Advanced statistical analysis of environmental data in the Gascoyne Inlet
In the marine environment, Times-series analysis is important to understand natural processes and their dynamics. 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 spec...
| Published in: | OCEANS 2021: San Diego – Porto |
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| Main Author: | |
| Format: | Conference Object |
| Language: | unknown |
| Published: |
IEEE
2021
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| Subjects: | |
| Online Access: | https://oro.open.ac.uk/83362/ https://doi.org/10.23919/oceans44145.2021.9705672 |
| Summary: | In the marine environment, Times-series analysis is important to understand natural processes and their dynamics. 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. Since the classical spectral analysis, namely the Blackman-Tukey method, requires not only linear and stationary data but also evenly-spaced data, the Lomb-Scargle algorithm is adapted to unevenly-spaced data and is first used as an alternative for the spectral analysis of high frequency sampled time series in nearshore waters of the Gascoyne Inlet, located within Nunavut and is nearby to Cape Ricketts, Caswall Tower, and Cape Lindon. We focus particularly on automatic measurements of temperature records, salinity, turbidity and chlorophyll data sets from deployments on an Ocean Networks Canada cabled platform. Then, the Hilbert-Huang Transform (HHT) is used to 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. |
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