Four-dimensional data assimilation for coupled physical-acoustic fields
Abstract
The estimation of oceanic environmental and acoustical fields is considered as a single
coupled data assimilation problem. The four-dimensional data assimilation methodology
employed is Error Subspace Statistical Estimation. Environmental fields and their dominant
uncertainties are predicted by an ocean dynamical model and transferred to acoustical
fields and uncertainties by an acoustic propagation model. The resulting coupled
dominant uncertainties define the error subspace. The available physical and acoustical
data are then assimilated into the predicted fields in accord with the error subspace and all
data uncertainties. The criterion for data assimilation is presently to correct the predicted
fields such that the total error variance in the error subspace is minimized. The approach
is exemplified for the New England continental shelfbreak region, using data collected
during the 1996 Shelfbreak Primer Experiment. The methodology is discussed, computational
issues are outlined and the assimilation of model-simulated acoustical data is
carried out. Results are encouraging and provide some insights into the dominant variability
and uncertainty properties of acoustical fields.
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