Principal Components Analysis of Vector Wind Measurements

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  • a Solar Energy Research Institute, Golden, CO 80401
  • | b Lawrence Livermore Laboratory, Livermore, CA 94550
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Abstract

The method of principal components analysis (also known as empirical eigenvector analysis) was generalized to the treatment of vector fields of data and applied to a 12-month record of mean hourly wind velocities from 10 measurement locations in a mesoscale region. The primary spatial distributions of regional wind velocities were derived for each month. Time-series analysis in terms of the primary spatial velocity patterns was used to determine the fundamental temporal patterns or principal components. Necessary mathematical procedures are given and geometric representations of eigenvectors that define the primary spatial velocity patterns are presented. Applications of the generalized vector formulation of the method to current and future problems of atmospheric science are discussed.

Abstract

The method of principal components analysis (also known as empirical eigenvector analysis) was generalized to the treatment of vector fields of data and applied to a 12-month record of mean hourly wind velocities from 10 measurement locations in a mesoscale region. The primary spatial distributions of regional wind velocities were derived for each month. Time-series analysis in terms of the primary spatial velocity patterns was used to determine the fundamental temporal patterns or principal components. Necessary mathematical procedures are given and geometric representations of eigenvectors that define the primary spatial velocity patterns are presented. Applications of the generalized vector formulation of the method to current and future problems of atmospheric science are discussed.

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