Statistical Inference of Weakly Correlated Subthermocline Fields from SatelliteAltimeter Data

摘要

To initialize the barotropic and baroclinic modes , numerical ocean predictionmodels need information both above and below the main thermocline. Forecasts of upper ocean mesoscale variability using real and simulated data show high sensitivity to the subthermocline pressure field. Results using simulated data indicate that the accuracy of this field may be the limiting factor on the time scale for mesoscale oceanic predictive skill. Satellite altimetry provides a potentially abundant source of sea surface height (SSH) data, but there is no comparable source of subthermocline information. We investigate statistical techniques to infer subthermocline pressure anomalies from SSH data, a problem complicated by the weak correlation between the fields. This problem is addressed by using the degrees of freedom available in the data and by describing them in an efficient manner to suppress noise, eliminate unskilled or redundant estimators and to prevent ill-conditioned matrix inversions. Multilinear regression, empirical orthogonal function regression and principal estimator patterns are compared using data simulation by a numerical ocean model and error models. Application of the statistical techniques are investigated. Topics include noise suppression and the impact of the noise on accuracy. These topics are studied as a function of decorrelation distance in the noise and the presence or absence of noise in dependent and independent data sets. Keywords: Reprints. (EDC)