Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 18
Articles | Volume 17, issue 18
https://doi.org/10.5194/gmd-17-7001-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7001-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 18
Development and technical paper
 | 
19 Sep 2024
Development and technical paper |  | 19 Sep 2024

Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community

Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam

Data sets

CMAQ Model Version 5.3.3 Input Data - 12/22/2015 - 01/31/2016 12km CONUS2 (12US2) Elizabeth Adams https://doi.org/10.15139/S3/CFU9UL

Model code and software

CMASCenter/pcluster-cmaq: CMAQ on AWS Tutorial Version 5.3.3 (v5.33) Liz Adams et al. https://doi.org/10.5281/zenodo.10696908

CMASCenter/cyclecloud-cmaq: CMAQ on Azure Tutorial Version 5.3.3 (v5.33) Liz Adams and Christos Efstathiou https://doi.org/10.5281/zenodo.10696804

CMAQ (5.3.3) U.S. EPA Office of Research and Development https://doi.org/10.5281/zenodo.5213949

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Short summary
We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
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