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首页> 外文期刊>Journal of Geophysical Research, C. Oceans: JGR >Non‐stationary wave height climate modeling and simulation
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Non‐stationary wave height climate modeling and simulation

机译：非平稳波高气候模拟与模拟

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摘要

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.
机译：模拟波高以及其他气象和海洋变量的时间序列的最流行方法是基于自回归模型的使用以及对变量进行转换以使其正常和平稳的基础。通常，当使用这些模型时，注意力集中在它们表示序列自相关的能力上。在本文中，提出了一个基于以下条件的仿真模型：（i）用于变量边际分布的非平稳参数混合模型，该模型结合了主要质量状态的对数正态分布和广义帕累托分布（ii）使用copulas为变量的时间依赖性建模。通过比较原始序列和模拟序列的自相关函数，均值，年度最大值和峰值阈值制度以及持久性制度，对模型进行了评估。它也已与ARMA模型进行了比较，发现产生了更令人满意的结果。

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