Time series of 16, 20 or 24 years of temperature data from 18 Italian stations were analyzed on a statistical base. The results of classical statistical analysis of climate data were compared with corresponding results of Fourier analysis. It was found that time series of temperature can be described by two components: a mean represented by a maximum of 19 Fourier complex coefficients, the stochastic component. A method is described to obtain a typical annual type function, a monthly mean day and other typical time functions, by means of coefficients of the Fourier Transforms.

Analysis of Temperature Time Series by Fourier Transforms and Markov Methods - 1. Deterministic Component.

BARBERI, Riccardo Cristoforo;
1987-01-01

Abstract

Time series of 16, 20 or 24 years of temperature data from 18 Italian stations were analyzed on a statistical base. The results of classical statistical analysis of climate data were compared with corresponding results of Fourier analysis. It was found that time series of temperature can be described by two components: a mean represented by a maximum of 19 Fourier complex coefficients, the stochastic component. A method is described to obtain a typical annual type function, a monthly mean day and other typical time functions, by means of coefficients of the Fourier Transforms.
Time Series; Fourier Transform; Meteorology
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/148287
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