Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic
Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic
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东北大西洋风场 (u, v) 分量的隐藏和观测状态切换自回归模型的比较
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发表时间：
2016
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通讯作者：
V. Monbet
中科院分区：
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作者：
J. Bessac;P. Ailliot;J. Cattiaux;V. Monbet
抽象的。本文提出了几种风的纬向和经向分量的多站点随机发电机。引入状态切换框架来解释由于不同天气类型的存在而在风况下观察到的强度和变化的交替。该建模将时间序列划分为由单个模型描述该序列的时期。状态切换由离散变量建模，该变量可以作为潜在（或隐藏）变量或观察变量引入。在后一种情况下，在拟合模型之前使用聚类算法来提取状态。根据状况，假设观测到的风况演变为线性高斯矢量自回归 (VAR) 模型。探讨了各种问题，例如在多站点环境中对气候进行建模，从额外变量或当地风数据中提取相关聚类，以及从风数据中提取的天气类型与从大气环流描述符中导出的大范围天气状况之间的联系。我们还讨论了隐藏和观察的政权转换模型的相对优势。对于风序列的人工随机生成，我们表明所提出的模型再现了风况的平均时空运动，并且我们强调了状态切换模型在再现风况的强度和变化性的交替方面的优势。
Abstract. Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. A regime-switching framework is introduced to account for the alternation of intensity and variability that is observed in wind conditions due to the existence of different weather types. This modeling blocks time series into periods in which the series is described by a single model. The regime-switching is modeled by a discrete variable that can be introduced as a latent (or hidden) variable or as an observed variable. In the latter case a clustering algorithm is used before fitting the model to extract the regime. Conditional on the regimes, the observed wind conditions are assumed to evolve as a linear Gaussian vector autoregressive (VAR) model. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes derived from a descriptor of the atmospheric circulation. We also discuss the relative advantages of hidden and observed regime-switching models. For artificial stochastic generation of wind sequences, we show that the proposed models reproduce the average space–time motions of wind conditions, and we highlight the advantage of regime-switching models in reproducing the alternation of intensity and variability in wind conditions.