A Fusion Method Based on Physical Modes and Satellite Remote Sensing for 3D Ocean State Reconstruction
Accurately and timely estimating three-dimensional ocean states is crucial for improving operational ocean forecasting capabilities. Although satellite observations provide valuable evolutionary information, they are confined to surface-level variables. While in situ observations can offer subsurfac...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/8/1468 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Accurately and timely estimating three-dimensional ocean states is crucial for improving operational ocean forecasting capabilities. Although satellite observations provide valuable evolutionary information, they are confined to surface-level variables. While in situ observations can offer subsurface information, their spatiotemporal distribution is highly uneven, making it difficult to obtain complete three-dimensional ocean structures. This study developed an operational-oriented lightweight framework for three-dimensional ocean state reconstruction by integrating multi-source observations through a computationally efficient multivariate empirical orthogonal function (MEOF) method. The MEOF method can extract physically consistent multivariate ocean evolution modes from high-resolution reanalysis data. We utilized these modes to further integrate satellite remote sensing and buoy observation data, thereby establishing physical connections between the sea surface and subsurface. The framework was tested in the South China Sea, with optimal data integration schemes determined for different reconstruction variables. The experimental results demonstrate that the sea surface height (SSH) and sea surface temperature (SST) are the key factors determining the subsurface temperature reconstruction, while the sea surface salinity (SSS) plays a primary role in enhancing salinity estimation. Meanwhile, current fields are most effectively reconstructed using SSH alone. The evaluations show that the reconstruction results exhibited high consistency with independent Argo observations, outperforming traditional baseline methods and effectively capturing the vertical structure of ocean eddies. Additionally, the framework can easily integrate sparse in situ observations to further improve the reconstruction performance. The high computational efficiency and reasonable reconstruction results confirm the feasibility and reliability of this framework for operational applications. |
|---|---|
| ISSN: | 2072-4292 |