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High resolution ADCP, CTD and Fluorometer time series analysis

机译:高分辨率ADCP,CTD和荧光计时间序列分析

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In the marine environment, the recorded time series are often nonlinear and nonstationary and interact with each other. Their analysis faces new challenges and thus requires the implementation of adequate and specific methods. We use the Hilbert-Huang Transform (HHT) for the spectral analysis of high frequency sampled time series in near shore waters of Cambridge Bay, located in the Canadian Arctic. We focus particularly on automatic measurements of temperature records, salinity, turbidity and chlorophyll data sets from deployments on an Ocean Networks Canada cabled platform. We look at the contribution of different Intrinsic Mode Functions (IMFs) obtained by the Empirical Mode Decomposition (EMD). The inertial wave and several low-frequency tidal waves are identified by the application of EMD. Furthermore, the correlation between two nonstationary time series is investigated. By Time Dependent Intrinsic Correlation (TDIC) analysis, it was concluded that the high-frequency modes have small correlation; whereas the trends are perfectly correlated. The methodologies presented in this paper are general. They can be applied for identification of main properties of other time series from the environmental and oceanic sciences, where the records are complex with fluctuations over a large range of different spatial and temporal scales.
机译:在海洋环境中,记录的时间序列通常是非线性且不稳定的,并且会相互影响。他们的分析面临新的挑战,因此需要实施适当而具体的方法。我们使用希尔伯特-黄变换(HHT)对位于加拿大北极地区剑桥湾近岸水域的高频采样时间序列进行频谱分析。我们特别专注于通过加拿大海洋网络有线平台上的部署对温度记录,盐度,浊度和叶绿素数据集进行自动测量。我们看看通过经验模式分解(EMD)获得的不同本征模式函数(IMF)的贡献。通过应用EMD可以识别出惯性波和几种低频潮汐波。此外,研究了两个非平稳时间序列之间的相关性。通过时变内在相关性(TDIC)分析,可以得出结论,高频模式具有较小的相关性。而趋势是完全相关的。本文介绍的方法是通用的。它们可用于从环境和海洋科学中识别其他时间序列的主要属性,在这些时间序列中,记录是复杂的,并且会在不同时空尺度的大范围内波动。

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