Print Email Facebook Twitter Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model Title Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model Author Janssens, M. (Wageningen University & Research) Hulshoff, S.J. (TU Delft Aerodynamics) Date 2022 Abstract Data-driven parameterizations offer considerable potential for improving the fidelity of General Circulation Models. However, ensuring that these remain consistent with the governing equations while still producing stable simulations remains a challenge. In this paper, we propose a combined Variational-Multiscale (VMS) Artificial Neural Network (ANN) discretization which makes no a priori assumptions on the model form, and is only restricted in its accuracy by the precision of the ANN. Using a simplified problem, we demonstrate that good predictions of the required closure terms can be obtained with relatively compact ANN architectures. We then turn our attention to the stability of the VMS-ANN discretization in the context of a single implicit time step. It is demonstrated that the ANN parameterization introduces nonphysical solutions to the governing equations that can significantly affect or prevent convergence. We show that enriching the training data with nonphysical states from intra-time step iterations is an effective remedy. This indicates that the lack of representative ANN-induced errors in our original, exact training inputs underpin the observed instabilities. In turn, this suggests that data set enrichment might aid in resolving instabilities that develop over several time steps. Subject Artificial Neural Networksatmospheric boundary layer turbulencemachine learningsubgrid-scale modelingvariational multiscale methods To reference this document use: http://resolver.tudelft.nl/uuid:b0ac2c6d-c005-4b22-aaaa-a04ad9753323 DOI https://doi.org/10.1029/2021MS002490 ISSN 1942-2466 Source Journal of Advances in Modeling Earth Systems, 14 (1) Part of collection Institutional Repository Document type journal article Rights © 2022 M. Janssens, S.J. Hulshoff Files PDF J_Adv_Model_Earth_Syst_20 ... ulence.pdf 2.14 MB Close viewer /islandora/object/uuid:b0ac2c6d-c005-4b22-aaaa-a04ad9753323/datastream/OBJ/view