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Updated: Jan 13, 2026

Using Generative Art to Convey Past and Future Climate Transitions
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On solving coordinate problems in climate model output and other geospatial datasets.

Clément Cherblanc1,2, Jeppe Peder Grejs Petersen1,2, Fredrick Bunt3

  • 1National Center for Climate Research, Danish Meteorological Institute, Copenhagen, 2100, Denmark.

Open Research Europe
|January 8, 2026
PubMed
Summary
This summary is machine-generated.

This study presents methods to simplify Regional Climate Model (RCM) data for easier use. Post-processing RCM outputs with Python and Climate Data Operators (CDO) enhances accessibility for climate science applications.

Keywords:
AntarcticArcticCDORegional climate modelsregriddingxarray

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Area of Science:

  • Climate Science
  • Geospatial Analysis
  • Data Science

Background:

  • Regional Climate Model (RCM) outputs often lack complete coordinate system metadata, complicating geospatial analysis.
  • Limited-domain RCMs, particularly in polar regions, frequently utilize rotated grids, adding complexity.
  • Standard tools require coordinate reference systems within file metadata, which is often missing or inconsistent in RCM data.

Purpose of the Study:

  • To provide accessible post-processing methods for Regional Climate Model (RCM) outputs.
  • To simplify the use of RCM data for non-specialists in climate and related sciences.
  • To enable easier integration of RCM data into Geographic Information Systems (GIS) and Python-based analysis workflows.

Main Methods:

  • Developed two post-processing techniques for RCM data.
  • Utilized free and widely available software: Python and Climate Data Operators (CDO).
  • Demonstrated methods to read RCM data on its native grid or resample it onto a regular grid with geographic coordinates.

Main Results:

  • Successfully made RCM outputs easier to handle for non-specialists.
  • Enabled RCM data to be read correctly without interpolation or reprojection, or resampled to a regular grid.
  • Facilitated the use of RCM data in GIS and Python libraries like xarray for analysis and visualization.

Conclusions:

  • Post-processing RCM outputs with Python and CDO significantly improves data usability.
  • These methods bridge the gap between complex climate model data and practical scientific applications.
  • Enhanced data accessibility supports broader use in climate research, GIS, and data analysis.