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Tal Ezer and George L. Mellor

Abstract

Satellite-derived surface data have become an important source of information for studies of the Gulf Stream system. The question of just how useful these datasets are for nowcasting the subsurface thermal fields, however, remains to be fully explored. Three types of surface data—sea surface temperature (SST), sea surface height (SSH), and Gulf Stream position (GSP)—are used here in a series of data assimilation experiments to test their usefulness when assimilated into a realistic primitive equation model. The U.S. Navy’s analysis fields from the Optimal Thermal Interpolation System are used to simulate the surface data and to evaluate nowcast errors. Correlation factors between variations of the surface data and variations of the subsurface temperature are used to project the surface information into the deep ocean, using data and model error estimates and an optimal interpolation approach to blend model and observed fields.

While assimilation of each surface data source shows some skill in nowcasting the subsurface fields (i.e., reducing errors compared to a control case without assimilation), SSH data reduce errors more effectively in middepths (around 500 m), and SST data reduce errors more effectively in the upper layers (above 100 m). Assimilation of GSP is effective in nowcasting the deep Gulf Stream, while the model dynamics produce eddies that are not included in the GSP analysis. An attempt to optimally combine SST and SSH data in the assimilation shows an improved skill at all depths compared to assimilation of each set of data separately.

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Tal Ezer, George L. Mellor, Dong-Shan Ko, and Ziv Sirkes

Abstract

Two types of satellite data, Geosat altimeter data and sea surface temperature data (SST), are compared and evaluated for their usefulness in assimilation into a numerical model of the Gulf Stream region. Synoptic sea surface height (SSH) fields are derived from the SST data in the following way: first three-dimensional temperature and salinity analysis fields are obtained through the Optimum Thermal Interpolation System (OTIS), and then SSH fields are calculated using a primitive equation, free-surface, numerical model running in a diagnostic mode. The aforementioned SSH fields are compared with SSH fields obtained from the Geosat altimeter data. Use of Geosat data requires an estimate of the cream SSH field relative to the earth geoid. Three different methods to obtain the mean SSH field are demonstrated. The first method uses altimetry and SST data, the second uses a diagnostic calculation with climatological data; and the third uses prognostic numerical calculations. The three estimates compared favorably with each other and with estimates obtained elsewhere.

The comparison of the synoptic SSH fields derived from both data types reveals similarity in the Gulf Stream meanders and some mesoscale features, but shows differences in strength of eddies and in variability far from the Gulf Stream. Due to the smoothed nature of the OTIS analysis fields, the SSH derived from altimetry data has larger variability amplitudes compared to that derived from SST data.

The statistical interpolation method, which is used to interpolate altimetry data from satellite tracks onto the model grid, is also evaluated for its filtering effect and its sensitivity to different parameters. The SSH variability of the Gulf Stream was calculated from two years of the exact repeat mission of the Geosat satellite, where altimeter data were interpolated daily onto the model grid. It is suggested here that some of the underestimation of mesoscale variations by statistical interpolation methods, as indicated by previous studies, may be explained by the filtering effect of the scheme.

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