An effective method based on dynamic sampling for data assimilation in a global wave model

doi:10.1007/s10236-017-1030-y

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

	An effective method based on dynamic sampling for data assimilation in a global wave model
	Sun, Meng 1,2; Yin, Xunqiang 2,3; Yang, Yongzeng 2,3; Wu, Kejian 1
刊名	OCEAN DYNAMICS
	2017-04
卷号	67 期号:3-4 页码:433-449
关键词	Dynamic/static ensemble Sampling method EAKF Computational cost
ISSN号	1616-7341
DOI	10.1007/s10236-017-1030-y
英文摘要	The ensemble Kalman filter (EnKF) performs well because that the covariance of background error is varying along time. It provides a dynamic estimate of background error and represents the reasonable statistic characters of background error. However, high computational cost due to model ensemble in EnKF is employed. In this study, two methods referred as static and dynamic sampling methods are proposed to obtain a good performance and reduce the computation cost. Ensemble adjustment Kalman filter (EAKF) method is used in a global surface wave model to examine the performance of EnKF. The 24-h interval difference of simulated significant wave height (SWH) within 1 year is used to compose the static samples for ensemble errors, and these errors are used to construct the ensemble states at each time the observations are available. And then, the same method of updating the model states in the EAKF is applied for the ensemble states constructed by a static sampling method. The dynamic sampling method employs a similar method to construct the ensemble states, but the period of the simulated SWH is changing with time. Here, 7 days before and after the observation time is used as this period. To examine the performance of three schemes, EAKF, static, or dynamic sampling method, observations from satellite Jason-2 in 2014 are assimilated into a global wave model, and observations from satellite Saral are used for validation. The results indicate that the EAKF performs best, while the static sampling method is relatively worse. The dynamic sampling method improves an assimilation effect dramatically compared to the static sampling method, and its overall performance is closed to the EAKF. In low latitudes, the dynamic sampling method has a slight advantage over the EAKF. In the dynamic or static sampling methods, only one wave model is required to run and their computational cost is reduced sharply. According to the performance of these three methods, the dynamic sampling method can treated as an effective alternative of EnKF, which could reduce the computational cost and provide a good performance of data assimilation.
电子版国际标准刊号	16167228
资助项目	National Key Research and Development Program of China[2016YFC1402000]
WOS研究方向	Oceanography
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000398103800007
内容类型	期刊论文
源URL	[http://ir.fio.com.cn/handle/2SI8HI0U/3191]
专题	业务部门_海洋环境与数值模拟研究室
作者单位	1.Ocean Univ China, Coll Ocean & Atmospher Sci, Qingdao 266100, Peoples R China; 2.SOA, Inst Oceanog 1, Qingdao 266061, Peoples R China; 3.Natl Lab Marine Sci & Technol, Lab Reg Oceanog & Numer Modeling, Qingdao 266071, Peoples R China
推荐引用方式 GB/T 7714	Sun, Meng,Yin, Xunqiang,Yang, Yongzeng,et al. An effective method based on dynamic sampling for data assimilation in a global wave model[J]. OCEAN DYNAMICS,2017,67(3-4):433-449.
APA	Sun, Meng,Yin, Xunqiang,Yang, Yongzeng,&Wu, Kejian.(2017).An effective method based on dynamic sampling for data assimilation in a global wave model.OCEAN DYNAMICS,67(3-4),433-449.
MLA	Sun, Meng,et al."An effective method based on dynamic sampling for data assimilation in a global wave model".OCEAN DYNAMICS 67.3-4(2017):433-449.