Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter

doi:10.1007/s11430-015-5187-2

CORC > 自然资源部第一海洋研究所 > 自然资源部第一海洋研究所 > 业务部门 > 海洋环境与数值模拟研究室

	Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter
	Chen Hui 1,2; Yin XunQiang 1,2; Bao Ying 1,2; Qiao FangLi 1,2
刊名	SCIENCE CHINA-EARTH SCIENCES
	2016-03
卷号	59 期号:3 页码:484-494
关键词	Earth system model Ocean satellite data Ensemble adjustment Kalman filter Data assimilation
ISSN号	1674-7313
DOI	10.1007/s11430-015-5187-2
英文摘要	Using Ensemble Adjustment Kalman Filter (EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model (FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly (SLA); another, to assimilate sea surface temperature (SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.
电子版国际标准刊号	18691897
资助项目	Scientific Research Foundation of the First Institute of Oceanography, State Oceanic Administration[2012G24]
WOS研究方向	Geology
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000371416000006
内容类型	期刊论文
源URL	[http://ir.fio.com.cn/handle/2SI8HI0U/3483]
专题	业务部门_海洋环境与数值模拟研究室
作者单位	1.State Ocean Adm, Inst Oceanog 1, Qingdao 266061, Peoples R China; 2.State Ocean Adm, Key Lab Marine Sci & Numer Modeling, Qingdao 266061, Peoples R China
推荐引用方式 GB/T 7714	Chen Hui,Yin XunQiang,Bao Ying,et al. Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter[J]. SCIENCE CHINA-EARTH SCIENCES,2016,59(3):484-494.
APA	Chen Hui,Yin XunQiang,Bao Ying,&Qiao FangLi.(2016).Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter.SCIENCE CHINA-EARTH SCIENCES,59(3),484-494.
MLA	Chen Hui,et al."Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter".SCIENCE CHINA-EARTH SCIENCES 59.3(2016):484-494.