Aplicación de análisis multivariado al campo de anomalías de precipitación en Centroamérica
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Date
1999
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Tópicos Meteorológicos y Oceanográficos
Abstract
Because climate signals have components in different time and space scales it is difficult to identify the causes of climate variability when only one or a few stations are analyzed. Multivariate analysis of one or several variables of stations with an adequate geographical distribution helps to identify the possible causes of variability, to separate in certain cases different time scales, and in quantifying the influence of those causes at different geographical points. Multivariate methods, such as principal components analysis, singular value decomposition and canonical correlation analysis were first applied to meteorological fields in the 1950’s and 1960’s. In the last ten years they have proved to be a valuable tool in the study of climate variability and their use has increased considerably. Vector Auto Regressive-Moving Average models are more recent and besides identifying the causes of variability they are also useful in forecasting. In this paper, we present the principles on which these methods are based and they are applied to the precipitation anomalies’ field in Central America. The results are interpreted within a discussion of the advantages and limitations of the different methods.
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Citation
Soley, F. y Alfaro, E. (1999). Aplicación de análisis multivariado al campo de anomalías de precipitación en Centroamérica. Tópicos Meteorológicos y Oceanográficos, 6(2), 66-88.