Accuracy Progressive Calculation of Lagrangian Trajectories from a Gridded Velocity Field

Peter C. Chu Naval Ocean Analysis and Prediction Laboratory, Department of Oceanography, Naval Postgraduate School, Monterey, California

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Chenwu Fan Naval Ocean Analysis and Prediction Laboratory, Department of Oceanography, Naval Postgraduate School, Monterey, California

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Abstract

Reduction of computational error is a key issue in computing Lagrangian trajectories using gridded velocities. Computational accuracy enhances from using the first term (constant velocity scheme), the first two terms (linear uncoupled scheme), the first three terms (linear coupled scheme), to using all four terms (nonlinear coupled scheme) of the two-dimensional interpolation. A unified “analytical form” is presented in this study for different truncations. Ordinary differential equations for predicting Lagrangian trajectory are linear using the constant velocity/linear uncoupled schemes (both commonly used in atmospheric and ocean modeling), the linear coupled scheme, and the nonlinear using the nonlinear coupled scheme (both proposed in this paper). The location of the Lagrangian drifter inside the grid cell is determined by two algebraic equations that are solved explicitly with the constant velocity/linear uncoupled schemes, and implicitly using the Newton–Raphson iteration method with the linear/nonlinear coupled schemes. The analytical Stommel ocean model on the f plane is used to illustrate great accuracy improvement from keeping the first term to keeping all the terms of the two-dimensional interpolation.

Corresponding author address: Peter C. Chu, Naval Ocean Analysis and Prediction Laboratory, Department of Oceanography, Naval Postgraduate School, 833 Dyer Road, Monterey, CA 93943. E-mail: pcchu@nps.edu

Abstract

Reduction of computational error is a key issue in computing Lagrangian trajectories using gridded velocities. Computational accuracy enhances from using the first term (constant velocity scheme), the first two terms (linear uncoupled scheme), the first three terms (linear coupled scheme), to using all four terms (nonlinear coupled scheme) of the two-dimensional interpolation. A unified “analytical form” is presented in this study for different truncations. Ordinary differential equations for predicting Lagrangian trajectory are linear using the constant velocity/linear uncoupled schemes (both commonly used in atmospheric and ocean modeling), the linear coupled scheme, and the nonlinear using the nonlinear coupled scheme (both proposed in this paper). The location of the Lagrangian drifter inside the grid cell is determined by two algebraic equations that are solved explicitly with the constant velocity/linear uncoupled schemes, and implicitly using the Newton–Raphson iteration method with the linear/nonlinear coupled schemes. The analytical Stommel ocean model on the f plane is used to illustrate great accuracy improvement from keeping the first term to keeping all the terms of the two-dimensional interpolation.

Corresponding author address: Peter C. Chu, Naval Ocean Analysis and Prediction Laboratory, Department of Oceanography, Naval Postgraduate School, 833 Dyer Road, Monterey, CA 93943. E-mail: pcchu@nps.edu
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  • Blanke, B., and Raynaud S. , 1997: Kinematics of the Pacific Equatorial Undercurrent: An Eulerian and Lagrangian approach from GCM results. J. Phys. Oceanogr., 27, 10381053, doi:10.1175/1520-0485(1997)027<1038:KOTPEU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., and Fan C. W. , 2010: Space-time transformation in flux-form semi-Lagrangian schemes. Terr. Atmos. Oceanic Sci., 21, 1726, doi:10.3319/TAO.2009.05.25.01(IWNOP).

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., Lu S. H. , and Chen Y. C. , 2001: Evaluation of the Princeton Ocean Model using the South China Sea Monsoon Experiment (SCSMEX) data. J. Atmos. Oceanic Technol., 18, 15211539, doi:10.1175/1520-0426(2001)018<1521:EOTPOM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., Ivanov L. M. , Korzhova T. P. , Margolina T. M. , and Melnichenko O. V. , 2003a: Analysis of sparse and noisy ocean current data using flow decomposition. Part I: Theory. J. Atmos. Oceanic Technol., 20, 478491, doi:10.1175/1520-0426(2003)20<478:AOSANO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., Ivanov L. M. , Korzhova T. P. , Margolina T. M. , and Melnichenko O. V. , 2003b: Analysis of sparse and noisy ocean current data using flow decomposition. Part II: Application to Eulerian and Lagrangian data. J. Atmos. Oceanic Technol., 20, 492512, doi:10.1175/1520-0426(2003)20<492:AOSANO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1007/s10872-005-0009-3.

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., Ivanov L. M. , and Melnichenko O. V. , 2005: Fall–winter current reversals on the Texas–Louisiana continental shelf. J. Phys. Oceanogr., 35, 902910, doi:10.1175/JPO2703.1.

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., Ivanov L. M. , Melnichenko O. V. , and Wells N. C. , 2007: On long baroclinic Rossby waves in the tropical North Atlantic observed from profiling floats. J. Geophys. Res., 112, C05032, doi:10.1029/2006JC003698.

    • Search Google Scholar
    • Export Citation
  • Doos, K., 1995: Interocean exchange of water masses. J. Geophys. Res., 100, 13 49913 514, doi:10.1029/95JC00337.

  • Galanis, G., Louka P. , Katsafados P. , Pytharoulis I. , and Kallos G. , 2006: Applications of Kalman filters based on non-linear functions to numerical weather predictions. Ann. Geophys., 24, 24512460, doi:10.5194/angeo-24-2451-2006.

    • Search Google Scholar
    • Export Citation
  • Gandin, L. S., 1965: Objective Analysis of Meteorological Fields. Israel Program for Scientific Translation, 242 pp.

  • Lozano, C. J., Robinson A. R. , Arango 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 Oceanography Series, Vol. 61, 413–452, doi:10.1016/S0422-9894(96)80018-3.

  • Song, Y. T., and Colberg F. , 2011: Deep ocean warming assessed from altimeters, Gravity Recovery and Climate Experiment, in situ measurements, and a non-Boussinesq ocean general circulation model. J. Geophys. Res., 116, C02020, doi:10.1029/2010JC006601.

    • Search Google Scholar
    • Export Citation
  • Stommel, H. M., 1948: The westward intensification of wind-driven ocean currents. Trans. Amer. Geophys. Union, 29, 202206, doi:10.1029/TR029i002p00202.

    • Search Google Scholar
    • Export Citation
  • Sun, L. C., 1999: Data inter-operability driven by oceanic data assimilation needs. Mar. Technol. Soc. J., 33, 5566, doi:10.4031/MTSJ.33.3.7.

    • Search Google Scholar
    • Export Citation
  • Vries, P. D., and Doos K. , 2001: Calculating Lagrangian trajectories using time-dependent velocity fields. J. Atmos. Oceanic Technol., 18, 10921101, doi:10.1175/1520-0426(2001)018<1092:CLTUTD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation