Advancements in various scientific fields have encouraged the development of novel tools,
techniques, components, methodologies, and innovations aimed at addressing the …

Accurate and timely rainfall nowcasting is important for protecting the public from heavy
rainfall-induced disasters. In recent years, deep-learning models have been demonstrated …

Precipitation nowcasting is one of the main tasks of weather forecasting that aims to predict
rainfall events accurately, even in low-rainfall regions. It has been observed that few studies …

Deep learning-based time series forecasting has dominated the short-term precipitation
forecasting field with the help of its ability to estimate motion flow in high-resolution datasets …

Precipitation nowcasting has long been a challenging problem in meteorology. While recent
studies have introduced deep neural networks into this area and achieved promising results …

This paper proposes a novel GAN framework with self-clustering approach for precipitation
nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using …

Precipitation nowcasting is gaining increasing attention in the signal processing community.
Existing deep learning-based studies focus on designing an effective model architecture …

The traditional method for retrieving cloud optical thickness (COT) is carried out through a
Look-Up Table (LUT). Researchers must make a series of idealized assumptions and …

Rainstorm prediction is of considerable importance for a wide range of applications, such as
weather forecasting, disaster management, and flood monitoring. Predicting rare and …

Physics-based numerical models have been the bedrock of atmospheric sciences for
decades, offering robust solutions but often at the cost of significant computational …