Continuous Assimilation of Geosat Altimeter Data into a Three-Dimensional Primitive Equation Gulf Stream Model

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  • 1 Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, New Jersey
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Abstract

A three-dimensional data assimilation scheme is described and tested, using the Geosat altimeter data and a high-resolution, primitive equation, numerical ocean model of the Gulf Stream region. The assimilation scheme is based on an optimal interpolation approach in which data along satellite tracks are continuously interpolated horizontally and vertically into the model grid and assimilated with the model prognostic fields. Preprocessed correlations between surface elevation anomalies and subsurface temperature and salinity anomalies are used to project surface information into the deep ocean; model and data error estimates are used to optimize the assimilation. Analysis fields derived from the Navy's Optimum Thermal Interpolation System are used to initialize the model and to provide some estimate of errors.

To evaluate the effectiveness of the assimilation scheme, the errors of model oceanic fields (surface elevation, Gulf Stream axis, temperature) with data assimilation are compared with errors without data assimilation (i.e., a pure forecast). Although some mesoscale meanders and rings are not well produced by the assimilation model, consistent reduction of errors by the assimilation is demonstrated. The vertical distribution of errors reveals that the scheme is most effective in nowcasting temperatures at middepth (around 500 m) and less effective near the surface and in the deep ocean. The scheme is also more effective in nowcasting the Gulf Stream axis location than in nowcasting temperature variations. A comparison of the assimilation scheme during two periods shows that the nowcast skill of the assimilated model is reduced in May–September 1988, compared to May–July 1987, due to poor coverage of the altimeter data during 1988.

This paper is one step toward a dynamic model and data assimilation system, which when fully developed, should provide useful nowcast and forecast information.

Abstract

A three-dimensional data assimilation scheme is described and tested, using the Geosat altimeter data and a high-resolution, primitive equation, numerical ocean model of the Gulf Stream region. The assimilation scheme is based on an optimal interpolation approach in which data along satellite tracks are continuously interpolated horizontally and vertically into the model grid and assimilated with the model prognostic fields. Preprocessed correlations between surface elevation anomalies and subsurface temperature and salinity anomalies are used to project surface information into the deep ocean; model and data error estimates are used to optimize the assimilation. Analysis fields derived from the Navy's Optimum Thermal Interpolation System are used to initialize the model and to provide some estimate of errors.

To evaluate the effectiveness of the assimilation scheme, the errors of model oceanic fields (surface elevation, Gulf Stream axis, temperature) with data assimilation are compared with errors without data assimilation (i.e., a pure forecast). Although some mesoscale meanders and rings are not well produced by the assimilation model, consistent reduction of errors by the assimilation is demonstrated. The vertical distribution of errors reveals that the scheme is most effective in nowcasting temperatures at middepth (around 500 m) and less effective near the surface and in the deep ocean. The scheme is also more effective in nowcasting the Gulf Stream axis location than in nowcasting temperature variations. A comparison of the assimilation scheme during two periods shows that the nowcast skill of the assimilated model is reduced in May–September 1988, compared to May–July 1987, due to poor coverage of the altimeter data during 1988.

This paper is one step toward a dynamic model and data assimilation system, which when fully developed, should provide useful nowcast and forecast information.

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