Application and comparison of Kalman filters for coastal ocean problems: An experiment with FVCOM
Application and comparison of Kalman filters for coastal ocean problems: An experiment with FVCOM
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DOI：
10.1029/2007jc004548
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发表时间：
2009-05
影响因子：
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通讯作者：
Changsheng Chen;P. Malanotte‐Rizzoli;Jun Wei;R. Beardsley;Z. Lai;P. Xue;Siwei Lyu;Qichun Xu;J. Qi;G. Cowles
中科院分区：
文献类型：
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作者：
Changsheng Chen;P. Malanotte‐Rizzoli;Jun Wei;R. Beardsley;Z. Lai;P. Xue;Siwei Lyu;Qichun Xu;J. Qi;G. Cowles
[1] 进行了双胞胎实验，以比较针对三种理想化状态下的沿海海洋问题的降阶卡尔曼滤波器（RRKF）、集合卡尔曼滤波器（EnKF）和集合平方根卡尔曼滤波器（EnSKF）：开放边界处由潮汐力驱动的平底圆形陆架；有河流排放的线性斜坡大陆架；一个矩形河口，有潮汐冲刷潮间带和淡水排放。本研究中使用的流体动力学模型是非结构化网格有限体积沿海海洋模型（FVCOM）。比较结果表明，资料同化方法的成功取决于采样位置、同化方法（单变量或多变量协方差方法）以及动力系统的性质。一般来说，对于这些应用，EnKF 和 EnSKF 比 RRKF 效果更好，特别是对于具有大扰动的时间相关情况。在EnKF和EnSKF中，同化中应使用多元协方差方法，以避免出现不切实际的数值振荡。由于近岸海洋具有时间和空间多尺度动力学特征，应根据具体情况，针对不同的动力系统确定最有效、最可靠的资料同化方法。
[1] Twin experiments were made to compare the reduced rank Kalman filter (RRKF), ensemble Kalman filter (EnKF), and ensemble square-root Kalman filter (EnSKF) for coastal ocean problems in three idealized regimes: a flat bottom circular shelf driven by tidal forcing at the open boundary; an linear slope continental shelf with river discharge; and a rectangular estuary with tidal flushing intertidal zones and freshwater discharge. The hydrodynamics model used in this study is the unstructured grid Finite-Volume Coastal Ocean Model (FVCOM). Comparison results show that the success of the data assimilation method depends on sampling location, assimilation methods (univariate or multivariate covariance approaches), and the nature of the dynamical system. In general, for these applications, EnKF and EnSKF work better than RRKF, especially for time-dependent cases with large perturbations. In EnKF and EnSKF, multivariate covariance approaches should be used in assimilation to avoid the appearance of unrealistic numerical oscillations. Because the coastal ocean features multiscale dynamics in time and space, a case-by-case approach should be used to determine the most effective and most reliable data assimilation method for different dynamical systems.