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dc.contributor.authorLermusiaux, Pierre, F.J.
dc.contributor.authorChiu, Ching-Sang
dc.contributor.authorGawarkiewicz, Glen G.
dc.contributor.authorAbbot, Phil
dc.contributor.authorRobinson, Allan R.
dc.contributor.authorMiller, Robert N.
dc.contributor.authorHaley, Patrick J.
dc.contributor.authorLeslie, Wayne G.
dc.contributor.authorMajumdar, Sharan J.
dc.contributor.authorPang, Alex
dc.contributor.authorLeklen, Francois
dc.date.accessioned2014-10-06T22:40:29Z
dc.date.available2014-10-06T22:40:29Z
dc.date.issued2006-03
dc.identifier.citationOceanography, Volume 19, Number 1, March 2006, pp. 93
dc.identifier.urihttp://hdl.handle.net/10945/43438
dc.description.abstractA multitude of physical and biological processes occur in the ocean over a wide range of temporal and spatial scales. Many of these processes are nonlinear and highly variable, and involve interactions across several scales and oceanic disciplines. For example, sound propagation is infl uenced by physical and biological properties of the water column and by the seabed. From observations and conservation laws, ocean scientists formulate models that aim to explain and predict dynamics of the sea. This formulation is intricate because it is challenging to observe the ocean on a sustained basis and to transform basic laws into generic but usable models. There are imperfections in both data and model estimates. It is important to quantify such uncertainties to understand limitations and identify the research needed to increase accuracies, which will lead to fundamental progress.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleQuantifying uncertainties in ocean preditions / Advances in Computational Oceanographyen_US
dc.typeArticleen_US
dc.contributor.departmentOceanography


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