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Found 4 papers, 0 papers with code
Super-resolution data assimilation
Increasing the resolution of a model can improve the performance of a data assimilation system: first because model field are in better agreement with high resolution observations, then the corrections are better sustained and, with ensemble data assimilation, the forecast error covariances are improved.
Combining data assimilation and machine learning to infer unresolved scale parametrisation
Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model.
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii) the unstable nature of the dynamics.
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
The output analysis is spatially complete and is used as a training set by the neural network to update the surrogate model.
