Abstract
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.
Item Type: | Journal article |
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Faculties: | Physics |
Subjects: | 500 Science > 530 Physics |
URN: | urn:nbn:de:bvb:19-epub-106925-7 |
ISSN: | 1023-5809 |
Language: | English |
Item ID: | 106925 |
Date Deposited: | 11. Sep 2023, 13:45 |
Last Modified: | 04. Oct 2023, 14:15 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 257899354 |