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Data assimilation in a system with two scales-combining two initialization techniques

机译：具有两个尺度的系统中的数据同化-结合了两种初始化技术

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摘要

An ensemble Kalman filter (EnKF) is used to assimilate data onto a non-linear chaotic model, coupling two kinds of variables. The first kind of variables of the system is characterized as large amplitude, slow, large scale, distributed in eight equally spaced locations around a circle. The second kind of variables are small amplitude, fast, and short scale, distributed in 256 equally spaced locations. Synthetic observations are obtained from the model and the observational error is proportional to their respective amplitudes. The performance of the EnKF is affected by differences in the spatial correlation scales of the variables being assimilated. This method allows the simultaneous assimilation of all the variables. The ensemble filter also allows assimilating only the large-scale variables, letting the small-scale variables to freely evolve. Assimilation of the large-scale variables together with a few small-scale variables significantly degrades the filter. These results are explained by the spurious correlations that arise from the sampled ensemble covariances. An alternative approach is to combine two different initialization techniques for the slow and fast variables. Here, the fast variables are initialized by restraining the evolution of the ensemble members, using a Newtonian relaxation toward the observed fast variables. Then, the usual ensemble analysis is used to assimilate the large-scale observations.
机译：集成卡尔曼滤波器（EnKF）用于将数据同化到非线性混沌模型中，耦合两种变量。该系统的第一类变量的特点是振幅大，速度慢，规模大，分布在围绕圆的八个等间隔位置。第二类变量是小幅度，快速和小规模的变量，分布在256个等距位置。从模型获得综合观测值，观测误差与它们各自的振幅成正比。 EnKF的性能受同化变量的空间相关性比例差异的影响。这种方法可以同时吸收所有变量。集成滤波器还允许仅吸收大型变量，从而使小型变量可以自由演化。将大型变量与几个小型变量同化会大大降低滤波器的性能。这些结果由采样的整体协方差引起的虚假相关性解释。一种替代方法是将两种不同的初始化技术结合用于慢速变量和快速变量。在此，通过对观察到的快速变量使用牛顿松弛，通过约束集合成员的演化来初始化快速变量。然后，使用通常的集成分析来吸收大型观测值。

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  • 来源
    《Tellus》 |2009年第4期|539-549|共11页
  • 作者

    JOAQUIM BALLABRERA-POY; EUGENIA KALNAY; SHU-CHIH YANG;

  • 作者单位

    Institut de Ciencies del Mar, CSIC, Passeig Maritim de la Barceloneta, 37-49, 08003 Barcelona, Spain;

    AOSC, University of Maryland, College Park, MD, USA;

    AOSC, University of Maryland, College Park, MD, USA Global Modeling and Assimilation Office (GMAO), NASA/GSFC, Greenbelt, MD, USA;

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