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Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

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  • Charakopoulos, A.K.
  • Katsouli, G.A.
  • Karakasidis, T.E.

Abstract

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Suggested Citation

  • Charakopoulos, A.K. & Katsouli, G.A. & Karakasidis, T.E., 2018. "Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 436-453.
    • Handle: RePEc:eee:phsmap:v:495:y:2018:i:c:p:436-453
    DOI: 10.1016/j.physa.2017.12.027
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    Cited by:

    1. Yu, Xuan & Shi, Suixiang & Xu, Lingyu & Yu, Jie & Liu, Yaya, 2020. "Analyzing dynamic association of multivariate time series based on method of directed limited penetrable visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Wang, Ze & Gao, Xiangyun & Tang, Renwu & Liu, Xueyong & Sun, Qingru & Chen, Zhihua, 2019. "Identifying influential nodes based on fluctuation conduction network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 355-369.
    3. Javier, Prince Joseph Erneszer A. & Liponhay, Marissa P. & Dajac, Carlo Vincienzo G. & Monterola, Christopher P., 2022. "Causal network inference in a dam system and its implications on feature selection for machine learning forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
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    5. Davide Astolfi & Ravi Pandit & Ludovico Terzi & Andrea Lombardi, 2022. "Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis," Energies, MDPI, vol. 15(15), pages 1-17, July.
    6. Zhang, Zehui & Wang, Fang & Shen, Luming & Xie, Qiang, 2022. "Multiscale time-lagged correlation networks for detecting air pollution interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).

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