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  GPU-based, interactive exploration of large spatiotemporal climate networks

Buschmann, S., Hoffmann, P., Agarwal, A., Marwan, N., Nocke, T. (2023): GPU-based, interactive exploration of large spatiotemporal climate networks. - Chaos, 33, 043129.
https://doi.org/10.1063/5.0131933

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 Creators:
Buschmann, Stefan1, Author
Hoffmann, Peter2, Author              
Agarwal, Ankit1, Author
Marwan, Norbert2, Author              
Nocke, Thomas2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

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 Abstract: This paper introduces the Graphics Processing Unit (GPU)-based tool Geo-Temporal eXplorer (GTX), integrating a set of highly interactive techniques for visual analytics of large geo-referenced complex networks from the climate research domain. The visual exploration of these networks faces a multitude of challenges related to the geo-reference and the size of these networks with up to several million edges and the manifold types of such networks. In this paper, solutions for the interactive visual analysis for several distinct types of large complex networks will be discussed, in particular, time-dependent, multi-scale, and multi-layered ensemble networks. Custom-tailored for climate researchers, the GTX tool supports heterogeneous tasks based on interactive, GPU-based solutions for on-the-fly large network data processing, analysis, and visualization. These solutions are illustrated for two use cases: multi-scale climatic process and climate infection risk networks. This tool helps one to reduce the complexity of the highly interrelated climate information and unveils hidden and temporal links in the climate system, not available using standard and linear tools (such as empirical orthogonal function analysis).

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Language(s): eng - English
 Dates: 2023-04-142023-04
 Publication Status: Finally published
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0131933
MDB-ID: yes - 3497
PIKDOMAIN: RD2 - Climate Resilience
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Regional keyword: Global
Model / method: Nonlinear Data Analysis
Model / method: Research Software Engineering (RSE)
Working Group: Hydroclimatic Risks
OATYPE: Hybrid - American Institute of Physics
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 33 Sequence Number: 043129 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)