南海北部集合卡曼滤波同化SST试验

doi:10.11978/j.issn.1009-5470.2010.05.010

热带海洋学报 ›› 2010, Vol. 29 ›› Issue (5): 10-16.doi: 10.11978/j.issn.1009-5470.2010.05.010cstr: 32234.14.j.issn.1009-5470.2010.05.010

南海北部集合卡曼滤波同化SST试验

舒业强1, 隋丹丹1, 王伟文1, 肖贤俊2

1. 中国科学院南海海洋研究所热带海洋环境动力学重点实验室, 广东广州 510301; 2. 国家气候中心, 北京 100081

收稿日期:2009-09-28 修回日期:2009-12-01 出版日期:2010-10-30 发布日期:2011-10-10
作者简介:舒业强(1978—), 男, 湖南省临澧县人, 博士, 主要从事海洋资料同化研究。E-mail: Shuyeq@scsio.ac.cn
基金资助:
财政部行业专项(GYHY200706005); 中国科学院南海海洋研究所所青年人才领域前沿项目(SQ200814); 中国气象局风云气象
卫星遥感开发与应用项目(FiDAF-2-05)

SST assimilation experiment in the northern South China Sea using ensemble Kalman filter

SHU Ye-qiang1, SUI Dan-dan1, WANG Wei-wen1, XIAO Xian-jun2

1. Key Laboratory of Tropical Environmental Dynamics, South China Sea Institute of Oceanology, CAS, Guangzhou 510301, China; 2. National Climate Center, Beijing 100081, China

Received:2009-09-28 Revised:2009-12-01 Online:2010-10-30 Published:2011-10-10
About author:舒业强(1978—), 男, 湖南省临澧县人, 博士, 主要从事海洋资料同化研究。E-mail: Shuyeq@scsio.ac.cn
Supported by:
财政部行业专项(GYHY200706005); 中国科学院南海海洋研究所所青年人才领域前沿项目(SQ200814); 中国气象局风云气象
卫星遥感开发与应用项目(FiDAF-2-05)

摘要/Abstract

摘要：

基于POM (Princeton Ocean Model)建立一个南海北部集合卡曼滤波的同化模式, 主要用于卫星海表面温度的同化。模式的平均水平分辨率为5km, 垂向分层为20层; 侧边界条件嵌套到一个大范围的南海海洋模式, 在同化方案上采用一个均方根集合卡曼滤波算法, 避免观测的扰动; 适当引入局地化算子, 消除样本在空间上的虚假相关, 同时增加集合样本的自由度。该同化试验同化了2008年夏季6月到7月的GHRSST (Global High-Resolution Sea Surface Temperature), 然后采用2008年夏季 SCOPE (Northern South China Sea Coastal Oceanographic Process Ex-periment)航次的温、盐数据对同化结果进行评估。结果表明, 相对于未同化模式模拟结果, 同化模式温度的改善比较明显, 表现在加强了南海北部的上升流, 校正了海表温度的偏差, 改善了温度的垂向分布。由于集合卡曼滤波是一种多变量调整的同化方法, 同化SST不仅能改善表层与次表层的温度分布, 而且对流场和盐度的调整也比较明显。

关键词: 集合卡曼滤波, 同化, 南海北部, SST

Abstract:

An ensemble Kalman filter (EnKF) scheme is applied to assimilate sea surface temperature (SST) in the northern South China Sea (SCS) using the Princeton Ocean Model (POM). The assimilation model has a horizontal resolution of 5km and a vertical resolution of 20 layers. Lateral boundary conditions are provided by a larger domain SCS model. A square root filter is applied to avoid perturbations induced by observations. Localization is used in the assimilation system to remove pseudo correlations and to add rank of ensemble. The Global High-Resolution Sea Surface Temperature (GHRSST) in June and July 2008 is assimilated in this study. To validate the assimilation results, hydrographic data from the Northern South China Sea Coastal Oceanographic Process Experiment (SCOPE) cruises are used. The results show that the assimilated SST can effectively improve the temperature distribution not only at surface but also in the subsurface. After the SST assimilation, upwelling in this region is strengthened and mixed layer is deepened. At the same time, because the EnKF is a multivariable assimilation scheme, salinity and currents are also corrected by assimilating SST.

Key words: ensemble Kalman filter, assimilation, northern South China Sea, SST

舒业强,隋丹丹,王伟文,肖贤俊. 南海北部集合卡曼滤波同化SST试验[J]. 热带海洋学报, 2010, 29(5): 10-16.

SHU Ye-qiang,SUI Dan-dan,WANG Wei-wen,XIAO Xian-jun. SST assimilation experiment in the northern South China Sea using ensemble Kalman filter[J]. Journal of Tropical Oceanography, 2010, 29(5): 10-16.

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南海北部集合卡曼滤波同化SST试验

SST assimilation experiment in the northern South China Sea using ensemble Kalman filter

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