Toward a global ocean data assimilation system based on ensemble optimum interpolation: altimetry data assimilation experiment

被引：0
作者：
Weiwei Fu
Jiang Zhu
Changxiang Yan
Hailong Liu
机构：
[1] Chinese Academy of Sciences,Nansen
[2] Chinese Academy of Sciences,Zhu International Research Center (NZC), Institute of Atmospheric Physics (IAP)
[3] Chinese Academy of Sciences,International Centre for Climate and Environmental Sciences (ICCES), Institute of Atmospheric Physics
来源：
Ocean Dynamics | 2009年 / 59卷
关键词：
Data assimilation; EnOI; GODAE; Satellite altimetric data; Global ocean;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A global ocean data assimilation system based on the ensemble optimum interpolation (EnOI) has been under development as the Chinese contribution to the Global Ocean Data Assimilation Experiment. The system uses a global ocean general circulation model, which is eddy permitting, developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences. In this paper, the implementation of the system is described in detail. We describe the sampling strategy to generate the stationary ensembles for EnOI. In addition, technical methods are introduced to deal with the requirement of massive memory space to hold the stationary ensembles of the global ocean. The system can assimilate observations such as satellite altimetry, sea surface temperature (SST), in situ temperature and salinity from Argo, XBT, Tropical Atmosphere Ocean (TAO), and other sources in a straightforward way. As a first step, an assimilation experiment from 1997 to 2001 is carried out by assimilating the sea level anomaly (SLA) data from TOPEX/Poseidon. We evaluate the performance of the system by comparing the results with various types of observations. We find that SLA assimilation shows very positive impact on the modeled fields. The SST and sea surface height fields are clearly improved in terms of both the standard deviation and the root mean square difference. In addition, the assimilation produces some improvements in regions where mesoscale processes cannot be resolved with the horizontal resolution of this model. Comparisons with TAO profiles in the Pacific show that the temperature and salinity fields have been improved to varying degrees in the upper ocean. The biases with respect to the independent TAO profiles are reduced with a maximum magnitude of about 0.25°C and 0.1 psu for the time-averaged temperature and salinity. The improvements on temperature and salinity also lead to positive impact on the subsurface currents. The equatorial under current is enhanced in the Pacific although it is still underestimated after the assimilation.
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页码：587 / 602
页数：15
相关论文
共 112 条
[1]  
Anderson JL(2003)A local least squares framework for ensemble filtering Mon Weather Rev 131 634-642
[2]  
Bishop CH(2001)Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects Mon Weather Rev 129 420-436
[3]  
Etherton BJ(2007)Using temperature–salinity relations in a global ocean implementation of a multivariate data assimilation scheme Mon Weather Rev 135 3785-3807
[4]  
Majumdar SJ(2003)Implementation of a multivariate data assimilation scheme for isopycnic coordinate ocean models: application to a 1993–96 hindcast of the North Atlantic Ocean circulation J Geophys Res 108 1-20
[5]  
Bellucci A(1999)Assimilation of altimetric observations in a primitive equation model of the Gulf Stream using the steady-state limit of a singular evolutive extended Kalman filter J. Mar. Syst. 22 269-294
[6]  
Masina S(1998)Analysis scheme in the ensemble Kalman filter Mon Weather Rev 126 1719-1724
[7]  
DiPietro P(2001)An inverse ocean modeling system Ocean Model 3 137-165
[8]  
Navarra A(2008)Joint altimetric and in-situ data assimilation using the GRACE mean dynamic topography: a 1993–1998 hindcast experiment in the tropical Pacific Ocean Ocean Dynamics 58 43-63
[9]  
Brankart J-M(1997)Dual formulation of four-dimensional variational assimilation Q J R Meteorol Soc 123B 2449-2461
[10]  
Testut C-E(2005)Operational multivariate ocean data assimilation Q J R Meteorol Soc 131 3583-3604
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