A Conserved Minimal Adjustment Scheme for Stabilization of Hydrographic Profiles

Peter C. Chu Department of Oceanography, Naval Postgraduate School, Monterey, California

Search for other papers by Peter C. Chu in
Current site
Google Scholar
PubMed
Close
and
Chenwu Fan Department of Oceanography, Naval Postgraduate School, Monterey, California

Search for other papers by Chenwu Fan in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Ocean (T, S) data analysis/assimilation, conducted in the three-dimensional physical space, is a generalized average of purely observed data (data analysis) or of modeled/observed data (data assimilation). Because of the high nonlinearity of the equation of the state of the seawater and nonuniform vertical distribution of the observational profile data, false static instability may be generated. A new analytical conserved adjustment scheme has been developed on the base of conservation of heat, salt, and static stability for the whole water column with predetermined (T, S) adjustment ratios. A set of well-posed combined linear and nonlinear algebraic equations has been established and is solved using Newton’s method. This new scheme can be used for ocean hydrographic data analysis and data assimilation.

Corresponding author address: Dr. Peter C. Chu, Naval Ocean Analysis and Prediction (NOAP) Lab, Naval Postgraduate School, 833 Dyer Rd., Monterey, CA 93940. Email: pcchu@nps.edu

Abstract

Ocean (T, S) data analysis/assimilation, conducted in the three-dimensional physical space, is a generalized average of purely observed data (data analysis) or of modeled/observed data (data assimilation). Because of the high nonlinearity of the equation of the state of the seawater and nonuniform vertical distribution of the observational profile data, false static instability may be generated. A new analytical conserved adjustment scheme has been developed on the base of conservation of heat, salt, and static stability for the whole water column with predetermined (T, S) adjustment ratios. A set of well-posed combined linear and nonlinear algebraic equations has been established and is solved using Newton’s method. This new scheme can be used for ocean hydrographic data analysis and data assimilation.

Corresponding author address: Dr. Peter C. Chu, Naval Ocean Analysis and Prediction (NOAP) Lab, Naval Postgraduate School, 833 Dyer Rd., Monterey, CA 93940. Email: pcchu@nps.edu

Save
  • Bryan, K., 1969: A numerical method for the study of the circulation of the world ocean. J. Comput. Phys., 4 , 347376.

  • Chu, P. C., Wang G. H. , and Fan C. W. , 2004: Evaluation of the U.S. Navy’s Modular Ocean Data Assimilation System (MODAS) using the South China Sea Monsoon Experiment (SCSMEX) data. J. Oceanogr., 60 , 10071021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cox, M., 1984: A primitive equation, three-dimensional model of the ocean. GFDL Ocean Group Tech. Rep. 1, 143 pp.

  • Galanis, G. N., Louka P. , Katsafados P. , Kallos G. , and Pytharoulis I. , 2006: Applications of Kalman filters based on nonlinear functions to numerical weather predictions. Ann. Geophys., 24 , 24512460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackett, D. R., and McDougall T. J. , 1995: Minimal adjustment of hydrographic profiles to achieve static stability. J. Atmos. Oceanic Technol., 12 , 381389.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.

  • Kelley, C. T., 1987: Solving Nonlinear Equations with Newton’s Method (Fundamentals of Algorithms). SIAM, 103 pp.

  • Killworth, P. D., 1989: On the parameterization of deep convection in ocean models. Parameterization of Small Scale Processes: Proc. ‘Aha Huliko‘a Hawaiian Winter Workshop, P. Muller, Ed., Hawaii Institute of Geophysics, 59–74.

    • Search Google Scholar
    • Export Citation
  • Levitus, S., 1982: Climatological Atlas of the World Ocean. NOAA Professional Paper 13, 173 pp.

  • Levitus, S., 1998: Introduction. Vol. 1, World Ocean Database 1998, NOAA Atlas NESDIS 18, 346 pp.

  • Locarnini, R. A., Mishonov A. V. , Antonov J. I. , Boyer T. P. , and Garcia H. E. , 2006: Temperature. Vol. 1, World Ocean Atlas 2005, NOAA Atlas NESDIS 61, 38 pp.

    • Search Google Scholar
    • Export Citation
  • Lozano, C. J., Robinson A. R. , Arrango H. G. , Gangopadhyay A. , Sloan Q. , Haley P. J. , Anderson L. , and Leslie W. , 1996: An interdisciplinary ocean prediction system: Assimilation strategies and structured data models. Modern Approaches to Data Assimilation in Ocean Modeling, P. Malanotte-Rizzoli, Ed., Elsevier, 413–452.

    • Search Google Scholar
    • Export Citation
  • Lynn, R. G., and Reid J. L. , 1968: Characteristics and circulation of deep and abyssal waters. Deep-Sea Res., 15 , 577598.

  • Smith, N., 1989: The Southern Ocean thermohaline circulation: A numerical study. J. Phys. Oceanogr., 19 , 713726.

  • Sun, L. C., 1999: Data inter-operability driven by oceanic data assimilation needs. Mar. Technol. Soc. J., 33 , 5566.

  • Tang, Y., and Kleeman R. , 2004: SST assimilation experiments in a tropical Pacific Ocean model. J. Phys. Oceanogr., 34 , 623642.

  • Yin, F. L., and Sarachik E. S. , 1994: An efficient convective adjustment scheme for ocean circulation models. J. Phys. Oceanogr., 24 , 14251430.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 309 242 4
PDF Downloads 63 35 4