Publication Date:
2022-10-26
Description:
© 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.
Description:
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.
Description:
I.G. and N.L. are supported by NSF OCE 1912332. R.A. is supported by NSF OCE 1912325. J.S. is supported by NSF OCE 1912302. S.B. and G.M. are supported by NSF OCE 1912420. A.G. and E.Y. are supported by NSF GEO 1912357 and NOAA CVP NA19OAR4310364.
Keywords:
Spatial filtering
;
Coarse graining
;
Data analysis
Repository Name:
Woods Hole Open Access Server
Type:
Article
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