Maximum likelihood estimation of Fourier coefficients to describe seasonal variations of parameters in stochastic daily precipitation models.

Abstract

Fourier series are convenient expressions for the seasonally fluctuating values of parameters in stochastic models of precipitation. Least-squares methods are often used to estimate the Fourier series coefficients, but this method has 2 important disadvantages. First the 'data' points are in fact estimates of parameters, and because of varying sample size, they may have unequal variances and should not be given equal weight. Second, there is no statistically sound procedure to test the significance of individual harmonics. In this paper, we investigate methods to obtain maximum likelihood estimates of the Fourier coefficients to describe the seasonal variability in the parameters for a stochastic rainfall model. Parameters are obtained from a 2-state Markov chain model for wet and dry occurrence, and from a mixed exponential model for distribution of depth on wet days. The procedure is demonstrated on 4 sample stations scattered across the continental United States. -from Authors