Abstract
The study of a time series is a standard exercise in statistical analysis. A time series, which is an ordered set of random variables, and its associated probability distribution are called a stochastic process. This mathematical construct can be applied to time series of climate variables. Strictly speaking, a climate variable is generated by deterministic processes. However since a myriad of processes contribute to the behavior of a climate variable, a climate time series behaves like one generated by a stochastic process. More detailed discussion of this problem is given by H. von Storch and Zwiers (1999).
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© 1999 Springer-Verlag Berlin Heidelberg
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von Storch, JS. (1999). Multivariate Statistical Modeling: POP-Model as a First Order Approximation. In: von Storch, H., Navarra, A. (eds) Analysis of Climate Variability. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03744-7_15
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DOI: https://doi.org/10.1007/978-3-662-03744-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-08560-4
Online ISBN: 978-3-662-03744-7
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