Spectral Analysis and Linear Prediction of Meteorological Time Series

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  • 1 Department of Statistics, The Johns Hopkins University
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Abstract

A method of estimating the spectral density of the nondeterministic component of a meteorological time series which is uncontaminated by the periodic mean variations is presented. This method does not require knowledge of the mean variations. The estimated spectrum is used to calculate the Wiener-Kolmogoroff prediction constants and to estimate the linear predictability of the series. Examples are given using meteorological and artificially generated time series.

Abstract

A method of estimating the spectral density of the nondeterministic component of a meteorological time series which is uncontaminated by the periodic mean variations is presented. This method does not require knowledge of the mean variations. The estimated spectrum is used to calculate the Wiener-Kolmogoroff prediction constants and to estimate the linear predictability of the series. Examples are given using meteorological and artificially generated time series.

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