The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part III - Observation impact and observation sensitivity in the California Current System
The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part III - Observation impact and observation sensitivity in the California Current System
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DOI：
10.1016/j.pocean.2011.05.005
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发表时间：
2011-10
影响因子：
4.1
通讯作者：
Andrew M. Moore;H. Arango;G. Broquet;Chris Edwards;M. Veneziani;B. Powell;Dave Foley;James D. Doyle;Dan Costa;P. Robinson
中科院分区：
文献类型：
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作者：
Andrew M. Moore;H. Arango;G. Broquet;Chris Edwards;M. Veneziani;B. Powell;Dave Foley;James D. Doyle;Dan Costa;P. Robinson
区域海洋模式系统(ROMS)是少数几个具有四维变分资料同化(4D-VAR)能力的社区海洋环流模式之一。ROMS 4D-Var能力是独特的，因为支持4D-Var的三种变体：增量强约束4D-Var的原始公式(I4D-Var)、基于物理空间统计分析系统的双重公式(4D-PSA)、以及4D-Var的基于双重公式表示的变体(R4D-Var)。在每种情况下，ROMS都与现有的观测结合使用，以确定基于一组关于4D-PSA和R4D-Var情况下的初始条件、边界条件、表面强迫和模型中的误差的先验假设的海洋环流的最佳估计。在I4D-Var的原始公式中，在模型控制向量的全空间中搜索最佳环流估计，而对于4D-PSA和R4D-Var的对偶公式，仅搜索由观测值跨越的模型状态向量的线性函数子空间(即对偶空间)。在海洋应用中，观测的数量通常比模型控制向量的维度少得多，因此将搜索限制在观测所覆盖的空间具有明显的优势。在4D-PSA和R4D-Var的情况下，可以放松强约束假设(即模型是无误差的)，从而产生所谓的弱约束公式。本文描述了在ROMS中实现的上述三种4D-Var变体。每种方法共有的关键组件是共轭梯度下降、预条件和误差协方差模型，也对其进行了描述。最后讨论了几种强大的四维Var诊断工具，即后验误差的计算、后验误差协方差的特征向量分析、观测影响和观测灵敏度。
The Regional Ocean Modeling System (ROMS) is one of the few community ocean general circulation models for which a 4-dimensional variational data assimilation (4D-Var) capability has been developed. The ROMS 4D-Var capability is unique in that three variants of 4D-Var are supported: a primal formulation of incremental strong constraint 4D-Var (I4D-Var), a dual formulation based on a physical-space statistical analysis system (4D-PSAS), and a dual formulation representer-based variant of 4D-Var (R4D-Var). In each case, ROMS is used in conjunction with available observations to identify a best estimate of the ocean circulation based on a set of a priori hypotheses about errors in the initial conditions, boundary conditions, surface forcing, and errors in the model in the case of 4D-PSAS and R4D-Var. In the primal formulation of I4D-Var the search for the best circulation estimate is performed in the full space of the model control vector, while for the dual formulations of 4D-PSAS and R4D-Var only the sub-space of linear functions of the model state vector spanned by the observations (i.e. the dual space) is searched. In oceanographic applications, the number of observations is typically much less than the dimension of the model control vector, so there are clear advantages to limiting the search to the space spanned by the observations. In the case of 4D-PSAS and R4D-Var, the strong constraint assumption (i.e. that the model is error free) can be relaxed leading to the so-called weak constraint formulation. This paper describes the three aforementioned variants of 4D-Var as they are implemented in ROMS. Critical components that are common to each approach are conjugate gradient descent, preconditioning, and error covariance models, which are also described. Finally, several powerful 4D-Var diagnostic tools are discussed, namely computation of posterior errors, eigenvector analysis of the posterior error covariance, observation impact, and observation sensitivity.