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相关论文: Maximizing simulated tropical cyclone intensity wi…

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Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger, larger and more…

大气与海洋物理 · 物理学 2023-11-28 Gabriele Accarino , Davide Donno , Francesco Immorlano , Donatello Elia , Giovanni Aloisio

Given the destructive impacts of tropical cyclones, it is critical to have a reliable system for cyclone intensity detection. Various techniques are available for this purpose, each with differing levels of accuracy. In this paper, we…

机器学习 · 计算机科学 2024-12-10 Vikas Dwivedi

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…

机器学习 · 计算机科学 2026-05-19 Yue Deng , Francisco Santos , Pang-Ning Tan , Lifeng Luo

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…

机器学习 · 计算机科学 2022-11-04 Léonard Boussioux , Cynthia Zeng , Théo Guénais , Dimitris Bertsimas

Identifying tropical cyclones that generate destructive storm tides for risk assessment, such as from large downscaled storm catalogs for climate studies, is often intractable because it entails many expensive Monte Carlo hydrodynamic…

大气与海洋物理 · 物理学 2025-01-07 Grace Jiang , Jiangchao Qiu , Sai Ravela

Tropical cyclone reconnaissance data, such as that of Hurricane Lane, often exhibits discrepancies between flight-level wind measurements and SFMR data, leading to uncertainty in determining peak intensity. In this study, I analyze…

大气与海洋物理 · 物理学 2024-04-18 Michael Igbinoba

In just the past few years multiple data-driven Artificial Intelligence Weather Prediction (AIWP) models have been developed, with new versions appearing almost monthly. Given this rapid development, the applicability of these models to…

The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain from input data (storm features) available in or derived from the HURDAT2 database models capable of simulating important hurricane…

大气与海洋物理 · 物理学 2022-09-16 Rikhi Bose , Adam L. Pintar , Emil Simiu

Determining the location of a tropical cyclone's (TC) surface circulation center -- "center-fixing" -- is a critical first step in the TC-forecasting process, affecting current/future estimates of track, intensity, and structure. Despite a…

大气与海洋物理 · 物理学 2025-06-13 Ryan Lagerquist , Galina Chirokova , Robert DeMaria , Mark DeMaria , Imme Ebert-Uphoff

A presumed impact of global climate change is the increase in frequency and intensity of tropical cyclones. Due to the possible destruction that occurs when tropical cyclones make landfall, understanding their formation should be of mass…

Precipitation from tropical cyclones (TCs) can cause disasters such as flooding, mudslides, and landslides. Predicting such precipitation in advance is crucial, giving people time to prepare and defend against these precipitation-induced…

机器学习 · 计算机科学 2025-05-20 Cheng Huang , Pan Mu , Cong Bai , Peter AG Watson

An open-source, physics-based tropical cyclone downscaling model is developed, in order to generate a large climatology of tropical cyclones. The model is composed of three primary components: (1) a random seeding process that determines…

大气与海洋物理 · 物理学 2023-06-19 Jonathan Lin , Raphael Rousseau-Rizzi , Chia-Ying Lee , Adam Sobel

The problem where a tropical cyclone intensifies dramatically within a short period of time is known as rapid intensification. This has been one of the major challenges for tropical weather forecasting. Recurrent neural networks have been…

机器学习 · 计算机科学 2017-02-12 Rohitash Chandra

Tropical cyclone (TC) forecasting is critical for disaster warning and emergency response. Deep learning methods address computational challenges but often neglect physical relationships between TC attributes, resulting in predictions…

机器学习 · 计算机科学 2026-03-03 Lei Liu , Xiaoning Yu , Kang Chen , Jiahui Huang , Tengyuan Liu , Hongwei Zhao , Bin Li

This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI-based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025,…

大气与海洋物理 · 物理学 2025-12-01 Yanmo Weng , Avantika Gori

Cloud radiative feedback impacts early tropical cyclone (TC) intensification, but limitations in existing diagnostic frameworks make them unsuitable for studying asymmetric or transient radiative heating. We propose a linear Variational…

大气与海洋物理 · 物理学 2024-10-07 Frederick Iat-Hin Tam , Tom Beucler , James H. Ruppert

Tropical cyclones are one of the most powerful and destructive natural phenomena on earth. Tropical storms and heavy rains can cause floods, which lead to human lives and economic loss. Devastating winds accompanying cyclones heavily affect…

大气与海洋物理 · 物理学 2021-07-15 Koushik Biswas , Sandeep Kumar , Ashish Kumar Pandey

Cyclone rapid intensification is the rapid increase in cyclone wind intensity, exceeding a threshold of 30 knots, within 24 hours. Rapid intensification is considered an extreme event during a cyclone, and its occurrence is relatively rare,…

机器学习 · 计算机科学 2025-06-11 Vamshika Sutar , Amandeep Singh , Rohitash Chandra

The fundamental interaction between tropical cyclones was investigated through a series of water tank experiements by Fujiwhara [20, 21, 22]. However, a complete understanding of tropical cyclones remains an open research challenge although…

流体动力学 · 物理学 2016-08-24 Raymond P Walsh , Jahrul M Alam

Tropical cyclones remain a major threat to the lives, property and economy of communities around the South West Indian ocean (SWIO), notably Southern Africa and Madagascar. This study uses the weather research forecast (WRF) model to…

大气与海洋物理 · 物理学 2019-06-21 Chibueze N. Oguejiofor , Babatunde J. Abiodun