dingling Yao, Andrea Polesello, Adeel Pervez, Caroline Muller, Francesco Locatello · 26 de mayo de 2026

  While Vision Foundation Models (VFMs) excel at predictive tasks on satellite imagery, their performance can arise from visual correlations rather than underlying structural invariants, making even perception-based out-of-distribution accuracy a poor proxy for scientific utility. As a result, models …

• Hybrid Quantum-Classical Corrective Diffusion Modeling for Meteorological Downscaling

  Rui Wang, Edoardo Pasetto, Amer Delilbasic, Morris Riedel, Kristel Michielsen, Gabriele Cavallaro · 25 de mayo de 2026

  Statistical downscaling is a crucial component of the weather modeling field, where high-resolution outputs must be reconstructed from coarse-resolution inputs with the full cost of dynamical refinement. In this work, we investigate a hybrid quantum-classical corrective diffusion model for probabili…

• A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

  Jennifer Nakamura, Upmanu Lall · 21 de mayo de 2026

  Reliable assessment of tropical cyclone (TC) risk is limited by the brevity and spatial sparsity of the historical record, particularly for the rare, high-intensity landfalls that dominate insured loss. We present WHITS (Wind-focused Hurricane Interactive Track Simulator), a non-parametric semi-Mark…

• Adversarial Attacks on Downstream Weather Forecasting Models: Application to Tropical Cyclone Trajectory Prediction

  Yue Deng, Francisco Santos, Pang-Ning Tan, Lifeng Luo · 19 de mayo de 2026

  Deep learning-based weather forecasting (DLWF) models leverage past weather observations to generate future forecasts, supporting a wide range of downstream applications, including tropical cyclone (TC) prediction. In this paper, we investigate their vulnerability to adversarial attacks, where subtl…

• Multi-site modelling and reconstruction of past extreme skew surges along the French Atlantic coast

  Nathan Huet, Philippe Naveau, Anne Sabourin · 7 de mayo de 2026

  Appropriate modelling of extreme skew surges is crucial, particularly for coastal risk management. Our study focuses on modelling extreme skew surges along the French Atlantic coast, with a particular emphasis on investigating the extremal dependence structure between stations. We employ the peak-ov…

• StormNet: Improving storm surge predictions with a GNN-based spatio-temporal offset forecasting model

  Noujoud Nader, Stefanos Giaremis, Clint Dawson, Carola Kaiser, Karame Mohammadiporshokooh, Hartmut Kaiser · 24 de abril de 2026

  Storm surge forecasting remains a critical challenge in mitigating the impacts of tropical cyclones on coastal regions, particularly given recent trends of rapid intensification and increasing nearshore storm activity. Traditional high fidelity numerical models such as ADCIRC, while robust, are ofte…

• Storm Surge Modeling, Bias Correction, Graph Neural Networks, Graph Convolution Networks

  Noujoud Nader, Stefanos Giaremis, Clint Dawson, Carola Kaiser, Karame Mohammadiporshokooh, Hartmut Kaiser · 23 de abril de 2026

  Storm surge forecasting remains a critical challenge in mitigating the impacts of tropical cyclones on coastal regions, particularly given recent trends of rapid intensification and increasing nearshore storm activity. Traditional high fidelity numerical models such as ADCIRC, while robust, are ofte…

• CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting

  Renlong Hang, Zihao Xu, Jiuwei Zhao, Runling Yu, Leye Cheng, Qingshan Liu · 15 de abril de 2026

  Tropical cyclones (TCs) rank among the most destructive natural hazards, yet their forecasting faces fundamental trade-offs: numerical weather prediction (NWP) models are computationally prohibitive and struggle to leverage historical data, while existing deep learning (DL)-based intelligent models …

• El Nino Prediction Based on Weather Forecast and Geographical Time-series Data

  Viet Trinh, Ha-Vy Luu, Quoc-Khiem Nguyen-Pham, Hung Tong, Thanh-Huyen Tran, Hoai-Nam Nguyen Dang · 8 de abril de 2026

  This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Ni\~no events, crucial for mitigating their global climatic, economic, and societal impacts. Traditional prediction models often rely on oceanic and atmospheric indices, which may lack the granularity or …