Review: Visual analytics of climate networks

Author(s): Nocke, T., S. Buschmann, J.F. Donges, N. Marwan, H.-J. Schulz, C. Tominski
In: Nonlinear Processes in Geophysics 22: 545–570
Year: 2015
Type: Journal / article
Theme affiliation: Patterns of the Anthropocene
Full reference: Nocke, T., S. Buschmann, J.F. Donges, N. Marwan, H.-J. Schulz, C. Tominski. 2015. Review: Visual analytics of climate networks. Nonlinear Processes in Geophysics 22: 545–570.

Summary

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields.

The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited.

To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science.

Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support.

Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

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