Description:

Radar systems serve as vital tools for monitoring and forecasting precipitation, providing invaluable data for various applications such as weather prediction, hydrology, and climate research. Moreover, dual-polarization weather radar technology has revolutionized our ability to observe and understand precipitation microphysics, severe weather phenomena, and atmospheric processes. This session aims to explore recent advancements, applications, and challenges in the utilization of dual-polarization algorithms and machine learning techniques in weather radar applications. Topics of interest include, but are not limited to, dual-polarization techniques and associated data sciences to enhance precipitation estimation (QPE) accuracy, especially in complex terrains, precipitation-type classification and hydrometeor identification, detection and characterization of severe weather phenomena, data fusion from multi-frequency radar observations across different platforms to enhance microphysical retrievals, with emphasis on modern machine learning techniques and AI. Assimilation of polarimetric radar data into numerical weather prediction models and applications of radar QPE in hydrological modeling and flood forecasting are also encouraged. Through presentations and discussions, this session aims to provide a comprehensive overview of recent advances in weather radar applications, fostering collaboration and knowledge exchange among researchers, practitioners, and graduate students interested in this rapidly evolving field.