An Empirical Method for the Spatial Interpolation of Monthly Precipitation within California

Abstract

An empirical method for interpolating monthly precipitation totals within California is described and evaluated. Using 120 monthly precipitation totals observed from 1961-1970 at each of 90 randomly selected stations in California and a P-mode principal components analysis of a co-variance matrix, four independent sources of precipitation variability were identified and quantitatively paraphrased. The four principal components were then linked to three representative stations by polynomial regression. From these relationships, monthly precipitation totals can be interpolated anywhere in the state by reversing the principal components computations. The required input includes: a monthly precipitation total, for the month of interest, from each of the three representative stations as well as isarithmically interpolated estimates of the component loadings and station means which were derived from the initial (1961-1970) data set. A major asset of the procedure is that it only requires three pieces of new information (i.e., the monthly totals from the representative stations) in order to interpolate monthly precipitation anywhere in California for any month of interest. Interpolations were quantitatively compared to measured values at 51 randomly selected stations over the period 1971-1975 and they were, in most cases, above 80% effective in reproducing the observed, 5-year records.