Evaluation of Volume Representation Networks for Meteorological Ensemble Compression
Kevin Höhlein1, Sebastian Weiss1, Tobias Necker2, Martin Weissmann2, Takemasa Miyoshi3, Rüdiger Westermann1
1 Chair for Computer Graphics and Visualization, Technical University of Munich, Germany
2 University of Vienna, Department of Meteorology and Geophysics, Austria
3 RIKEN Center for Computational Science, Kobe, Japan
Abstract
Recent studies have shown that volume scene representation networks constitute powerful means to transform 3D scalar fields into extremely compact representations, from which the initial field samples can be randomly accessed. In this work, we evaluate the capabilities of such networks to compress meteorological ensemble data, which are comprised of many separate weather forecast simulations. We analyze whether these networks can effectively exploit similarities between the ensemble members, and how alternative classical compression approaches perform in comparison. Since meteorological ensembles contain different physical parameters with various statistical characteristics and variations on multiple scales of magnitude, we analyze the impact of data normalization schemes on learning quality. Along with an evaluation of the trade-offs between reconstruction quality and network model parameterization, we compare compression ratios and reconstruction quality for different model architectures and alternative compression schemes.
Associated publications
Evaluation of Volume Representation Networks for Meteorological Ensemble Compression
Kevin Höhlein, Sebastian Weiss, Tobias Necker, Martin Weissmann, Takemasa Miyoshi, Rüdiger Westermann
Vision, Modeling, and Visualization 2022 (VMV 2022)
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