Modelling Atmospheric Dynamics with Spherical Fourier Neural Operators (Papers Track)
Boris Bonev (NVIDIA); Thorsten Kurth (Nvidia); Christian Hundt (NVIDIA AI Technology Center); Jaideep Pathak (NVIDIA Corporation); Maximilian Baust (NVIDIA); Karthik Kashinath (NVIDIA); Anima Anandkumar (NVIDIA/Caltech)
Fourier Neural Operators (FNOs) have established themselves as an efficient method for learning resolution-independent operators in a wide range of scientific machine learning applications. This can be attributed to their ability to effectively model long-range dependencies in spatio-temporal data through computationally ef- ficient global convolutions. However, the use of discrete Fourier transforms (DFTs) in FNOs leads to spurious artifacts and pronounced dissipation when applied to spherical coordinates, due to the incorrect assumption of flat geometry. To ad- dress the issue, we introduce Spherical FNOs (SFNOs), which use the generalized Fourier transform for learning operators on spherical geometries. We demonstrate the effectiveness of the method for forecasting atmospheric dynamics, producing stable auto-regressive results for a simulated time of one year (1,460 steps) while retaining physically plausible dynamics. This development has significant implica- tions for machine learning-based climate dynamics emulation, which could play a crucial role in accelerating our response to climate change.