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Assimilating Altimetry Data into a HYCOM Model of the Pacific: Ensemble Optimal Interpolation versus Ensemble Kalman Filter

机译：将测高数据同化为HYCOM太平洋模型：集合最优插值与集合卡尔曼滤波器

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摘要

The ensemble Kalman filter (EnKF) has proven its efficiency in strongly nonlinear dynamical systems but is demanding in its computing power requirements, which are typically about the same as those of the four-dimensional variational data assimilation (4DVAR) systems presently used in several weather forecasting centers. A simplified version of EnKF, the so-called ensemble optimal interpolation (EnOI), requires only a small fraction of the computing cost of the EnKF, but makes the crude assumption of no dynamical evolution of the errors. How do both these two methods compare in realistic settings of a Pacific Ocean forecasting system where the computational cost is a primary concern? In this paper the two methods are used to assimilate real altimetry data via a Hybrid Coordinate Ocean Model of the Pacific. The results are validated against the independent Argo temperature and salinity profiles and show that the EnKF has the advantage in terms of both temperature and salinity and in all parts of the domain, although not with a very striking difference.
机译：集成卡尔曼滤波器（EnKF）已证明其在强非线性动力学系统中的效率，但对计算能力的要求却很高，通常与目前在几种天气中使用的四维变差数据同化（4DVAR）系统的计算能力要求大致相同预报中心。 EnKF的简化版本，即所谓的集成最优插值（EnOI），仅需要EnKF的计算成本的一小部分，但是粗略地假设误差不会动态变化。在以计算成本为首要考虑因素的太平洋预报系统的实际设置中，这两种方法如何进行比较？在本文中，这两种方法用于通过太平洋的混合坐标海洋模型来吸收真实的测高数据。针对独立的Argo温度和盐度曲线验证了结果，结果表明EnKF在温度和盐度方面以及整个域的各个方面均具有优势，尽管差异不大。

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    《Journal of atmospheric and oceanic technology》 |2010年第4期|p.753-765|共13页
  • 作者

    Liying Wan; Laurent Bertino; Jiang Zhu;

  • 作者单位

    National Marine Environmental Forecasting Center, Beijing, China;

    Mohn-Sverdrup Center, Nansen Environmental and Remote Sensing Center, Bergen, Norway;

    Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China;

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