首页 | 期刊简介 | 编委会 | 投稿指南 | 常用下载 | 联系我们 | 期刊订阅 | In English
引用本文:孙盟,尹训强,杨永增,吴克俭,孙宝楠.MASNUM-WAM海浪模式集合Kalman滤波同化研究——II.集合样本对同化效果的影响.海洋与湖沼,2017,48(2):210-220.
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  下载PDF阅读器  关闭
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 1846次   下载 1408 本文二维码信息
码上扫一扫!
分享到: 微信 更多
MASNUM-WAM海浪模式集合Kalman滤波同化研究——II.集合样本对同化效果的影响
孙盟1,2, 尹训强2,3, 杨永增2,3, 吴克俭1, 孙宝楠1,2
1.中国海洋大学海洋与大气学院 青岛 266100;2.国家海洋局第一海洋研究所海洋环境与数值模拟研究室 青岛 266061;3.海洋国家实验室区域海洋动力学与数值模拟功能实验室 青岛 266071
摘要:
背景误差相关结构的确定是影响海浪同化效果的关键因素之一。集合Kalman滤波是一种较为成熟的同化方法,其可以对背景误差进行实时更新和动态估计,现已广泛应用于海洋和大气领域的研究。本文基于MASNUM-WAM海浪模式,分别采用静态样本集合Kalman滤波和EAKF方法,针对2014年全球海域开展海浪数据同化实验,同化资料为Jason-2卫星高度计数据,利用Saral卫星高度计资料对同化实验结果进行检验。结果表明,两组同化方案均有效提高了海浪模式的模拟水平,EAKF方案在风场变化较大的西风带区域表现显著优于静态样本集合Kalman滤波方案,但总体上两者相差不大。综合考虑计算成本和同化效果,静态样本集合Kalman滤波方案更适用于海浪业务化预报。
关键词:  EAKF  海浪数据同化  静态样本集合
DOI:10.11693/hyhz20160900193
分类号:
基金项目:国家高技术研究发展计划-南海及周边海域风浪流耦合同化精细化数值预报与信息服务系统项目,2013AA09A506号;国家重点研发计划项目,2016YFC1402001号,2016YFC1402004号。
附件
ON EAKF DATA ASSIMILATION BASED ON MASNUM-WAM——Ⅱ.ASSIMILATION EXPERIMENT AND RESULT
SUN Meng1,2, YIN Xun-Qiang2,3, YANG Yong-Zeng2,3, WU Ke-Jian1, SUN Bao-Nan1,2
1.College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China;2.Key Lab of Marine Science and Numerical Modeling, First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;3.Laboratory for Regional Oceanography and Numerical Modeling, National Laboratory for Marine Science and Technology, Qingdao 266071, China
Abstract:
We applied two wave data assimilation schemes:static sample ensemble Kalman filter and EAKF (Ensemble Adjustment Kalman Filter), to assimilate the altimeter data of the Jason-2 into a global wave model MASNUM-WAM (marine science and numerical modeling-wave modelling part) over the period of 2014. A practical assimilation design was proposed for numerical implement. Results were validated against the altimeter data of the Saral satellite. In the first scheme, static sample ensemble that consists of the difference in 24h-interval SWH from long-term history model results are superposed to SWH field at time window for assimilation to construct a model state variable ensemble that will be updated by two-part filter method. In the second scheme, wave model ensemble is driven by wind field with random field perturbation. The results show that these two assimilation schemes could improve the ability of numerical simulation significantly compared with the control run without assimilation. In addition, EnKF (Ensemble Kalman Filter) scheme has a remarkable advantage over static sample ensemble Kalman filter scheme in mid-high latitudes where wind field varies rapidly. Overall, the results of two assimilation schemes are similar. However, the static sample ensemble Kalman filter could be applied to operational wave forecast at a lower computation cost.
Key words:  EAKF(ensemble adjustment Kalman filter)  wave data assimilation  static sample ensemble
版权所有 海洋与湖沼 Oceanologia et Limnlolgia Sinica Copyright©2008 All Rights Reserved
主管单位:中国科协技术协会 主办单位:中国海洋湖沼学会
地址:青岛市福山路32号  邮编:266071  电话:0532-82898753  E-mail:ols@qdio.ac.cn
技术支持:北京勤云科技发展有限公司