Implementation of implicit filters for spatial spectra extraction

Abstract

Scale analysis based on coarse graining has been proposed recently as an alternative to Fourier analysis. It is now broadly used to analyze energy spectra and energy transfers in eddy-resolving ocean simulations. However, for data from unstructured-mesh models it requires interpolation to a regular grid. We present a high-performance Python implementation of an alternative coarse-graining method which relies on implicit filters using discrete Laplacians. This method can work on arbitrary (structured or unstructured) meshes and is applicable to the direct output of unstructured-mesh ocean circulation atmosphere models. The computation is split into two phases: preparation and solving. The first one is specific only to the mesh. This allows for auxiliary arrays that are then computed to be reused, significantly reducing the computation time. The second part consists of sparse matrix algebra and solving the linear system. Our implementation is accelerated by GPUs to achieve excellent performance and scalability. This results in processing data based on meshes with more than 10 million surface vertices in a matter of seconds. As an illustration, the method is applied to compute spatial spectra of ocean currents from high-resolution FESOM2 simulations.