Using neural networks to improve simulations in the gray zone

Using neural networks to improve simulations in the gray zone

Blog Article

Machine learning represents a potential method to cope with the gray zone problem of representing motions in dynamical systems on scales comparable to the model resolution.Here we explore the possibility of using a neural network to directly learn read more the error caused by unresolved scales.We use a modified shallow water model which includes highly nonlinear processes mimicking atmospheric convection.

To create the training dataset, we run the model in a high- and a low-resolution setup and compare the difference after one low-resolution time step, starting from the same initial conditions, thereby obtaining an exact target.The neural network is able to learn a large portion of the difference when evaluated on single time step predictions on a validation dataset.When coupled to the low-resolution model, we find large forecast improvements up to 1 d on average.

After this, the accumulated error due to the mass conservation violation of the neural network starts to dominate and deteriorates the forecast.This deterioration can effectively be delayed by adding a penalty term to the acure face lotion loss function used to train the ANN to conserve mass in a weak sense.This study reinforces the need to include physical constraints in neural network parameterizations.