An Index of Arizona Summer Rainfall Developed through Eigenvector Analysis

Daniel Morgan Johnson Portland State University, Portland, OR 97207

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Abstract

Summer rainfall in Arizona exhibits tremendous spatial variability due to topographic irregularities and the fact that convection is the dominant precipitation generating mechanism. As such it has proven difficult to document changes over time, changes in either total amounts or in spatial distribution. In this study, eigenvector analysis is utilized to generate an effective index of this climatic parameter. From an original data matrix consisting of summer rainfall totals at 101 climatological stations for 50 years, the first time-series eigenvector is extracted and accounts for 70% of the variance. Geographic mapping of the multipliers (coefficients) reveals that the first eigenvector does adhere to physical reality, reflecting the dominant pattern of rainfall over the state. Thus, its validity as an effective index is established.

Abstract

Summer rainfall in Arizona exhibits tremendous spatial variability due to topographic irregularities and the fact that convection is the dominant precipitation generating mechanism. As such it has proven difficult to document changes over time, changes in either total amounts or in spatial distribution. In this study, eigenvector analysis is utilized to generate an effective index of this climatic parameter. From an original data matrix consisting of summer rainfall totals at 101 climatological stations for 50 years, the first time-series eigenvector is extracted and accounts for 70% of the variance. Geographic mapping of the multipliers (coefficients) reveals that the first eigenvector does adhere to physical reality, reflecting the dominant pattern of rainfall over the state. Thus, its validity as an effective index is established.

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