Estimating multiscale ocean-surface states from sparse observations is challenging because the state is high-dimensional, sampling is irregular, and posterior distributions can be strongly non-Gaussian. We develop Latent-LWETKF, a structured latent-space implementation of the localized weighted ensemble transform Kalma...
Meng-Ge Zhou, Xiao-Qun Cao, Yan Chen et al.· Remote Sensing· 0 citations
This work introduces Slow-OCast, a transfer-learning based model designed for high-resolution ocean environmental forecasting that incorporates the slow-varying motion characteristics of the ocean and comprises two insightful modules.
Qi-Xiu Li, Xiang Zhu, Xiao-Yong Li et al.· Proceedings of the 32nd ACM...· 0 citations
Ocean dissolved oxygen (DO) is a key indicator for assessing the health of marine ecosystems. However, constrained by sparse observational data and insufficient spatiotemporal resolution, traditional reconstruction methods struggle to accurately characterize its nonlinear spatial distribution. This paper proposes a con...
Qian-Hao Li, Jian-Feng Chen, Shenyao Wu et al.· International Conference on...· 0 citations
Regional high-resolution ocean environmental forecasting combines spatial numerical modeling with temporal prediction, and is essential for monitoring the ecological security of specific ocean regions. In recent years, deep learning methods are generally more computationally efficient than traditional numerical models...
Qixiu Li, Xiang Zhu, Xiaoyong Li et al.· Proceedings of the 32nd ACM...· 0 citations
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
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