本文主要目的是建立具有实际运行能力的集合卡尔曼滤波资料同化系统。
So the actual background error covariance is the key to success of data assimilation technique.
背景误差协方差是变分资料同化系统中的一个重要组成部分，能将观测信息从观测点传播到周围的模式格点和垂直层上。
Background error covariance is an important part of variational data assimilation system, which is used to spread the observation information to other grid points and vertical levels of the model.
方法是目前在强非线性系统中应用最广泛，效果最明显的集合资料同化方法。
The Ensemble Kalman Filter (EnKF) is a powerful data assimilation method and has proven its efficiency for strongly non-linear dynamical systems but is demanding in computing power.
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