Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1635-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1635-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
12 Mar 2025
Model evaluation paper |
| 12 Mar 2025
| 12 Mar 2025
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Wei Li
CORRESPONDING AUTHOR
Air Resources Laboratory, NOAA/OAR, College Park, MD, USA
Cooperative Institute for Satellite Earth System Studies, George Mason University, Fairfax, VA, USA
Beiming Tang
Air Resources Laboratory, NOAA/OAR, College Park, MD, USA
Cooperative Institute for Satellite Earth System Studies, George Mason University, Fairfax, VA, USA
Patrick C. Campbell
Air Resources Laboratory, NOAA/OAR, College Park, MD, USA
Cooperative Institute for Satellite Earth System Studies, George Mason University, Fairfax, VA, USA
Youhua Tang
Air Resources Laboratory, NOAA/OAR, College Park, MD, USA
Cooperative Institute for Satellite Earth System Studies, George Mason University, Fairfax, VA, USA
Barry Baker
Air Resources Laboratory, NOAA/OAR, College Park, MD, USA
Zachary Moon
Air Resources Laboratory, NOAA/OAR, College Park, MD, USA
Earth Resources Technology (ERT), Inc., Laurel, MD, USA
Daniel Tong
Air Resources Laboratory, NOAA/OAR, College Park, MD, USA
Cooperative Institute for Satellite Earth System Studies, George Mason University, Fairfax, VA, USA
Jianping Huang
Environmental Modeling Center, NOAA/NWS/NCEP, College Park, MD, USA
Kai Wang
Environmental Modeling Center, NOAA/NWS/NCEP, College Park, MD, USA
Lynker, Leesburg, VA, USA
Ivanka Stajner
Environmental Modeling Center, NOAA/NWS/NCEP, College Park, MD, USA
Raffaele Montuoro
Environmental Modeling Center, NOAA/NWS/NCEP, College Park, MD, USA
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Chi-Tsan Wang, Patrick C. Campbell, Paul Makar, Siqi Ma, Irena Ivanova, Bok H. Baek, Wei-Ting Hung, Zachary Moon, Youhua Tang, Barry Baker, Rick Saylor, and Daniel Tong
EGUsphere, https://doi.org/10.5194/egusphere-2025-485, https://doi.org/10.5194/egusphere-2025-485, 2025
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Forests influence air quality by altering ozone levels, but most air pollution models overlook canopy effects. Our study improves ozone predictions by incorporating forest canopy shading and turbulence into a widely used model. We found that tree cover reduces near-surface ozone by decreasing photolysis rates and diffusion inside canopy, resulting in lower ozone concentrations in densely forested areas. These findings enhance ozone surface prediction accuracy and improve air quality modeling.
Xiaodan Ma, Jianping Huang, Michaela I. Hegglin, Patrick Jöckel, and Tianliang Zhao
Atmos. Chem. Phys., 25, 943–958, https://doi.org/10.5194/acp-25-943-2025, https://doi.org/10.5194/acp-25-943-2025, 2025
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Our research explored changes in ozone levels in the northwest Pacific region over 30 years, revealing a significant increase in the middle-to-upper troposphere, especially during spring and summer. This rise is influenced by both stratospheric and tropospheric sources, which affect climate and air quality in East Asia. This work underscores the need for continued study to understand underlying mechanisms.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
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TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Qindan Zhu, Rebecca H. Schwantes, Matthew Coggon, Colin Harkins, Jordan Schnell, Jian He, Havala O. T. Pye, Meng Li, Barry Baker, Zachary Moon, Ravan Ahmadov, Eva Y. Pfannerstill, Bryan Place, Paul Wooldridge, Benjamin C. Schulze, Caleb Arata, Anthony Bucholtz, John H. Seinfeld, Carsten Warneke, Chelsea E. Stockwell, Lu Xu, Kristen Zuraski, Michael A. Robinson, J. Andrew Neuman, Patrick R. Veres, Jeff Peischl, Steven S. Brown, Allen H. Goldstein, Ronald C. Cohen, and Brian C. McDonald
Atmos. Chem. Phys., 24, 5265–5286, https://doi.org/10.5194/acp-24-5265-2024, https://doi.org/10.5194/acp-24-5265-2024, 2024
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Volatile organic compounds (VOCs) fuel the production of air pollutants like ozone and particulate matter. The representation of VOC chemistry remains challenging due to its complexity in speciation and reactions. Here, we develop a chemical mechanism, RACM2B-VCP, that better represents VOC chemistry in urban areas such as Los Angeles. We also discuss the contribution of VOCs emitted from volatile chemical products and other anthropogenic sources to total VOC reactivity and O3.
