Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry
Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry
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DOI：
10.5194/npg-13-177-2006
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发表时间：
2006-01-01
影响因子：
2.2
通讯作者：
Fernandes, M. J.
中科院分区：
文献类型：
--
作者：
Barbosa, S. M.;Silva, M. E.;Fernandes, M. J.
本文对TOPEX/Poseidon卫星测高使命的海平面时间序列进行了自回归建模。来自遥感应用的数据集通常非常大，在时间和空间上都是相互关联的。多变量分析方法是从如此大的时空数据集中总结和提取信息的有用工具。多变量自回归分析是主振荡模式（POP）分析的推广，广泛用于地球科学中，用于通过拟合到多变量观测数据集的一阶自回归模型的特征分解来提取动力学模式。POP方法的扩展，高阶自回归，虽然增加了估计的困难，允许一个更大的一类复杂的系统建模。在这里，海平面变化在北大西洋的三阶多变量自回归模型逐步最小二乘法估计。拟合模型的特征分解产生物理上可解释的季节模式。主导的自回归模式是一个年度振荡，并表现出非常均匀的空间结构的振幅反映了大规模的相干行为的年度模式在北方。相位结构反映了与信风制度相关的热带北大西洋西部和东部地区之间的跷跷板模式。第二种模式接近于半年振荡。多元自回归模型提供了一个有用的框架，描述随时间变化的领域，同时封闭的预测潜力。
This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a generalisation of Principal Oscillation Pattern (POP) analysis, widely used in the geosciences for the extraction of dynamical modes by eigen-decomposition of a first order autoregressive model fitted to the multivariate dataset of observations. The extension of the POP methodology to autoregressions of higher order, although increasing the difficulties in estimation, allows one to model a larger class of complex systems. Here, sea level variability in the North Atlantic is modelled by a third order multivariate autoreerressive model estimated by stepwise least squares. Eigen-decomposition of the fitted model yields physically-interpretable seasonal modes. The leading autoregressive mode is an annual oscillation and exhibits a very homogeneous spatial structure in terms of amplitude reflecting the large scale coherent behaviour of the annual pattern in the Northern hemisphere. The phase structure reflects the seesaw pattern between the western and eastern regions in the tropical North Atlantic associated with the trade winds regime. The second mode is close to a semi-annual oscillation. Multivariate autoregressive models provide a useful framework for the description of time-varying fields while enclosing a predictive potential.