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Yanqiu Gao
The ensemble Kalman filter is often used in parameter estimation, which plays an essential role in reducing model errors. However, filter divergence is often encountered in an estimation process, resulting in the convergence of parameters to the improper...
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Qianlong Jin, Yu Tian, Weicong Zhan, Qiming Sang, Jiancheng Yu and Xiaohui Wang
Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-tim...
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Xingxia Kou, Zhekun Huang, Hongnian Liu, Meigen Zhang, Si Shen and Zhen Peng
The four-dimensional variational data assimilation (4DVar) method is one of the most popular techniques used in numerical weather prediction. Nevertheless, the needs of the adjoint model and the linearization of the forecast model largely limit the wider...
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Elias D. Nino-Ruiz
In this paper, a matrix-free posterior ensemble Kalman filter implementation based on a modified Cholesky decomposition is proposed. The method works as follows: the precision matrix of the background error distribution is estimated based on a modified C...
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