Implementation of the Ensemble Kalman Filter to a double gyre ocean and sensitivity test using twin experiments

Young Ho Kim, Sang Jin Lyu, Byoung Ju Choi, Yang Ki Cho, Young Gyu Kim

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

Original languageEnglish
Pages (from-to)129-140
Number of pages12
JournalOcean and Polar Research
Volume30
Issue number2
DOIs
StatePublished - Jun 2008

Keywords

  • Data assimilation
  • Ensemble Kalman Filter
  • Ensemble spread
  • Localization and inflation of the background error covariance
  • Ocean modeling

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