AUTHOR=Smith Philip A. H. , Sørensen Kristian Aa. , Buongiorno Nardelli Bruno , Chauhan Anshul , Christensen Asbjørn , St. John Michael , Rodrigues Filipe , Mariani Patrizio TITLE=Reconstruction of subsurface ocean state variables using Convolutional Neural Networks with combined satellite and in situ data JOURNAL=Frontiers in Marine Science VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2023.1218514 DOI=10.3389/fmars.2023.1218514 ISSN=2296-7745 ABSTRACT=

Subsurface ocean measurements are extremely sparse and irregularly distributed, narrowing our ability to describe deep ocean processes and thus also limiting our understanding of the role of ocean and marine ecosystems in the Earth system. To overcome these observational limitations, neural networks combining remotely-sensed surface measurements and in situ vertical profiles are increasingly being used to retrieve high-quality three-dimensional estimates of the ocean state. This study proposes a convolutional neural network (CNN) architecture for the reconstruction of vertical profiles of temperature and salinity starting from surface observation-based data. The model is trained on satellite and in situ data collected between 2005 and 2020 in the Atlantic Ocean. Rather than using spatially gridded in situ observations, we use directly measured vertical profiles. Different combinations of surface variables are analyzed and compared in order to determine the most effective inputs for the CNN. Furthermore, the relative importance of each of these variables in the vertical reconstruction is assessed using Shapley values, originally developed in the framework of cooperative game theory. The model performance is shown to be superior to current state-of-the-art methods and the same approach can easily be extended to other basins or to the global ocean.