Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3407–3420, 2021
https://doi.org/10.5194/gmd-14-3407-2021
Geosci. Model Dev., 14, 3407–3420, 2021
https://doi.org/10.5194/gmd-14-3407-2021

Model description paper 07 Jun 2021

Model description paper | 07 Jun 2021

The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2

Benjamin N. Murphy et al.

Related authors

Modeling secondary organic aerosol formation from volatile chemical products
Elyse A. Pennington, Karl M. Seltzer, Benjamin N. Murphy, Momei Qin, John H. Seinfeld, and Havala O. T. Pye
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-547,https://doi.org/10.5194/acp-2021-547, 2021
Preprint under review for ACP
Short summary
Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States
Xiaoyang Chen, Yang Zhang, Kai Wang, Daniel Tong, Pius Lee, Youhua Tang, Jianping Huang, Patrick C. Campbell, Jeff Mcqueen, Havala O. T. Pye, Benjamin N. Murphy, and Daiwen Kang
Geosci. Model Dev., 14, 3969–3993, https://doi.org/10.5194/gmd-14-3969-2021,https://doi.org/10.5194/gmd-14-3969-2021, 2021
Short summary
The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation
K. Wyat Appel, Jesse O. Bash, Kathleen M. Fahey, Kristen M. Foley, Robert C. Gilliam, Christian Hogrefe, William T. Hutzell, Daiwen Kang, Rohit Mathur, Benjamin N. Murphy, Sergey L. Napelenok, Christopher G. Nolte, Jonathan E. Pleim, George A. Pouliot, Havala O. T. Pye, Limei Ran, Shawn J. Roselle, Golam Sarwar, Donna B. Schwede, Fahim I. Sidi, Tanya L. Spero, and David C. Wong
Geosci. Model Dev., 14, 2867–2897, https://doi.org/10.5194/gmd-14-2867-2021,https://doi.org/10.5194/gmd-14-2867-2021, 2021
Short summary
Particle dry deposition algorithms in CMAQ version 5.3: characterization of critical parameters and land use dependence using DepoBoxTool version 1.0
Qian Shu, Benjamin Murphy, Jonathan E. Pleim, Donna Schwede, Barron H. Henderson, Havala O.T. Pye, Keith Wyat Appel, Tanvir R. Khan, and Judith A. Perlinger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-129,https://doi.org/10.5194/gmd-2021-129, 2021
Preprint under review for GMD
Short summary
Technical Note – AQMEII4 Activity 1: Evaluation of Wet and Dry Deposition Schemes as an Integral Part of Regional-Scale Air Quality Models
Stefano Galmarini, Paul Makar, Olivia Clifton, Christian Hogrefe, Jesse Bash, Roberto Bianconi, Roberto Bellasio, Johannes Bieser, Tim Butler, Jason Ducker, Johannes Flemming, Alma Hozdic, Christopher Holmes, Ioannis Kioutsioukis, Richard Kranenburg, Aurelia Lupascu, Juan Luis Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Sam Silva, Marta Garcia Vivanco, and Ralf Wolke
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-313,https://doi.org/10.5194/acp-2021-313, 2021
Preprint under review for ACP
Short summary

Related subject area

Atmospheric sciences
Data reduction for inverse modeling: an adaptive approach v1.0
Xiaoling Liu, August L. Weinbren, He Chang, Jovan M. Tadić, Marikate E. Mountain, Michael E. Trudeau, Arlyn E. Andrews, Zichong Chen, and Scot M. Miller
Geosci. Model Dev., 14, 4683–4696, https://doi.org/10.5194/gmd-14-4683-2021,https://doi.org/10.5194/gmd-14-4683-2021, 2021
Short summary
Comparison of source apportionment approaches and analysis of non-linearity in a real case model application
Claudio A. Belis, Guido Pirovano, Maria Gabriella Villani, Giuseppe Calori, Nicola Pepe, and Jean Philippe Putaud
Geosci. Model Dev., 14, 4731–4750, https://doi.org/10.5194/gmd-14-4731-2021,https://doi.org/10.5194/gmd-14-4731-2021, 2021
Short summary
APFoam 1.0: integrated computational fluid dynamics simulation of O3–NOx–volatile organic compound chemistry and pollutant dispersion in a typical street canyon
Luolin Wu, Jian Hang, Xuemei Wang, Min Shao, and Cheng Gong
Geosci. Model Dev., 14, 4655–4681, https://doi.org/10.5194/gmd-14-4655-2021,https://doi.org/10.5194/gmd-14-4655-2021, 2021
Short summary
Exploring deep learning for air pollutant emission estimation
Lin Huang, Song Liu, Zeyuan Yang, Jia Xing, Jia Zhang, Jiang Bian, Siwei Li, Shovan Kumar Sahu, Shuxiao Wang, and Tie-Yan Liu
Geosci. Model Dev., 14, 4641–4654, https://doi.org/10.5194/gmd-14-4641-2021,https://doi.org/10.5194/gmd-14-4641-2021, 2021
Short summary
Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing
Christian Zeman, Nils P. Wedi, Peter D. Dueben, Nikolina Ban, and Christoph Schär
Geosci. Model Dev., 14, 4617–4639, https://doi.org/10.5194/gmd-14-4617-2021,https://doi.org/10.5194/gmd-14-4617-2021, 2021
Short summary

Cited articles

Allen, D. J., Pickering, K. E., Pinder, R. W., Henderson, B. H., Appel, K. W., and Prados, A.: Impact of lightning-NO on eastern United States photochemistry during the summer of 2006 as determined using the CMAQ model, Atmos. Chem. Phys., 12, 1737–1758, https://doi.org/10.5194/acp-12-1737-2012, 2012. 
Appel, K. W., Bhave, P. V., Gilliland, A. B., Sarwar, G., and Roselle, S. J.: Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: sensitivities affecting model performance; part II – particulate matter, Atmos. Environ., 42, 6057–6066, https://doi.org/10.1016/j.atmosenv.2008.03.036, 2008. 
Baek, B. H. and Seppanen, C.: Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System (Version SMOKE User's Documentation), Zenodo, https://doi.org/10.5281/zenodo.1421403, 2018. 
Bash, J. O.: Description and initial simulation of a dynamic bidirectional air-surface exchange model for mercury in CMAQ, J. Geophys. Res., 115, D06305, https://doi.org/10.1029/2009JD012834, 2010. 
Download
Short summary
The algorithms for applying air pollution emission rates in the Community Multiscale Air Quality (CMAQ) model have been improved to better support users and developers. The new features accommodate emissions perturbation studies that are typical in atmospheric research and output a wealth of metadata for each model run so assumptions can be verified and documented. The new approach dramatically enhances the transparency and functionality of this critical aspect of atmospheric modeling.