xclim: xarray-based climate data analytics

Bourgault, Pascal ORCID: https://orcid.org/0000-0003-1192-0403; Huard, David ORCID: https://orcid.org/0000-0003-0311-5498; Smith, Trevor James ORCID: https://orcid.org/0000-0001-5393-8359; Logan, Travis ORCID: https://orcid.org/0000-0002-2212-9580; Aoun, Abel ORCID: https://orcid.org/0000-0003-2289-2890; Lavoie, Juliette ORCID: https://orcid.org/0000-0002-4708-5182; Dupuis, Eric ORCID: https://orcid.org/0000-0001-7976-4596; Rondeau-Genesse, Gabriel ORCID: https://orcid.org/0000-0003-3389-9406; Alegre, Raquel ORCID: https://orcid.org/0000-0002-6081-0721; Barnes, Clair ORCID: https://orcid.org/0000-0002-7806-7913; Beaupre Laperriere, Alexis; Biner, Sebastien ORCID: https://orcid.org/0000-0001-7515-490X; Caron, David; Ehbrecht, Carsten; Fyke, Jeremy ORCID: https://orcid.org/0000-0002-4522-3019; Keel, Tom ORCID: https://orcid.org/0000-0001-9193-5271; Labonte, Marie-Pier ORCID: https://orcid.org/0000-0003-0738-3940; Lierhammer, Ludwig ORCID: https://orcid.org/0000-0002-7207-0003; Low, Jwen-Fai ORCID: https://orcid.org/0000-0001-6618-7443; Quinn, Jamie ORCID: https://orcid.org/0000-0003-1239-7589; Roy, Philippe ORCID: https://orcid.org/0000-0003-3271-6874; Squire, Dougie ORCID: https://orcid.org/0000-0002-1038-7988; Stephens, Ag; Tanguy, Maliko ORCID: https://orcid.org/0000-0002-1516-6834; Whelan, Christopher. 2023 xclim: xarray-based climate data analytics. Journal of Open Source Software, 8 (85), 5415. 4, pp. https://doi.org/10.21105/joss.05415

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Abstract/Summary

xclim is a Python library that enables computation of climate indicators over large, hetero- geneous data sets. It is built using xarray objects and operations, can seamlessly benefit from the parallelization handling provided by dask, and relies on community conventions for data formatting and metadata attributes. xclim is meant as a tool to facilitate both climate science research and the delivery of operational climate services and products. In addition to climate indicator calculations, xclim also includes utilities for bias correction and statistical adjustment, ensemble analytics, model diagnostics, data quality assurance, and metadata standards compliance.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.21105/joss.05415
UKCEH and CEH Sections/Science Areas:	Water Resources (Science Area 2017-)
ISSN:	2475-9066
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Computer Science
Date made live:	22 May 2023 11:17 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/534578

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