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Large-scale atmospheric phenomena under the lens of ordinal time-series analysis and information theory measures

Deza, J. I.; Tirabassi, G.; Barreiro, M.; Masoller, C.

Authors

Ignacio Deza Ignacio.Deza@uwe.ac.uk
Research Fellow in Machine Learning for Building Energy Analysis

G. Tirabassi

M. Barreiro

C. Masoller



Contributors

Anastasios Tsonis aatsonis@uwm.edu
Editor

Abstract

This review presents a synthesis of our work done in the framework of the European project Learning about Interacting Networks in Climate (LINC, climatelinc.eu). We have applied tools of information theory and ordinal time series analysis to investigate large scale atmospheric phenomena from climatological datasets. Specifically, we considered monthly and daily Surface Air Temperature (SAT) time series (NCEP reanalysis) and used the climate network approach to represent statistical similarities and interdependencies between SAT time series in different geographical regions. Ordinal analysis uncovers how the structure of the climate network changes in different time scales (intra-season, intra-annual, and longer). We have also analyzed the directionally of the links of the network, and we have proposed novel approaches for uncovering communities formed by geographical regions with similar SAT properties.

Publication Date Jan 1, 2018
Peer Reviewed Peer Reviewed
Pages 87-99
Book Title Advances in Nonlinear Geosciences
ISBN 9783319588940
Institution Citation Deza, J. I., Tirabassi, G., Barreiro, M., & Masoller, C. (2018). Large-scale atmospheric phenomena under the lens of ordinal time-series analysis and information theory measures. In A. Tsonis (Ed.), Advances in Nonlinear Geosciences, 87-99. Springer International Publishing. https://doi.org/10.1007/978-3-319-58895-7_4
DOI https://doi.org/10.1007/978-3-319-58895-7_4
Keywords climate networks, nonlinear time series analysis, climate communities, information transfer
Publisher URL http://doi.org/10.1007/978-3-319-58895-7_4
Related Public URLs https://www.springer.com/gp/book/9783319588940
Additional Information Corporate Creators : Springer International Publishing