Evaluation of Volume Representation Networks for Meteorological Ensemble Compression
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
CCS Concepts: Computing methodologies --> Learning latent representations; Applied computing --> Earth and atmospheric sciences
@inproceedings{10.2312:vmv.20221198,
booktitle = {Vision, Modeling, and Visualization},
editor = {Bender, Jan and Botsch, Mario and Keim, Daniel A.},
title = {{Evaluation of Volume Representation Networks for Meteorological Ensemble Compression}},
author = {Höhlein, Kevin and Weiss, Sebastian and Necker, Tobias and Weissmann, Martin and Miyoshi, Takemasa and Westermann, Rüdiger},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-189-2},
DOI = {10.2312/vmv.20221198}
}