气候变化领域集成服务门户(CCPortal): Impact of the Hierarchical Ensemble Filter Covariance Localization Method on EnKF Radar Data Assimilation: Observing system simulation experiments

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DOI	10.1016/j.atmosres.2020.105070
	Impact of the Hierarchical Ensemble Filter Covariance Localization Method on EnKF Radar Data Assimilation: Observing system simulation experiments
	Gao S.; Yu H.; Min J.; Liu L.; Ren C.
发表日期	2020
ISSN	0169-8095
卷号	245
英文摘要	An adaptive localization method using the hierarchical ensemble filter (HEF) technique was first applied to the ensemble Kalman filter (EnKF) radar data assimilation (DA) though observing system simulation experiments (OSSEs) for an idealized supercell storm. The HEF method calculates localization by minimizing the sampling error without defining the physical distance between observation and state variables. Four experiments using regular Gaspari and Cohn (GC) and HEF localization methods with different group sizes (group numbers of ensembles) were performed. The HEF localization experiments perform better than GC experiment, producing smaller analysis errors and larger ensemble spreads for most model state variables, and stronger wind, vertical vorticity, and cold pools. The best results are obtained when the largest group size is used in the HEF experiment. With this improved analysis, the forecast error is reduced for most variables and levels. The dynamical and thermal distributions of HEF experiments are found to better promote the development of the supercell storm which helps to improve the forecast reflectivity in terms of location, intensity, and area coverage. This suggests the potential of HEF localization for an EnKF radar DA. A larger group size for this method is expected to lead to improved analysis and forecast results compared with the regular GC localization method. © 2020 Elsevier B.V.
关键词	Errors Forecasting Radar Storms Adaptive localizations Ensemble Kalman Filter Hierarchical ensemble Localization method Observing system simulation experiments Radar data assimilation Thermal distributions Vertical vorticity Kalman filters covariance analysis data assimilation ensemble forecasting error analysis experiment Kalman filter radar thunderstorm
语种	英语
来源机构	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/132386
推荐引用方式 GB/T 7714	Gao S.,Yu H.,Min J.,et al. Impact of the Hierarchical Ensemble Filter Covariance Localization Method on EnKF Radar Data Assimilation: Observing system simulation experiments[J]. Atmospheric Research,2020,245.
APA	Gao S.,Yu H.,Min J.,Liu L.,&Ren C..(2020).Impact of the Hierarchical Ensemble Filter Covariance Localization Method on EnKF Radar Data Assimilation: Observing system simulation experiments.,245.
MLA	Gao S.,et al."Impact of the Hierarchical Ensemble Filter Covariance Localization Method on EnKF Radar Data Assimilation: Observing system simulation experiments".245(2020).