Diffusion‐Based Smoothers for Spatial Filtering of Gridded Geophysical Data
Diffusion‐Based Smoothers for Spatial Filtering of Gridded Geophysical Data
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DOI：
10.1029/2021ms002552
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发表时间：
2021-06
影响因子：
6.8
通讯作者：
I. Grooms;Nora Loose;R. Abernathey;Jacob M. Steinberg;S. Bachman;Gustavo M. Marques;Arthur Guillaumin;E. Yankovsky
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作者：
I. Grooms;Nora Loose;R. Abernathey;Jacob M. Steinberg;S. Bachman;Gustavo M. Marques;Arthur Guillaumin;E. Yankovsky
我们描述了一种将空间滤波器应用于来自模型或观测的网格数据的新方法，重点关注低通滤波器。新方法类似于通过扩散进行平滑，并且其实现仅需要适合于数据的离散拉普拉斯算子。新方法可以近似任意形状的滤波器，包括高斯滤波器，并可以扩展到空间变化和各向异性滤波器。新的基于扩散的平滑器的属性与海洋模型数据和海洋观测产品的例子进行了说明。目前正在开发一个实现该算法的开源Python包，称为gcm-filters。
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.