Diffusion‐Based Smoothers for Spatial Filtering of Gridded Geophysical Data

Journal of Advances in Modeling Earth Systems

E. Yankovsky¹  A. P. Guillaumin¹  S. D. Bachman²  G. Marques²  N. Loose³  I. Grooms³  R. Abernathey⁴  J. M. Steinberg⁵ 

[1] Center for Atmosphere Ocean Science Courant Institute of Mathematical Sciences New York University New York NY USA;Climate and Global Dynamics Division National Center for Atmospheric Research Boulder CO USA;Department of Applied Mathematics University of Colorado Boulder CO USA;Department of Earth and Environmental Sciences Lamont Doherty Earth Observatory of Columbia University Palisades NY USA;Department of Physical Oceanography Woods Hole Oceanographic Institution Woods Hole MA USA;

关键词: spatial filtering;    coarse graining;    data analysis;   

DOI  :  10.1029/2021MS002552

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown