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Xcdat

An extension of xarray for climate data analysis on structured grids.

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/learn @xCDAT/Xcdat
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

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Xarray Climate Data Analysis Tools

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xCDAT is an extension of xarray_ for climate data analysis on structured grids. It serves as a modern successor to the Community Data Analysis Tools (CDAT_) library.

Useful links: Documentation <https://xcdat.readthedocs.io>__ | Code Repository <https://github.com/xCDAT/xcdat>__ | Issues <https://github.com/xCDAT/xcdat/issues>__ | Discussions <https://github.com/xCDAT/xcdat/discussions>__ | Releases <https://github.com/xCDAT/xcdat/releases>__ | Mailing List <https://groups.google.com/g/xcdat>__

Project Motivation

The goal of xCDAT is to provide generalizable features and utilities for simple and robust analysis of climate data. xCDAT’s scope focuses on routine climate research analysis operations such as loading, averaging, and regridding data on structured grids (e.g., rectilinear, curvilinear). Some key xCDAT features are inspired by or ported from the core CDAT library, while others leverage powerful libraries in the xarray ecosystem (e.g., xESMF, xgcm) to deliver robust APIs. xCDAT has the ability to operate generally across model and observational datasets that follow the CF Metadata Convention_ by interpreting CF Metadata through the cf_xarray_ package.

The xCDAT core team's mission is to provide a maintainable and extensible package that serves the needs of the climate community in the long-term. We are excited to be working on this project and hope to have you onboard!

.. _xarray: https://github.com/pydata/xarray .. _CDAT: https://github.com/CDAT/cdat

Getting Started

The best resource for getting started is the xCDAT documentation website. Our documentation provides general guidance for setting up xCDAT in an Anaconda environment on your local computer or on an HPC/Jupyter_ environment. We also include an API Overview_ and Gallery_ to highlight xCDAT functionality.

.. _xCDAT documentation website: https://xcdat.readthedocs.io/en/stable/ .. _computer: https://xcdat.readthedocs.io/en/stable/getting-started.html .. _HPC/Jupyter: https://xcdat.readthedocs.io/en/stable/getting-started-hpc-jupyter.html .. _API Overview: https://xcdat.readthedocs.io/en/stable/api.html .. _Gallery: https://xcdat.readthedocs.io/en/stable/gallery.html

Community

xCDAT is a community-driven open source project. We encourage discussion on topics such as version releases, feature suggestions, and architecture design on the GitHub Discussions_ page.

Subscribe to our mailing list_ for news and announcements related to xCDAT, such as software version releases or future roadmap plans.

Please note that xCDAT has a Code of Conduct_. By participating in the xCDAT community, you agree to abide by its rules.

.. _GitHub Discussions: https://github.com/xCDAT/xcdat/discussions .. _Code of Conduct: CODE-OF-CONDUCT.rst .. _mailing list: https://groups.google.com/g/xcdat

Contributing

We welcome and appreciate contributions to xCDAT. Users and contributors can view and open issues on our GitHub Issue Tracker_.

For more instructions on how to contribute, please checkout our Contributing Guide_.

.. _GitHub Issue Tracker: https://github.com/xCDAT/xcdat/issues .. _Contributing Guide: https://xcdat.readthedocs.io/en/stable/contributing.html

Features

  • Extension of xarray's open_dataset() and open_mfdataset() with post-processing options

    • Generate bounds for axes supported by xcdat if they don't exist in the Dataset

    • Optional selection of single data variable to keep in the Dataset (bounds are also kept if they exist)

    • Optional decoding of time coordinates

      • In addition to CF time units, also decodes common non-CF time units ("months since ...", "years since ...")

    • Optional centering of time coordinates using time bounds

    • Optional conversion of longitudinal axis orientation between [0, 360) and [-180, 180)

  • Temporal averaging

    • Time series averages (single snapshot and grouped), climatologies, and departures
    • Weighted or unweighted
    • Optional seasonal configuration (e.g., DJF vs. JFD, custom seasons)

  • Geospatial weighted averaging

    • Supports rectilinear grid
    • Optional specification of regional domain

  • Horizontal structured regridding

    • Supports rectilinear and curvilinear grids
    • Extends the xESMF_ horizontal regridding API
    • Python implementation of regrid2_ for handling cartesian latitude longitude grids

  • Vertical structured regridding

    • Support rectilinear and curvilinear grids
    • Extends the xgcm_ vertical regridding API

Things We Are Striving For

  • xCDAT supports CF compliant datasets, but will also strive to support datasets with common non-CF compliant metadata (e.g., time units in "months since ..." or "years since ...")

    • xCDAT leverages cf_xarray_ to interpret CF attributes on xarray objects
    • Refer to CF Metadata Convention_ for more information on CF attributes

  • Robust handling of dimensions and their coordinates and coordinate bounds

    • Coordinate variables are retrieved with cf_xarray using CF axis names or coordinate names found in xarray object attributes. Refer to Metadata Interpretation_ for more information.
    • Bounds are retrieved with cf_xarray using the "bounds" attr
    • Ability to operate on both longitudinal axis orientations, [0, 360) and [-180, 180)

  • Support for parallelism using dask_ where it is both possible and makes sense

.. _Metadata Interpretation: https://xcdat.readthedocs.io/en/stable/faqs.html#metadata-interpretation .. _xESMF: https://xesmf.readthedocs.io/en/latest/ .. _regrid2: https://cdms.readthedocs.io/en/latest/regrid2.html .. _xgcm: https://xgcm.readthedocs.io/en/latest/index.html .. _dask: https://dask.org/ .. _cf_xarray: https://cf-xarray.readthedocs.io/en/latest/index.html .. _CF Metadata Convention: http://cfconventions.org/

Releases

xCDAT (released as xcdat) follows a feedback-driven release cycle using continuous integration/continuous deployment. Software releases are performed based on the bandwidth of the development team, the needs of the community, and the priority of bug fixes or feature updates.

After releases are performed on GitHub Releases, the corresponding xcdat package version will be available to download through Anaconda conda-forge usually within a day.

Subscribe to our mailing list_ to stay notified of new releases.

.. _conda-forge: https://anaconda.org/conda-forge/xcdat .. _GitHub Releases: https://anaconda.org/conda-forge/xcdat

Useful Resources

We highly encourage you to checkout the awesome resources below to learn more about Xarray and Xarray usage in climate science!

  • Official Xarray Tutorials <https://tutorial.xarray.dev/intro.html>_
  • Xarray GitHub Discussion Forum <https://github.com/pydata/xarray/discussions>_
  • Pangeo Forum <https://foundations.projectpythia.org/core/xarray.html>_
  • Project Pythia <https://foundations.projectpythia.org/core/xarray.html>_

Projects Using xCDAT

xCDAT is actively being integrated as a core component of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package_ and the `Ene

xCDAT

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GitHub Stars137
CategoryData
Updated9d ago
Forks16
xCDAT/xcdat

Languages

Python

Security Score

100/100

Audited on Mar 14, 2026

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