Non-stationary wave height climate modeling and simulation

Abstract

[1] The most popular methods of simulating time series for wave heights and other meteorological and oceanic variables are based on the use of autoregressive models and the transformation of variables to make them normal and stationary. Generally, when these models are used, attention is centered on their capacity to represent the autocorrelation of the series. In this article, a simulation model is proposed that is based on the following: (i) a non-stationary parametric mixture model for the marginal distribution of the variable, that combines a log-normal distribution for main-mass regime and generalized Pareto distributions for upper and lower tail regimes, and (ii) the use of copulas to model the time dependency of the variable. The model has been evaluated by comparing the original series and the simulated series in terms of the autocorrelation function, the mean, the annual maxima and peaks-over-threshold regimes, and the persistences regime. It has also been compared to an ARMA model and found to yield more satisfactory results.