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A regionalization of Austria's precipitation climate using principal component analysis
Author(s) -
Ehrendorfer Martin
Publication year - 1987
Publication title -
journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0196-1748
DOI - 10.1002/joc.3370070107
Subject(s) - principal component analysis , varimax rotation , empirical orthogonal functions , statistics , total variation , precipitation , mathematics , homogeneous , variance (accounting) , climatology , mode (computer interface) , environmental science , econometrics , meteorology , geography , computer science , geology , descriptive statistics , cronbach's alpha , accounting , combinatorics , business , operating system
S‐mode principal component analysis (PCA) is performed on correlation matrices of precipitation data of Austria for summer and winter half‐year totals for the period 1951–1980. Application of the dominant‐variance selection Rule N (Overland and Preisendorfer, 1982) proves two or three eigenvalues to be significant, where three principal components (PCs) on average account for 68.3 and 79.4 per cent of total variance in the summer and winter half‐years, respectively. The Varimax‐rotated PC loadings allow for a subdivision of Austria into three homogeneous (with respect to the underlying processes) regions. Intercomparison of the PC primary patterns, variances and time series of the principal components of three different networks confirms the spatial stability of these regions and turns attention towards seasonal differences. The three PCs are identified by assignment of large‐scale weather types to them which are known to be precipitation producing in the region wherein the respective PC is dominant. The results of this study and their wide variety of applicability reveal PCA and Rule N as useful tools in identifying homogeneous groups of variables which can be ascribed a physical meaning. The paper contains a short account of the analysis of empirical orthogonal functions, the theory of PCA and its connection to factor analysis.