Towards Met.3D version 2: Rapid exploration of numerical weather prediction data by means of interactive 3-D visualization

Visualization is an important and ubiquitous tool in the daily work of weather forecasters and atmospheric researchers to analyse data from simulations and observations. The domain-specific meteorological visualization tool Met.3D (documentation including installation instructions available at https://met3d.readthedocs.org) is an open-source effort to make interactive, 3-D, feature-based, and ensemble visualization techniques accessible to the meteorological community. Since the public release of version 1.0 in 2015, Met.3D has been used in multiple visualization research projects targeted at atmospheric science applications, and has evolved into a feature-rich visual analysis tool facilitating rapid exploration of gridded atmospheric data. The software is based on the concept of “building a bridge” between “traditional” 2-D visual analysis techniques and interactive 3-D techniques powered by modern graphics hardware. It allows users to analyse data using combinations of feature-based displays (e.g., atmospheric fronts and jet streams), “traditional” 2-D maps and cross-sections, meteorological diagrams, ensemble displays, and 3-D visualization including direct volume rendering, isosurfaces and trajectories, all combined in an interactive 3-D context.

In the past year, we have been able to significantly advance the Met.3D code base (available at https://gitlab.com/wxmetvis/met.3d) to make the tool more stable, usable, and to integrate visualization techniques not commonly available in other visualization tools. In this presentation, we present recent updates to the software and describe the status of our effort towards achieving a fully documented version 2.0. We demonstrate how Met.3D can be used for forecasting tasks, with examples including the utility of interactive visual analysis of 3-D cloud fields in combination with feature-based displays, and interactive visual analysis of trajectory data.