GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data
Nora Loose, Ryan Abernathey, Ian Grooms, Julius Busecke, Arthur P. Guillaumin, Elizabeth Yankovsky, Gustavo Marques, Jacob M. Steinberg, Andrew Slavin Ross, Hemant Khatri, Scott Bachman, Laure Zanna, Paige Martin
Abstract
GCM-Filters is a python package that allows scientists to perform spatial filtering analysis in an easy, flexible and efficient way. The package implements the filtering method based on the discrete Laplacian operator that was introduced by Grooms et al. (2021). The filtering algorithm is analogous to smoothing via diffusion; hence the name diffusion-based filters. GCM-Filters can be used with either gridded observational data or gridded data that is produced by General Circulation Models (GCMs) of ocean, weather, and climate. Spatial filtering of observational or GCM data is a common analysis method in the Earth Sciences, for example to study oceanic and atmospheric motions at different spatial scales or to develop subgrid-scale parameterizations for ocean models.