气候变化领域集成服务门户(CCPortal): The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation

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DOI	10.1016/j.atmosres.2019.06.024
	The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation
	Gao S.; Min J.; Liu L.; Ren C.
发表日期	2019
ISSN	0169-8095
起始页码	208
结束页码	223
卷号	229
英文摘要	A coupled hybrid ensemble square root filter and three-dimensional ensemble-variational (EnSRF-En3DVar) radar data assimilation system, developed for the Weather Research and Forecasting model, was applied to a mesoscale convective system (MCS) that occurred over southeastern China on 5 June 2009. This hybrid system uses the extended control variable method to combine the static and ensemble flow-dependent forecast error covariance. The potential of the hybrid EnSRF-En3DVar system was first explored by comparing the derived results with those obtained using 3DVar and EnSRF approaches alone. Assimilation results showed the hybrid EnSRF-En3DVar system reduces the root mean square innovations for reflectivity and radial velocity, and better represents the pattern of the MCS than either 3DVar or EnSRF. This successful analysis improved quantitative reflectivity and precipitation forecasting skills, and helped forecast the MCS more realistically with regard to location, structure and intensity. Moreover, the root mean square error of the forecast wind, temperature and water vapor mixing ratio were found reduced by the hybrid EnSRF-En3DVar system when compared with the other methods. Diagnoses of the forecast fields showed the hybrid EnSRF-En3DVar system increases low-level cooling and mid-level warming within the convective area. Furthermore, sensitivity experiments testing the ensemble covariance weighting factor effect in the hybrid EnSRF-En3DVar method suggested that a stronger ensemble covariance weighting value led to improved precipitation forecast results. © 2019 Elsevier B.V.
英文关键词	Hybrid EnSRF-En3DVar system; Mesoscale convective system; Radar data assimilation
语种	英语
scopus关键词	Hybrid systems; Mean square error; Radar; Reflection; Storms; Ensemble square root filter; Hybrid EnSRF-En3DVar system; Mesoscale Convective System; Precipitation forecasting; Radar data assimilation; Root mean square errors; Water vapor mixing ratio; Weather research and forecasting models; Weather forecasting; convective system; data assimilation; ensemble forecasting; error analysis; mesoscale motion; precipitation (climatology); radar; reflectivity; China
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/162068
作者单位	Department of Atmospheric Sciences, Agronomy College, Shenyang Agricultural University, Shenyang, 110866, China; School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
推荐引用方式 GB/T 7714	Gao S.,Min J.,Liu L.,et al. The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation[J],2019,229.
APA	Gao S.,Min J.,Liu L.,&Ren C..(2019).The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation.Atmospheric Research,229.
MLA	Gao S.,et al."The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation".Atmospheric Research 229(2019).