Fitting Dynamic Models to the Geosat Sea Level Observations in the Tropical Pacific Ocean. Part II: A Linear, Wind-driven Model

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  • 1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • | 2 College of Oceanography, Oregon State University, Corvallis, Oregon
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Abstract

The Geosat altimeter sea level observations in the tropical Pacific Ocean are used to evaluate the Performance of a linear wind-driven equatorial wave model. The question posed is the extent to which such a model can describe the observed sea level variations. The Kalman filter and optimal smoother are used to obtain a solution that is an optimal fit to the observation in a weighted least-squares sense. The total mean variance of the Geosat sea level observation is 98.1 cm2, of which 36.6 cm2 is due to measurement errors, leaving 61.5 cm2 for the oceanographic signal to be explained. The model is found to account for about 68% of this signal Variance and the remainder is ascribed to the effects of physical mechanisms missing from the model. This result suggests that the Geosat data contains sufficient information for testing yet more sophisticated models. Utility of an approximate filter and smoother based on the asymptotic time limit of the estimation error covariance is also examined and compared with the estimates of the full time-evolving filter. The results are found to be statistically indistinguishable from each other, but the computational requirements are more than an order of magnitude less for the approximate filter/smoother. Corrections to the wind field that drives the model are also obtained by the smoother, but they are found only to be marginally improved when compared with in situ wind measurements. The substantial errors in the Geosat data and the simplicity of the present model prevents a reliable wind estimate from being made.

Abstract

The Geosat altimeter sea level observations in the tropical Pacific Ocean are used to evaluate the Performance of a linear wind-driven equatorial wave model. The question posed is the extent to which such a model can describe the observed sea level variations. The Kalman filter and optimal smoother are used to obtain a solution that is an optimal fit to the observation in a weighted least-squares sense. The total mean variance of the Geosat sea level observation is 98.1 cm2, of which 36.6 cm2 is due to measurement errors, leaving 61.5 cm2 for the oceanographic signal to be explained. The model is found to account for about 68% of this signal Variance and the remainder is ascribed to the effects of physical mechanisms missing from the model. This result suggests that the Geosat data contains sufficient information for testing yet more sophisticated models. Utility of an approximate filter and smoother based on the asymptotic time limit of the estimation error covariance is also examined and compared with the estimates of the full time-evolving filter. The results are found to be statistically indistinguishable from each other, but the computational requirements are more than an order of magnitude less for the approximate filter/smoother. Corrections to the wind field that drives the model are also obtained by the smoother, but they are found only to be marginally improved when compared with in situ wind measurements. The substantial errors in the Geosat data and the simplicity of the present model prevents a reliable wind estimate from being made.

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