Reconstruction of Subsurface Velocities From Satellite Observations Using Iterative Self-Organizing Maps — Australian Meteorological and Oceanographic Society

Reconstruction of Subsurface Velocities From Satellite Observations Using Iterative Self-Organizing Maps (#154)

Chris C Chapman 1 , Anastase A Charantonis 2
  1. CSIRO Oceans and Atmosphere, Battery Point, TASMANIA, Australia
  2. École Nationale Supérieure d’Informatique pour l’Industrie et l’entreprise, Évry, France

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. No assumptions regarding the s

tructure of the water column, nor the underlying dynamics of the flow field, are made. 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 1000 m. Validation reveals promising results, with a speed root mean squared error of 2.8 cm/s, morethan a factor of two smaller than competing methods, and direction errors consistently smaller than 30°. Finally, we discuss the merits and shortcomings of this methodology

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