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Ref.: SiPLAB Report 02/06, FCT, University of Algarve,2006.
The space-time oceanographic mapping of given areas assumes an obvious importance in environmental and acoustic assessment, from regional to large scales. Since the 70’s, deterministic models for oceanographic processes have been coupled with statistical interpolation tools, in order to cope with the inevitable sparsity of oceanographic measurements. These tools treat physical quantities as random, and assume one from a collection of possible models for their inter-correlation. One of these models, a modified Gaussin with space-time dependence, is here fitted to temperature data correlations from the acoustic-oceanographic MREA ’03 sea trial, in order to characterize the temperature space-time distribution and evolution. The model assumes the temperature shift from the mean as anisotropic, homogeneous and stationary. The correlation model is parameterized by four correlation lengths and a scaling factor. The multiple-instrumental sea trial setup allowed the estimation of the unknown parameters, by fitting the model with two differently originated data correlations. The first is from data measured by a spatially fixed single instrument or by CTD casts in the area. The second is from stacked data measured by all temperature monitoring instruments. The results show that the actual temperature perturbation field is not homogeneous, as expected. Significant differences exist for the parameter estimates, comparing that obtained with shallower instruments with those obtained by instruments with a deeper covering array aperture. The question about the usefulness of combining the information coming from several instruments has not a trivial answer. If the objective is to interpolate along depth and time, the information coming only from instruments localized on the interest area is likely to give better results. Instead, for horizontal interpolations, it is essential to combine various instruments, considering the data at hand. This was the unique possibility to obtain a reasonable number of data correlation lags, to be fitted by the model, and give realistic horizontal correlation lengths.
ACKNOWLEDGMENT: this work was partially supported by
project NUACE, funded by FCT, Portugal.