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

Viewed

Total article views: 558 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
406 113 39 558 16 18
  • HTML: 406
  • PDF: 113
  • XML: 39
  • Total: 558
  • BibTeX: 16
  • EndNote: 18
Views and downloads (calculated since 26 Mar 2024)
Cumulative views and downloads (calculated since 26 Mar 2024)

Viewed (geographical distribution)

Total article views: 558 (including HTML, PDF, and XML) Thereof 562 with geography defined and -4 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Sep 2024
Download
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.
Read more