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Ocean dynamics constitute a source of incertitude in determining the ocean'srole in complex climatic phenomena. Current observation systems havelimitations in achieving sufficiently statistical precision forthree-dimensional oceanic data. It is crucial knowledge to describe thebehavior of internal ocean structures. We present the data-driven approacheswhich explore latent class regressions and deep regression neural networks inmodeling ocean dynamics in the extensions of Gulf Stream and Kuroshio currents.The obtained results show a promising data-driven direction for understandingthe ocean's characteristics, including salinity and temperature, in bothspatial and temporal dimensions in the turbulent regions. Our source codes arepublicly available at https://github.com/v18nguye/gulfstream-lrm and athttps://github.com/sagudelor/Kuroshio.

Synergy between Observation Systems Oceanic in Turbulent Regions