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Rotated principal component analysis (RPCA) is a powerful tool for studying upper air height data because of its ability to distill information about the variance existing in a large number of maps to a much smaller set of physically meaningful maps which together explain a large fraction of the variance of she input dataset. However, in order to achieve this, one faces the problem of deciding how many eigenmodes to rotate. A discussion of the dangers of incorrectly choosing the rotation point and a quasi-objective technique that leads to a good compromise between over- and underrotation are presented. Finally, the use of RPCA for detecting errors and inconsistencies in upper air data along with two examples is discussed.
O’Lenic et al. (Mon,) studied this question.