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.