Classification of Temperature Regime of Iran Using PCA and CA

In this study for searching the tempo-spatial patterns and classification of monthly temperature regimes of Iran during the last half century has been analyzed using Principal Component Analysis and Cluster Analysis (CA).Analysis among the Empirical Orthogonal Functions (EOF) from principal component analysis is a powerful tool to detect temporal and spatial patterns of climatic variables. Principal component analysis may result to some empirical modes representing climatic tempo-spatial patterns. Additionally this method approach to decline the mass: of data and convert primary variables into several components which can illustrate the most effects of primary variables. A principal component analysis applied to correlation matrix of temperature data as a data reduction technique. It showed that three principal components explain about 99.7 percent of variance. The first principal component explains about 95.3 percent of variance. This component dominates southern parts of the country from Khuzestan to Chabahar shows completely different characteristic in compare with another region throughout the year. This study showed that principal component analysis is a useful tool for detecting temperature regimes in Iran.A cluster analysis on principal component scores resulted to three main temperature regions in Iran. This includes mountainous region, lowland and foothill region and south region.