Fei Liu, Steffen Beirle, Joanna Joiner, Sungyeon Choi, Zhining Tao, K. Emma Knowland, Steven J. Smith, Daniel Q. Tong, Siqi Ma, Zachary T. Fasnacht, and Thomas Wagner
Atmos. Chem. Phys., 24, 3717–3728, https://doi.org/10.5194/acp-24-3717-2024, https://doi.org/10.5194/acp-24-3717-2024, 2024
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Using satellite data, we developed a coupled method independent of the chemical transport model to map NOx emissions across US cities. After validating our technique with synthetic data, we charted NOx emissions from 2018–2021 in 39 cities. Our results closely matched EPA estimates but also highlighted some inconsistencies in both magnitude and spatial distribution. This research can help refine strategies for monitoring and managing air quality.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
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A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
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To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Yunyao Li, Daniel Tong, Siqi Ma, Saulo R. Freitas, Ravan Ahmadov, Mikhail Sofiev, Xiaoyang Zhang, Shobha Kondragunta, Ralph Kahn, Youhua Tang, Barry Baker, Patrick Campbell, Rick Saylor, Georg Grell, and Fangjun Li
Atmos. Chem. Phys., 23, 3083–3101, https://doi.org/10.5194/acp-23-3083-2023, https://doi.org/10.5194/acp-23-3083-2023, 2023
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Plume height is important in wildfire smoke dispersion and affects air quality and human health. We assess the impact of plume height on wildfire smoke dispersion and the exceedances of the National Ambient Air Quality Standards. A higher plume height predicts lower pollution near the source region, but higher pollution in downwind regions, due to the faster spread of the smoke once ejected, affects pollution exceedance forecasts and the early warning of extreme air pollution events.
James D. East, Barron H. Henderson, Sergey L. Napelenok, Shannon N. Koplitz, Golam Sarwar, Robert Gilliam, Allen Lenzen, Daniel Q. Tong, R. Bradley Pierce, and Fernando Garcia-Menendez
Atmos. Chem. Phys., 22, 15981–16001, https://doi.org/10.5194/acp-22-15981-2022, https://doi.org/10.5194/acp-22-15981-2022, 2022
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We present a framework that uses a computer model of air quality, along with air pollution data from satellite instruments, to estimate emissions of nitrogen oxides (NOx) across the Northern Hemisphere. The framework, which advances current methods to infer emissions from satellite observations, provides observationally constrained NOx estimates, including in regions of the world where emissions are highly uncertain, and can improve simulations of air pollutants relevant for health and policy.
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022, https://doi.org/10.5194/gmd-15-7977-2022, 2022
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This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
Li Zhang, Raffaele Montuoro, Stuart A. McKeen, Barry Baker, Partha S. Bhattacharjee, Georg A. Grell, Judy Henderson, Li Pan, Gregory J. Frost, Jeff McQueen, Rick Saylor, Haiqin Li, Ravan Ahmadov, Jun Wang, Ivanka Stajner, Shobha Kondragunta, Xiaoyang Zhang, and Fangjun Li
Geosci. Model Dev., 15, 5337–5369, https://doi.org/10.5194/gmd-15-5337-2022, https://doi.org/10.5194/gmd-15-5337-2022, 2022
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The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
Patrick C. Campbell, Youhua Tang, Pius Lee, Barry Baker, Daniel Tong, Rick Saylor, Ariel Stein, Jianping Huang, Ho-Chun Huang, Edward Strobach, Jeff McQueen, Li Pan, Ivanka Stajner, Jamese Sims, Jose Tirado-Delgado, Youngsun Jung, Fanglin Yang, Tanya L. Spero, and Robert C. Gilliam
Geosci. Model Dev., 15, 3281–3313, https://doi.org/10.5194/gmd-15-3281-2022, https://doi.org/10.5194/gmd-15-3281-2022, 2022
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NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the harmful effects of air pollution, while saving billions of dollars per year. Here we describe and evaluate the development of the most advanced version of the NAQFC to date, which became operational at NOAA on 20 July 2021. The new NAQFC is based on a coupling of NOAA's operational Global Forecast System (GFS) version 16 with the Community Multiscale Air Quality (CMAQ) model version 5.3.1.
