Significance test for empirical orthogonal function (EOF) analysis of meteorological and oceanic data

Li Xiao-feng; L. Pietrafesa; Lan Shu-fang; Xie Li-an

doi:10.1007/BF02842536

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Significance test for empirical orthogonal function (EOF) analysis of meteorological and oceanic data

Updated：2023-05-16

- Significance test for empirical orthogonal function (EOF) analysis of meteorological and oceanic data
- Journal of Oceanology and Limnology Vol. 18, Issue 1, Pages: 10-17(2000)
- Affiliations：
  
  1. NOAA Science Center
  2. Department of Marine Earth and Atmosphere Sciences North Carolina State University
  3. Institute of Oceanology Chinese Academy of Sciences
- Author bio：
- Funds：
- DOI：10.1007/BF02842536
  CLC：
- Published：2000
- Accepted：
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Xiao-feng, L., Pietrafesa, L., Shu-fang, L. et al. Significance test for empirical orthogonal function (EOF) analysis of meteorological and oceanic data., Chin. J. Ocean. Limnol. 18, 10–17 (2000). https://doi.org/10.1007/BF02842536
DOI：

Xiao-feng, L., Pietrafesa, L., Shu-fang, L. et al. Significance test for empirical orthogonal function (EOF) analysis of meteorological and oceanic data., Chin. J. Ocean. Limnol. 18, 10–17 (2000). https://doi.org/10.1007/BF02842536 DOI：

摘要

Abstract

A Monte Carlo experiment was done to create a statistically significant test table for selecting EOF modes whose signals are above the noise level. Numerical analysis showed that it is feasible to choose a shorter data set between temporal and spatial dimensions for conducting an EOF analysis

and thus simplify calculation of the covariance matrix and solution of its eigenvalues and eigenvectors. The experiment showed that the dominant EOF modes remained the same. This approach is more computationally efficient

reducing both computing time and computer memory requirement

while still retaining the dominant signal.

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Keywords

references

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