GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data

Loose, Nora; Abernathey, Ryan; Grooms, Ian; Busecke, Julius; Guillaumin, Arthur; Yankovsky, Elizabeth; Marques, Gustavo; Steinberg, Jacob; Ross, Andrew; Khatri, Hemant; Bachman, Scott; Zanna, Laure; Martin, Paige

doi:10.21105/joss.03947

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GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data

2022
By Loose, Nora ; Abernathey, Ryan ; Grooms, Ian ; ...
Source: Journal of Open Source Software, 7(70), 3947

[PDF-2.49 MB]

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Journal Title:

Journal of Open Source Software
Personal Author:

Loose, Nora ; Abernathey, Ryan ; Grooms, Ian ; Busecke, Julius ; Guillaumin, Arthur ; ... More +

Loose, Nora ; Abernathey, Ryan ; Grooms, Ian ; Busecke, Julius ; Guillaumin, Arthur ; Yankovsky, Elizabeth ; Marques, Gustavo ; Steinberg, Jacob ; Ross, Andrew ; Khatri, Hemant ; Bachman, Scott ; Zanna, Laure ; Martin, Paige Less -
NOAA Program & Office:

CIMES (Cooperative Institute for Modeling the Earth System) ; OAR (Oceanic and Atmospheric Research)

CIMES (Cooperative Institute for Modeling the Earth System) ; OAR (Oceanic and Atmospheric Research) Less -
Description:

GCM-Filters is a python package that allows scientists to perform spatial filtering analysis in an easy, flexible and efficient way. The package implements the filtering method based on the discrete Laplacian operator that was introduced by Grooms et al. (2021). The filtering algorithm is analogous to smoothing via diffusion hence the name diffusion-based filters. GCM-Filters can be used with either gridded observational data or gridded data that is produced by General Circulation Models (GCMs) of ocean, weather, and climate. Spatial filtering of observational or GCM data is a common analysis method in the Earth Sciences, for example to study oceanic and atmospheric motions at different spatial scales or to develop subgrid-scale parameterizations for ocean models.
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Python (Computer program language)
Source:

Journal of Open Source Software, 7(70), 3947
DOI:

https://doi.org/10.21105/joss.03947
Document Type:

Journal Article
Funding:

Grant no. NA18OAR4320123
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CC BY
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Submitted
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urn:sha256:f5030e2dfbf5bc17b96ba5923145547215c13dd93626de1fb09601e928972ea3
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https://repository.library.noaa.gov/view/noaa/43147/noaa_43147_DS1.pdf
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GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data

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