Coastal-Trapped Waves in the East China Sea Observed by a Mooring Array in Winter 2006

Yin, Liping; Zheng, Quanan

doi:10.1175/jpo-d-13-07.1

Cited by 35 publications

(30 citation statements)

References 32 publications

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“…The SSH and current responses to synoptic winter wind in the BYES have been recognized to be energetic based on data from several coastal tide gauge stations (Chen & Su, ; Ding et al, ; Hsueh & Romea, ; Yin et al, ) and some in situ current observations (Hsueh, ; Huang et al, ; Lin et al, ; Li et al, ; Teague & Jacobs, ; Yin et al, ). However, the basin‐scale SSH and current variations have not yet been fully revealed, possibly due to the limited spatial and temporal coverage of the observed data and the lack of an effective method to decompose the overall variations into several leading modes.…”

Section: Discussionmentioning

confidence: 99%

“…The bottom topographies and wind conditions facilitate the generation of CTWs in the BYES in winter. In fact, CTWs in the BYES have been observed with both tidal gauge data (Chen & Su, ; Hsueh & Romea, ; Yin et al, ) and TOPEX/POSEIDON altimeter satellite data (Jacobs, ; Jacobs, Preller, et al, ; Jacobs, Teague, et al, ). These studies have confirmed that CTWs with dominant periods of 2–10 days propagate southward and northward along the Chinese and Korean coasts, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, regionally distinct features in the CTWs in the western ECS and BYS were identified based on theoretical models and in situ current observations in the two regions. In the western ECS, the offshore decay of both SSH and current can be well explained by a theoretical CTW model (Yin et al, ). In the semienclosed BYS, several leading CTWs derived from double‐shelf theoretical models indicated that both SSH and current variations decrease from both the Chinese and Korean sides (Hsueh & Pang, ; Qu et al, ), leading to very weak variations in the central BYS.…”

Section: Introductionmentioning

confidence: 99%

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Sea Surface Height and Current Responses to Synoptic Winter Wind in the Bohai, Yellow, and East China Seas: Two Leading Coastal Trapped Waves

Huang

2019

JGR Oceans

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This study investigates the sea surface height (SSH) and current responses to synoptic winter wind in the Bohai, Yellow, and East China Seas (BYES) using a numerical model. The basin‐scale responses are primarily interpreted as two leading coastal trapped waves (CTWs) identified through the complex empirical orthogonal function analysis of SSH. One CTW is a Kelvin wave (KW) explaining 76% of the total SSH variance; the other is a wind‐forced lowest shelf wave (SW1) explaining 17% of the total SSH variance. The KW dominates the current in the Bohai and Yellow Seas (BYS), and the SW1 dominates the current in the western East China Sea (ECS). The KW is a free relaxation response to large wind‐driven SSH variations and propagates cyclonically in the BYES around one SSH node. The SW1 propagates cyclonically in the semienclosed BYS around two nodes and southward along the western ECS with two nodes on the shelf. The wavelength and e‐folding scale of amplitude vary spatially, producing regionally distinct features and mechanisms. In the semienclosed BYS, the associated geostrophic current is along the cophase line of SSH, and its magnitude is proportional to both wave amplitude and wavenumber. In the western ECS, the associated geostrophic current is perpendicular to the cophase line of SSH, and its magnitude is proportional to the wave amplitude and inversely proportional to the e‐folding scale of amplitude. This study reveals not only the basin‐scale SSH and current variations in the BYES but also new features of CTWs in a semienclosed embayment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sea Surface Height and Current Responses to Synoptic Winter Wind in the Bohai, Yellow, and East China Seas: Two Leading Coastal Trapped Waves

Huang

2019

JGR Oceans

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“…The EMD scheme was used in studying sea level rise (Ezer et al, 2013). Yin et al (2014) also applied the method and identified three kinds of low-frequency waves using some observations in the coastal water of the East China Sea.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Statistical properties and time-frequency analysis of temperature, salinity and turbidity measured by the MAREL Carnot station in the coastal waters of Boulogne-sur-Mer (France)

