Removal of Systematic Biases in S-Mode Principal Components Arising from Unequal Grid Spacing

Abstract

Abstract Frequently, physical variables are analyzed using gridded fields, on regular latitude–longitude frameworks. Such networks often concentrate a disproportionate number of observations over polar regions. If these types of grids are used for an S-mode principal component analysis, they produce a bias of the component patterns toward the temporal patterns observed at higher latitudes. A method to potentially eliminate this effect, while employing the covariance similarity matrix, is to weight the variables by the square root of the cosine of the latitude of the point at which the datum was observed. However, this processing is not useful when using the correlation similarity matrix. In this case, a spatially uniform or equal density grid can be designed by means of the criteria of a constant density of points in each circle of latitude. Then, the variable values are linearly interpolated into this new equal-density grid. This technique is easy to program and to adapt to any regular latitude–longitude...