Abstract Short‐term Quantitative Precipitation Forecasting is important for flood forecasting,
early flood warning, and natural hazard management. This study proposes a precipitation …

This study aims to compare two machine learning techniques, random forests (RF) and
support vector machine (SVM), for real-time radar-derived rainfall forecasting. The real-time …

Quantitative precipitation nowcasting (QPN) has become an essential technique in various
application contexts, such as early warning or urban sewage control. A common heuristic …

We use a conditional deep convolutional generative adversarial network to predict the
geopotential height of the 500 hPa pressure level, the two-meter temperature and the total …

Radar echo extrapolation is a basic but essential task in meteorological services. It could
provide radar echo prediction results with high spatiotemporal resolution in a …

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic
context makes a great challenge in the development of accurate short-term rainfall forecasts …

Short-term radar-based forecasts of precipitation can be achieved through the
implementation of nowcasting models, essentially based on the rainfall extrapolation from a …

Abstract Accurate Quantitative Precipitation Forecasts (QPF) at high spatial and temporal
resolution are a perquisite for urban flood prediction. Commonly Lagrangian extrapolation of …

Precipitation nowcasting is a critical task for various applications, such as disaster mitigation,
water resource management, traffic safety, and agricultural planning. In recent years, deep …

Short-term high-resolution Quantitative Precipitation Nowcasting (QPN) has important
implications for navigation, flood forecasting, and other hydrological and meteorological …