CLOINet: Ocean state reconstructions through remote-sensing, in-situ sparse observations and Deep Learning

TL;DR

CLOINet is a deep learning framework that combines optimal interpolation with clustering to improve 3D ocean state reconstructions from sparse data, significantly reducing errors and resolving finer scales.

Contribution

The paper introduces CLOINet, a novel neural network that integrates optimal interpolation with self-supervised clustering for enhanced ocean state reconstruction.

Findings

Reduced reconstruction error by up to 40%.

Resolved scales 50% smaller than baseline methods.

Successfully reconstructed unseen SST fields using only sparse observations.

Abstract

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.