Analysis of Meteorological Data by Means of Canonical Decomposition and Biplots

View More View Less
  • 1 The Hebrew University, Jerusalem, Israel
© Get Permissions
Full access

Abstract

The biplot is a graphical display of a two-dimensional approximation to a matrix. Its usefulness in the display and analysis of matrices of meteorological data is demonstrated by a detailed exposition of two illustrations based on Israeli rainfall. In the first illustration, the biplot for the sample matrix for monthly rainfall averages is shown to provide a visual display of patterns existing in the data. In the second illustration, data from a rainmaking experiment is the basis for the generation of a biplot which is a graphical approximation to simultaneous tests of a variety of sub-hypotheses in the multivariate analysis of variance (MA.NOVA) one-way layout.

The biplot is useful whenever the two largest characteristic roots of the matrix times its transpose account for most of the variance. When this is the case, relationships and trends in the data are displayed which may be difficult to obtain by common analytic methods.

Abstract

The biplot is a graphical display of a two-dimensional approximation to a matrix. Its usefulness in the display and analysis of matrices of meteorological data is demonstrated by a detailed exposition of two illustrations based on Israeli rainfall. In the first illustration, the biplot for the sample matrix for monthly rainfall averages is shown to provide a visual display of patterns existing in the data. In the second illustration, data from a rainmaking experiment is the basis for the generation of a biplot which is a graphical approximation to simultaneous tests of a variety of sub-hypotheses in the multivariate analysis of variance (MA.NOVA) one-way layout.

The biplot is useful whenever the two largest characteristic roots of the matrix times its transpose account for most of the variance. When this is the case, relationships and trends in the data are displayed which may be difficult to obtain by common analytic methods.

Save