Introduction

Figure from Rautenhaus et al. (2015)
Figure from Rautenhaus et al. (2015)

Welcome to the documentation of Met.3D, the open-source visualization research software for interactive, three-dimensional visual analysis of gridded atmospheric data.

Before you start

The Getting started page is the best place to start if you are new to Met.3D. You will find Installation instructions and a tutorial for First steps with Met.3D.

Note

The documentation you are reading is work in progress. We are adding bits and pieces whenever we find time. If you don’t find the information you are looking for, please contact us. If you like to contribute to the documentation, please let us know as well!

About Met.3D

Met.3D is being developed as a research effort to improve visual analysis of 3-D meteorological data in research and forecasting. The software is implemented in C++ and OpenGL 4 and runs on standard commodity hardware. Its only “special” requirement is an OpenGL 4.3 capable graphics card. Met.3D currently runs under Linux. It has originally been designed for weather forecasting during atmospheric research field campaigns, however, is not restricted to this application anymore. Besides being used as a visualization tool, Met.3D is intended to serve as a framework to implement and evaluate new 3-D and ensemble visualization techniques for the atmospheric sciences.

Met.3D development is lead by the Visual Data Analysis Group at the Hub of Computing and Data Science of the University of Hamburg (UHH), Germany. Previously, development was also done within the Computing Graphics & Visualization Group of the Technical University of Munich (TUM), Germany. We much acknowledge funding we have received from the German Research Foundation (DFG), from 2015 until 2024 within the Collaborative Research Centre SFB/TRR 165 Waves to Weather (both at UHH and TUM), and ongoing within the UHH Cluster of Excellence CLICCS. Previously, development at TUM was also partly funded by the European Union under the ERC Advanced Grant 291372 SaferVis and the ERC Proof-of-Concept Grant “Vis4Weather”.

Met.3D research and development is led by Marc Rautenhaus, UHH (previously TUM).

Background and design goals

Met.3D was originally built to support weather forecasting during atmospheric research field campaigns, and several of its characteristic design decisions come directly from that setting.

A bridge from 2-D to 3-D.

Forecasters work with well-established 2-D products: maps on constant pressure surfaces, vertical cross-sections along a flight track. Met.3D deliberately reproduces the look of these products — filled and line contours, wind barbs, coastlines and graticule — but embeds them in a 3-D scene where they can be moved interactively. Sliding a horizontal section up and down through the atmosphere, or dragging a waypoint of a vertical section, is a very direct way of exploring vertical structure, and it lets users transition into the 3-D environment from something they already trust.

Ensembles are not just an add-on.

Ensemble member, forecast valid time and forecast initialisation time are part of every data request, so navigating them is as cheap as navigating anything else. Animating through the members resembles the widely used stamp maps, with side-by-side display traded for animation — regions in which the forecast fluctuates attract attention naturally. Statistical quantities (mean, standard deviation, minimum, maximum, threshold probabilities) are derived on demand rather than precomputed.

Comparison is a first-class use case.

Actors can be synchronized in time and ensemble member, scene views can be synchronized to a common camera, and the same actor can appear in several scenes at once. Keeping the valid time fixed while stepping the initialisation time, for instance, helps forecasters to understand how consistent successive forecasts are.

Use native data for minimal delay.

Data is read in the format in which it arrives, without a preprocessing or conversion step, so that the time between “the forecast has arrived” and “the forecast is on screen” stays as short as possible.

Interactive on commodity hardware.

The target platform is a regular desktop or notebook with a reasonably recent GPU.

Developers will find these goals traced through to the code in the Architecture overview.

Organization of the documentation

The documentation for Met.3D is organized into the following sections:

  • The Getting started section provides Installation instructions, and a tutorial for First steps with Met.3D.

  • Read the User Manual if you are a user (e.g., an atmospheric researcher, meteorology student, interested lay person). It provides information about supported data, user interface components, and visualization modules of Met.3D.

  • The Contributing section provides information on how to contribute to Met.3D. Please carefully read the information about the GIT workflow and about our coding conventions.

  • Finally, the Developer Manual contains information about the internal structure of Met.3D, including system modules, data pipeline, and actors. Conceptual descriptions of the architecture of selected features in Met.3D are provided in Architecture overview. Note that large parts of the Met.3D source code have been documented with the Doxygen code documentation system. A Doxygen configuration file is available in the Met.3D repository (in the doc/doxygen subdirectory). Please run Doxygen locally to build the corresponding documentation.

In addition to this documentation, you may also want to take a look at the reference publication. Also, Marc’s PhD thesis contains additional details.

  • Rautenhaus, M., Kern, M., Schäfler, A., and Westermann, R.: Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0), Geosci. Model Dev., 8, 2329-2353, doi:10.5194/gmd-8-2329-2015, 2015.

  • Rautenhaus, M.: Interactive 3D visualization of ensemble weather forecasts, Ph.D. thesis, Technical University of Munich, Munich, Germany, 195pp., 2015.

Credits

The Met.3D core development team currently consists of:

We acknowledge the following individuals for their contribution:

  • Andreas Beckert, UHH (former core member - many thanks!)

  • Susanne Fuchs, UHH (former core member - many thanks!)

  • Kamesh Modali, UHH (former core member - many thanks!)

  • Justus Jakobi, UHH student

  • Alexander Klassen, UHH student

  • Jonas Lefert, UHH student

  • Marcel Meyer, UHH

  • Michael Kern, TUM (former core member - many thanks!)

  • Alexander Kumpf, TUM (former core member - many thanks!)

  • Bianca Tost, TUM (former core member - many thanks!)

  • Mathias Kanzler, TUM

  • Florian Ferstl, TUM

  • Fabian Schöttl, TUM student

  • Christoph Heidelmann, TUM student

  • Florian Hauer, TUM student

  • Max Bandle, TUM student

  • Jessica Blankenburg, TUM student

  • Raphael Kriegmair, LMU student

  • Philipp Kaiser, TUM student

  • Theresa Diefenbach, LMU student

  • Robert Redl, LMU

  • Alexander Thole, TUM student

We acknowledge support from ECMWF, in particular by Tim Hewson and Stephan Siemen and his group. Access to ECMWF data for our work has been kindly provided in the context of the ECMWF special project “Support Tool for HALO Missions”.