The effects of El Niño on the sea-level fluctuations along the coast of Taiwan
Date Issued
2007
Date
2007
Author(s)
Chang, Yu-Yao
DOI
zh-TW
Abstract
We use the new Hilbert – Huang Transform (HHT) method, developed by Huang et al (1998), to analyze the coastal sea-level fluctuations in Taiwan. This new approach can be used to analyze non-linear and non-stationary data. Traditional analysis method like Fourier transform and Wavelet transform can only used in linear system, but in real world, the data are usually non-linear. Tide gauge usually located at near shore sites. So when we analyze the gauge data, we can not ignore its non-linear property. The key part of the HHT is the “ empirical mode decomposition “ method with which any complicated data set can be decomposed into a finite and often small number of “ intrinsic mode function “(IMF). Each IMF represents different driving mechanism. When the driving force appears intermittency, it will cause the decomposed IMF consisting of oscillations of dramatically disparate scales, hereafter called “mode mixing”. Wu and Huang (2005) presented a new Ensemble Empirical Mode Decomposition (EEMD). This new approach consists of sifting an ensemble of white noise-added signal and treats the mean as the final true result, thus prevents “mode mixing”. We analyze the sea level data in Fu-Kang Harbor, and find that the annual cycle oscillation in Fu-Kang is primarily affected by air pressure: When the air pressure is high in winter, the sea level is low. On the contrary, the air pressure drops in summer while the sea level rises. The amplitude of annual cycle increase during El Niño event. The cross-correlation between upper envelope of annual cycle and El Niño 3.4 index reaches 0.6 with -7 month lag time (envelope leads index). We also find that the sea level around Taiwan rises during the period 1991~2000.
Subjects
聖嬰現象
水位
HHT
經驗模態分解法
El Niñ
o
sea-level
EEMD
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-96-R90241108-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):543f22afe1710124d7f0172e12cb4d30