ILAMB

Python software used in the International Land Model Benchmarking project.
https://github.com/rubisco-sfa/ILAMB

Category: Natural Resources
Sub Category: Soil and Land

Keywords

benchmarking earth-system-model model python3

Keywords from Contributors

geoscience climate cmip6 climate-data cmip5 petsc climate-model

Last synced: about 6 hours ago
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Repository metadata

Python software used in the International Land Model Benchmarking (ILAMB) project

README.md

ILAMB - International Land Model Benchmarking

The python package designed to help confront earth system models with reference data products, and then present the results in a hierarchical set of webpages. Please see ilamb.org where we have details about the datasets we use, the results we catalog, and the methods we employ.

v2.7.2 Release - September 2024

This update contains many bugfixes found while supporting papers and other model-data comparison work in the community. You may sense that the development of ILAMB has slowed down. Code development at this repository has slowed, but ILAMB continues to change. Many datasets are being added to our comparisons but that activity happens in a different repository (see ILAMB-Data).

We have also been developing a rewrite of the ILAMB software which leverages more modern analysis packages, in particular xarray. The new ILAMB version also is meant to address a shift in how scientists work. Originally ILAMB was envisioned as a replacement for diagnostics packages that modeling centers run. However in the past 10 years, it has become more common for scientists to run small analyses in notebooks as part of their model development process. This new version of ILAMB will find a balance between the operational diagnostic package and the ability to import functionality for creative uses. At the moment, we are developing this as a separate package we call ilamb3. Check it out along with its accompanying documentation.

Funding

This research was performed for the Reducing Uncertainties in
Biogeochemical Interactions through Synthesis and Computation

(RUBISCO) Scientific Focus Area, which is sponsored by the Regional
and Global Climate Modeling (RGCM) Program in the Climate and
Environmental Sciences Division (CESD) of the Biological and
Environmental Research (BER) Program in the U.S. Department of Energy
Office of Science.


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GitHub Events

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Last synced: 8 days ago

Total Commits: 1,165
Total Committers: 19
Avg Commits per committer: 61.316
Development Distribution Score (DDS): 0.076

Commits in past year: 39
Committers in past year: 4
Avg Commits per committer in past year: 9.75
Development Distribution Score (DDS) in past year: 0.154

Name Email Commits
Nathan Collier n****r@g****m 1076
Ke Xu k****u@u****u 26
Ana Ordonez o****4@l****v 14
Mark Piper m****r@c****u 10
Min Xu m****u@g****m 9
Jitendra Kumar j****r@c****g 8
Keith Oleson o****n@u****u 4
rbeucher r****r@a****u 4
Ke Xu x****d@g****m 2
Jem Stachelek j****a 2
Joseph H Kennedy me@j****g 2
Gautam Bisht g****t@l****v 1
Forrest M. Hoffman f****t@c****g 1
Mingquan Mu m****u@u****u 1
Nathan Collier n****f@m****v 1
Nathaniel Collier n****f@d****v 1
thomas colligan 4****t 1
Sean Bryan 3****1 1
Nathaniel Collier n****f@w****v 1

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Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 55
Total pull requests: 63
Average time to close issues: 4 months
Average time to close pull requests: 22 days
Total issue authors: 25
Total pull request authors: 11
Average comments per issue: 1.84
Average comments per pull request: 0.98
Merged pull request: 56
Bot issues: 0
Bot pull requests: 0

Past year issues: 15
Past year pull requests: 13
Past year average time to close issues: 4 months
Past year average time to close pull requests: about 15 hours
Past year issue authors: 7
Past year pull request authors: 4
Past year average comments per issue: 1.93
Past year average comments per pull request: 1.77
Past year merged pull request: 10
Past year bot issues: 0
Past year bot pull requests: 0

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Package metadata

pypi.org: ilamb

The International Land Model Benchmarking Package

  • Homepage: https://github.com/rubisco-sfa/ILAMB.git
  • Documentation: https://ilamb.readthedocs.io/
  • Licenses: BSD-3-Clause
  • Latest release: 2.7.2 (published 8 months ago)
  • Last Synced: 2025-04-29T15:03:15.030Z (1 day ago)
  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 384 Last month
  • Rankings:
    • Forks count: 6.927%
    • Dependent packages count: 7.31%
    • Stargazers count: 10.79%
    • Dependent repos count: 11.798%
    • Average: 13.517%
    • Downloads: 30.757%
  • Maintainers (1)
conda-forge.org: ilamb

The International Land Model Benchmarking (ILAMB) project is a model-data intercomparison and integration project designed to improve the performance of land models and, in parallel, improve the design of new measurement campaigns to reduce uncertainties associated with key land surface processes. Building upon past model evaluation studies, the goals of ILAMB are to: * develop internationally accepted benchmarks for land model performance, promote the use of these benchmarks by the international community for model intercomparison, * strengthen linkages between experimental, remote sensing, and climate modeling communities in the design of new model tests and new measurement programs, and * support the design and development of a new, open source, benchmarking software system for use by the international community. It is the last of these goals to which this repository is concerned. We have developed a python-based generic benchmarking system, initially focused on assessing land model performance.

  • Homepage: https://github.com/rubisco-sfa/ILAMB
  • Licenses: BSD-3-Clause
  • Latest release: 2.3.1 (published about 6 years ago)
  • Last Synced: 2025-04-02T02:08:51.153Z (29 days ago)
  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Rankings:
    • Dependent repos count: 24.103%
    • Forks count: 31.405%
    • Average: 37.976%
    • Stargazers count: 44.857%
    • Dependent packages count: 51.54%

Dependencies

setup.py pypi
  • cartopy >=0.17.0
  • cf_units >=2.0.0
  • cftime *
  • matplotlib >=2.2
  • mpi4py >=1.3.1
  • netCDF4 >=1.1.4
  • numpy >=1.11.0
  • pandas >=1.0.0
  • scipy >=0.9.0
  • sympy >=0.7.6

Score: 13.274892171256734