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Advances in Atmospheric Sciences

, Volume 18, Issue 5, pp 767–786 | Cite as

Assimilation of Satellite Altimetry into a Western North Pacific Operational Model

  • Masafumi Kamachi
  • Tsurane Kuragano
  • Noriya Yoshioka
  • Jiang Zhu
  • Francesco Uboldi
Article

Abstract

An ocean data assimilation system, COMPASS-K (the Comprehensive Ocean Modeling, Prediction, Analysis and Synthesis System in the Kuroshio-region), has been developed at the Meteorological Research Institute (MRI). The purposes of the development are understanding ocean variability in the Kuroshio region as a local response to a global climate change with assimilated four-dimensional data sets, development of an operational system in the Japan Meteorological Agency, and for the GODAE (Global Ocean Data Assimilation Experiment) project.

The model is an eddy permitting version of an MRI-OGCM. Space-time decorrelation scales of ocean variability are estimated with TOPEX I POSEIDON (TIP) altimeter data. Subsurface temperature and salinity fields are projected from the TIP altimeter data with a statistical correlation method and are assimilated into the model with a time-retrospective nudging scheme.

Seasonal variation in the western North Pacific is investigated. Realistic space-time distribution of the physical quantities, the path of Kuroshio and its separation from Honshu are captured well. The Kuroshio volume transport is well reproduced in a reanalysis experiment of 1993. Preliminary predictability experiments are done in February and March, 1994. Predictability diagram shows the time scale of the predictability for temperature field is about 17 days in the Kuroshio south of Japan. This time scale is smaller than that in the North Atlantic.

