Implementation of the Ensemble Kalman Filter into a Northwest Pacific Ocean Circulation Model

  • Gwang-Ho Seo
  • Sangil Kim
  • Byoung-Ju Choi
  • Yang-Ki Cho
  • Young-Ho Kim


The Ensemble Kalman Filter (EnKF) was implemented to an ocean circulation modeling system of the Northwest Pacific Ocean. The study area includes the northwestern part of the Pacific Ocean, the East China Sea, the Yellow Sea and the East/Japan Sea. The numerical model used for the system was the Regional Ocean Model System, which is a 3-dimensional primitive-equation ocean circulation model. The performance of EnKF was evaluated by assimilating satellite-observed Sea Surface Temperature (SST) data into the numerical ocean model every 7 day for year 2003. SST data were obtained from 30 fixed points at a time. The number $N$ of ensemble members used in this study was 16. Without localization of covariance matrix, ensemble spread (EnSP) drastically decreased due to rank deficiency and the large correlation between two distant state variables. To resolve the ensemble collapse, localization of covariance matrix was performed and EnSP did not collapse throughout the experiment. Root -mean-square (RMS) error of SST from the assimilative model (RMS error= 2.2°C) was smaller than that of the non-assimilative model (RMS error= 3.2°C). This work provides promising results that can be further explored in establishing operational ocean prediction systems for the Northwest Pacific including its marginal seas.


Root Mean Square Error Data Assimilation Ensemble Member Ensemble Spread Ocean Circulation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson JL (1996) A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J Climate 9:1518–1530CrossRefGoogle Scholar
  2. Anderson JL, Anderson SL (1999) A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon Wea Rev 127:2741–2758CrossRefGoogle Scholar
  3. Anderson JL (2007) An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus 59A:210–224Google Scholar
  4. Bennett AF (1992) Inverse methods in physical oceanography. Cambridge University Press, CambridgeGoogle Scholar
  5. Bennett AF (2002) Inverse modeling of the ocean and atmosphere. Cambridge University Press, CambridgeGoogle Scholar
  6. Burgers G et al (1998) Analysis scheme in the ensemble Kalman filter. Mon Wea Rev 126:1719–1724CrossRefGoogle Scholar
  7. Chassignet EP et al (2000) DAMEE–NAB: The base experiments. Dyn Atmospheres Oceans 32:155–184CrossRefGoogle Scholar
  8. Cho Y-K et al (2008) Connectivity among straits of the northwest Pacific marginal seas. J Geophys Res SubmittedGoogle Scholar
  9. Curchitser EN et al (2005) Multi-scale modeling of the North Pacific Ocean: Assesment and analysis of simulated basin-scale variability (1996–2003), J Geophys Res 110: C11021, doi:10.1029/2005JC002902CrossRefGoogle Scholar
  10. Courtier P et al (1993) Important literature on the use of adjoint, variational methods and the Kalman filter in meteorology. Tellus 45A:342–357Google Scholar
  11. De Mey P (1997) Data assimilation at the oceanic mesoscale: A review. J Meteor Soc Japan 75:415–427Google Scholar
  12. Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99:10143–10162CrossRefGoogle Scholar
  13. Evensen G (2004) Sampling strategies and square root analysis schemes for the EnKF. Ocean Dyn 54:539–560CrossRefGoogle Scholar
  14. Ezer T et al (2002) Developments in terrain-following ocean models: intercomparisons of numerical aspects. Ocean Model 4:249–267CrossRefGoogle Scholar
  15. Fairall CW et al (1996) Bulk parameterization of air-sea fluxes for tropical ocean-global atmosphere coupled-ocean atmosphere response experiment. J Geophys Res 101:3747–3764CrossRefGoogle Scholar
  16. Gaspari G, Cohn SE (1999) Construction of correlationfunctions in two and three dimensions. Quart J Roy Meteor Soc 125:723–757CrossRefGoogle Scholar
  17. Guo X et al (2006) The Kuroshio onshore intrusion along the shelf break of the East China Sea: the origin of the Tsushima Warm Current. J Phys Oceanogr 36:2205–2231CrossRefGoogle Scholar
  18. Haidvogel DB et al (2000) Model evaluation experiments in the North Atlantic Basin: Simulations in nonlinear terrain-following coordinates. Dyn Atmos Oceans 32:239–281CrossRefGoogle Scholar
  19. Hamill TM et al (2001) Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon Wea Rev 129:2776–2790CrossRefGoogle Scholar
  20. Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter technique. Mon Wea Rev 126:796–811CrossRefGoogle Scholar
  21. Houtekamer, PL, Mitchell HL (2001) A sequential ensemble Kalman filter for atmospheric data assimilation. Mon Wea Rev 129:123–137CrossRefGoogle Scholar
  22. Isobe A. (1999) On the origin of the Tsushima warm current and its seasonality. Cont Shelf Res 19:117–133CrossRefGoogle Scholar
  23. Keppenne CL, Rienecker MM (2001) Design and implementation of a parallel multivariate ensemble Kalman filter for the Poseidon ocean general circulation model. NASA Tech. Memo-2001-104606, Vol. 21, 35ppGoogle Scholar
  24. Large WG et al (1994) Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev Geophys 32:363–403CrossRefGoogle Scholar
  25. Sasajima Y et al (2007) Structure of the subsurface counter current beneath the tsushima warm current simulated by an ocean general circulation model. J Oceanogr 63(6):913–926CrossRefGoogle Scholar
  26. Senjyu T et al (2006) Interannual salinity variations in the Tsushima Strait and its relation to the Changjiang discharge. J Oceanogr 62:681–692, 2006.09CrossRefGoogle Scholar
  27. Seung Y et al (2007) Seasonal characteristics of the Tsushima current in the Tsushima/Korea strait obtained by a fine-resolution numerical model. Cont Shelf Res 27(1):117–133CrossRefGoogle Scholar
  28. Song Y, Haidvogel DB (1994) A semi-implicit ocean circulation model using a generalized topography-following coordinate system. J Comp Phys 115(1):228–244CrossRefGoogle Scholar
  29. van Leeuwen PJ (1999) Comment on “Data assimilation using an Ensemble Kalman Filter Technique.” Mon Wea Rev 127:1374–1377CrossRefGoogle Scholar
  30. Wang X, Bishop CH (2003) A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J Atmos Sci 60:1140–1158CrossRefGoogle Scholar
  31. Wantanabe M (2007) Simulation of temperature, salinity and suspended matter distributions induced by the discharge into the East China Sea during the 1998 flood of the Yangtze River. Estuarine Coast Shelf Sci 71(1–2):81–97CrossRefGoogle Scholar
  32. Wunsch C (1996) The ocean circulation inverse problem. Cambridge University Press, CambridgeGoogle Scholar
  33. Xia C et al (2006) Three-dimensional structure of the summertime circulation in the Yellow Sea from a wave-tide-circulation coupled model. J Geophy Res 111 (C11):Art. No. C11S03Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gwang-Ho Seo
    • 1
  • Sangil Kim
  • Byoung-Ju Choi
  • Yang-Ki Cho
  • Young-Ho Kim
  1. 1.The College of Oceanic and Atmospheric SciencesOregon State UniversityCorvallisUSA

Personalised recommendations