Improving Quantitative Precipitation Estimation with Solid-State X-Band Radar

Radar-based measurements are crucial for accurately estimating precipitation and capturing the spatial variability of rainfall, which enhances both precipitation forecasting and hydrological modeling. This study focuses on quantitative precipitation estimation (QPE) using radar data in the Lombardy Region of northern Italy, examining the limitations of radar measurements and identifying optimal configurations. Specifically, data from two newly installed X-band radars with solid-state transmitters, operated by the Regional Environmental Protection Agency (ARPA Lombardia), were analyzed.The goal of this research is to determine radar settings that maximize QPE performance at an operational level and explore post-processing methods to address radar limitations, particularly during extreme precipitation events that could lead to flooding. The methodology is twofold: first, to identify radar configurations that accurately correlate rainfall intensity with radar data, and second, to address radar challenges during severe events, focusing on attenuation correction, signal extinction, and the integration of third-party data sources.Extreme and non-extreme precipitation events affecting the Milan hydraulic node were analyzed, highlighting opportunities to enhance the radar network through post-processing techniques that could aid future hydrological modeling. The study compares different QPE methods, including basic Z-R relationships and Z-R matching techniques based on previous research.This work provides a foundation for optimizing operational QPE and proposes strategies for overcoming radar limitations during extreme weather events. Additionally, it supports future improvements, such as integrating real-time rain gauge data to enhance flood forecasting accuracy.