Community detection analysis in wind speed-monitoring systems using mutual information-based complex network

Laib, Mohamed; Guignard, Fabian; Kanevski, Mikhaïl; Telesca, Luciano

doi:10.1063/1.5054724

Cited by 5 publications

(3 citation statements)

References 42 publications

(42 reference statements)

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“…Using observational data, some studies emphasize how the network-based clustering decodes the spatial relationship on a regional scale. 16,[19][20][21] A recent investigation reveals the global structure of synchronization of EREs based on high-resolution satellite data. 22 However, it relies on a given region of interest, and therefore, it provides a partial view.…”

Section: Introductionmentioning

confidence: 99%

The climatic interdependence of extreme-rainfall events around the globe

Meyerhenke

Kurths

2022

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The identification of regions of similar climatological behavior can be utilized for the discovery of spatial relationships over long-range scales, including teleconnections. Additionally, it provides insights for the improvement of corresponding interaction processes in general circulation models. In this regard, the global picture of the interdependence patterns of extreme-rainfall events (EREs) still needs to be further explored. To this end, we propose a top-down complex-network-based clustering workflow, with the combination of consensus clustering and mutual correspondences. Consensus clustering provides a reliable community structure under each dataset, while mutual correspondences build a matching relationship between different community structures obtained from different datasets. This approach ensures the robustness of the identified structures when multiple datasets are available. By applying it simultaneously to two satellite-derived precipitation datasets, we identify consistent synchronized structures of EREs around the globe, during boreal summer. Two of them show independent spatiotemporal characteristics, uncovering the primary compositions of different monsoon systems. They explicitly manifest the primary intraseasonal variability in the context of the global monsoon, in particular, the “monsoon jump” over both East Asia and West Africa and the mid-summer drought over Central America and southern Mexico. Through a case study related to the Asian summer monsoon, we verify that the intraseasonal changes of upper-level atmospheric conditions are preserved by significant connections within the global synchronization structure. Our work advances network-based clustering methodology for (i) decoding the spatiotemporal configuration of interdependence patterns of natural variability and for (ii) the intercomparison of these patterns, especially regarding their spatial distributions over different datasets.

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Section: Introductionmentioning

confidence: 99%

The climatic interdependence of extreme-rainfall events around the globe

Meyerhenke

Kurths

2022

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…This knowledge is important for the planning of wind energy production and for evaluation of predictive models for wind speed and wind power. (Koçak, 2009;Laib, Golay, Telesca, & Kanevski, 2018;Laib, Guignard, Kanevski, & Telesca, 2019;Q. Li & Zuntao, 2014).…”

Section: Introductionmentioning

confidence: 99%

Multiscale entropy analysis of wind speed dynamics in Petrolina, Northeast Brazil

Silva

Barreto

Stošić

2021

RSD

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Purpose: In this paper, we analyzed the intra-annual variability of complexity of wind dynamics in Petrolina, Brazil and its relation with the wind potential. Methodology: We applied the Multiscale Sample Entropy (MSE) method on wind speed temporal series for each month of 2010. The data are recorded every 10 min at 50m height. Results: The results showed higher entropy values at higher temporal scales indicating that wind speed fluctuations are les regular and less predictable when wind speed is observed at lower temporal frequency. For all months, average wind speed is above a cut in level 3.5 m, the speed at which turbines start operating and producing electricity, indicating that the location of Petrolina is promising for wind energy generation. We also found that the wind speed is positively correlated with entropy values for all months when recorded at 10min frequency and between August and December when recorded t 1 h frequency. Conclusion: In these periods wind speed temporal fluctuations are more irregular, which is considered as unfavorable condition for the operation of wind turbines, leading to lower efficiency in the capture of wind energy for electricity production.

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“…Using observational data, some studies emphasize how the network-based clustering decodes the spatial relationship on a regional scale. 16,[19][20][21] A recent investigation reveals the global structure of synchronization of EREs based on high-resolution satellite data. 22 But it relies on a given region of interest, and therefore it provides a partial view.…”

Section: Introductionmentioning

confidence: 99%

The climatic interdependence of extreme-rainfall events around the globe

Su,

Meyerhenke,

Kurths

2021

Preprint

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Community detection analysis in wind speed-monitoring systems using mutual information-based complex network

Cited by 5 publications

References 42 publications

The climatic interdependence of extreme-rainfall events around the globe

The climatic interdependence of extreme-rainfall events around the globe

Multiscale entropy analysis of wind speed dynamics in Petrolina, Northeast Brazil

The climatic interdependence of extreme-rainfall events around the globe

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