New method to obtain the power spectra of hidden variables and its application to ocean data

Wei en-bo; Tian Ji-wei; Xu Jin-shan

doi:10.1007/BF02842786

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New method to obtain the power spectra of hidden variables and its application to ocean data

OriginalPaper | Updated：2023-05-16

- New method to obtain the power spectra of hidden variables and its application to ocean data
- New method to obtain the power spectra of hidden variables and its application to ocean data
- 海洋湖沼学报（英文） 2001年19卷第1期页码：29-34
- Affiliations：
  
  1. College of Power Engineering University of Shanghai for Science and Technology
  2. Physical Oceanography Laboratory Ocean University of Qingdao
- Author bio：
- Funds：
  
  Project 49476254 supported by NSFC.
- DOI：10.1007/BF02842786
  中图分类号：
- 纸质出版：2001
- Accepted：
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New method to obtain the power spectra of hidden variables and its application to ocean data[J]. 海洋湖沼学报（英文）, 2001,19(1):29-34.

en-bo, W., Ji-wei, T. & Jin-shan, X. New method to obtain the power spectra of hidden variables and its application to ocean data., Chin. J. Ocean. Limnol. 19, 29–34 (2001). https://doi.org/10.1007/BF02842786
New method to obtain the power spectra of hidden variables and its application to ocean data[J]. 海洋湖沼学报（英文）, 2001,19(1):29-34. DOI： 10.1007/BF02842786.

en-bo, W., Ji-wei, T. & Jin-shan, X. New method to obtain the power spectra of hidden variables and its application to ocean data., Chin. J. Ocean. Limnol. 19, 29–34 (2001). https://doi.org/10.1007/BF02842786 DOI：

摘要

Abstract

A novel method is proposed to obtain the power spectra of hidden variables in a chaotic time series. By embedding the data in phase space

and recording the conditional probability density of points that the trajectory encounters as it evolves in the reconstructed phase space

it is possible to recover the power spectra of hidden variables in chaotic time series through a spectral analysis over the conditional probability density time series. The method is robust in the application to Lorenz system

4-dimension Rössler system and rigid body motion by linear feedback system (LFRBM). Applying the method to the time series of sea surface temperature (SST) of the South China Sea

we obtained the power spectra of the wind speed (WS) from SST data. Furthermore

the results showed that there exists an important nonlinear interaction between the SST and the WS.

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Keywords

references

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