气候变化领域集成服务门户(CCPortal): Model-Space Localization in Serial Ensemble Filters

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DOI	10.1029/2018MS001514
	Model-Space Localization in Serial Ensemble Filters
	Shlyaeva A.; Whitaker J.S.; Snyder C.
发表日期	2019
ISSN	19422466
起始页码	1627
结束页码	1636
卷号	11 期号:6
英文摘要	Ensemble-based data assimilation systems typically use covariance localization to dampen spurious correlations associated with sampling error while increasing the rank of the covariance estimate. Variational methods use model-space localization, in which localization is applied to ensemble estimates of covariances between model variables and is based on distances between those variables, while ensemble filters apply observation-space localization to estimates of model-observation covariances, based on distances between model variables and observations. It has been shown that for nonlocal observations, such as satellite radiances, model-space localization can be superior. This paper demonstrates a new method for performing model-space localization in serial ensemble filters using the linearized observation operators (or Jacobians). Results of radiance-only assimilation in a global forecast system show the benefit of using model-space localization relative to observation-space localization. The serial ensemble square root filter with vertical model-space localization gives results similar to those of the Ensemble Variational system (without outer loops or extra balance constraints) while increasing the runtime compared to the filter with observation-space localization by a factor between 2 and 8, depending on how sparse the Jacobian matrices are. The results are also similar to another approach to model-space localization in ensemble filters: ensemble Kalman filter with modulated ensembles. ©2019. The Authors.
英文关键词	background error covariances; EnKF; ensemble data assimilation; localization
语种	英语
scopus关键词	Jacobian matrices; Background-error covariances; EnKF; Ensemble based data assimilation; Ensemble data assimilation; Ensemble Kalman Filter; Ensemble square root filter; Global forecast systems; localization; Kalman filters; climate modeling; covariance analysis; data assimilation; ensemble forecasting; error analysis; Kalman filter; sampling
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156895
作者单位	Physical Sciences Division, Cooperative Institute for Research in Environmental Sciences at the NOAA Earth System Research Laboratory, University of Colorado Boulder, Boulder, CO, United States; Physical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO, United States; National Center for Atmospheric Research, Boulder, CO, United States
推荐引用方式 GB/T 7714	Shlyaeva A.,Whitaker J.S.,Snyder C.. Model-Space Localization in Serial Ensemble Filters[J],2019,11(6).
APA	Shlyaeva A.,Whitaker J.S.,&Snyder C..(2019).Model-Space Localization in Serial Ensemble Filters.Journal of Advances in Modeling Earth Systems,11(6).
MLA	Shlyaeva A.,et al."Model-Space Localization in Serial Ensemble Filters".Journal of Advances in Modeling Earth Systems 11.6(2019).