Development of PBL Parameterization Emulator using Neural Networks

Physical parameterization is one of the major components of Numerical Weather Prediction system. In Korean Integrated Model (KIM), physical parameterizations account for about 30 % of the total computation time. There are many studies of developing neural network based emulators to replace and accelerate physics based parameterization. In this study, we develop a planetary boundary layer(PBL) emulator which is based on Shin-Hong (Hong et al., 2006, 2010; Shin and Hong, 2013, 2015) scheme that computes the parameterized effects of vertical turbulent eddy diffusion of momentum, water vapor, and sensible heat fluxes. We compare the emulator performance with Multi-Layer Perceptron (MLP) based architectures: simple MLP, MLP application version, and MLP-mixer(Tolstikhin et al., 2021). MLP application version divides data into several vertical groups for better approximation of each vertical group layers. MLP-mixer is MLP based architecture that performs well in computer vision without using convolution and self-attention. We evaluate the resulting MLP based emulator performance. MLP application version and MLP-mixer showed significant performance improvement over simple MLP.