Preprints
https://doi.org/10.5194/egusphere-2024-1164
https://doi.org/10.5194/egusphere-2024-1164
19 Apr 2024
 | 19 Apr 2024
Status: this preprint is open for discussion.

Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms

Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli

Abstract. Our study focuses on the improvement of Absolute Dynamic Topography (ADT) and Sea Surface Temperature (SST) mapping from satellite observations. Retrieving consistent high resolution ADT and SST information from space is challenging, due to instrument limitations, sampling constraints and degradations introduced by the interpolation algorithms used to obtain gap free (L4) analyses. To address these issues, we developed and tested different deep learning methodologies, specifically Convolutional Neural Network (CNN) models that were originally proposed for single-image super-resolution. Building upon recent findings, we conduct an Observing System Simulation Experiments (OSSE) relying on Copernicus numerical model outputs and we present a strategy for further refinements. Previous OSSEs combined low resolution L4 satellite equivalent ADTs with high resolution "perfectly known" SSTs to derive high resolution sea surface dynamical features. Here, we introduce realistic SST L4 processing errors and modify the network to concurrently predict high resolution SST and ADT from synthetic, satellite equivalent L4 products. This modification allows us to evaluate the potential enhancement in the ADT and SST mapping while integrating dynamical constraints through tailored, physics informed loss functions. The neural networks are thus trained using OSSE data and subsequently applied to the Copernicus Marine Service satellite derived ADTs and SSTs, with the primary goal of reconstructing super resolved ADTs and geostrophic currents. A 12 years long time series of super resolved geostrophic currents (2008–2019) is thus presented and validated against in situ measured currents from drogued drifting buoys. This study suggests that CNNs are beneficial for improving standard Altimetry mapping: they generally sharpen the ADT gradients with consequent correction of the surface currents direction and intensities with respect to the altimeter derived products. Our investigation is focused on the Mediterranean Sea, a quite challenging region due to its small Rossby deformation radius (around 10 km).

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Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli

Status: open (until 27 Jun 2024)

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Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli

Data sets

Mediterranean Sea Super Resolved Geostrophic Currents Daniele Ciani, Bruno Buongiorno Nardelli, and Elodie Charles https://zenodo.org/records/10727432

Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli

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Short summary
Ocean surface currents are routinely derived from satellite observations of the sea level, allowing a regional to global scale synoptic monitoring. In order to overcome the theoretical and instrumental limits of this methodology, we exploit the synergy of multisensor satellite observations. We rely on deep learning, physics informed algorithms to predict ocean currents from sea surface height and sea surface temperature observations. Results are validated by means of in-situ measurements