Deep learning methods dominate short-term high-resolution precipitation nowcasting in
terms of prediction error. However, their operational usability is limited by difficulties …

This paper presents a convolutional neural network model for precipitation nowcasting that
combines data-driven learning with physics-informed domain knowledge. We propose …

Effective training of Deep Neural Networks requires massive amounts of data and compute.
As a result, longer times are needed to train complex models requiring large datasets, which …

Natural disasters caused by heavy rainfall often cost huge loss of life and property. To avoid
it, the task of precipitation nowcasting is imminent. To solve the problem, increasingly deep …

With the goal of making high-resolution forecasts of regional rainfall, precipitation
nowcasting has become an important and fundamental technology underlying various …

Short-term precipitation forecasting remains challenging due to the difficulty in capturing
long-term spatiotemporal dependencies. Current deep learning methods fall short in …

Sudden extreme rainfall events have a severe impact on daily life and production activities,
such as traffic disruption, property loss, and even serious casualties. Nowcasting …

Precipitation nowcasting is crucial across various industries and plays a significant role in
mitigating and adapting to climate change. We introduce an efficient deep learning model for …

Deep learning approaches have been widely adopted for precipitation nowcasting in recent
years. Previous studies mainly focus on proposing new model architectures to improve pixel …

Precipitation nowcasting, which predicts rainfall intensity in the near future, has been studied
by meteorologists for decades. Currently, computer vision techniques, especially optical flow …