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Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0)
Author(s) -
Marc Rautenhaus,
Michael Kern,
Andreas Schäfler,
Rüdiger Westermann
Publication year - 2015
Publication title -
geoscientific model development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.238
H-Index - 86
eISSN - 1991-9603
pISSN - 1991-959X
DOI - 10.5194/gmd-8-2329-2015
Subject(s) - visualization , computer science , weather forecasting , interactive visualization , grid , numerical weather prediction , visual analytics , context (archaeology) , data visualization , scientific visualization , data mining , meteorology , geology , geography , paleontology , geodesy
We present "Met.3D", a new open-source tool for the interactive three-dimensional (3-D) visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns; however, it is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output – 3-D visualization, ensemble visualization and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts (ECMWF) and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantities. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment) campaign

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