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Diffusion-based smoothers for spatial filtering of gridded geophysical data
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  • Ian Grooms,
  • Nora Loose,
  • Ryan Abernathey,
  • Jacob Steinberg,
  • Scott Daniel Bachman,
  • Gustavo Marques,
  • Arthur Paul Guillaumin,
  • Elizabeth Yankovsky
Ian Grooms
University of Colorado Boulder, University of Colorado Boulder

Corresponding Author:[email protected]

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Nora Loose
University of Colorado Boulder, University of Colorado Boulder
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Ryan Abernathey
Columbia University, Columbia University
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Jacob Steinberg
University of Washington, University of Washington
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Scott Daniel Bachman
NCAR, NCAR
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Gustavo Marques
NCAR, NCAR
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Arthur Paul Guillaumin
NYU, NYU
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Elizabeth Yankovsky
Princeton University, NOAA-GFDL, Princeton University, NOAA-GFDL
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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.
Sep 2021Published in Journal of Advances in Modeling Earth Systems volume 13 issue 9. 10.1029/2021MS002552