Kai Wang, Yang Zhang, Shaocai Yu, David C. Wong, Jonathan Pleim, Rohit Mathur, James T. Kelly, and Michelle Bell
Geosci. Model Dev., 14, 7189–7221, https://doi.org/10.5194/gmd-14-7189-2021, https://doi.org/10.5194/gmd-14-7189-2021, 2021
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The two-way coupled WRF-CMAQ model accounting for complex chemistry–meteorology feedbacks has been applied to the long-term predictions of regional meteorology and air quality over the US. The model results show superior performance and importance of chemistry–meteorology feedbacks when compared to the offline coupled WRF and CMAQ simulations, which suggests that feedbacks should be considered along with other factors in developing future model applications to inform policy making.
Siqi Ma, Daniel Tong, Lok Lamsal, Julian Wang, Xuelei Zhang, Youhua Tang, Rick Saylor, Tianfeng Chai, Pius Lee, Patrick Campbell, Barry Baker, Shobha Kondragunta, Laura Judd, Timothy A. Berkoff, Scott J. Janz, and Ivanka Stajner
Atmos. Chem. Phys., 21, 16531–16553, https://doi.org/10.5194/acp-21-16531-2021, https://doi.org/10.5194/acp-21-16531-2021, 2021
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Predicting high ozone gets more challenging as urban emissions decrease. How can different techniques be used to foretell the quality of air to better protect human health? We tested four techniques with the CMAQ model against observations during a field campaign over New York City. The new system proves to better predict the magnitude and timing of high ozone. These approaches can be extended to other regions to improve the predictability of high-O3 episodes in contemporary urban environments.
Adrian Chappell, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-337, https://doi.org/10.5194/gmd-2021-337, 2021
Revised manuscript not accepted
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Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Our results indicate that tuning dust emission models to dust in the atmosphere has hidden dust emission modelling weaknesses and its poor performance. Our new approach will reduce uncertainty and driven by prognostic albedo improve Earth System Models of aerosol effects on future environmental change.
Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
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Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Haipeng Lin, Daniel J. Jacob, Elizabeth W. Lundgren, Melissa P. Sulprizio, Christoph A. Keller, Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Patrick C. Campbell, Barry Baker, Rick D. Saylor, and Raffaele Montuoro
Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021, https://doi.org/10.5194/gmd-14-5487-2021, 2021
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Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to select, add, and scale emissions from different sources through a configuration file with no change to the model source code. We demonstrate the implementation of HEMCO in several models, all sharing the same HEMCO core code and database library.
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
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The continuously updated National Air Quality Forecast Capability (NAQFC) provides air quality forecasts. To support the development of the next-generation NAQFC, we evaluate a prototype of GFSv15-CMAQv5.0.2. The performance and the potential improvements for the system are discussed. This study can provide a scientific basis for further development of NAQFC and help it to provide more accurate air quality forecasts to the public over the contiguous United States.
Youhua Tang, Huisheng Bian, Zhining Tao, Luke D. Oman, Daniel Tong, Pius Lee, Patrick C. Campbell, Barry Baker, Cheng-Hsuan Lu, Li Pan, Jun Wang, Jeffery McQueen, and Ivanka Stajner
Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, https://doi.org/10.5194/acp-21-2527-2021, 2021
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Chemical lateral boundary condition (CLBC) impact is essential for regional air quality prediction during intrusion events. We present a model mapping Goddard Earth Observing System (GEOS) to Community Multi-scale Air Quality (CMAQ) CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species. Influence depends on distance from the inflow boundary and species and their regional characteristics. We use aerosol optical thickness to derive CLBCs, achieving reasonable prediction.
Xiaodan Ma, Jianping Huang, Tianliang Zhao, Cheng Liu, Kaihui Zhao, Jia Xing, and Wei Xiao
Atmos. Chem. Phys., 21, 1–16, https://doi.org/10.5194/acp-21-1-2021, https://doi.org/10.5194/acp-21-1-2021, 2021
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The present work aims at identifying and quantifying the relative contributions of the key factors in driving a rapid increase in summertime surface O3 over the North China Plain during 2013–2019. In addition to anthropogenic emission reduction and meteorological variabilities, our study highlights the importance of inclusion of aerosol absorption and scattering properties rather than aerosol abundance only in accurate assessment of aerosol radiative effect on surface O3 formation and change.
Cited articles
Appel, K. W., Gilliam, R. C., Davis, N., Zubrow, A., and Howard, S. C.: Overview of the atmospheric model evaluation tool (AMET) v1.1 for evaluating meteorological and air quality models, Environ. Modell. Softw., 26, 434–443, https://doi.org/10.1016/j.envsoft.2010.09.007, 2011.
