标题： 一种4D-Var数据同化的降阶策略 显示英文标题

标题： A reduced-order strategy for 4D-Var data assimilation

摘要： 本文提出了一种用于四维变分数据同化的降阶方法，该方法基于对模型轨迹的先验EO F分析。 该方法具有两个主要优势：一种自然的基于模型的多变量背景误差协方差矩阵$\textbf{B}_r$的定义，以及由于控制空间维度的大幅减少而使方法的计算负担显著降低。 % 在赤道太平洋海洋模型的双实验学术框架中给出了该方法可行性和有效性的说明。 结果表明，$\textbf{B}_r$的多变量特性带来了额外的信息，显著提高了识别过程。 此外，与全空间4D-Var方法相比，计算成本可以降低一个数量级。 显示英文摘要

摘要： This paper presents a reduced-order approach for four-dimensional variational data assimilation, based on a prior EO F analysis of a model trajectory. This method implies two main advantages: a natural model-based definition of a mul tivariate background error covariance matrix $\textbf{B}_r$, and an important decrease of the computational burden o f the method, due to the drastic reduction of the dimension of the control space. % An illustration of the feasibility and the effectiveness of this method is given in the academic framework of twin experiments for a model of the equatorial Pacific ocean. It is shown that the multivariate aspect of $\textbf{B}_r$ brings additional information which substantially improves the identification procedure. Moreover the computational cost can be decreased by one order of magnitude with regard to the full-space 4D-Var method.