Tropical cyclones have always been a concern of meteorologists, and there are many
studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting …

At present, there is still a bottleneck in tropical cyclone (TC) forecasting due to its complex
dynamical mechanisms and various impact factors. Machine learning (ML) methods have …

In this study, a set of deep convolutional neural networks (CNNs) was designed for
estimating the intensity of tropical cyclones (TCs) over the Northwest Pacific Ocean from the …

A novel tropical cyclone (TC) intensity classification and estimation model (TCICENet) is
proposed using infrared geostationary satellite images from the northwest Pacific Ocean …

Over the past decade the use of machine learning in meteorology has grown rapidly.
Specifically neural networks and deep learning have been used at an unprecedented rate …

Huge losses of life and economy are brought by Tropical Cyclones (TCs). Accurate TC
intensity prediction is crucial for disaster prevention and emergency decision-making, but …

A deep learning–based method augmented by prior knowledge of tropical cyclones (TCs),
called DeepTCNet, is introduced to estimate TC intensity and wind radii from infrared (IR) …

This paper presents the official release of the Digital Typhoon dataset, the longest typhoon
satellite image dataset for 40+ years aimed at benchmarking machine learning models for …

In this report, we propose a deep learning technique for high-accuracy estimation of the
intensity class of a typhoon from a single satellite image, by incorporating meteorological …

This study develops a probabilistic model based on a convolutional neural network to
predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical …