CoAuthors

Ronan Fablet (IMT Atlantique, France); Quentin Febvre (IMT Atlantique, France); Mohamed Mahmoud Amar (IMT Atlantique, France); Benjamin Carpentier (CLS, France)

The reconstruction of sea surface currents is a key challenge in spatial oceanography. We recently proposed the so-called 4DVarNN algorithm, a generic end-to-end deep learning scheme for inverse problems using a variational formulation. Based on Observing System Simulation Experiment (OSSE) involving high-resolution numerical simulations in the Gulf Stream region, the preliminary applications of the 4DVarNN algorithm using an LSTM-based parametrization of the solver have shown promising results. We propose here to present the recent evolutions of the 4DVarNN framework applied to spatio-temporal interpolations of satellite-derived datasets. First, 4DVarNN embeds a variational formalism which is a natural framework for exploiting multi-tracer synergies (e.g. SSH and SST) in the reconstruction of altimetric fields benefiting from high-resolution satellite products. Using a similar OSSE configuration, we demonstrate how the use of SST may help for the identification of ocean fronts resulting in a better reconstruction of the SSH. Second, the variational formulation also enables to design optimal monitoring and sampling strategies to retrieve the best reconstruction of the submesoscale processes. Last, from an operational perspective, a new version of the code able to deal with datasets scaling up to an ocean basin has been recently distributed. Training the model involves a new strategy based on iterating the entire datasets in small batches. This code is open-source and enables its future use for both design and participation in ocean data challenges.