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James A. Cummings and Ole Martin Smedstad

1. Introduction Assessment of the impact of observations on reducing ocean model forecast error from data assimilation is a fundamental aspect of any ocean analysis and forecasting system. The purpose of assimilation is to reduce the model initial condition error. Improved initial conditions should lead to an improved forecast. However, it is likely that not all observations assimilated have equal value in reducing forecasting error. Estimation of which observations are best and the

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David Halpern, Dimitris Menemenlis, and Xiaochun Wang

, wind stress and its curl are modified by the data assimilation. Because the OGCM is nonlinear, several such forward-adjoint iterations are needed to reach a solution that is statistically consistent with model and data error estimates. Table 1 lists assimilated datasets and prescribed errors in the cost function, which differ for 2004–05 and 2009–11. The European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis and the Japan Meteorological Agency (JMA) reanalysis of surface

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