Ocean Dynamics

, Volume 62, Issue 7, pp 1059–1071 | Cite as

Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean

  • Xunqiang Yin
  • Fangli Qiao
  • Yongzeng Yang
  • Changshui Xia
  • Xianyao Chen
Article
Part of the following topical collections:
  1. Topical Collection on the 3rd International Workshop on Modelling the Ocean 2011

Abstract

The Argo temperature and salinity profiles in 2005–2009 are assimilated into a coastal ocean general circulation model of the Northwest Pacific Ocean using the ensemble adjustment Kalman filter (EAKF). Three numerical tests, including the control run (CTL) (without data assimilation, which serves as the reference experiment), ensemble free run (EnFR) (without data assimilation), and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states, and no ensemble inflation is necessary for this EAKF experiment. Different experiment results are compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10 % on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85 % and 80 %, respectively. The standard deviations of sea surface height are employed to examine the simulation ability, and it is shown that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models.

Keyword

Argo profiles Ensemble adjustment Kalman filter Ensemble free runs Ensemble spread Mesoscale variability 

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Xunqiang Yin
    • 1
    • 2
  • Fangli Qiao
    • 1
    • 2
  • Yongzeng Yang
    • 1
    • 2
  • Changshui Xia
    • 1
    • 2
  • Xianyao Chen
    • 1
    • 2
  1. 1.First Institute of OceanographyState Oceanic Administration (SOA)QingdaoChina
  2. 2.Key Laboratory of Marine Science and Numerical Modeling (MASNUM)SOAQingdaoChina

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