Ismail

Lazure

Puillat

2016

Journal of Marine Systems

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In marine sciences, many fields display high variability over a large range of spatial and temporal scales, from seconds to thousands of years. The longer recorded time series, with an increasing sampling frequency, in this field are often nonlinear, nonstationary, multiscale and noisy. Their analysis faces new challenges and thus requires the implementation of adequate and specific methods. The objective of this paper is to highlight time series analysis methods already applied in econometrics, signal processing, health, etc. to the environmental marine domain, assess advantages and inconvenients and compare classical techniques with more recent ones. Temperature, turbidity and salinity are important quantities for ecosystem studies. The authors here consider the fluctuations of sea level, salinity, turbidity and temperature recorded from the MAREL Carnot system of Boulogne-sur-Mer (France), which is a moored buoy equipped with physico-chemical measuring devices, working in continuous and autonomous conditions. In order to perform adequate statistical and spectral analyses, it is necessary to know the nature of the considered time series. For this purpose, the stationarity of the series and the occurrence of unit-root are addressed with the Augmented-Dickey Fuller tests. As an example, the harmonic analysis is not relevant for temperature, turbidity and salinity due to the nonstationary condition, except for the nearly stationary sea level datasets. In order to consider the dominant frequencies associated to the dynamics, the large number of data provided by the sensors should enable the estimation of Fourier spectral analysis. Different power spectra show a complex variability and reveal an influence of environmental factors such as tides. However, the previous classical spectral analysis, namely the Blackman-Tukey method, requires not only linear and stationary data but also evenly-spaced data. Interpolating the time series introduces numerous artifacts to the data. The Lomb-Scargle algorithm is adapted to unevenly-spaced data and is used as an alternative. The limits of the method are also set out. It was found that beyond 50% of missing measures, few significant frequencies are detected, several seasonalities are no more visible, and even a whole range of high frequency disappears progressively. Furthermore, two time-frequency decomposition methods, namely wavelets and Hilbert-Huang Transformation (HHT), are applied for the analysis of the entire dataset. Using the Continuous Wavelet Transform (CWT), some properties of the time series are determined. Then, the inertial wave and several low-frequency tidal waves are identified by the application of the Empirical Mode Decomposition (EMD). Finally, EMD based Time Dependent Intrinsic Correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series.

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“…Schmitt et al [10] applied the HHT method to characterize the scale invariance of velocity fluctuations in the surf zone. Ying et al [11] applied the method and identified three kinds of low-frequency waves using some observations in the coastal water of the East China Sea. The EMD scheme has also been used in studying sea level rise [12].…”

Section: A Brief Description Of Hhtmentioning

confidence: 99%

Advanced Spectral Analysis and Cross Correlation Based on the Empirical Mode Decomposition: Application to the Environmental Time Series

Ismail

Lazure

Puillat

2015

IEEE Geosci. Remote Sensing Lett.

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In marine sciences, time series are often nonlinear and nonstationary. Adequate and specific methods are needed to analyze such series. In this letter, an application of the empirical mode decomposition method (EMD) associated to the Hilbert spectral analysis (HSA) is presented. Furthermore, EMD-based time-dependent intrinsic correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series. Four temperature time series obtained from automatic measurements in nearshore waters of the Réunion island are considered, recorded every 10 min from July 2011 to January 2012. The application of the EMD on these series and the estimation of their power spectra using the HSA are illustrated. The authors identify low-frequency tidal waves and display the pattern of correlations at different scales and different locations. By TDIC analysis, it was concluded that the high-frequency modes have small correlation, whereas the trends are perfectly correlated.

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Coastal-Trapped Waves in the East China Sea Observed by a Mooring Array in Winter 2006

Cited by 35 publications

References 32 publications

Sea Surface Height and Current Responses to Synoptic Winter Wind in the Bohai, Yellow, and East China Seas: Two Leading Coastal Trapped Waves

Sea Surface Height and Current Responses to Synoptic Winter Wind in the Bohai, Yellow, and East China Seas: Two Leading Coastal Trapped Waves

Statistical properties and time-frequency analysis of temperature, salinity and turbidity measured by the MAREL Carnot station in the coastal waters of Boulogne-sur-Mer (France)

Advanced Spectral Analysis and Cross Correlation Based on the Empirical Mode Decomposition: Application to the Environmental Time Series

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