Metadata-Version: 2.1
Name: ILAMB
Version: 2.6
Summary: The International Land Model Benchmarking Package
Home-page: https://github.com/rubisco-sfa/ILAMB.git
Author: Nathan Collier
Author-email: nathaniel.collier@gmail.com
License: UNKNOWN
Description: ILAMB 2.6 Release
        =================
        
        It has been a while since our last release, but ILAMB continues to
        evolve. Many of the changes are 'under the hood' or bugfixes that are
        not readily seen. In the following, we present a few key changes and
        draw attention in particular to those that will change scores. We also
        have worked to make ILAMB ready to integrate with tools being
        developed as part of the Coordinated Model Evaluation Capabilities (`CMEC
        <https://cmec.llnl.gov/>`_).
        
        Changes - May 2021
        ------------------
        
        CMEC
        ~~~~
        
        * Added CMEC-compliant JSON output to the standard outputs
        * Added an alternative landing page for ILAMB results which uses the
          `LMT Unified Dashboard
          <https://github.com/climatemodeling/unified-dashboard>`_
        * Added support files for using `cmec-driver
          <https://github.com/cmecmetrics/cmec-driver>`_ as an alternative run
          environment
        
        Quality of Life
        ~~~~~~~~~~~~~~~
        
        * Top page overhaul moving to a single result panel with a colorblind
          friendly palette
        * Shifted score colormaps to be more qualitative and colorblind
          friendly
        * ILAMB now has continuous integration testing using Azure Pipelines
          on each commit or pull request
        * ModelResults can be passed a list of paths to search for results,
          objects are cached as pickle files
        * Plotting limits are now based on the middle 98% across all models to
          help reduce the effect of a single model with extreme values washing
          out all the map plots
        * The configure file used to generate a run is now copied into the
          output directory as `ilamb.cfg`
        * ILAMB logfiles will now provide an estimate for peak memory usage in
          each confrontation which can be used in debugging and when running
          on large clusters with limited memory
        
        Scoring
        ~~~~~~~
        
        * For scoring coupled models, we find that scoring the RMSE of the
          annual cycle is more reasonable. While the default is still set to
          score the full time series, this may be changed at runtime with
          `--rmse_score_basis {series|cycle}`
        * We have found that when comparing a set of models which contain a
          multimodel mean, the mean model's interannual variability is
          typically lower which serendipitously better matches that of our
          reference data products. This makes the multimodel mean look even
          better relative to individual models but not for good reasons. We
          have disabled the interannual variability in our scoring.
        * We have updated a number of reference datasets to their most current
          version as well as many new datasets and comparions, run
          `ilamb-fetch` to update
        * Support for using observational uncertainty in scoring, currently
          disabled
          
        
        Useful Information
        ------------------
        
        * `Documentation <https://www.ilamb.org/doc/>`_ - installation and
          basic usage tutorials
        * Sample Output
          
          * `ILAMB <https://www.ilamb.org/CMIP5v6/historical/>`_ - land
            comparison against a collection of CMIP5 and CMIP6 models
          * `IOMB <https://www.ilamb.org/CMIP5v6/IOMB/>`_ - ocean comparison
            against a collection of CMIP5 and CMIP6 models
        
        * `Paper <https://doi.org/10.1029/2018MS001354>`_ published in JAMES
          which details the design and methodology employed in the ILAMB
          package. If you find the package or the output helpful in your
          research or development efforts, we kindly ask you to cite this
          work.
        
        Description
        -----------
        
        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.
        
        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.
        
Keywords: benchmarking,earth system modeling,climate modeling,model intercomparison
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