Appel, K. W., Bash, J. O., Fahey, K. M., Foley, K. M., Gilliam, R. C., Hogrefe, C., Hutzell, W. T., Kang, D., Mathur, R., Murphy, B. N., Napelenok, S. L., Nolte, C. G., Pleim, J. E., Pouliot, G. A., Pye, H. O. T., Ran, L., Roselle, S. J., Sarwar, G., Schwede, D. B., Sidi, F. I., Spero, T. L., and Wong, D. C.: The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation, Geosci. Model Dev., 14, 2867–2897, https://doi.org/10.5194/gmd-14-2867-2021, 2021.
Bai, H., Li, B., Mehra, A., Meixner, J., Moorthi, S., Ray, S., Stefanova, L., Wang, J., Wang, J., Worthen, D., Yang, F., and Stan, C.: The Impact of Tropical SST Biases on the S2S Precipitation Forecast Skill over the Contiguous United States in the UFS Global Coupled Model, Weather Forecast., 38, 937–952, https://doi.org/10.1175/WAF-D-22-0162.1, 2023.
Baker, B. and Pan, L.: Overview of the Model and Observation Evaluation Toolkit (MONET) Version 1.0 for Evaluating Atmospheric Transport Models, Atmosphere, 8, 210, https://doi.org/10.3390/atmos8110210, 2017.
Balbus, J. M. and Malina, C.: Identifying Vulnerable Subpopulations for Climate Change Health Effects in the United States, J. Occup. Environ. Med., 51, 33–37, https://doi.org/10.1097/JOM.0b013e318193e12e, 2009.
Briggs, G. A.: A Plume Rise Model Compared with Observations, JAPCA J. Air Waste Ma., 15, 433–438, https://doi.org/10.1080/00022470.1965.10468404, 1965.
Burkholder, J. B., Sander, S. P., Abbatt, J. P. D. A. D., Barker, J. R., Huie, R. E., Kolb, C. E., Iii, M. J. K., Orkin, V. L., Wilmouth, D. M., and Wine, P. H.: Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies: Evaluation number 18, JPL Publication 15-10, Jet Propulsion Laboratory, Pasadena, CA, 2019.
Campbell, P. C., Baker, B., Saylor, R., Tong, D., Tang, Y., and Lee, P.: Initial Development of a NOAA Emissions and eXchange Unified System (NEXUS), 100th American Meteorological Society Annual Meeting, 12–16 January 2020, Boston, MA, https://doi.org/10.13140/RG.2.2.21070.20806, 2020.
Campbell, P. C., Tang, Y., Lee, P., Baker, B., Tong, D., Saylor, R., Stein, A., Huang, J., Huang, H.-C., Strobach, E., McQueen, J., Pan, L., Stajner, I., Sims, J., Tirado-Delgado, J., Jung, Y., Yang, F., Spero, T. L., and Gilliam, R. C.: Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16, Geosci. Model Dev., 15, 3281–3313, https://doi.org/10.5194/gmd-15-3281-2022, 2022.
Campbell, P. C., Li, W., and Tang, B.: noaa-oar-arl/AQM: CMAQv5.4 Paper (CMAQ54_Paper), Zenodo [code], https://doi.org/10.5281/zenodo.10833128, 2024.
Carlton, A. G., Bhave, P. V., Napelenok, S. L., Edney, E. O., Sarwar, G., Pinder, R. W., Pouliot, G. A., and Houyoux, M.: Model Representation of Secondary Organic Aerosol in CMAQv4.7, Environ. Sci. Technol., 44, 8553–8560, https://doi.org/10.1021/es100636q, 2010.
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chen, J.-H. and Lin, S.-J.: The remarkable predictability of inter-annual variability of Atlantic hurricanes during the past decade, Geophys. Res. Lett., 38, L11804, https://doi.org/10.1029/2011GL047629, 2011.
Chen, J.-H. and Lin, S.-J.: Seasonal Predictions of Tropical Cyclones Using a 25-km-Resolution General Circulation Model, J. Climate, 26, 380–398, https://doi.org/10.1175/JCLI-D-12-00061.1, 2013.
Chen, J.-H., Lin, S.-J., Zhou, L., Chen, X., Rees, S., Bender, M., and Morin, M.: Evaluation of Tropical Cyclone Forecasts in the Next Generation Global Prediction System, Mon. Weather Rev., 147, 3409–3428, https://doi.org/10.1175/MWR-D-18-0227.1, 2019.
