A Comparison of Gulf Stream Sea Surface Height Fields Derived from Geosat Altimeter Data and Those Derived from Sea Surface Temperature Data

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.