An open source framework for atmospheric model and observational column comparison.
Project description
An open source framework for atmospheric model and observational column comparison. Supported by the Atmospheric Systems Research (ASR) program of the United States Department of Energy.
The Earth Model Column Collaboratory (EMC2) is inspired from past work comparing remotely sensed zenith-pointing measurements to earth system and global climate models (GCMs) and their single-column model modes (SCMs) (e.g., Lamer et al. 2018; Swales et al. 2018)
EMC2 provides an open source software framework to:
Represent both ARM measurements and GCM columns in the Python programming language building on the Atmospheric Community Toolkit (ACT, Theisen et. al. 2019) and leveraging the EMC2 team’s success with Py-ART (Helmus and Collis 2016).
Scale GCM outputs (using the cloud fraction) to compare with sub-grid-scale column measurements using a modular sub column generator designed to run off-line on time series extracted from existing GCM/SCM output.
Enable a suite of comparisons between ARM (and other) column measurements and the GCM model subcolumns.
Detailed description will soon be provided in a dedicated manuscript (Silber et al., in prep).
Usage
Installation
In order to install EMC^2, you can use either pip or anaconda. In a terminal, simply type either of:
$ pip install emc2 $ conda install -c conda-forge emc2
In addition, if you want to build EMC^2 from source and install, type in the following commands:
$ git clone https://github.com/columncolab/EMC2 $ cd EMC2 $ pip install .
Requirements
- EMC^2 requires Python 3.6+ as well as:
Atmoshperic Community Toolkit (https://arm-doe.github.io/ACT).
Numpy (https://numpy.org)
Scipy (https://scipy.org)
Matplotlib (https://matplotlib.org)
Xarray (http://xarray.pydata.org)
Licence
Copyright 2021 Authors
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
References
Swales, D.J., Pincus, R., Bodas-Salcedo, A., 2018. The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2. Geosci. Model Dev. 11, 77–81. https://doi.org/10.5194/gmd-11-77-2018
Lamer, K. Relative Occurrence of Liquid Water, Ice and Mixed-Phase Conditions within Various Cloud and Precipitation Regimes: Long Term Ground-Based Observations for GCM Model Evaluation. 2018. The Pennsylvania State University, PhD dissertation.
Theisen et. al.: Atmospheric Community Toolkit: https://github.com/ANL-DIGR/ACT
Helmus, J., Collis, S., 2016. The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software 4. https://doi.org/10.5334/jors.119
Eynard-Bontemps, G., R Abernathey, J. Hamman, A. Ponte, W. Rath, 2019: The Pangeo Big Data Ecosystem and its use at CNES. In P. Soille, S. Loekken, and S. Albani, Proc. of the 2019 conference on Big Data from Space (BiDS’2019), 49-52. EUR 29660 EN, Publications Office of the European Union, Luxembourg. ISBN: 978-92-76-00034-1, doi:10.2760/848593.
Jupyter et al., “Binder 2.0 - Reproducible, Interactive, Sharable Environments for Science at Scale.” Proceedings of the 17th Python in Science Conference. 2018. 10.25080/Majora-4af1f417-011
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