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 …

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 …

Precipitation nowcasting is an important task in the fields of transportation, traffic, agriculture,
and tourism. One of the main challenges is radar echo maps forecasting. It is regarded as a …

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

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 …

In recent years traditional numerical methods for accurate weather prediction have been
increasingly challenged by deep learning methods. Numerous historical datasets used for …

Precipitation nowcasting is to predict the future rainfall intensity over a short period of time,
which mainly relies on the prediction of radar echo sequences. Though convolutional neural …

Precipitation nowcasting is a critical task that can facilitate multiple applications including
urban warnings and traffic. Deep learning methods combining convolutional neural …

Nowcasting is an observation-based method that uses the current state of the atmosphere to
forecast future weather conditions over several hours. Recent studies have shown the …