EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using weak constrained neural networks to improve simulations in the gray zone

Yvonne Ruckstuhl1, Raphael Kriegmair1, Stephan Rasp2, and George Craig1
Yvonne Ruckstuhl et al.
  • 1Meteorology, LMU, Munich
  • 2ClimateAi, Inc., San Francisco, USA

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 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 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.

How to cite: Ruckstuhl, Y., Kriegmair, R., Rasp, S., and Craig, G.: Using weak constrained neural networks to improve simulations in the gray zone, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5523,, 2023.