Oceanology

, Volume 56, Issue 6, pp 774–781 | Cite as

ARGO data assimilation into the ocean dynamics model with high spatial resolution using Ensemble Optimal Interpolation (EnOI)

Marine Physics

Abstract

The article proposes parallel implementation of the Ensemble Optimal Interpolation (EnOI) data assimilation (DA) method in eddy-resolving World Ocean circulation model. The results of DA experiments in North Atlantic with ARGO drifters are compared with the multivariate optimal interpolation (MVOI) DA scheme. The sensitivity of the model error, i.e., the difference between the model and observations depending on the number of ensemble elements, is also assessed and presented. The effectiveness of this method over the MVOI scheme is confirmed. The model outputs with and without assimilation are also compared with independent sea surface temperature data from ARMOR 3d.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    V. I. Agoshkov, V. M. Ipatova, V. B. Zalesnyi, E. I. Parmuzin, and V. P. Shutyaev, “Problems of variational assimilation of observational data for ocean general circulation models and methods for their solution,” Izv. Atmos. Ocean. Phys. 46 (6), 677–712 (2010).CrossRefGoogle Scholar
  2. 2.
    V. I. Agoshkov, E. I. Parmuzin, and V. P. Shutyaev, “Observational data assimilation in the problem of Black Sea circulation and sensitivity analysis of its solution,” Izv., Atmos. Ocean. Phys. 49 (6), 592–602 (2013).CrossRefGoogle Scholar
  3. 3.
    K. P. Belyaev, C. A. S. Tanajura, and N. P. Tuchkova, “Comparison of methods for argo drifters data assimilation into a hydrodynamical model of the ocean,” Oceanology (Engl. Transl.) 52 (5), 593–603 (2012).Google Scholar
  4. 4.
    R. A. Ibrayev, R. N. Khabeev, and K. V. Ushakov, “Eddy-resolving 1/10° model of the World Ocean,” Izv., Atmos. Ocean. Phys. 48 (1), 37–46 (2012).CrossRefGoogle Scholar
  5. 5.
    V. V. Kalmykov, R. A. Ibrayev, “The overlapping algorithm for solving shallow water equations on massivelyparallel architectures with distributed memory,” Vestnik UGATU 17 (5 (58)), 252–259 (2013).Google Scholar
  6. 6.
    V. V. Kalmykov, R. A. Ibrayev, and K. V. Ushakov, “The problems and challenges in the modeling of the high resolution Earth system,” in Supercomputer Technologies in Science, Education, and Industry: Almanac, Ed. by V. A. Sadovnichii, (Moscow State Univ., Moscow, 2014), pp. 14–22.Google Scholar
  7. 7.
    V. V. Kalmykov and R. A. Ibrayev, “A framework for the ocean-ice-atmosphere-land coupled modeling on massively-parallel architectures” Vychisl. Metody Programm., No. 14, 88–95 (2013).Google Scholar
  8. 8.
    M. N. Kaurkin, R. A. Ibrayev, and K. P. Belyaev, “Data assimilation in the ocean circulation model of high spatial resolution using the methods of parallel programming,” Russ. Meteorol. Hydrol. 41 (7), 479–486 (2016).CrossRefGoogle Scholar
  9. 9.
    V. V. Knysh, G. K. Korotaev, A. I. Mizyuk, and A. S. Sarkisyan, “Assimilation of hydrological observation data for calculating currents in seas and oceans,” Izv., Atmos. Ocean. Phys. 48 (1), 57–73 (2012).CrossRefGoogle Scholar
  10. 10.
    G. I. Marchuk and V. P. Shutyaev, “Conjugate equations and iterative algorithms in the tasks of variational data assimilation,” Tr. Inst. Matem. Mekh. im. N.N. Krasovskogo 17 (2), 136–150 (2011).Google Scholar
  11. 11.
    K. V. Ushakov, T. B. Grankina, and R. A. Ibrayev, “Modeling the water circulation in the North Atlantic in the scope of the CORE-II experiment,” Izv., Atmos. Ocean. Phys. 52 (4), 365–375 (2016).CrossRefGoogle Scholar
