基于集合的增量变分资料同化的评估
Evaluation of an ensemble-based incremental variational data assimilation
摘要：
在这项工作中，我们的目标是研究集合的数据同化的最优控制策略。这种提法很好地结合了集合卡尔曼滤波和变分资料同化（4DVAR）的成分。与变分同化方案一样，它也是目标函数的最小化，但与集合滤波器类似，它在目标函数中引入了基于经验集合的背景误差协方差，并以离线平滑模式工作，而不是像顺序滤波器那样顺序工作。这些技术有很大的优势，以避免引入切线线性和伴随模型，这是必要的标准增量变分技术。它们还允许处理表示估计解和背景解之间的误差演变的时变背景协方差矩阵。由于这种背景误差协方差矩阵-在实践中减少秩-在变分过程中起着关键作用，我们的研究特别侧重于本地化技术的分析系综状态的生成。此外，为了澄清不同方法之间的差异，并突出不同方法的潜在陷阱和优势，我们提出了与其设置中涉及的不同选择相关的关键理论属性。我们比较实验的几个变化的合奏技术的利益与增量4DVAR方法的性能。的比较已导致浅水模型的基础上，并已进行了合成数据，并通过一个封闭的实验装置。特别讨论了系统各组成部分得到充分观察或仅得到部分观察的情况。
英文摘要：
In this work, we aim at studying ensemble based optimal control strategies for data assimilation. Such formulation nicely combines the ingredients of ensemble Kalman filters and variational data assimilation (4DVar). In the same way as variational assimilation schemes, it is formulated as the minimization of an objective function, but similarly to ensemble filter, it introduces in its objective function an empirical ensemble-based background-error covariance and works in an off-line smoothing mode rather than sequentially like sequential filters. These techniques have the great advantage to avoid the introduction of tangent linear and adjoint models, which are necessary for standard incremental variational techniques. They also allow handling a time varying background covariance matrix representing the error evolution between the estimated solution and a background solution. As this background error covariance matrix -- of reduced rank in practice -- plays a key role in the variational process, our study particularly focuses on the generation of the analysis ensemble state with localization techniques. Besides, to clarify well the differences between the different methods and to highlight the potential pitfall and advantages of the different methods, we present key theoretical properties associated to different choices involved in their setup. We compared experimentally the performances of several variations of an ensemble technique of interest with an incremental 4DVar method. The comparisons have been leaded on the basis of a Shallow Water model and have been carried out both with synthetic data and through a close experimental setup. The cases where the system's components are either fully observed or only partially have been in particular addressed.
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