Advances in Atmospheric Sciences

, Volume 26, Issue 5, pp 1042–1052 | Cite as

A “dressed” Ensemble Kalman Filter using the Hybrid Coordinate Ocean Model in the Pacific

  • Liying Wan (万莉颖)
  • Jiang Zhu (朱江)
  • Hui Wang (王辉)
  • Changxiang Yan (闫长香)
  • Laurent Bertino


The computational cost required by the Ensemble Kalman Filter (EnKF) is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation (OI) or three-dimension variational assimilation (3DVAR). Ensemble optimal interpolation (EnOI), a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of “dressing” a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term “dressing” means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF (DrEnKF for short) scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model (HYCOM) over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors (RMSE) at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.

Key words

Dressing Ensemble Kalman Filter (DrEnKF) HYbrid Coordinate Ocean Model root mean square errors 


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Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer Berlin Heidelberg 2009

Authors and Affiliations

  • Liying Wan (万莉颖)
    • 1
  • Jiang Zhu (朱江)
    • 2
  • Hui Wang (王辉)
    • 1
  • Changxiang Yan (闫长香)
    • 2
  • Laurent Bertino
    • 3
  1. 1.National Marine Environmental Forecasting CenterBeijingChina
  2. 2.Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.Mohn-Sverdrup Center/Nansen Environmental and Remote Sensing CenterBergenNorway

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