Technical Note: Sequential ensemble data assimilation in convergent and divergent systems
Technical Note: Sequential ensemble data assimilation in convergent and divergent systems
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DOI：
10.5194/hess-25-3319-2021
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发表时间：
2021-06
影响因子：
6.3
通讯作者：
H. H. Bauser-H.;Daniel Berg;K. Roth
中科院分区：
文献类型：
--
作者：
H. H. Bauser-H.;Daniel Berg;K. Roth
抽象。数据同化方法在整个地球科学中用于将来自不确定模型和不确定测量数据的联合收割机信息结合起来。然而，地球物理系统的特征是不同的，可以区分为发散系统和收敛系统。在发散系统中，最初附近的状态会漂移，而在收敛系统中，它们会合并。这种差异对序列集合数据同化方法的应用有影响。本研究探讨了两个典型系统的这些影响，即，发散的Lorenz 96模型和收敛的理查兹方程描述土壤水分运动。结果表明，序列集合资料同化方法需要足够的发散分量。这使得从发散系统到收敛系统的方法的转移具有挑战性。我们证明，通过一组案例研究，这是必要的，以充分代表模式误差，并将参数的不确定性集合数据同化收敛系统。
Abstract. Data assimilation methods are used throughout the geosciences to combine information from uncertain models and uncertain measurement data. However, the characteristics of geophysical systems differ and may be distinguished between divergent and convergent systems. In divergent systems initially nearby states will drift apart, while they will coalesce in convergent systems. This difference has implications for the application of sequential ensemble data assimilation methods. This study explores these implications on two exemplary systems, i.e., the divergent Lorenz 96 model and the convergent description of soil water movement by the Richards equation. The results show that sequential ensemble data assimilation methods require a sufficient divergent component. This makes the transfer of the methods from divergent to convergent systems challenging. We demonstrate, through a set of case studies, that it is imperative to represent model errors adequately and incorporate parameter uncertainties in ensemble data assimilation in convergent systems.