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Xarray-based climate data analytics · Issue #73 · pyOpenSci/software-submission · GitHub

Submitting Author: Trevor James Smith (@Zeitsperre)
All current maintainers: (@Zeitsperre, @tlogan2000, @aulemahal)
Package Name: xclim
One-Line Description of Package: Climate indices computation package based on Xarray
Repository Link:  https://github.com/Ouranosinc/xclim
Version submitted: v0.40.0
Editor: @Batalex
Reviewer 1: @jmunroe
Reviewer 2: @aguspesce
Archive:
JOSS DOI:
Version accepted: v0.42.0
Date accepted (month/day/year): 04/11/2023

xclim is an operational Python library for climate services, providing numerous climate-related indicator tools with an extensible framework for constructing custom climate indicators, statistical downscaling and bias adjustment of climate model simulations, as well as climate model ensemble analysis tools.

xclim is built using xarray_ and can seamlessly benefit from the parallelization handling provided by dask. Its objective is to make it as simple as possible for users to perform typical climate services data treatment workflows. Leveraging xarray and dask, users can easily bias-adjust climate simulations over large spatial domains or compute indices from large climate datasets.

• Please indicate which category or categories this package falls under:
• Data retrieval
• Data extraction
• Data munging
• Data deposition
• Reproducibility
• Geospatial
• Education
• Data visualization*

Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.

• For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each): 

  - Who is the target audience, and what are scientific applications of this package?

xclim aims to position itself as a climate services tool for any researchers interested in using Climate and Forecast Conventions compliant datasets to perform climate analyses. This tool is optimized for working with Big Data in the climate science domain and can function as an independent library for one-off analyses in Jupyter notebooks or as a backend engine for performing climate data analyses over PyWPS (e.g. Finch). It was primarily developed targeting earth and environmental science audiences and researchers, originally for calculating climate indicators for the Canadian government web service ClimateData.ca.

The primary domains that xclim is built for are in calculating climate indicators, performing statistical correction / bias adjustment of climate model output variables/simulations, and in performing climate model simulation ensemble statistics.

  - Are there other Python packages that accomplish the same thing? If so, how does yours differ?

icclim is another library for the computation of climate indices. Starting with version 5.0 of icclim, some of the core computations rely on xclim. See explanations about differences between xclim and icclim.

scikit-downscale is a library offering algorithms for statistical downscaling. xclim drew inspiration for its fit-predict architecture. The suite of downscaling algorithms offered differ.

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box.  This package:

• does not violate the Terms of Service of any service it interacts with. 
• has an OSI approved license.
• contains a README with instructions for installing the development version. (Under CONTRIBUTING)
• includes documentation with examples for all functions.
• contains a vignette with examples of its essential functions and uses.
• has a test suite.
• has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

• Do you wish to automatically submit to the Journal of Open Source Software? If so:

• The package has an obvious research application according to JOSS's definition in their submission requirements. Be aware that completing the pyOpenSci review process does not guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS.
• The package is not a "minor utility" as defined by JOSS's submission requirements: "Minor 'utility' packages, including 'thin' API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria.
• The package contains a paper.md matching JOSS's requirements with a high-level description in the package root or in inst/.
• The package is deposited in a long-term repository with the DOI: https://doi.org/10.5281/zenodo.2795043

Note: Do not submit your package separately to JOSS
  

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

• Yes, I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

• I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

• Last but not least, please fill out our pre-review survey. This helps us track
  submission and improve our peer review process. We will also ask our reviewers 
  and editors to fill this out.

P.S. *Have feedback/comments about our review process? Leave a comment here

Editor and review templates can be found here

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