Isopycnic and Hybrid Ocean Modeling in the Context of GODAE

  • Eric P. Chassignet


An ocean forecasting system has three essential components (observations, data assimilation, numerical model). Observational data, via data assimilation, form the basis of an accurate model forecast; the quality of the ocean forecast will depend primarily on the ability of the ocean numerical model to faithfully represent the ocean physics and dynamics. Even the use of an infinite amount of data to constrain the initial conditions will not necessarily improve the forecast against persistence of a poorly performing ocean numerical model. In this chapter, some of the challenges associated with global ocean modeling are introduced and the current state of numerical models formulated in isopycnic and hybrid vertical coordinates is reviewed within the context of operational global ocean prediction systems.


Data Assimilation Vertical Coordinate Potential Density Surface Mixed Layer Bottom Boundary Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



As stated in the introduction, a lot of material presented in this chapter relies heavily on articles, notes, and review papers by R. Bleck, S. Griffies, A. Adcroft, and R. Hallberg. I also would like to acknowledge contributions by H. Hurlburt and B. Arbic. The development of the HYCOM ocean prediction system was sponsored by the National Oceanographic Partnership Program (NOPP) and the Office of Naval Research (ONR).


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© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Center for Ocean-Atmospheric Predictions Studies, Florida State UniversityTallahasseeUSA

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