Diffusion-based smoothers for spatial filtering of gridded geophysical
data
- Ian Grooms
, - Nora Loose
, - Ryan Abernathey
, - Jacob Steinberg
, - Scott Daniel Bachman
, - Gustavo Marques
, - Arthur Paul Guillaumin,
- Elizabeth Yankovsky

Ian Grooms

University of Colorado Boulder, University of Colorado Boulder
Corresponding Author:ian.grooms@colorado.edu
Author ProfileNora Loose

University of Colorado Boulder, University of Colorado Boulder
Author ProfileJacob Steinberg

University of Washington, University of Washington
Author ProfileElizabeth Yankovsky

Princeton University, NOAA-GFDL, Princeton University, NOAA-GFDL
Author ProfileAbstract
We describe a new way to apply a spatial filter to gridded data from
models or observations, focusing on low-pass filters. The new method is
analogous to smoothing via diffusion, and its implementation requires
only a discrete Laplacian operator appropriate to the data. The new
method can approximate arbitrary filter shapes, including Gaussian
filters, and can be extended to spatially-varying and anisotropic
filters. The new diffusion-based smoother's properties are illustrated
with examples from ocean model data and ocean observational products. An
open-source python package implementing this algorithm, called
gcm-filters, is currently under development.Sep 2021Published in Journal of Advances in Modeling Earth Systems volume 13 issue 9. 10.1029/2021MS002552