  Combining remote-sensing data with in-situ observations to achieve a
comprehensive 3D reconstruction of the ocean state presents significant
challenges for traditional interpolation techniques. To address this, we
developed the CLuster Optimal Interpolation Neural Network (CLOINet), which
combines the robust mathematical framework of the Optimal Interpolation (OI)
scheme with a self-supervised clustering approach. CLOINet efficiently segments
remote sensing images into clusters to reveal non-local correlations, thereby
enhancing fine-scale oceanic reconstructions. We trained our network using
outputs from an Ocean General Circulation Model (OGCM), which also facilitated
various testing scenarios. Our Observing System Simulation Experiments aimed to
reconstruct deep salinity fields using Sea Surface Temperature (SST) or Sea
Surface Height (SSH), alongside sparse in-situ salinity observations. The
results showcased a significant reduction in reconstruction error up to $40\%$
and the ability to resolve scales $50\%$ smaller compared to baseline OI
techniques. Remarkably, even though CLOINet was trained exclusively on
simulated data, it accurately reconstructed an unseen SST field using only
glider temperature observations and satellite chlorophyll concentration data.
This demonstrates how deep learning networks like CLOINet can potentially lead
the integration of modeling and observational efforts in developing an ocean
digital twin.
