  Understanding local currents in the North Atlantic region of the ocean is a
key part of modelling heat transfer and global climate patterns. Satellites
provide a surface signature of the temperature of the ocean with a high
horizontal resolution while in situ autonomous probes supply high vertical
resolution, but horizontally sparse, knowledge of the ocean interior thermal
structure. The objective of this paper is to develop a methodology to combine
these complementary ocean observing systems measurements to obtain a
three-dimensional time series of ocean temperatures with high horizontal and
vertical resolution. Within an observation-driven framework, we investigate the
extent to which mesoscale ocean dynamics in the North Atlantic region may be
decomposed into a mixture of dynamical modes, characterized by different local
regressions between Sea Surface Temperature (SST), Sea Level Anomalies (SLA)
and Vertical Temperature fields. Ultimately we propose a Latent-class
regression method to improve prediction of vertical ocean temperature.
