The purpose of this study is to describe an economical approach to an existing adaptive localization technique and its implementation in the proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method (PODEn4DVar). Owing to the applications of the sparse processing and EOF decomposition techniques, the computational costs of this proposed sparse flow-adaptive moderation (SFAM) localization scheme are significantly reduced. The effectiveness of PODEn4DVar with SFAM localization is demonstrated by using the Lorenz-96 model in comparison with the Smoothed ENsemble Correlations Raised to a Power (SENCORP) and static localization schemes, separately. The performance of PODEn4DVar with SFAM localization shows a moderate improvement over the schemes with SENCORP and static localization, with low computational costs under the imperfect model.
本文通过将稀疏化以及特征值分解技术引入到一种国际上最新提出自适应局地化方法（SENCORP），从而发展了一种计算高效且流依赖的自适应局地化方案(SFAM)，进而将其应用一种先进的集合四维变分同化方法PODEn4DVar之中。稀疏化与特征值分解技术的引入使得SFAM的计算代价与内存需要得以显著降低。基于Lorenz-96模式，将文中新发展的SFAM、已有的自适应的技术SENCORP以及静态局地化技术分别应用到PODEn4DVar方法中，比较新技术SFAM的有效性。结果表明：在Lorenz-96模式有误差的条件下，SFAM的同化效果较其他两种有适当的改善，且计算存储代价小。