Evaluation of an ocean data assimilation system for Chinese marginal seas with a focus on the South China Sea

Dazhi Xu; Xichen Li; Jiang Zhu; Yiquan Qi

doi:10.1007/s00343-011-0044-4

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Evaluation of an ocean data assimilation system for Chinese marginal seas with a focus on the South China Sea

Updated：2023-05-16

- Evaluation of an ocean data assimilation system for Chinese marginal seas with a focus on the South China Sea
- Journal of Oceanology and Limnology Vol. 29, Issue 2, Pages: 414-426(2011)
- Affiliations：
  
  1. Key Laboratory of Tropical Marine Environmental Dynamics (LED) South China Sea Institute of Oceanology Chinese Academic of Sciences
  2. Graduate University of Chinese Academy of Sciences
  3. Institute of Atmospheric Physics Chinese Academic of Sciences
- Author bio：
- Funds：
- DOI：10.1007/s00343-011-0044-4
  CLC：
- Online First：02 March 2011，
  
  Published：2011
- Accepted：
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Xu, D., Li, X., Zhu, J. et al. Evaluation of an ocean data assimilation system for Chinese marginal seas with a focus on the South China Sea., Chin. J. Ocean. Limnol. 29, 414–426 (2011). https://doi.org/10.1007/s00343-011-0044-4
DOI：

Xu, D., Li, X., Zhu, J. et al. Evaluation of an ocean data assimilation system for Chinese marginal seas with a focus on the South China Sea., Chin. J. Ocean. Limnol. 29, 414–426 (2011). https://doi.org/10.1007/s00343-011-0044-4 DOI：

摘要

Abstract

Data assimilation is a powerful tool to improve ocean forecasting by reducing uncertainties in forecast initial conditions. Recently

an ocean data assimilation system based on the ensemble optimal interpolation (EnOI) scheme and HYbrid Coordinate Ocean Model (HYCOM) for marginal seas around China was developed. This system can assimilate both satellite observations of sea surface temperature (SST) and along-track sea level anomaly (SLA) data. The purpose of this study was to evaluate the performance of the system. Two experiments were performed

which spanned a 3-year period from January 1

2004 to December 30

2006

with and without data assimilation. The data assimilation results were promising

with a positive impact on the modeled fields. The SST and SLA were clearly improved in terms of bias and root mean square error over the whole domain. In addition

the assimilations provided improvements in some regions to the surface field where mesoscale processes are not well simulated by the model. Comparisons with surface drifter trajectories showed that assimilated SST and SLA also better represent surface currents

with drifter trajectories fitting better to the contours of SLA field than that without assimilation. The forecasting capacity of this assimilation system was also evaluated through a case study of a birth-and-death process of an anticyclone eddy in the Northern South China Sea (NSCS)

in which the anticyclone eddy was successfully hindcasted by the assimilation system. This study suggests the data assimilation system gives reasonable descriptions of the near-surface ocean state and can be applied to forecast mesoscale ocean processes in the marginal seas around China.

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