The estimation of missing climatological data

Abstract

Abstract Various methods of estimating montly means and extremes of climatological data are examined. Any generalized method is likely to be based on a correlation matrix, but the incompleteness of the data introduces problems with this approach. These are illustrated by program BMDPAM of the BMDP suite, which produces estimates inferior to those using traditional methods based on single station comparisons. Principal component analysis is considered likely to be the best statistical technique for estimating missing values among highly correlated data. The high quality correlation matrix required as input can be obtained by using a simple estimating procedure to produce a preliminary set of complete data. A simple technique devised for estimating climatological data in the U.K. was found to give results similar to those obtained from an eigenvector scheme used for quality control purposes. The accuracy of the technique is such that it is suggested that satisfactory averages could be estimated for stations with only 10 years of data, and possibly less.