Diffusion-based smoothers for spatial filtering of gridded geophysical data
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.
document
https://n2t.org/ark:/85065/d7q243p2
eng
geoscientificInformation
Text
publication
2016-01-01T00:00:00Z
publication
2021-09-01T00:00:00Z
Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
None
OpenSky Support
UCAR/NCAR - Library
PO Box 3000
Boulder
80307-3000
name: homepage
pointOfContact
OpenSky Support
UCAR/NCAR - Library
PO Box 3000
Boulder
80307-3000
name: homepage
pointOfContact
2025-07-11T16:12:22.947038