  12. 12.
    K. V. Ushakov, R. A. Ibrayev, and V. V. Kalmykov, “Simulation of the World ocean climate with a massively parallel numerical model,” Izv., Atmos. Ocean. Phys. 51 (4), 362–380 (2015).CrossRefGoogle Scholar
  13. 13.
    J. I. Antonov, D. Seidov, T. P. Boyer, et al., World Ocean Atlas 2009, Ed. by S. Levitus (US Government Printing Office, Washington, 2010).Google Scholar
  14. 14.
    R. Bleck, “An oceanic general circulation model framed in hybrid isopycnic Cartesian coordinates,” Ocean Model. 4, 55–88 (2002).CrossRefGoogle Scholar
  15. 15.
    G. Evensen, Data Assimilation, the Ensemble Kalman Filter, 2nd ed. (Berlin, Springer-Verlag, 2009).Google Scholar
  16. 16.
    GODAE Ocean View Science Team, Work Plan 2009, 2013, http://www.godae-oceanview.org.Google Scholar
  17. 17.
    S. M. Griffies, A. Biastoch, C. Böning, et al., “Coordinated ocean-ice reference experiments (COREs),” Ocean Model. 26 (1–2), 1–46 (2009).CrossRefGoogle Scholar
  18. 18.
    E. Kalnay, H. Li, T. Miyoshi, et al., “4-D-Var or ensemble Kalman filter?” Tellus A 59 (5), 758–773 (2007).CrossRefGoogle Scholar
  19. 19.
    E. Kalnay, Atmospheric Modeling, Data Assimilation, and Predictability (Cambridge Univ. Press, Cambridge, 2003).Google Scholar
  20. 20.
    W. Large and S. Yeager, “The global climatology of an interannually varying air–sea flux data set,” Clim. Dyn. 33 (2–3), 341–364 (2009).CrossRefGoogle Scholar
  21. 21.
    G. Larnicol, S. Guinehut, M.-H. Rio, et al., “The global observed ocean products of the French Mercator project,” Proceedings of the Symp. on 15 Years of Progress in Radar Altimetry, March 13–18, 2006 (Venice, 2006).Google Scholar
  22. 22.
    P. R. Oke, G. B. Brassington, D. A. Griffin, and A. Schiller, “Ocean data assimilation: a case for ensemble optimal interpolation,” Austral. Meteorol. Oceanogr. J. 59, 67–76 (2010).Google Scholar
  23. 23.
    P. Sakov, F. Counillon, L. Bertino, et al., “TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic,” Ocean Sci. 8, 633–656 (2012).CrossRefGoogle Scholar
  24. 24.
    A. Schiller and G. B. Brassington, Operational Oceanography in the 21st Century, Ed. by A. Schiller and G. B. Brassington (Springer-Verlag, Dordrecht, 2011).Google Scholar
  25. 25.
    C. Schrum and J. Backhaus, “Sensitivity of atmosphere-ocean heat exchange and heat content in North Sea and Baltic Sea. A comparative assessment,” Tellus A 51, 526–549 (1999).CrossRefGoogle Scholar
  26. 26.
    C. A. S. Tanajura and K. Belyaev, “A sequential data assimilation method based on the properties of a diffusion-type process,” Appl. Math. Model. 33 (5), 2165–2174 (2009).CrossRefGoogle Scholar
  27. 27.
    J. Xie and J. Zhu, “Ensemble optimal interpolation schemes for assimilating Argo profiles into a hybrid coordinate ocean model,” Ocean Model. 33, 283–298 (2010).CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2016

Authors and Affiliations

  • M. N. Kaurkin
    • 1
    • 2
    • 3
  • R. A. Ibrayev
    • 1
    • 2
    • 3
    • 4
  • K. P. Belyaev
    • 2
    • 5
  1. 1.Institute of Numerical MathematicsRussian Academy of SciencesMoscowRussia
  2. 2.P.P. Shirshov Institute of OceanologyRussian Academy of SciencesMoscowRussia
  3. 3.Hydrometcentre of RussiaMoscowRussia
  4. 4.Moscow Institute of Physics and Technology (State University)Dolgoprudny, Moscow RegionRussia
  5. 5.Dorodnicyn Computing CenterRussian Academy of SciencesMoscowRussia

Personalised recommendations