AN INTERCOMPARISON OF COMPUTER-ASSISTED CIRCULATION CLASSIFICATION METHODS

Five different methods that have been used for classification of circulation patterns (correlation method, sums-of-squares method, average linkage, K-means, and rotated principal component analysis) are examined as to their ability to detect dominant circulation types. The performance of the methods is evaluated according to the degree of meeting the following demands made on the groups formed: The groups should (i) be consistent when pre-set parameters are changed, (ii) be well separated both from each other and from the entire data set, (iii) be stable in space and time, and (iv) reproduce the predefined types. All the methods proved to be capable of yielding meaningful classifications. None of them can be thought of as the best in all aspects. Which method to use will depend mainly on the aim of the classification. Nevertheless, the principal component analysis is most successful in reproducing the predefined types and is therefore considered as the most promising method among those examined.