Radar Based Precipitation

Radar-based precipitation research focuses on improving the accuracy and temporal resolution of precipitation estimations from radar data for applications in weather forecasting and hydrological modeling. Current research employs advanced deep learning architectures, including generative models like transformers and convolutional neural networks, to enhance nowcasting capabilities, super-resolve temporal data, and improve the accuracy of precipitation retrievals from both active and passive microwave sensors. These advancements lead to more precise and timely precipitation information, improving flood risk assessment, climate change studies, and ultimately, public safety.