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Unravelling the community structure of the climate system by using lags and symbolic time-series analysis
Tirabassi, Giulio; Masoller Alonso, Cristina
Universitat Politècnica de Catalunya. Departament de Física; Universitat Politècnica de Catalunya. DONLL - Dinàmica no lineal, òptica no lineal i làsers
This work is licensed under a Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/
Many natural systems can be represented by complex networks of dynamical units with modular structure in the form of communities of densely interconnected nodes. Unraveling this community structure from observed data requires the development of appropriate tools, particularly when the nodes are embedded in a regular space grid and the datasets are short and noisy. Here we propose two methods to identify communities, and validate them with the analysis of climate datasets recorded at a regular grid of geographical locations covering the Earth surface. By identifying mutual lags among time-series recorded at different grid points, and by applying symbolic time-series analysis, we are able to extract meaningful regional communities, which can be interpreted in terms of large-scale climate phenomena. The methods proposed here are valuable tools for the study of other systems represented by networks of dynamical units, allowing the identification of communities, through time-series analysis of the observed output signals.
Peer Reviewed
Àrees temàtiques de la UPC::Física
Àrees temàtiques de la UPC::Matemàtiques i estadística
Mathematical models
Information theory
Applied mathematics
Information theory and computation
Complex networks
Nonlinear phenomena
Matemàtica aplicada
Models matemàtics
Informació, Teoria de la
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/publishedVersion
Artículo
Macmillan Publishers
         

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