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Xiao-Yong Li

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Oct 2026

A localized weighted ensemble adjustment Kalman filter for high-dimensional nonlinear data assimilation

Particle filters provide a nonlinear Bayesian framework for data assimilation, but global weights often degenerate in high-dimensional geophysical systems. Ensemble Kalman filters are stable with practical ensemble sizes, yet their Gaussian and locally linear updates can be restrictive for nonlinear observations and...

Meng-Ge Zhou, Xiao-Qun Cao, Yan Chen et al. · 0 citations
Open access Sep 2026

Nonlinear Latent-Space Data Assimilation for Sea Surface Height Reconstruction from Sparse Observations

Latent-LWETKF, a structured latent-space implementation of the localized weighted ensemble transform Kalman filter, is developed, supporting tractable nonlinear ensemble analysis with 40 members while retaining physical observation geometry and complete-field decoding context.

Meng-Ge Zhou, Xiao-Qun Cao, Yan Chen et al. · 0 citations
Book Open access Aug 2026

Slow-OCast: Slow-Varying Motion Inspired Transfer Learning for Regional High-Resolution Ocean Environmental Forecasting

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. · 0 citations
Conference Aug 2026

Super-resolution reconstruction of ocean dissolved oxygen via conditional diffusion modeling with auxiliary physical fields

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. · 0 citations
Book Open access Aug 2026

Slow-OCast: Slow-Varying Motion Inspired Transfer Learning for Regional High-Resolution Ocean Environmental Forecasting

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. · 0 citations
Conference Open access Sep 2026

4DVarGen: A 4D Variational-Inspired Generative Model for Eddy-Resolving Surface Ocean Reconstruction

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. · 0 citations

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