An Optimal Nudging Data Assimilation Scheme Using Parameter Estimation

doi:10.1002/qj.49711850808

An Optimal Nudging Data Assimilation Scheme Using Parameter Estimation

2006-12-15

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DOI：10.1002/qj.49711850808

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AbstractA new optimal nudging dynamical relaxation technique is tested in the framework of 4‐dimensional variational data assimilation, applied to an adiabatic T40 version of the National Meteorological Center (NMC) spectral model with 18 vertical layers. Several experiments are performed using the NMC operationally analysed data. the variational data assimilation algorithm is also employed in a parameter‐estimation mode to determine the vector of optimal nudging coefficients. Results of data‐assimilation experiments involving estimated nudging, optimal nudging and variational data assimilation are compared. Issues are addressed related to the dependence of the assimilation on the length of the assimilation period as well as to the ability of retrieving high‐quality model initial conditions.The study outlines the ability to obtain optimal nudging coefficients, which can vary in space, in the framework of a parameter‐estimation approach using variational data assimilation. Based on our preliminary results the optimal nudging seems to be a most promising data‐assimilation scheme.

An Optimal Nudging Data Assimilation Scheme Using Parameter Estimation

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