Evaluation of Volume Representation Networks for Meteorological Ensemble Compression

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dc.contributor.authorHöhlein, Kevinen_US
dc.contributor.authorWeiss, Sebastianen_US
dc.contributor.authorNecker, Tobiasen_US
dc.contributor.authorWeissmann, Martinen_US
dc.contributor.authorMiyoshi, Takemasaen_US
dc.contributor.authorWestermann, Rüdigeren_US
dc.contributor.editorBender, Janen_US
dc.contributor.editorBotsch, Marioen_US
dc.contributor.editorKeim, Daniel A.en_US
dc.date.accessioned2022-09-26T09:28:37Z
dc.date.available2022-09-26T09:28:37Z
dc.date.issued2022
dc.description.abstractRecent 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.en_US
dc.description.sectionheadersJoint Session
dc.description.seriesinformationVision, Modeling, and Visualization
dc.identifier.doi10.2312/vmv.20221198
dc.identifier.isbn978-3-03868-189-2
dc.identifier.pages9-16
dc.identifier.pages8 pages
dc.identifier.urihttps://doi.org/10.2312/vmv.20221198
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20221198
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies --> Learning latent representations; Applied computing --> Earth and atmospheric sciences
dc.subjectComputing methodologies
dc.subjectLearning latent representations
dc.subjectApplied computing
dc.subjectEarth and atmospheric sciences
dc.titleEvaluation of Volume Representation Networks for Meteorological Ensemble Compressionen_US
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