Diffusion-Based smoothers for spatial filtering of gridded geophysical data

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Date
2021-08-29
Authors
Grooms, Ian
Loose, Nora
Abernathey, Ryan
Steinberg, Jacob M.
Bachman, Scott D.
Marques, Gustavo
Guillaumin, Arthur P.
Yankovsky, Elizabeth
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DOI
10.1029/2021MS002552
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Keywords
Spatial filtering
Coarse graining
Data analysis
Abstract
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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© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Grooms, I., Loose, N., Abernathey, R., Steinberg, J. M., Bachman, S. D., Marques, G., Guillaumin, A. P., & Yankovsky, E. Diffusion-Based smoothers for spatial filtering of gridded geophysical data. Journal of Advances in Modeling Earth Systems, 13(9), (2021): e2021MS002552, https://doi.org/10.1029/2021MS002552.
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Grooms, I., Loose, N., Abernathey, R., Steinberg, J. M., Bachman, S. D., Marques, G., Guillaumin, A. P., & Yankovsky, E. (2021). Diffusion-Based smoothers for spatial filtering of gridded geophysical data. Journal of Advances in Modeling Earth Systems, 13(9), e2021MS002552.
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