Key words

Assimilation Kuroshio Predictability 

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References

  1. Adamec, D., 1989: Predictability of a quasi-geostrophic ocean flow; Sensitivity of varying model vertical resolution. J. Phys. Oceanogr., 19, 1753–1764.CrossRefGoogle Scholar
  2. Anderson, D.L.T., 1991: Data assimilation in ocean models. In: M. Latif (Ed.), Strategies for Future Climate Research. Max-Plank-Institutefuer Meteorologie, Hamburg, Germany, 193–225.Google Scholar
  3. Arakawa, A., 1966: Computational design for long-term numerical integration of the equations of fluid motion: Two-dimensional incompressible flow, Part. I. J. Comp. Phys., 1, 119–143.CrossRefGoogle Scholar
  4. Arakawa, A., and V. R. Lamb, 1977: Computational design of the basic processes of the UCLA general circulation model. Methods in Comp. Phys., 17, 173–265.Google Scholar
  5. Auer, S.J., 1987: Five-year climatological survey of the Gulf Stream system and its associated rings. J. Geophys. Res., 92, Cll, 11709–11726.CrossRefGoogle Scholar
  6. Bennett, A.F., 1992: Inverse Methods in Physical Oceanography. Cambridge University Press, Cambridge, 346 pp.CrossRefGoogle Scholar
  7. Bourtier, F., and P. Courtier, 1999: Data assimilation concepts and methods. Rep. of ECMWF, 75 pp.Google Scholar
  8. Brasseur, P., E. Blayo, and J. Verron, 1996: Predictability experiments in the North Atlantic Ocean: Outcome of a quasi-geostrophic model with assimilation of TOPEX I POSEIDON altimeter data. J. Geophys. Res., 101, 14161–14173.CrossRefGoogle Scholar
  9. Bryan, K., 1969: A numerical method for the study of the world ocean. J. Comp. Phys., 4, 347–376.CrossRefGoogle Scholar
  10. Carton, J. A., 1987: How predictable are the geostrophic currents in the recirculation zone of the north Atlantic? J. Phys. Ocenogr., 17, 751–762.CrossRefGoogle Scholar
  11. Cohn, S.E., 1997: An introduction to estimation theory. J. Met. Soc., Japan., 75, 257–288.CrossRefGoogle Scholar
  12. Cooper, M., and K. Haines, 1996: Altimetric assimilation with water property conservation. J. Geophys. Res., 101, 1059–1077.CrossRefGoogle Scholar
  13. Courtier, P., 1997: Dual formulation of four-dimensional variational assimilation. Q. J. Roy. Met. Soc., 123, 2449–2461.CrossRefGoogle Scholar
  14. De Mey, P., and A. Robinson, 1987: Assimilation of altimeter eddy fields in a limited-area quasi-geostrophic model. J. Phys. Oceanogr., 17, 2280–2293.CrossRefGoogle Scholar
  15. Gent, P. R., and J.C. McWilliams, 1990: Isopycnal mixing in ocean general circulation model. J. Phys. Oceanogr., 20, 150–155.CrossRefGoogle Scholar
  16. Haines K., 1994: Dynamics and data assimilation in oceanography in Data Assimilation: Tools for Modelling the Ocean in a Global Change Perspective, ed. by P.P. Brasseur and J.C.C. Nihoul, NATO ASI Series I, 19, 1–32.Google Scholar
  17. Hellerman, S., and M. Rosenstein, 1983: Normal monthly wind stress over the world ocean with error estimates. J. Phys. Oceanogr., 13, 1093–1104.CrossRefGoogle Scholar
  18. Hsieh, W., 1985: Modal bias in sea level and sea surface temperature, with application to remote sensing. J. Phys. Oceanogr., 15, 351–356.CrossRefGoogle Scholar
  19. Imawaki, S., H. Uchida, H. Ichikawa, M. Fukasawa, S. Umatani, and ASUKA Group, 1997: Time series of the Kuroshio transport derived from field observations and altimetry data. Int. WOCE Newsletter, 25, 15–18.Google Scholar
  20. Ishizaki H., 1994: A simulation of the abyssal circulation in the North Pacific Ocean. Part I: Flow field and comparison with observations. J. Phys. Oceanogr., 24, 1941–1954.CrossRefGoogle Scholar
  21. Ishizaki, H., and T. Motoi, 1999: Reevaluation of the Takano-Oonishi scheme for momentum advection on bottom relief in ocean models. J. of Atmos. Ocean. Tech., 16, 1994–2010.CrossRefGoogle Scholar
  22. Kagimoto, T., and T. Yamagata, 1997: Seasonal transport variations of the Kuroshio: An OGCM simulation, J. Phys. Oceanogr., 27, 403–418.CrossRefGoogle Scholar
  23. Kamachi M., 1995: Toward assimilation of altimetry in combination with conventional data in the North Pacific Ocean. Proceeding of Int. Workshop on Num. Pred. of Ocean. Cond., Tokyo, 59–66.Google Scholar
