Annual Sea Level Amplitude Analysis over the North Pacific Ocean Coast by Ensemble Empirical Mode Decomposition Method
نویسندگان
چکیده
Understanding spatial and temporal changes of seasonal sea level cycles is important because direct influence on coastal systems. The annual cycle substantially larger than semi-annual in most parts the ocean. Ensemble empirical mode decomposition (EEMD) method has been widely used to study tidal component, long-term rise, decadal variation. In this work, EEMD analyze observed monthly anomalies detect characteristics. Considering that variations variation Northeast Pacific Ocean are poorly studied, trend characteristics amplitudes related mechanisms North investigated using tide gauge records covering 1950–2016. average amplitude exhibits interannual-to-decadal variability within range 14–220 mm. largest value ~174 mm west coast South China Sea. other regions Ocean, mean relatively low between 77 124 for western 84 87 eastern coast. estimated values areas Sea have statistically decreased over 1952–2014 with a −0.77 mm·yr−1 −0.11 mm·yr−1. Our results suggested decreasing good agreement wind stress associated Decadal Oscillation (PDO). This phenomenon also explains especially high correlations since 1980 (R = 0.61−0.72).
منابع مشابه
A Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کاملA Fault Diagnosis Method for Automaton Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کاملEnsemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method
A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the p...
متن کاملThe Multi-Dimensional Ensemble Empirical Mode Decomposition Method
A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multidimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition (EEMD) to slices of data in each and every dimension involved. The final reconstruction of the corresponding intrinsic mode function (IMF) is based on ...
متن کاملA Near-Annual Pacific Ocean Basin Mode
Some fairly regular and nearly annual variability in the equatorial Pacific after the major 1997/98 El Niño event is studied. Sea level, sea surface temperature (SST), and surface wind anomalies of this variability are tied together in a way similar to the slow cycles of El Niño–Southern Oscillations (ENSO). Despite a slightly longer-than-annual time scale, similar variability was also found in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2072-4292']
DOI: https://doi.org/10.3390/rs13040730