  In this letter a new method based on modified self-organizing maps is
presented for the reconstruction of deep ocean current velocities from surface
information provided by satellites. This method takes advantage of local
correlations in the data-space to improve the accuracy of the reconstructed
deep velocities. Unlike previous attempts to reconstruct deep velocities from
surface data, our method makes no assumptions regarding the structure of the
water column, nor the underlying dynamics of the flow field. Using satellite
observations of surface velocity, sea-surface height and sea-surface
temperature, as well as observations of the deep current velocity from
autonomous Argo floats to train the map, we are able to reconstruct realistic
high--resolution velocity fields at a depth of 1000m. Validation reveals
extremely promising results, with a speed root mean squared error of ~2.8cm/s,
a factor more than a factor of two smaller than competing methods, and
direction errors consistently smaller than 30 degrees. Finally, we discuss the
merits and shortcomings of this methodology and its possible future
applications.
