Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO 2 data assimilation
Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO 2 data assimilation
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技术说明：CO 2 数据同化的集合卡尔曼滤波器和变分方法的比较
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
A. Michalak
中科院分区：
文献类型：
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作者：
A. Chatterjee;A. Michalak
抽象。数据同化（DA）方法，包括变分和集合卡尔曼滤波方法，提供了一个计算有效的框架，解决CO2源汇估计问题。然而，与天气预报和成分同化的DA应用程序不同，DA方法用于CO2通量估计的优点和缺点尚未得到广泛探讨。在这项研究中，我们比较和评估估计从两个先进的DA方法（合奏平方根滤波器和变分技术）使用一批逆建模设置为基准，在一个简单的一维对流扩散原型反问题的背景下，已被设计为捕捉的细微差别的真实的CO2通量估计问题。实验旨在确定观测密度、异质性和不确定性以及操作约束（即，总体大小、下降迭代次数）对DA估计的影响。没有明确指定的DA方法，以保持类似于一个典型的真实的CO2通量估计问题的问题设置的动态模型。结果表明，DA方法的性能取决于测量网络和操作约束之间复杂的相互作用。总的来说，变分的方法（视情况而定的一个伴随的运输模式的可用性）更可靠地捕捉到大规模的源-汇模式。相反，集合平方根滤波器提供更现实的不确定性估计。因此，在选择一种方法而不是另一种方法时，必须以所提出的碳科学问题和应用这些方法时所面临的操作限制为指导。
Abstract. Data assimilation (DA) approaches, including variational and the ensemble Kalman filter methods, provide a computationally efficient framework for solving the CO2 source–sink estimation problem. Unlike DA applications for weather prediction and constituent assimilation, however, the advantages and disadvantages of DA approaches for CO2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA approaches (an ensemble square root filter and a variational technique) using a batch inverse modeling setup as a benchmark, within the context of a simple one-dimensional advection–diffusion prototypical inverse problem that has been designed to capture the nuances of a real CO2 flux estimation problem. Experiments are designed to identify the impact of the observational density, heterogeneity, and uncertainty, as well as operational constraints (i.e., ensemble size, number of descent iterations) on the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA approaches to keep the problem setup analogous to a typical real CO2 flux estimation problem. Results demonstrate that the performance of the DA approaches depends on a complex interplay between the measurement network and the operational constraints. Overall, the variational approach (contingent on the availability of an adjoint transport model) more reliably captures the large-scale source–sink patterns. Conversely, the ensemble square root filter provides more realistic uncertainty estimates. Selection of one approach over the other must therefore be guided by the carbon science questions being asked and the operational constraints under which the approaches are being applied.