Ocean Dynamics

, Volume 62, Issue 4, pp 645–659 | Cite as

Open and coastal seas interactions south of Japan represented by an ensemble Kalman filter

  • Yasumasa Miyazawa
  • Toru Miyama
  • Sergey M. Varlamov
  • Xinyu Guo
  • Takuji Waseda
Part of the following topical collections:
  1. Topical Collection on the 3rd International Workshop on Modelling the Ocean 2011


We investigated the feasibility of the ensemble Kalman filter (EnKF) to reproduce oceanic conditions south of Japan. We have adopted the local ensemble transformation Kalman filter algorithm based on 20 members’ ensemble simulations of the parallelized Princeton Ocean Model (the Stony Brook Parallel Ocean Model) with horizontal resolution of 1/36°. By assimilating satellite sea surface height anomaly, satellite sea surface temperature, and in situ temperature and salinity profiles, we reproduced the Kuroshio variation south of Japan for the period from 8 to 28 February 2010. EnKF successfully reproduced the Kuroshio path positions and the water mass property of the Kuroshio waters as observed. It also detected the variation of the steep thermohaline front in the Kii Channel due to the intrusion of the Kuroshio water based on the observation, suggesting efficiency of EnKF for detection of open and coastal seas interactions with highly complicated spatiotemporal variability.


Ensemble Kalman filter Kuroshio Kii channel front OGCM 



This work is part of the Japan Coastal Ocean Predictability Experiment (JCOPE) promoted by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). The code of the local ensemble transformation Kalman filter (LETKF) was obtained from, which is maintained by Dr. Takamasa Miyoshi. Sea surface height anomaly data of Jasons-1 and Jasons-2 and sea surface temperature of NOAA MCSST were downloaded from the US-GODAE website: AMSR-E data were produced by remote sensing systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at In situ temperature and salinity profiles were obtained from the Global Temperature-Salinity Profile Program (GTSPP) website: Comments from two anonymous reviewers were useful for improvements of the earlier version of the manuscript.


