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.
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.
The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow.
Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model.
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.
The output analysis is spatially complete and is used as a training set by the neural network to update the surrogate model.
In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-inclusion of complex dynamics in numerical models) but sometimes can be observed.
We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state.