Oct 2026· IEEE Sensors Letters· Vol 10, pp. 6010004-6010004· 0 citations· 12 references
Abstract
Sea surface height (SSH) is a key variable for characterizing ocean dynamics, yet its high-resolution reconstruction remains challenging due to sparse satellite observations and the limited ability of conventional methods to represent multiscale nonlinear processes. This study proposes a physics-constrained SSH reconstruction model, 4-D variational (4DVar) Attention, built upon a 4DVar framework. The model approximates gradient-based updates using neural networks and jointly assimilates satellite altimeter and in situ pressure-recording inverted echo sounder (PIES) observations, while combining a dual-scale U-Net and a Vision Transformer to model cross-scale spatiotemporal dependencies. Experiments in the Gulf of Mexico demonstrate that 4DVarAttention outperforms several representative methods in terms of reconstruction accuracy and spatiotemporal continuity, and further confirm that incorporating PIES observations significantly enhances the resolution of the reconstructed SSH fields, providing a foundation for extending physics-constrained deep learning-based data assimilation frameworks to broader oceanic regions.
4DVarGen is proposed, a 4DVar-inspired generative framework for reconstructing sea surface variable fields at eddy-resolving scales from sparse remote-sensing observations that establishes a mathematical equivalence between 4DVar and an observation-guided denoising process.
Jun-Peng Huang, Wu-Xin Wang, Xiao-Yong Li et al.· Proceedings of the Thirty-Fi...· 0 citations
Mapping seafloor topography is of great significance for deep-sea navigation, marine resource exploration, and aquatic ecosystem conservation. Advances in bathymetric surveying technology have progressively enriched our understanding of the oceans. However, due to the high cost and low coverage of ship-based surveys, e...
Jia Hu, Yi-Feng Luo, Chao Wang et al.· Deutsche Hydrographische Zei...· 1 citation
Subsurface ocean observations remain severely limited in spatial coverage due to the high cost and operational difficulty of
in-situ
deployment. Although moored buoys and profiling floats enable continuous, minute-level sampling at fixed locations, the temporal evolution information they record is largely underutil...
Lu-,-Hong-Feng-,-Li-Zheng-Bao-,-Guo-Zhong-Wen Hong, Meng-Yao Wang, Qing Xu et al.· Frontiers in Marine Science· 0 citations
Accurate sea surface wind speed fields are essential for marine navigation, offshore operations, and air–sea interaction studies. However, limited communication bandwidth makes it difficult to receive forecasts from land-based centers, motivating wind speed reconstruction using sparse observations. To address this chal...
Ruisheng Hu, Jia-Qi Ding, Jin-Hui Yang et al.· Remote Sensing· 0 citations
High-resolution oceanographic fields are critical for resolving mesoscale and sub-mesoscale coastal dynamics, yet their generation remains constrained by both computational cost and observational sparsity. We present OcDiffSR, a conditional denoising diffusion probabilistic model (DDPM) for oceanographic super-resoluti...
R. Srivastava, Muhammad Sarmad, Emanuele Mele et al.· 0 citations
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