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.

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Author Grooms, I.
Loose, N.
Abernathey, R.
Steinberg, J. M.
Bachman, Scott
Marques, Gustavo M.
Guillaumin, A. P.
Yankovsky, E.
Publisher UCAR/NCAR - Library
Publication Date 2021-09-01T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Topic Category geoscientificInformation
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Metadata Date 2025-07-11T16:12:22.947038
Metadata Record Identifier edu.ucar.opensky::articles:24752
Metadata Language eng; USA
Suggested Citation Grooms, I., Loose, N., Abernathey, R., Steinberg, J. M., Bachman, Scott, Marques, Gustavo M., Guillaumin, A. P., Yankovsky, E.. (2021). Diffusion-based smoothers for spatial filtering of gridded geophysical data. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7q243p2. Accessed 03 August 2025.

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