Enhancing Ocean Temperature and Salinity Reconstruction with Deep Learning: The Role of Surface Waves DOI Creative Commons
Xiaoyu Yu, Daling Li Yi, Peng Wang

et al.

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(5), P. 910 - 910

Published: May 3, 2025

In oceanographic research, reconstructing the three-dimensional (3D) distribution of temperature and salinity is essential for understanding global climate dynamics, predicting marine environmental changes, evaluating their impacts on ecosystems. While previous studies have largely concentrated effects various modeling approaches oceanic variables, limited attention has been paid to role surface waves in reconstruction. This study, based sea data, employs a deep learning-based neural network model, U-Net, reconstruct 3D across North Pacific Equatorial within upper 200 m. The input wave information includes significant height (SWH), Langmuir number (La), enhancement factor (ε); latter two indicate strength turbulence, which promotes vertical mixing ocean layer thereby affects profiles salinity. results that incorporating information, particularly La ε, significantly enhances model’s ability highlights critical enhancing reconstruction

Language: Английский

Enhancing Ocean Temperature and Salinity Reconstruction with Deep Learning: The Role of Surface Waves DOI Creative Commons
Xiaoyu Yu, Daling Li Yi, Peng Wang

et al.

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(5), P. 910 - 910

Published: May 3, 2025

In oceanographic research, reconstructing the three-dimensional (3D) distribution of temperature and salinity is essential for understanding global climate dynamics, predicting marine environmental changes, evaluating their impacts on ecosystems. While previous studies have largely concentrated effects various modeling approaches oceanic variables, limited attention has been paid to role surface waves in reconstruction. This study, based sea data, employs a deep learning-based neural network model, U-Net, reconstruct 3D across North Pacific Equatorial within upper 200 m. The input wave information includes significant height (SWH), Langmuir number (La), enhancement factor (ε); latter two indicate strength turbulence, which promotes vertical mixing ocean layer thereby affects profiles salinity. results that incorporating information, particularly La ε, significantly enhances model’s ability highlights critical enhancing reconstruction

Language: Английский

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