Framework for weather radar data processing.

This study introduces a comprehensive framework for generating 3D Cartesian products from weather radar network data to support operational forecasting and numerical weather prediction. Traditional radar data is typically represented in spherical-polar coordinates, which complicates composite product generation, geospatial referencing, and assimilation into weather models. To address this we propose a system that converts these data into a unified 3D Cartesian format, enabling more flexible product generation and visualization.

The framework is modular and includes components for data acquisition, transformation, visualization, and export. It supports various radar data formats, including proprietary ones, and implements multiple interpolation techniques and sampling strategies. The system was developed in C# as a standalone Windows Forms application for interactive analysis and rapid development.

Experimental evaluation demonstrated the system’s ability to produce 72 products, such as CAPPI, VIL, and Echo Tops, from both single and multi-radar datasets efficiently. The processing time scaled predictably with domain size and number of radars. Though current evaluations focused on runtime and functional validation, future work will emphasize optimizing interpolation methods and enabling autonomous operations for real-time forecasting.

The proposed framework represents a robust solution for converting complex radar datasets into actionable 3D products, improving forecasters’ situational awareness and enhancing input for numerical weather models.