计算机工程与应用Issue(7)：209-214,6.DOI:10.3778/j.issn.1002-8331.1405-0421
小集合数条件下的数据同化策略研究
Data assimilation strategy research with small ensembles circum- stance
摘要
Abstract
In recent year, widely attentions have been paid to ensemble-based data assimilation methods and application researches have been carried out to test in the operational data assimilation systems in order to replace the variational data assimilation systems. Ensemble Kalman Filter(EnKF)methods depend highly on the sizes of the ensemble. If ensemble numbers are too small, they will bring the related issues such as under sampling, covariance underestimation, filter divergence and distanced spurious correlations. Local technology can effectively solve the related problems in the small ensembles circumstances. On the basis of the Lorenz-96 model, this paper studies the differences of data assimilation with or without localization and discusses the advantages of local analysis under the condition of small ensemble. It develops a method based on Power Spectral Density(PSD)to judge the effect of ensemble data assimilation. The results show that with a finite ensemble of numbers, Kalman gain values and PSD can be used to evaluate the assimilation effect combined with the local technology.关键词
数据同化/Lorenz-96模型/集合Kalman滤波/协方差局地化/局地化分析Key words
data assimilation/lorenz-96 model/Ensemble Kalman Filter(EnKF)/covariance localization/local analysis分类
信息技术与安全科学引用本文复制引用
黄智慧,摆玉龙,邵宇,徐宝兄..小集合数条件下的数据同化策略研究[J].计算机工程与应用,2015,(7):209-214,6.基金项目
国家自然科学基金（No.41461078，No.41061038）；甘肃省科技支撑计划（No.1204GKCA067）。 （）
