Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations

Abstract

Using the nonlinear shallow water equations as benchmark, we demonstrate that a simulation with the ICON-O ocean model with a 20km resolution that is frequently corrected by a U-net-type neural network can achieve discretization errors of a simulation with 10km resolution. The network, originally developed for image-based super-resolution in post-processing, is trained to compute the difference between solutions on both meshes and is used to correct the coarse mesh every 12h. Our setup is the Galewsky test case, modeling transition of a barotropic instability into turbulent flow. We show that the ML-corrected coarse resolution run correctly maintains a balance flow and captures the transition to turbulence in line with the higher resolution simulation. After 8 day of simulation, the $L_2$-error of the corrected run is similar to a simulation run on the finer mesh. While mass is conserved in the corrected runs, we observe some spurious generation of kinetic energy.

17 pages, 12 figures

References in corpus (5)

• Super-resolution reconstruction of turbulent flows with machine learning
• Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
• Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows
• Super-resolution data assimilation
• DNN-MG: A Hybrid Neural Network/Finite Element Method with Applications to 3D Simulations of the Navier-Stokes Equations