Prediction of Convective Boundary Layer in the Gray Zone Using Fourier Neural Operator

In recent years, with the development of computing power, kilometer-scale resolution has become possible in regional numerical weather prediction (NWP) and climate simulation. While the refined numerical mesh allows for a more detailed representation of weather and climate, it also moves atmospheric modeling into gray zones, where the parameterization of turbulence and convection becomes a challenge in NWP. The newly developed machine learning (ML) methods would be a better choice to address this challenge. Previous ML weather prediction models primarily focus on global mesoscale forecasting. This study develops a purely data-driven three-dimensional Fourier neural operator (FNO) model for simulating the idealized convective boundary layer (CBL) at 800-m grid spacing, which is a resolution in the gray zone. The filtered large-eddy simulation (LES) data of the CBL are used for training the FNO models. The FNO models can accurately predict the instantaneous spatial structures and flow statistics of the boundary layer. The structures of vertical velocity near the surface predicted by the FNO models are consistent with those of the filtered LES, overcoming the issue of overly large cell structures predicted by traditional numerical simulations. The FNO models perform better than the gray-zone CM1 simulations in predicting profiles of flow statistics. Furthermore, the temperature and velocity spectra predicted by the FNO models are close to the results of filtered LES. The FNO models, trained using data for a few surface heat flux values Q_s from 0.14 to 0.26 K m s^-1, demonstrate certain generalization capabilities for other Q_s within and out of this range. Overall, the FNO model is a promising ML method for fast and accurate weather prediction in the gray zone.