From research software to operational forecasting: Advancing Met.3D for interactive 3-D meteorological analysis in field campaigns

Visualization plays a central role in the analysis of numerical weather prediction data for both research and operational forecasting. In recent years, the open-source visualization framework Met.3D (https://met3d.readthedocs.io) has been developed to enable interactive, three-dimensional, and feature-based exploration of gridded atmospheric data. Since its first public release in 2015, Met.3D has evolved into a feature-rich visual analysis tool facilitating rapid exploration of atmospheric datasets. It is designed to bridge established 2-D visualization techniques commonly used in meteorology with interactive 3-D analysis by combining both within a unified visual context. Following the introduction of Met.3D version 2.0, which improved usability and functionality, we now focus on advancing the software towards a mature research software system and demonstrating its applicability in real-world forecasting environments.

We present recent developments that enhance the robustness, portability, and reproducibility of Met.3D. A central contribution is the introduction of a reproducible CI/CD workflow. It generates test datasets (synthetic and from open sources), executes Met.3D in batch mode to produce visualization outputs, and compares the resulting images against reference baselines using image-based similarity metrics (e.g. SSIM). This enables automated evaluation of visualization results and ensures consistent behaviour across heterogeneous environments. Further developments include extended cross-platform support (Linux and Windows), improved memory management, enhancements to the rendering pipeline, and refinements to usability and user interfaces.

Many of these developments were driven by user feedback from real-world applications. For example, Met.3D was used during the North Atlantic Waveguide and Downstream Impact Campaign (NAWDIC) in a forecasting context for research flight planning under various constraints.

The presented developments lower the barrier for adoption in operational and educational settings and contribute to sustainable open-source software development in the meteorological community.