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ESMValCore

ESMValTool Core: core functionalities and driver for ESMValTool, a community diagnostic and performance metrics tool for routine evaluation of Earth System Models in CMIP.

71
mentions
45
contributors

Cite this software

What ESMValCore can do for you

  • Finding data in a directory structure typically used for CMIP data
  • ESMValTool preprocessor functions based on iris for e.g. regridding, vertical interpolation, statistics, correcting (meta)data errors, extracting a time range, etcetera.
  • High flexibility: new diagnostics and more observational data can be easily added.
  • Multi-language support: Python, NCL, R, Julia... other open-source languages are possible.
  • CF/CMOR compliant: data from many different projects can be handled (CMIP, obs4mips, ana4mips, CCMI, CCMVal, AEROCOM, etc.). Routines are provided to CMOR-ize non-compliant data.
  • Integration in modeling workflows: for EMAC, NOAA-GFDL and NEMO, can be easily extended.

The ESMValCore software package provides the core functionality for ESMValTool, the Earth System Model eValuation Tool. It provides a configurable framework for finding CMIP files using a “data reference syntax”, applying commonly used pre-processing functions to them, running analysis scripts, and recording provenance. Numerous pre-processing functions, e.g. for data selection, regridding, and statistics are readily available and the modular design makes it easy to add more. The ESMValCore package is easy to install with relatively few dependencies, written in Python 3, based on state-of-the-art open-source libraries such as Iris and Dask, and widely used standards such as YAML, NetCDF, CF-Conventions, and W3C PROV. An extensive set of automated tests and code quality checks ensure the reliability of the package.

The ESMValCore package uses human-readable recipes to define which variables and datasets to use, how to pre-process that data, and what scientific analysis scripts to run. The package provides convenient interfaces, based on the YAML and NetCDF/CF-convention file formats, for running diagnostic scripts written in any programming language. Because the ESMValCore framework takes care of running the workflow defined in the recipe in parallel, most analyses run much faster, with no additional programming effort required from the authors of the analysis scripts. For example, benchmarks show a factor of 30 speedup with respect to version 1 of the tool for a representative recipe on a 24 core machine. A large collection of standard recipes and associated analysis scripts is available in the ESMValTool package for reproducing selected peer-reviewed analyses.

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Keywords
Programming languages
  • Python 94%
  • Jupyter Notebook 3%
  • HTML 2%
  • Other 1%
License
  • Apache-2.0
</>Source code
Packages
pypi.org
anaconda.org
hub.docker.com

Participating organisations

Environment & Sustainability
Environment & Sustainability
Alfred Wegener Institute
Barcelona Supercomputing Center
DLR
ETH Zurich
Ludwig Maximilian University of Munich
Met Office
Netherlands eScience Center
Plymouth Marine Laboratory
Swedish Meteorological and Hydrological Institute
University of Bremen
University of Reading

Reference papers

Mentions

Analysis-ready climate data with ESMValCore

Author(s): Peter Kalverla and Bouwe Andela
Published by Netherlands eScience Center in 2023

Contributors

Abel Soares Siqueira
Abel Soares Siqueira
Barbara Vreede
Barbara Vreede
BM
Benjamin Müller
Ludwig Maximilian University of Munich
BG
Bouwe Andela
Bouwe Andela
EG
Evgenia Galytska
Fakhereh Alidoost
Fakhereh Alidoost

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