Xclim : Xarray-based climate data analytics

[Zeitsperre]

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

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Description

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.

Scope 

• Please indicate which category or categories this package falls under:
  • [ ] Data retrieval
  • [ ] Data extraction
  • [x] Data munging
  • [ ] Data deposition
  • [x] Reproducibility
  • [x] 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.

Technical checks

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

Publication options

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

 

JOSS Checks

  - [x] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. 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. - [x] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor 'utility' packages, including 'thin' API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [x] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [x] 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*   

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• [x] 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.

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Editor and Review Templates

Editor and review templates can be found here

[tlogan2000]

@Zeitsperre, @tlogan2000, @aulemahal We would love to have you on our Slack if you'd like! Give me a shout, and we will do so

Yes please. That would be great

[allcontributors[bot]]

@Batalex

I've put up a pull request to add @aulemahal! :tada:

I've put up a pull request to add @aguspesce! :tada:

We had trouble processing your request. Please try again later.

[Batalex]

@all-contributors please add @jmunroe for review

bad bad bot

[allcontributors[bot]]

@Batalex

I've put up a pull request to add @jmunroe! :tada:

[Zeitsperre]

Hi @Batalex and @lwasser

The article was just published yesterday in JOSS! Here's the review: https://github.com/openjournals/joss-reviews/issues/5415. I think we're good to close this issue!

[lwasser]

hi there @Zeitsperre !! what wonderful news!! congratulations on the JOSS acceptance. I just tagged this review as JOSS-approved and will close it!! were there any hiccups in the JOSS side of things from your perspective? or did it go smoothly?

[lwasser]

hey there @Zeitsperre just following up on this review! i hope the JOSS component is going well. I have a small request - can you take 5-10 minutes to fill out our post-review survey please? i'd greatly appreciate it. The most important part for us it how the review impacted / improved etc your package (if it did) and feedback on the process. many thanks in advance for doing this!!

[Zeitsperre]

Hi Leah, for sure. I have some time this evening.

The JOSS side of things went relatively well. I think there was some confusion over the order in which things needed to get done (the release with all reviewer comment changes needs to happen first before it can be formally approved and published in JOSS, which wasn't clear at first).

Other than that, no issues. It was relatively easy. I have some thoughts that could maybe benefit the pyOpenSci-side, so I'll share them in the survey.

Thanks again!

[lwasser]

oh yes - any thoughts you have that could help us clarify what the process looks like would be greatly appreciated. many thanks for this. i'm happy to update our review guides with clearer guidance on that side of things as it makes sense as well. Thank you so much, Trevor for both filling this out and for the input on the JOSS process!

[lwasser]

hey @Zeitsperre i wanted to invite you / your maintainer team to write a blog post (totally optional) on your package for us to promote your work! if you are interested - here are a few examples of other blog posts:

pandera movingpandas

and here is a markdown example that you could use as a guide when creating your post.

it can even be a tutorial like post that highlights what your package does. then we can share it with people to get the word out about your package.

If you are too busy for this no worries. But if you have time - we'd love to spread the word about your package!