EGU2020-15469, updated on 21 Apr 2023
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Met.3D: Interactive 3D ensemble visualization for rapid exploration of atmospheric simulation data

Marc Rautenhaus
Marc Rautenhaus
  • Universität Hamburg, Regional Computing Centre, Hamburg, Germany

Visualization is an important and ubiquitous tool in the daily work of atmospheric researchers and weather forecasters to analyse data from simulations and observations. Visualization research has made much progress in recent years, in particular with respect to techniques for ensemble data, interactivity, 3D depiction, and feature-detection. Transfer of new techniques into the atmospheric sciences, however, is slow.

Met.3D ( is an open-source research software aiming at making novel interactive 3D and ensemble visualization techniques accessible to the atmospheric community. Since its first public release in 2015, Met.3D has been used in multiple visualization research projects targeted at atmospheric science applications, and also has evolved into a feature-rich visual analysis tool facilitating rapid exploration of atmospheric simulation data. The software is based on the concept of “building a bridge” between “traditional” 2D visual analysis techniques and interactive 3D techniques and allows users to analyse their data using combinations of 2D maps and cross-sections, meteorological diagrams and 3D techniques including direct volume rendering, isosurfaces and trajectories, all combined in an interactive 3D context.

This PICO will provide an overview of the Met.3D project and highlight recent additions and improvements to the software. We will show several examples of how the combination of 2D and 3D visualization elements in an interactive context can be used to explore atmospheric simulation data, including the analysis of forecast errors, analysis of synoptic-scale features including jet-streams and fronts, and analysis of forecast uncertainty in ensemble forecasts.

How to cite: Rautenhaus, M.: Met.3D: Interactive 3D ensemble visualization for rapid exploration of atmospheric simulation data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15469,, 2020.


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displays version 1 – uploaded on 03 May 2020
  • CC1: Comment on EGU2020-15469, Scarlet Stadtler, 06 May 2020

    Thank you for your great visualizations.

    I wonder how to induce the change from 2D visualization to 3D. It all looks really cool, nevertheless there is some effort needed in order to set it up. And then, how to get this again into a 2D version for the publications? I mean, yes, links to videos or repositories can be included in the paper, but anyway there is still the need for some 2D figures.

    Any thoughts on how to change 2D culture to 3D culture?



    Scarlet Stadtler

    • AC1: Reply to CC1, Marc Rautenhaus, 07 May 2020

      Dear Scarlet,

      thank you for your comment. The idea behind Met.3D is not to fully replace the well-proven 2D techniques but rather make them interactive for rapid exploration and add benefit by combining them with 3D visualization elements. We find that when confronted with a new unknown dataset the interactivity and 3D context helps a lot to quickly get to know the data and to find regions or features of interest that you might miss when "just" looking at a selected number of 2D plots. Also, 3D visualization elements add a new perspective to the data. This is the "exploration" step of data analysis. When you publish, you of course need to decide what to communicate and find suitable depictions, which may be classical 2D plots. We found that well-chosen 3D images or as you say supplementary videos are also well suited to communicate specific aspects in publications. Hence, don't consider our work as as aiming at a complete change from a 2D to a 3D culture but rather as adding an additional means to the analysis toolbox for atmospheric data.

      Best regards,

      • CC2: Reply to AC1, Scarlet Stadtler, 07 May 2020

        Dear Marc,

        Thank you for your detailed reply. I see your contribution clearer and I want to keep it in mind as an option for data exploration.