Articles | Volume 17, issue 18
https://doi.org/10.5194/gmd-17-7001-2024
https://doi.org/10.5194/gmd-17-7001-2024
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

Related authors

Evaluation of gas-particle partitioning in a regional air quality model for organic pollutants
Christos I. Efstathiou, Jana Matejovičová, Johannes Bieser, and Gerhard Lammel
Atmos. Chem. Phys., 16, 15327–15345, https://doi.org/10.5194/acp-16-15327-2016,https://doi.org/10.5194/acp-16-15327-2016, 2016
Short summary
Bidirectional air–sea exchange and accumulation of POPs (PAHs, PCBs, OCPs and PBDEs) in the nocturnal marine boundary layer
Gerhard Lammel, Franz X. Meixner, Branislav Vrana, Christos I. Efstathiou, Jiři Kohoutek, Petr Kukučka, Marie D. Mulder, Petra Přibylová, Roman Prokeš, Tatsiana P. Rusina, Guo-Zheng Song, and Manolis Tsapakis
Atmos. Chem. Phys., 16, 6381–6393, https://doi.org/10.5194/acp-16-6381-2016,https://doi.org/10.5194/acp-16-6381-2016, 2016
Short summary

Related subject area

Atmospheric sciences
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025,https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025,https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025,https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025,https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025,https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary

Cited articles

Adams, E.: CMAQ Model Version 5.3.3 Input Data – 12/22/2015 – 01/31/2016 12km CONUS2 (12US2), UNC Dataverse, V1 [data set], https://doi.org/10.15139/S3/CFU9UL, 2024. 
Adams, L. and Efstathiou, C.: CMASCenter/cyclecloud-cmaq: CMAQ on Azure Tutorial Version 5.3.3 (v5.33), Zenodo [code], https://doi.org/10.5281/zenodo.10696804, 2024a. 
Adams, E. and Efstathiou, C.: CMAQv5.3.3 on Azure Tutorial, https://cyclecloud-cmaq.readthedocs.io/en/cmaqv5.3.3/, last access: 20 June 2024b. 
Adams, L., Foley, K., and Efstathiou, C.: CMASCenter/pcluster-cmaq: CMAQ on AWS Tutorial Version 5.3.3 (v5.33), Zenodo [code], https://doi.org/10.5281/zenodo.10696908, 2024b. 
Adams, E., Foley, K., and Efstathiou, C.: CMAQv5.3.3 on AWS Tutorial, https://pcluster-cmaq.readthedocs.io/en/cmaqv5.3.3/, last access: 20 June 2024b. 
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.
Share