Extrapolating oceanic signals from surface data to deeper layers:
Bruno Buongiorno Nardelli(1) , Olga Cavalieri(2) , Rosalia Santoleri(1) , and Marie-Helene Rio(3)
Via del Fosso del Cavaliere 100,
(2) ENEA, via anguillarese 301, 00060 S. Maria di Galeria (Roma) , Italy
(3) CLS (previously CNR-ISAC), 8-10, rue Hermès, 31520 Ramonville Saint-Agne, France
Different approaches can be followed to extract as much information as possible from sea surface and vertically integrated measurements, potentially allowing to infer vertical profiles from sea surface level measured by satellite altimeters or acoustic round trip travel time measurements from inverted echo sounders, coupled to other remotely sensed data. Among these, data assimilation in numerical models is obviously crucial in order to obtain accurate analyses and forecasts, but its results are also strongly dependent on the models’ assumptions and characteristics. On the other hand, the approach explored here, namely the direct analysis of the sole observations and of their covariances, can help to identify what is the real information content of the data, how this can be extracted more efficiently, and also what measurements are needed operatively to optimize an observational network. Recently proposed Coupled Pattern Reconstruction (CPR) and multivariate Empirical Orthogonal Function Reconstruction (mEOF-R) directly couple steric height, temperature and/or salinity and chlorophyll concentration profiles (Buongiorno Nardelli and Santoleri, JTECH 2004; Buongiorno Nardelli and Santoleri, JTECH 2005; Buongiorno Nardelli et al., CIESM 2005). The two techniques have been first applied to time series of in situ measurements at fixed locations, and successively tested in an area of fundamental importance for the Mediterranean sea dynamics as the Sicily Channel. The more relevant results of these analyses will be summarized and presented.