Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms
<p>Our study focuses on absolute dynamic topography (ADT) and sea surface temperature (SST) mapping from satellite observations, with the primary objective of improving the satellite-derived ADT (and derived geostrophic currents) spatial resolution. Retrieving consistent high-resolution ADT an...
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Copernicus Publications
2025-01-01
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| Series: | Ocean Science |
| Online Access: | https://os.copernicus.org/articles/21/199/2025/os-21-199-2025.pdf |
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| author | D. Ciani C. Fanelli B. Buongiorno Nardelli |
| author_facet | D. Ciani C. Fanelli B. Buongiorno Nardelli |
| author_sort | D. Ciani |
| collection | DOAJ |
| description | <p>Our study focuses on absolute dynamic topography (ADT) and sea surface temperature (SST) mapping from satellite observations, with the primary objective of improving the satellite-derived ADT (and derived geostrophic currents) spatial resolution. 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 Experiment (OSSE) relying on Copernicus numerical model outputs (with respective temporal and spatial resolutions of 1 d and 1/24°), 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, allowing us to reconstruct super-resolved ADTs and geostrophic currents at the same spatiotemporal resolution of the model outputs employed for the OSSE. A 12-year-long time series of super-resolved geostrophic currents (2008–2019) is thus presented and validated against in situ-measured currents from drogued drifting buoys and via spectral analyses. 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, quite a challenging region due to its small Rossby deformation radius (around 10 <span class="inline-formula">km</span>).</p> |
| format | Article |
| id | doaj-art-7c3b85bcb6d44d13a881e74c2fd79a94 |
| institution | Kabale University |
| issn | 1812-0784 1812-0792 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Ocean Science |
| spelling | doaj-art-7c3b85bcb6d44d13a881e74c2fd79a942025-01-27T14:56:15ZengCopernicus PublicationsOcean Science1812-07841812-07922025-01-012119921610.5194/os-21-199-2025Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithmsD. Ciani0C. Fanelli1B. Buongiorno Nardelli2Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 00133, Rome, ItalyConsiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 80133, Naples, ItalyConsiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 80133, Naples, Italy<p>Our study focuses on absolute dynamic topography (ADT) and sea surface temperature (SST) mapping from satellite observations, with the primary objective of improving the satellite-derived ADT (and derived geostrophic currents) spatial resolution. 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 Experiment (OSSE) relying on Copernicus numerical model outputs (with respective temporal and spatial resolutions of 1 d and 1/24°), 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, allowing us to reconstruct super-resolved ADTs and geostrophic currents at the same spatiotemporal resolution of the model outputs employed for the OSSE. A 12-year-long time series of super-resolved geostrophic currents (2008–2019) is thus presented and validated against in situ-measured currents from drogued drifting buoys and via spectral analyses. 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, quite a challenging region due to its small Rossby deformation radius (around 10 <span class="inline-formula">km</span>).</p>https://os.copernicus.org/articles/21/199/2025/os-21-199-2025.pdf |
| spellingShingle | D. Ciani C. Fanelli B. Buongiorno Nardelli Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms Ocean Science |
| title | Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms |
| title_full | Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms |
| title_fullStr | Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms |
| title_full_unstemmed | Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms |
| title_short | Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms |
| title_sort | estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms |
| url | https://os.copernicus.org/articles/21/199/2025/os-21-199-2025.pdf |
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