  24. Kamachi, M., T. Kuragano, T. Yoshida, F. Uboldi, and N. Yoshioka, 1998: An Ocean Data Assimilation System. Weather Service Bulletin, JMA, 65, 1–19 (in Japanese).Google Scholar
  25. Kamachi, M., T. Kuragano, and D. F. Uboldi, 1999: A reproduction of Kuroshio separation and data assimilation of KWCR in the confluence area. In “Ecosystem Dynamics of the Kuroshio-Oyashio Transition Region”, Japan Marine Science Foundation, 9–23.Google Scholar
  26. Killworth, P.D., 1996: Time interpolation of forcing fields in ocean models. J. Phys. Oceanogr., 26, 136–143.CrossRefGoogle Scholar
  27. Kimura Y., and M. Endoh, 1989: Response experiment of the Pacific Ocean to anomalous wind stress with ocean general circulation model. Tech. Rep. of the Met. Res. Inst., 24, 96 pp.Google Scholar
  28. Kuragano, T., A. Shibata, 1997: Sea surface dynamic height of the Pacific Ocean derived from TOPEX I POSEIDON altimeter data: calculation method and accuracy. J. of Oceanogr., 53, 585–599.Google Scholar
  29. Kuragano, T. and M. Kamachi, 1997: Investigation of temperature and salinity fields statistically derived from TOPEX I POSEIDON altimeter data. Proceedings of the Symposium Monitoring the Oceans in the 2000s: An integrated approach., Biarritz, France, 4–29.Google Scholar
  30. Kuragano, T. and M. Kamachi, 2000: The global statistical space-time scales of oceanic variability estimated from the TOPEX I POSEIDON altimeter. J. Geophys. Res., 105, 955–974.CrossRefGoogle Scholar
  31. Levitus, S., 1982: Climatological atlas of the world ocean. NOAA Prof. Paper 13, 174 pp.Google Scholar
  32. Levitus, S. And T.P. Boyer, 1994: World Ocean Atlas 1994, 4: Temperature. NOAA Atlas NESDIS 4, 117 pp.Google Scholar
  33. Levitus, S., R. Gurgett, and T. P. Boyer, 1994: World Ocean Atlas 1994, 3: Salinity. NOAA Atlas NESDIS 3, 99 pp.Google Scholar
  34. Lorenz, E.N., 1982: Atmospheric predictability experiments with a large numerical model. Tellus, 34, 505–513.CrossRefGoogle Scholar
  35. Mellor, G.L., and T. Ezer, 1991: A Gulf stream model and an altimetry assimilation scheme. J. Geophys. Res., 96, 8779–8795.CrossRefGoogle Scholar
  36. Misumi A., H. Yamamoto, H. Yoshikawa, K. Ishikawa, N. Kanno, N. Yoshioka, H. Kinosita, H. Yoritaka, N. Shikama, T. Yamashiro, M. Sakurai, and A. Maeda, 1997: Report ofKuroshio variability in the Tokara strait. Proc. of the 1997 spring Mtg. Of the Jpn Oen. Soc., No. 118 (in Japanese).Google Scholar
  37. Redi, M.H., 1982: Oceanic isopycnal mixing by coordinate rotation. J. Phys. Oceanogr., 12, 1154–1158.CrossRefGoogle Scholar
  38. Richardson, L.F., and H. Stommel, 1948: Note on eddy diffusion in the sea. J. Meteorol., 5, 238–240.CrossRefGoogle Scholar
  39. Stommel, H., 1949: Horizontal diffusion due to oceanic turbulence. J. Mar. Res., 8, 199–225.Google Scholar
  40. Tsuyuki, T., 1997: Variational data assimilation. JM A-NPD Rep., special volume, 43, 102–165 (in Japanese).Google Scholar
  41. Uboldi, F., 1998: Report on Eddy Synthetic Models, Reports in COMPASS Group No.2. Met. Res. Inst., 49 pp.Google Scholar
  42. Uehara, K., H. Miyake, and T. Iwao, 1997: Geostrophic volume transport of Oyashio and Tsugaru strait calculated from CTD observations along 41°30’N. Proceeding of the 1997 spring Mtg. Of the Jpn Oen. Soc., No. 133 (in Japanese).Google Scholar
  43. Woodgate, R.A., and P.D. Killworth, 1993: Can we derive pressure from density using normal modes? Ocean Modelling, 100, 5–6.Google Scholar
  44. Wunsch, C., 1996: The Ocean Circulation Inverse Problem. Cambridge University Press, Cambridge, 442 pp.CrossRefGoogle Scholar
  45. Zhu, J., and M. Kamachi, 2000: An adaptive variational method for data assimilation with imperfect models. Tellus, 52A, 265–279.CrossRefGoogle Scholar

Copyright information

© Advances in Atmospheric Sciences 2001

Authors and Affiliations

  • Masafumi Kamachi
    • 1
  • Tsurane Kuragano
    • 1
  • Noriya Yoshioka
    • 2
  • Jiang Zhu
    • 3
  • Francesco Uboldi
    • 4
  1. 1.Meteorological Research InstituteTsukubaJapan
  2. 2.Japan Meteorological AgencyTokyoJapan
  3. 3.Institute of Atmospheric PhysicsChinese Academy of ScienceBeijingChina
  4. 4.Magritte SNCMilanoItaly

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