Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

Journal metrics

  • IF value: 1.329 IF 1.329
  • IF 5-year<br/> value: 1.394 IF 5-year
  • CiteScore<br/> value: 1.27 CiteScore
  • SNIP value: 0.903 SNIP 0.903
  • SJR value: 0.709 SJR 0.709
  • IPP value: 1.455 IPP 1.455
  • h5-index value: 20 h5-index 20
Nonlin. Processes Geophys., 24, 661-671, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
20 Oct 2017
Network-based study of Lagrangian transport and mixing
Kathrin Padberg-Gehle and Christiane Schneide Leuphana Universität Lüneburg, Institute of Mathematics and its Didactics, Applied Mathematics Group, Universitätsallee 1, 21335 Lüneburg, Germany
Abstract. Transport and mixing processes in fluid flows are crucially influenced by coherent structures and the characterization of these Lagrangian objects is a topic of intense current research. While established mathematical approaches such as variational methods or transfer-operator-based schemes require full knowledge of the flow field or at least high-resolution trajectory data, this information may not be available in applications. Recently, different computational methods have been proposed to identify coherent behavior in flows directly from Lagrangian trajectory data, that is, numerical or measured time series of particle positions in a fluid flow. In this context, spatio-temporal clustering algorithms have been proven to be very effective for the extraction of coherent sets from sparse and possibly incomplete trajectory data. Inspired by these recent approaches, we consider an unweighted, undirected network, where Lagrangian particle trajectories serve as network nodes. A link is established between two nodes if the respective trajectories come close to each other at least once in the course of time. Classical graph concepts are then employed to analyze the resulting network. In particular, local network measures such as the node degree, the average degree of neighboring nodes, and the clustering coefficient serve as indicators of highly mixing regions, whereas spectral graph partitioning schemes allow us to extract coherent sets. The proposed methodology is very fast to run and we demonstrate its applicability in two geophysical flows – the Bickley jet as well as the Antarctic stratospheric polar vortex.

Citation: Padberg-Gehle, K. and Schneide, C.: Network-based study of Lagrangian transport and mixing, Nonlin. Processes Geophys., 24, 661-671,, 2017.
Publications Copernicus
Short summary
Transport and mixing processes in fluid flows are crucially influenced by coherent structures, such as eddies, gyres, or jets in geophysical flows. We propose a very simple and computationally efficient approach for analyzing coherent behavior in fluid flows. The central object is a flow network constructed directly from particle trajectories. The network's local and spectral properties are shown to give a very good indication of coherent as well as mixing regions in the underlying flow.
Transport and mixing processes in fluid flows are crucially influenced by coherent structures,...