Framework for generation of 3D weather radar data composite products

Modern weather radar networks play an indispensable role in nowcasting and short-term weather forecasting. They provide high-resolution, volumetric data crucial for identifying convective structures, precipitation intensity, and storm dynamics. However, the native spherical-polar coordinate system used by radar instruments presents significant challenges when integrating this data into numerical weather prediction (NWP) models and generating standardized meteorological products. To overcome these limitations, we present a novel, modular framework for the generation of 3D Cartesian radar products derived from both single and multi-radar network data. This framework is designed for use in operational meteorology, data assimilation, and research applications.

The system transforms raw radar observations into a unified, geospatially referenced Cartesian grid, enabling the production of key volumetric weather products such as Constant Altitude Plan Position Indicator (CAPPI), Vertically Integrated Liquid (VIL), and Echo Tops. Developed both as a command line service and standalone Windows Forms application using C# , the framework is equipped with configurable modules for radar data acquisition, spatial transformation through interpolation, product visualization, and standardized data export. It supports both proprietary and open radar data formats and offers flexible configuration of domain size, resolution, and compositing strategies.

In operational testing, the system generated 72 distinct product types, demonstrating consistent performance and scalability. Benchmarking revealed that processing times scale linearly with domain volume and the number of radar sources, which confirms the suitability of the approach for near-real-time deployment. Additional capabilities include automated data acquisition, dynamic product scheduling, and improved interpolation schemes that enhance spatial fidelity.

By bridging the gap between raw radar data and application-ready volumetric products, the framework significantly improves radar data accessibility and usability. It facilitates better situational awareness for forecasters and supports integration into NWP workflows. Its modular, extensible design lays the foundation for future developments, including full automation and real-time nowcasting integration across national and regional radar networks.