Result: On solving coordinate problems in climate model output and other geospatial datasets.

Title:
On solving coordinate problems in climate model output and other geospatial datasets.
Authors:
Cherblanc C; National Center for Climate Research, Danish Meteorological Institute, Copenhagen, 2100, Denmark.; Niels Bohr Institute, University of Copenhagen, Copenhagen, 2200, Denmark., Grejs Petersen JP; National Center for Climate Research, Danish Meteorological Institute, Copenhagen, 2100, Denmark.; Niels Bohr Institute, University of Copenhagen, Copenhagen, 2200, Denmark., Bunt F; Department of Computer Sciences, University of Montana, Missoula, Montana, 59812, USA., Torres-Alavez JA; National Center for Climate Research, Danish Meteorological Institute, Copenhagen, 2100, Denmark., Mottram R; National Center for Climate Research, Danish Meteorological Institute, Copenhagen, 2100, Denmark.
Source:
Open research Europe [Open Res Eur] 2025 Dec 16; Vol. 5, pp. 269. Date of Electronic Publication: 2025 Dec 16 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: European Commission Country of Publication: Belgium NLM ID: 9918230081006676 Publication Model: eCollection Cited Medium: Internet ISSN: 2732-5121 (Electronic) Linking ISSN: 27325121 NLM ISO Abbreviation: Open Res Eur Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: [Brussels] : European Commission, [2021]-
References:
Nat Methods. 2020 Mar;17(3):261-272. (PMID: 32015543)
Contributed Indexing:
Keywords: Antarctic; Arctic; CDO; Regional climate models; regridding; xarray
Local Abstract: [plain-language-summary] The datasets exist on grids with coordinates. The coordinates of one grid can be related to those of another using a coordinate reference system. Lack or poor encoding of coordinate reference systems can lead to incompatibilities, errors, approximations, and bottlenecks. We present ways to read the coordinate reference systems correctly and to recover them if they are missing, using two programming languages. We provide real case examples and discuss the implications of the presented methods.
Entry Date(s):
Date Created: 20260108 Date Completed: 20260108 Latest Revision: 20260110
Update Code:
20260110
PubMed Central ID:
PMC12770884
DOI:
10.12688/openreseurope.20467.2
PMID:
41502465
Database:
MEDLINE

Further Information

The output from Regional Climate Models (RCMs) can be difficult for non-specialists to handle, especially in cases where metadata describing coordinate systems is incomplete or absent. Standard geospatial analysis tools expect coordinate reference systems to be encoded inside file metadata. In addition to different metadata conventions, RCMs that are run over limited domains in the Arctic and Antarctic frequently have rotated longitude and latitude grids that add additional complexity compared to geographic datasets. In this article, we describe two post-processing methods that make RCM outputs easier to use for applications in the climate and related sciences. We demonstrate two different approaches that allow output from RCMs to be 1) read on the correct grid without interpolating or reprojecting the dataset, or 2) resampled onto a regular grid that includes geographic coordinates. These two approaches use the widely available and free software tools Python and Climate Data Operators (CDO). These transformations make outputs simple to use in Geographic Information Systems (GIS) and allow the full use of Python libraries, such as xarray, for plotting and analysis.
(Copyright: © 2025 Cherblanc C et al.)

No competing interests were disclosed.