Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion

Empirical Orthogonal Function (EOF) analysis is often used in Meteorology and Climatology

이 제출물을 팔로우합니다

In statistics and signal processing, the method of empirical orthogonal function (EOF) analysis is a decomposition of a signal or data set in terms of orthogonal basis functions which are determined from the data. It is the same as performing a principal components analysis on the data, except that the EOF method finds both time series and spatial patterns. The term is also interchangeable with the geographically weighted PCAs in geophysics.
if there are too many spatial grids, the spatiotemporal convertion is often performed to quicken the process, other than EOF_analysis.
As required by users, a new version of Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion is provided here.

인용 양식

Zhou Chunlüe (2026). Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion (https://kr.mathworks.com/matlabcentral/fileexchange/54675-empirical-orthogonal-function-eof-with-spatiotemporal-convertion), MATLAB Central File Exchange. 검색 날짜: .

도움

도움 받은 파일: Empirical Orthogonal Function (EOF) analysis

도움 준 파일: EOF

카테고리

Help CenterMATLAB Answers에서 Weather and Atmospheric Science에 대해 자세히 알아보기

일반 정보

MATLAB 릴리스 호환 정보

  • 모든 릴리스와 호환

플랫폼 호환성

  • Windows
  • macOS
  • Linux
버전 퍼블리시됨 릴리스 정보 Action
1.2.0.0

update the figure
add some example figures
As required by users, a new version of Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion is provided here.

1.1.0.0

As required by users, a new version of Empirical Orthogonal Function (EOF) with Spatiotemporal Convertion is provided here.

1.0.0.0