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 …

Radar rainfall nowcasting has mostly been applied to relatively large (often rural) domains
(eg, river basins), although rainfall nowcasting in small urban areas is expected to be more …

为探究深度学习的雷达降雨临近预报在流域洪水预报中的适用性, 采用U-Net, 嵌入注意力门的
Attention-Unet 和添加转换器的多级注意力TransAtt-Unet 开展雷达降雨临近预报 …

Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water
resource scheduling. Based on the structural features of the U-Net model, we proposed the …

Rain gauge networks provide direct precipitation measurements and have been widely used
in hydrology, synoptic-scale meteorology, and climatology. However, rain gauge …

In this study, the nowcast models provided by the Python pySTEPS library were evaluated
using radar derived rain rate data and the satellite product Split-Window Difference (SWD) …

scoresis a Python package containing mathematical functions for the verification, evaluation
and optimisation of forecasts, predictions or models. It primarily supports the geoscience …

To explore the applicability of deep learning methods to radar rainfall nowcasting and flood
forecasting, U-Net, Attention-Unet and TransAtt-Unet are used to carry out rainfall …

In this study, a spatial and temporal analysis of three‐dimensional motion fields was
conducted to formulate an ensemble nowcasting scheme. These motion fields were derived …

Information about the weather has profound implications for numerous sectors, including
agriculture, energy, and disaster management. Accurate and reliable predictions are …