Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations
This work addresses computational efficiency for ocean modelers by providing an incremental improvement in simulation accuracy with reduced resolution.
The paper tackled the problem of reducing computational cost in ocean modeling by using a U-net neural network to correct coarse-resolution simulations, achieving discretization errors comparable to a higher-resolution simulation after 8 days with similar L2-error.
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.