Estimation and study of mesoscale variability in the Strait of Sicily

Considering mesoscale variability in the Strait of Sicily during September 1996, the four-dimensional physical fields and their dominant variability a nd error covariances are estimated and studied. The methodology applied in real-time combines an intensive data survey and primitive equation dynamics based on the error subspace statistical estimation approach. A sequence of filtering and prediction problems are solved for a period of ten days, wit h adaptive learning of the dominant errors. Intercomparisons with optimal i nterpolation fields, clear sea surface temperature images and available in situ data are utilized for qualitative and quantitative evaluations. The pr esent estimation system is shown to be a comprehensive nonlinear and adapti ve assimilation scheme, capable of providing real-time forecasts of ocean f ields and associated dominant variability and error covariances. The initia lization and evolution of the error subspace is explained. The dominant err or eigenvectors, variance and covariance fields are illustrated and their m ultivariate, multiscale properties described. Five coupled features associa ted with the dominant variability in the Strait during August-September 199 6 emerge from the dominant decomposition of the initial primitive equation variability covariance matrix: the Adventure Bank Vortex, Maltese Channel C rest, Ionian Shelfbreak Vortex, Messina Rise Vortex, and subbasin-scale tem perature and salinity fronts of the Ionian slope. From the evolution of the estimated fields and dominant predictability error covariance decompositio ns, several of the primitive equation processes associated with the variati ons of these features are revealed, decomposed and studied. In general, the estimation of the evolving dominant decompositions of the multivariate pre dictability error and variability covariances appears promising for ocean s ciences and technology. The practical feedbacks of the present approach whi ch include the determination of data optimals and the refinements of dynami cal and measurement models are considered. (C) 1999 Elsevier Science B.V. A ll rights reserved.