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 …

Extreme weather events pose significant challenges, thereby demanding techniques for
accurate analysis and precise forecasting to mitigate its impact. In recent years, deep …

This study develops an objective deep-learning-based model for tropical cyclone (TC)
intensity estimation. The model's basic structure is a convolutional neural network (CNN) …

Convective storms and lightning are among the most important weather phenomena that are
challenging to forecast. In this study, a novel multi-task learning (MTL) encoder-decoder U …

Tropical cyclones (TCs) are dangerous weather events; accurate monitoring and forecasting
can provide significant early warning to reduce loss of life and property. However, the study …

To improve understanding of essential aspects that influence forecasting of tropical cyclones
(TCs), the National Key Research and Development Program, Ministry of Science and …

Accurate and instant estimation of tropical cyclone (TC) intensity is crucial for emergency
decision making. Although deep neural networks and satellite images have been …

Brightness temperature (TB) observations at an infrared channel (10.3 μ m) of the Advanced
Baseline Imager (ABI) on board the US 16th Geostationary Operational Environmental …

Convective initiation (CI) nowcasting is crucial for reducing loss of human life and property
caused by severe convective weather. A novel deep learning method based on the U-Net …

In this paper, a data-driven transfer learning (TL) model for locating tropical cyclone (TC)
centers from satellite infrared images in the northwest Pacific is developed. A total of 2450 …