nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Large-Scale Atmospheric Phenomena Under the Lens of Ordinal Time-Series Analysis and Information Theory Measures

verfasst von : J. I. Deza, G. Tirabassi, M. Barreiro, C. Masoller

Erschienen in: Advances in Nonlinear Geosciences

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Stochastic Parameterization of Subgrid-Scale Processes: A Review of Recent Physically Based Approaches

Nächstes Kapitel Supermodeling: Synchronization of Alternative Dynamical Models of a Single Objective Process

Albert, R., and A.L. Barabasi. 2002. Statistical mechanics of complex networks. Reviews of Modern Physics 74: 47–97.CrossRef

Arizmendi, F., M. Barreiro, and C. Masoller. 2017. Identifying large-scale patterns of nonlinearity and unpredictability in atmospheric data. Scientific Reports 7: 45676.CrossRef

Bandt, C., and B. Pompe. 2002. Permutation entropy: a natural complexity measure for time series. Physical Review Letters 88: 174102.CrossRef

Barreiro, M., A.C. Marti, and C. Masoller. 2011. Inferring long memory processes in the climate network via ordinal pattern analysis. Chaos 21: 013101.CrossRef

Boccaletti, S., V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang. 2006. Complex networks: structure and dynamics. Physics Reports 424: 175–308.CrossRef

Deza, J.I., M. Barreiro, and C. Masoller. 2013. Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales. The European Physical Journal Special Topics 222: 511–523.CrossRef

Deza, J.I., M. Barreiro, and C. Masoller. 2015. Assessing the direction of climate interactions by means of complex networks and information theoretic tools. Chaos 25: 033105.

Donges, J.F., Y. Zou, N. Marwan, and J. Kurths. 2009. The backbone of the climate network. EPL 87: 48007.CrossRef

Fountalis, I., A. Bracco, and C. Dovrolis. 2014. Spatio-temporal network analysis for studying climate patterns. Climate Dynamics 42: 879–899.CrossRef

Hlinka, J., D. Hartman, M. Vejmelka, D. Novotna, and M. Palus. 2014. Non-linear dependence and teleconnections in climate data: sources, relevance, nonstationarity. Climate Dynamics 42: 1873–1886.CrossRef

Newman, M.E.J. 2003. The structure and function of complex networks. SIAM Review 45: 167–256.CrossRef

Rosvall, R., and C.T. Bergstrom. 2007. An information-theoretic framework for resolving community structure in complex networks. PNAS 104: 7327–7331.CrossRef

Rubido, N., A.C. Martí, E. Bianco-Martínez, C. Grebogi, M.S. Baptista, and C. Masoller. 2014. Exact detection of direct links in networks of interacting dynamical units. New Journal of Physics 16: 093010.CrossRef

Serrano, M.A., M. Boguña, and A. Vespignani. 2009. Extracting the multiscale backbone of complex weighted networks. PNAS 106: 6483–6488.CrossRef

Shandilya, S.G., and M. Timme. 2011. Inferring network topology from complex dynamics. New Journal of Physics 13: 013004.CrossRef

Timme, M. 2007. Revealing network connectivity from response dynamics. Physical Review Letters 98: 224101.CrossRef

———. 2016. Unravelling the community structure of the climate system by using lags and symbolic time-series analysis. Scientific Reports 6: 29804.CrossRef

Tirabassi, G., C. Masoller, and M. Barreiro. 2015a. A study of the air–sea interaction in the South Atlantic Convergence Zone through Granger causality. International Journal of Climatology 35: 3440.CrossRef

Tirabassi, G., R. Sevilla-Escoboza, J.M. Buldú, and C. Masoller. 2015b. Inferring the connectivity of coupled oscillators from time series statistical similarity analysis. Scientific Reports 5: 10829.CrossRef

Tirabassi, G., L. Sommerlade, and C. Masoller. 2017. Inferring directed climatic interactions with renormalized partial directed coherence and directed partial correlation. Chaos 27: 035815.CrossRef

Tsonis, A.A., and P. Roebber. 2004. The architecture of the climate network. Physica A 333: 497–504.CrossRef

Tsonis, A.A., and K.L. Swanson. 2008. Topology and predictability of El Nino and La Nina networks. Physical Review Letters 100: 228502.CrossRef

Yamasaki, K., A. Gozolchiani, and S. Havlin. 2008. Climate networks around the globe are significantly affected by El Nino. Physical Review Letters 100: 228501.CrossRef

Yu, D., and U. Parlitz. 2011. Inferring network connectivity by delayed feedback control. PloS One 6: e24333.CrossRef

Zappalà, D.A., M. Barreiro, and C. Masoller. 2016. Global atmospheric dynamics investigated by using Hilbert frequency analysis. Entropy 18: 408.CrossRef

Zebiak, S.E. 1993. Air–sea interaction in the equatorial Atlantic region. Journal of Climate 6: 1567.CrossRef

Titel: Large-Scale Atmospheric Phenomena Under the Lens of Ordinal Time-Series Analysis and Information Theory Measures
verfasst von: J. I. Deza
G. Tirabassi
M. Barreiro
C. Masoller
Verlag: Springer International Publishing
Buch: Advances in Nonlinear Geosciences
Print ISBN: 978-3-319-58894-0

Electronic ISBN: 978-3-319-58895-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-58895-7_4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"