  1. Akitomo K, Imasato N, Awaji T (1990) A numerical study of a shallow sea front generated by buoyancy flux: generation mechanism. J Phys Oceanogr 20:172–189CrossRefGoogle Scholar
  2. Cohn SE (1997) An introduction to estimation theory. J Meteor Soc Jpn 75:257–288Google Scholar
  3. Conkright ME, Antonov JI, Baranova O, Boyer TP, Garcia HE, Gelfeld R, Johnson D, Locarnini RA, Murphy PP, O’Brien TD, Smolyar I, Stephens C (2002) World Ocean Database 2001, vol1: Introduction. In: Levitus S (ed) NOAA Atlas NESDIS 42. US Government Printing Office, Washington, p 167Google Scholar
  4. 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
  5. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367CrossRefGoogle Scholar
  6. Harashima A, Oonishi Y (1981) The Coriolis effect against frontogenesis in steady buoyancy-driven circulation. J Oceanogr Soc Jpn 37:49–59CrossRefGoogle Scholar
  7. Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D 230:112–126CrossRefGoogle Scholar
  8. Isobe A, Guo X, Takeoka H (2010) Hindcast and predictability of sporadic Kuroshio–water intrusion (kyucho in the Bungo Channel) into the shelf and coastal waters. J Geophys Res 115:C04023. doi: 10.1029/2009JC005818 CrossRefGoogle Scholar
  9. Jameson L, Waseda T, Mitsudera H (2002) Scale utilization and optimization from wavelet analysis for data assimilation: SUgOiWADAi. J Atmos Oceanic Tech 19:747–758CrossRefGoogle Scholar
  10. Kagimoto T, Miyazawa Y, Guo X, Kawajiri H (2008) High resolution Kuroshio forecast system: description and its applications. In: Hamilton K, Ohfuchi W (eds) High resolution numerical modeling of the atmosphere and ocean. Springer, New York, pp 209–239CrossRefGoogle Scholar
  11. Kuragano T, Kamachi M (2000) Global statistical space-time scales of oceanic variability estimated from the TOPEX/POSEIDON altimetry data. J Geophys Res 105:955–974. doi: 10.1029/1999JC900247 CrossRefGoogle Scholar
  12. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141CrossRefGoogle Scholar
  13. Miyama T, Miyazawa Y (2010) Sudden acceleration of Kuroshio jet off the Cape Shionomisaki in JCOPE2 ocean reanalysis data. Abstracts of 2010 Ocean Sciences Meeting, PO23Google Scholar
  14. Miyazawa Y, Yamane S, Guo X, Yamagata T (2005) Ensemble forecast of the Kuroshio meandering. J Geophys Res 110:C10026. doi: 10.1029/2004JC002426 CrossRefGoogle Scholar
  15. Miyazawa Y, Kagimoto T, Guo X, Sakuma H (2008) The Kuroshio large meander formation in 2004 analyzed by an eddy-resolving ocean forecast system. J Geophys Res 113:C10015. doi: 10.1029/2007JC004226 CrossRefGoogle Scholar
  16. Miyazawa Y, Zhang R, Guo X, Tamura H, Ambe D, Lee J-S, Okuno A, Yoshinari H, Setou T, Komatsu K (2009) Water mass variability in the western North Pacific detected in a 15-year eddy resolving ocean reanalysis. J Oceanogr 65:737–756CrossRefGoogle Scholar
  17. Miyoshi T, Sato Y, Kadowaki T (2010) Ensemble Kalman Filter and 4D-Var intercomparison with the Japanese operational global analysis and prediction system. Mon Wea Rev 138:2846–2866CrossRefGoogle Scholar
  18. Moteki Q, Yoneyama K, Shirooka R, Kubota H, Yasunaga K, Suzuki J, Seiki A, Sato N, Enomoto T, Miyoshi T, Yamane S. (2011) The influence of observations propagated by convectively coupled equatorial waves. Quart J Royal Meteoro Soc 137:641.655. doi: 10.1002/qj.779
  19. Toda T (1992) Double structure of the coastal front in the Kii Channel, Japan, during winter. J Geophys Res 97:11333–11342CrossRefGoogle Scholar
  20. Vignudelli S, Cipollini P, Astraldi M, Gasparini GP, Manzella G (2000) Integrated use of altimeter and in situ data for understanding the water exchange between the Tyrrhenian and Ligurian Seas. J Geophys Res 105:19649–19663CrossRefGoogle Scholar
  21. Wang D, Liu Y, Qi Y, Shi P (2001) Seasonal variability of thermal fronts in the northern South China Sea from satellite data. Geophys Res Lett 28:3963–3966CrossRefGoogle Scholar
  22. Xie X, Iwata S, Nemoto M, Nagashima H (2006) A method for obtaining daily high-resolution sea surface temperature in the KUROSHIO regions. Bull Japanese Soc Fish Oceanogr 70:122–130 (in Japanese)Google Scholar
  23. Yanagi T, Sanuki T (1991) Variation in the thermohaline front at the mouse of Tokyo Bay. J Oceanogr Soc Jpn 47:105–110CrossRefGoogle Scholar
  24. Yanagi T, Guo X, Saino T, Ishimaru T, Noriki S (1997) Thermohaline front at the mouse of Ise Bay. J Oceanogr 53:403–409Google Scholar
  25. Yosioka H (1983) Sea surface temperature and its application to the investigation of oceanic condition in Kii-Channel. J Marine Meteorol Society Japan (‘Umi to Sora’) 58:37–51 (in Japanese)Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Yasumasa Miyazawa
    • 1
  • Toru Miyama
    • 1
  • Sergey M. Varlamov
    • 1
  • Xinyu Guo
    • 1
    • 2
  • Takuji Waseda
    • 1
    • 3
  1. 1.Research Institute for Global ChangeJapan Agency for Marine-Earth Science and Technology, YokohamaYokohamaJapan
  2. 2.Center for Marine Environmental StudiesEhime UniversityMatsuyamaJapan
  3. 3.Graduate School of Frontier ScienceThe University of TokyoKashiwaJapan

Personalised recommendations