Dynamic analysis of meteorological time series in Hong Kong: A nonlinear perspective

Yan, Bowen; Chan, P. W.; Li, Qiusheng; He, Yuncheng; Shu, Z.R.

doi:10.1002/joc.7106

Cited by 19 publications

(10 citation statements)

References 71 publications

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“…Essentially, RP and RQA are well-established methods for diagnosing the deterministic dependences in time series, which have been successfully applied to describe the dynamic properties of natural processes. To illustrate, Yan et al (2021) used recurrence analysis to quantify and compare the nonlinear dynamic characteristics of wind, pressure, temperature and relative humidity time series. Shu et al (2021a, c) applied a series of nonlinear dynamic analysis techniques (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…To illustrate,Yan et al (2021) used recurrence analysis to quantify and compare the nonlinear dynamic characteristics of wind, pressure, temperature and relative humidity time series.Shu et al (2021a, c) applied a series of nonlinear dynamic analysis techniques (e.g. phase space reconstruction, recurrence plot and recurrence quantification analysis) to diagnose the chaotic nature of wind and rainfall time series recorded at different surface weather stations across Hong Kong, and investigate how such chaotic nature of meteorological time series can be influenced by various terrain condition.…”

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confidence: 99%

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Dynamic characterization of wind pressure fluctuations in separated and reattaching flows

Shu

2022

Advances in Structural Engineering

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Flow separation and reattachment over bluff bodies has been a primary concern in wind engineering community as it may generate large suction pressure. This study re-visits the characteristics of wind pressure underneath the separated shear layer, with emphasis on diagnosing the underlying dynamic properties. Nonlinear dynamic analysis techniques, namely recurrence plot and recurrence quantification analysis are applied to reveal and compare the wind pressure dynamics associated with different test conditions. The results indicate that the dynamic behaviour of wind pressure is closely tied with leading edge shape, turbulence intensity, as well as turbulence scale. Generally, the underlying dynamics of wind pressure become less deterministic as the measurement position moves from leading edge towards downstream. Such pattern is reversed once the descriptive indicator reaches its minimum. The position of such minimum value corresponds well to the downstream boundary of the reverse flow region. Moreover, the minimum value of dynamic descriptive indicator, as well as the reverse position in its distribution pattern, is correlated with the leading edge shape. On the other hand, the minimum value of dynamic descriptive indicator is also dependent on turbulence scale, while the reverse position in the distribution pattern of dynamic descriptive factor is dependent on turbulence intensity.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Dynamic characterization of wind pressure fluctuations in separated and reattaching flows

Shu

2022

Advances in Structural Engineering

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“…In the analysis of marine time-series data, we used CRQA to quantify the frequency of similar changes between the time series of two water quality factors and then confirmed the dynamic spatial correlation of the time series of different water quality factors. CRQA indexes include mean diagonal line length (MDL), determinism (DET), laminarity (LAM), and recurrence rate (RR) [ 14 , 15 , 16 , 17 , 18 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

Sun

Wang

et al. 2023

Entropy

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In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.

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“…The natural wind in the atmosphere is known to be chaotic (Yan et al, 2020(Yan et al, , 2021Shu et al, 2020Shu et al, , 2021aShu et al, , 2021bShu et al, , 2021c, which often leads to random-like behavior in time domain. Hence, in order to properly diagnose the wind effects on buildings and structures, several different methods have been proposed, in which physical simulation in wind tunnel is one of the most popular and well used methods.…”

Section: Experimental Set-upmentioning

confidence: 99%

Nonlinear dynamic analysis of wind pressure variability on standard tall building

He,

Shu,

Chen

et al. 2023

Advances in Structural Engineering

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Understanding the variability of wind pressure on tall building surfaces is of essential importance regarding the control of wind-induced response. In this study, advanced time series analysis techniques, e.g., phase space reconstruction and recurrence analysis, were applied to diagnose the wind pressure variability from a nonlinear dynamic perspective. It is shown that, the wind pressure acting on tall building surfaces exhibit distinct chaotic nature. Due to the complex flow patterns around the building, the underlying dynamics of wind pressure is subject to pronounced region-to-region variability. For windward surface, the wind pressure dynamics at the stagnation region is more deterministic than those of downstream. For the side surfaces, the distribution of recurrence quantification analysis (RQA) indicators follows a clear pattern, where the values are larger at the near-front edge, and decreases towards the rear. The distribution of RQA indicators on the leeward surface is opposite to that of windward surface, in which larger values occur more often at near-ground level. Further upstream, the values are found to decrease. The outcomes provide valuable insights to the wind-structural interaction, thus could help wind-resistant design.

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Dynamic analysis of meteorological time series in Hong Kong: A nonlinear perspective

Cited by 19 publications

References 71 publications

Dynamic characterization of wind pressure fluctuations in separated and reattaching flows

Dynamic characterization of wind pressure fluctuations in separated and reattaching flows

Spatio-Temporal Analysis of Marine Water Quality Data Based on Cross-Recurrence Plot (CRP) and Cross-Recurrence Quantitative Analysis (CRQA)

Nonlinear dynamic analysis of wind pressure variability on standard tall building

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