scholarly journals Denoising Effect of Jason-1 Altimeter Waveforms with Singular Spectrum Analysis: A Case Study of Modelling Mean Sea Surface Height over South China Sea

2020 ◽  
Vol 8 (6) ◽  
pp. 426 ◽  
Author(s):  
Jiajia Yuan ◽  
Jinyun Guo ◽  
Yupeng Niu ◽  
Chengcheng Zhu ◽  
Zhen Li ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Spectrum Analysis ◽  
Noise Level ◽  
Singular Spectrum Analysis ◽  
Sea Surface Height ◽  
Sea Surface ◽  
China Sea ◽  
Singular Spectrum ◽  
Mean Sea Surface

Altimeter waveforms are usually contaminated due to nonmarine surfaces or inhomogeneous sea state conditions. The present work aimed to present how the singular spectrum analysis (SSA) can be used to reduce the noise level in Jason-1 altimeter waveforms to obtain SSA-denoised waveforms, improving the accuracy of a mean sea surface height (MSSH) model. Comparing the retracked sea surface heights (SSHs) by a 50% threshold retracker for the SSA-denoised waveforms with those for the raw waveforms, the results indicated that SSA allowed a noise reduction on Jason-1 waveforms, improving the accuracy of retracked SSHs. The MSSH model (called Model 1) over the South China Sea with a grid of 2′ × 2′ was established from the retracked SSHs of Jason-1 by the 50% threshold retracker for the SSA-denoised waveforms. Comparing Model 1 and Model 2 (established from the retracked SSHs by the 50% threshold retracker for the raw waveforms) with the CLS15 and DTU18 models in the South China Sea, it was found that the accuracy of Model 1 was higher than that of Model 2, which indicates that using SSA to reduce noise level in Jason-1 waveforms can effectively improve the accuracy of the MSSH model.

Application of deep learning technique to the sea surface height prediction in the South China Sea

2021 ◽  
Vol 40 (7) ◽  
pp. 68-76
Author(s):  
Tao Song ◽  
Ningsheng Han ◽  
Yuhang Zhu ◽  
Zhongwei Li ◽  
Yineng Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
South China Sea ◽  
South China ◽  
Sea Surface Height ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
China Sea ◽  
Learning Technique ◽  
Height Prediction

Variability of sea surface height in the South China Sea and its relationship to Pacific oscillations

2015 ◽  
Vol 34 (12) ◽  
pp. 80-92 ◽  
Author(s):  
Yuhua Pei ◽  
Rong-Hua Zhang ◽  
Xiangming Zhang ◽  
Lianghong Jiang ◽  
Yanzhou Wei
Keyword(s):  
South China Sea ◽  
South China ◽  
Sea Surface Height ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
China Sea

scholarly journals Intraseasonal variability in sea surface height over the South China Sea

2010 ◽  
Vol 115 (C4) ◽  
Author(s):  
Wei Zhuang ◽  
Shang-Ping Xie ◽  
Dongxiao Wang ◽  
Bunmei Taguchi ◽  
Hidenori Aiki ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Intraseasonal Variability ◽  
Sea Surface Height ◽  
The South China Sea ◽  
Sea Surface ◽  
The South ◽  
China Sea

Seasonal variability of sea surface height in the South China Sea observed with TOPEX/Poseidon altimeter data

2000 ◽  
Vol 105 (C6) ◽  
pp. 13981-13990 ◽  
Author(s):  
Chung-Ru Ho ◽  
Quanan Zheng ◽  
Yin S. Soong ◽  
Nan-Jung Kuo ◽  
Jian-Hua Hu
Keyword(s):  
South China Sea ◽  
South China ◽  
Seasonal Variability ◽  
Sea Surface Height ◽  
The South China Sea ◽  
Sea Surface ◽  
Altimeter Data ◽  
The South ◽  
China Sea

scholarly journals Statistical Prediction of the South China Sea Surface Height Anomaly

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Caixia Shao ◽  
Weimin Zhang ◽  
Chunjian Sun ◽  
Xinmin Chai ◽  
Zhimin Wang
Keyword(s):  
Time Series ◽  
South China Sea ◽  
South China ◽  
Decadal Variability ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Statistical Prediction ◽  
Height Anomaly ◽  
China Sea ◽  
Sea Surface Height Anomaly

Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007.

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