output files from CMAQ runs. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

Annual CMAQ model output for 2016 for approximately six different simulations. This dataset is not publicly accessible because: Data are too large to upload to ScienceHub. It can be accessed through the following means: These data can be requested from the author. Format: Data formats include I/O API and csv. This dataset is associated with the following publication: Appel, K.W., J. Bash, K. Fahey, K. Foley, R. Gilliam, C. Hogrefe, B. Hutzell, D. Kang, R. Mathur, B. Murphy, S. Napelenok, C. Nolte, J. Pleim, G. Pouliot, H. Pye, G. Sarwar, D. Schwede, F. Sidi, T. Spero, D. Wong, L. Ran, and S. Roselle. The Community Multiscale Air Quality (CMAQ) Model Version 5.3: System Updates and Evaluation. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 14: 2867-2897, (2021).

Annual CMAQ model output for 2016 for approximately six different simulations. This dataset is not publicly accessible because: Data are too large to upload to ScienceHub. It can be accessed through the following means: These data can be requested from the author. Format: Data formats include I/O API and csv. This dataset is associated with the following publication: Appel, K.W., J. Bash, K. Fahey, K. Foley, R. Gilliam, C. Hogrefe, B. Hutzell, D. Kang, R. Mathur, B. Murphy, S. Napelenok, C. Nolte, J. Pleim, G. Pouliot, H. Pye, G. Sarwar, D. Schwede, F. Sidi, T. Spero, D. Wong, L. Ran, and S. Roselle. The Community Multiscale Air Quality (CMAQ) Model Version 5.3: System Updates and Evaluation. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 14: 2867-2897, (2021).

CMAQ and CMAQ-VBS model output. This dataset is not publicly accessible because: Files too large. It can be accessed through the following means: via EPA's NCC tape archive system (ASM) or by contacting the PI. Format: netCDF CMAQ model output. This dataset is associated with the following publication: Woody , M., K. Baker , P. Hayes, J. Jimenez, B. Koo, and H. Pye. Understanding sources of organic aerosol during CalNex-2010 using the CMAQ-VBS. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 16: 4081-4100, (2016).

Files contain values from Figures 1, 2, and 3 of the article by Pye et al., "Leveraging scientific community knowledge for air quality model chemistry parameterizations," scheduled for publication in EM in January 2024. Figures 2 and 3 are available in csv and excel spreadsheet format. Figure 1 is only available in spreadsheet format. Figure 1 shows gas and aerosol-phase chemistry representations in CMAQ since 2010. Figure 2 shows ozone and SOA formation potential (in g/g) for CRACMM species. Figure 3 shows the size (number of species and reactions) for various chemical mechanisms. This dataset is associated with the following publication: Pye, H., R. Schwantes, K. Barsanti, V.F. McNeill, and G. Wolfe. Leveraging scientific community knowledge for air quality model chemistry parameterizations. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 24-31, (2024).

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US. This dataset is not publicly accessible because: Outside data. It can be accessed through the following means: contact : Amir Hakami . Format: Model outputs in ioapi format, and analysis results in database format. This dataset is associated with the following publication: Zhao, S., A. Hakami, S. Capps, M. Turner, D. Henze, P. Percell, J. Resler, H. Shen, A. Russell, A. Nenes, A. Pappin, S. Napelenok, J. Bash, K. Fahey, J. Baek, G. Carmichael, C. Stanier, A. Sandu, and T. Chai. A multiphase CMAQ version 5.0 adjoint. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 13(7): 2925-2944, (2020).

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US. This dataset is not publicly accessible because: Outside data. It can be accessed through the following means: contact : Amir Hakami . Format: Model outputs in ioapi format, and analysis results in database format. This dataset is associated with the following publication: Zhao, S., A. Hakami, S. Capps, M. Turner, D. Henze, P. Percell, J. Resler, H. Shen, A. Russell, A. Nenes, A. Pappin, S. Napelenok, J. Bash, K. Fahey, J. Baek, G. Carmichael, C. Stanier, A. Sandu, and T. Chai. A multiphase CMAQ version 5.0 adjoint. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 13(7): 2925-2944, (2020).

Near Real Time CMAQ Simulations compared to AQS Observations. This dataset is associated with the following publication: Eder, B., R. Gilliam, G. Pouliot, R. Mathur, and J. Pleim. Continuous, Near Real-Time Evaluation of Air Quality Models: An Approach for the Rapid Scientific Evolution of Modeling Systems. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-6, (2017).

Data zip file contains the exact CMAQ code used by Wiser et al. in the development of CRACMM1AMORE. In addition, CMAQ predictions and AQS observations of formaldehyde and ozone appearing in Figure 8 are included as csv files. For information on CMAQ file conventions as well as site compare output for model/observation evaluation, see the linked github repository for CMAQ. Linked dois include the standard CMAQv5.3.3 code and AMORE supplementary files.

Data zip file contains the exact CMAQ code used by Wiser et al. in the development of CRACMM1AMORE. In addition, CMAQ predictions and AQS observations of formaldehyde and ozone appearing in Figure 8 are included as csv files. For information on CMAQ file conventions as well as site compare output for model/observation evaluation, see the linked github repository for CMAQ. Linked dois include the standard CMAQv5.3.3 code and AMORE supplementary files.