A Bayesian approach to Lagrangian data assimilation
A Bayesian approach to Lagrangian data assimilation
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DOI：
10.1111/j.1600-0870.2007.00295.x
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发表时间：
2008-03-01
影响因子：
2
通讯作者：
Stuart, A. M.
中科院分区：
文献类型：
--
作者：
Apte, A.;Jones, C. K. R. T.;Stuart, A. M.
拉格朗日数据来自于流体场中由流体携带的仪器。由于在相对简单的流场中拉格朗日轨迹的潜在复杂性，将这些数据同化到海洋模型中提出了一个挑战。我们在这个问题上采用贝叶斯的观点，从而考虑到底层模型的完全非线性特征。在完美的模型场景中，系统初始状态的后验分布包含了可以从给定的观测实现和模型动力学中提取的所有信息。我们在平滑的背景下工作，其中初始条件的后验是由未来的观察决定的。这种后验分布给出了用于数据同化的最优集合。接下来的问题是对这个分布进行抽样。我们开发，实施和测试抽样方法，基于马尔可夫链蒙特卡罗（MCMC），这是特别适合于低维，但高度非线性的拉格朗日数据的性质。我们将这些方法与成熟的集合卡尔曼滤波（EnKF）方法进行了比较。可以看出，基于MCMC的方法正确地采样了期望的后验分布，而EnKF可能由于不频繁的观测或潜在流中的非线性结构而失败。
Lagrangian data arise from instruments that are carried by the flow in a fluid field. Assimilation of such data into ocean models presents a challenge due to the potential complexity of Lagrangian trajectories in relatively simple flow fields. We adopt a Bayesian perspective on this problem and thereby take account of the fully non-linear features of the underlying model.In the perfect model scenario, the posterior distribution for the initial state of the system contains all the information that can be extracted from a given realization of observations and the model dynamics. We work in the smoothing context in which the posterior on the initial conditions is determined by future observations. This posterior distribution gives the optimal ensemble to be used in data assimilation. The issue then is sampling this distribution. We develop, implement, and test sampling methods, based on Markov-chain Monte Carlo (MCMC), which are particularly well suited to the low-dimensional, but highly non-linear, nature of Lagrangian data. We compare these methods to the well-established ensemble Kalman filter (EnKF) approach. It is seen that the MCMC based methods correctly sample the desired posterior distribution whereas the EnKF may fail due to infrequent observations or non-linear structures in the underlying flow.