Accurate modeling of the daily evolution of sea ice concentration (SIC) is central to improving the credibility and operational forecasting capability of deep learning-based sea ice prediction. However, existing deep learning methods often couple the underlying sea ice evolution relationships and data errors within hig...
Ma-Qun Zhang, Feng Gao, Wan-Kun Chen et al.· 0 citations
A numerical-prior-guided, physics-constrained method trained without ground-truth trajectory supervision that reduces long-time extrapolation error relative to the numerical prior and outperforms the best competing baseline in each case, thereby improving long-time simulation accuracy across different PDEs without grou...
Ma-Qun Zhang, Feng Gao, Wan-Kun Chen et al.· 0 citations
Results indicate enhanced ice-edge preservation and error-growth control in reanalysis?forced simulation, while PIHIM retains measurable short-range prediction skill under forecast-forced conditions.
Maqun Zhang, Feng Gao, Wan-Kun Chen et al.· 0 citations
This work offers a scalable pathway for next-generation Earth system models to learn directly from sparse, incomplete real-world observations and derives an optimization framework based on the expectation-maximization (EM) algorithm that enable learning directly from sparse and noisy observations.
Yangyang Kong, Yutong Jiang, Yanhai Gan et al.· 0 citations
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