Precipitation nowcasting underlying various public services from rainstorm warning to flight
safety is quite important and remains challenging due to the fast change in convective …

The utilization of 3D-Convolutional Long Short-Term Memory (3D-Conv-LSTM) networks for
precipitation nowcasting through satellite data integration has emerged as a significant …

In machine learning (ML)-based hydrological forecasting, particularly in medium-and long-
term prediction, judicious predictor selection is paramount, as it ultimately determines the …

Precipitation nowcasting is an important but hard problem. Currently, with the landing of
deep learning, it has been treated as an image prediction problem based on radar echo …

Object-based radar rainfall nowcasting is a widely used technique for convective storm
prediction. Currently, most existing object-based nowcasting methods primarily focus on …

Quantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts,
especially for lead times up to 12 h, but their evaluation depends on a variety of factors …

Precipitation nowcasting refers to the prediction of small-scale precipitation events at minute
and kilometer scales within the upcoming 0 to 2 h, which significantly impacts both human …

Many post‐processing methods improve forecasts at individual locations but remove their
correlation structure. However, this information is essential for forecasting larger‐scale …

Accurate and reliable precipitation forecasting is vital for effective water resource
management and disaster mitigation, especially in geographically diverse and climatically …

Traditional deep learning-based prediction methods predominantly rely on weather radar
data to quantify precipitation, often neglecting the integration of the thermal processes …