Chen, X., Zhang, Y., Wang, K., Tong, D., Lee, P., Tang, Y., Huang, J., Campbell, P. C., Mcqueen, J., Pye, H. O. T., Murphy, B. N., and Kang, D.: 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, Geosci. Model Dev., 14, 3969–3993, https://doi.org/10.5194/gmd-14-3969-2021, 2021.
Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005.
Cohen, A. J., Ross Anderson, H., Ostro, B., Pandey, K. D., Krzyzanowski, M., Künzli, N., Gutschmidt, K., Pope, A., Romieu, I., Samet, J. M., and Smith, K.: The Global Burden of Disease Due to Outdoor Air Pollution, J. Toxicol. Env. Heal. A, 68, 1301–1307, https://doi.org/10.1080/15287390590936166, 2005.
Dong, X., Fu, J. S., Huang, K., Tong, D., and Zhuang, G.: Model development of dust emission and heterogeneous chemistry within the Community Multiscale Air Quality modeling system and its application over East Asia, Atmos. Chem. Phys., 16, 8157–8180, https://doi.org/10.5194/acp-16-8157-2016, 2016.
Eder, B., Kang, D., Mathur, R., Yu, S., and Schere, K.: An operational evaluation of the Eta–CMAQ air quality forecast model, Atmos. Environ., 40, 4894–4905, https://doi.org/10.1016/j.atmosenv.2005.12.062, 2006.
Eder, B., Kang, D., Mathur, R., Pleim, J., Yu, S., Otte, T., and Pouliot, G.: A performance evaluation of the National Air Quality Forecast Capability for the summer of 2007, Atmos. Environ., 43, 2312–2320, https://doi.org/10.1016/j.atmosenv.2009.01.033, 2009.
EEA (European Environment Agency): Advancing towards climate resilience in Europe – Status of reported national adaptation actions in 2021, ISBN 978-92-9480-516-4, 2022.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Emery, C., Jung, J., Koo, B., and Yarwood, G.: Improvements to CAMx snow cover treatments and Carbon Bond chemical mechanism for winter ozone, Final report for Utah Department of Environmental Quality, Salt Lake City, UT, prepared by Ramboll Environ, Novato, CA, 2015.
Emery, C., Liu, Z., Russell, A. G., Odman, M. T., Yarwood, G., and Kumar, N.: Recommendations on statistics and benchmarks to assess photochemical model performance, J. Air Waste Manage. Assoc., 67, 582–598, https://doi.org/10.1080/10962247.2016.1265027, 2017.
Fares, S., Weber, R., Park, J.-H., Gentner, D., Karlik, J., and Goldstein, A. H.: Ozone deposition to an orange orchard: Partitioning between stomatal and non-stomatal sinks, Environ. Pollut., 169, 258–266, https://doi.org/10.1016/j.envpol.2012.01.030, 2012.
Fu, X., Wang, S. X., Cheng, Z., Xing, J., Zhao, B., Wang, J. D., and Hao, J. M.: Source, transport and impacts of a heavy dust event in the Yangtze River Delta, China, in 2011, Atmos. Chem. Phys., 14, 1239–1254, https://doi.org/10.5194/acp-14-1239-2014, 2014.
Gantt, B., Kelly, J. T., and Bash, J. O.: Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2, Geosci. Model Dev., 8, 3733–3746, https://doi.org/10.5194/gmd-8-3733-2015, 2015.
Grell, A., Dudhia, J., and Stauffer, D.: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5), NCAR tech, Note NCAR TN-398-1-STR, 117 pp., https://doi.org/10.5065/D60Z716B, 1994.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Han, J. and Bretherton, C. S.: TKE-Based Moist Eddy-Diffusivity Mass-Flux (EDMF) Parameterization for Vertical Turbulent Mixing, Weather Forecast., 34, 869–886, https://doi.org/10.1175/WAF-D-18-0146.1, 2019.
Han, J. and Pan, H.-L.: Revision of Convection and Vertical Diffusion Schemes in the NCEP Global Forecast System, Weather Forecast., 26, 520–533, https://doi.org/10.1175/WAF-D-10-05038.1, 2011.
Han, J., Wang, W., Kwon, Y. C., Hong, S.-Y., Tallapragada, V., and Yang, F.: Updates in the NCEP GFS Cumulus Convection Schemes with Scale and Aerosol Awareness, Weather Forecast., 32, 2005–2017, https://doi.org/10.1175/WAF-D-17-0046.1, 2017.
Harris, L. M. and Lin, S.-J.: A Two-Way Nested Global-Regional Dynamical Core on the Cubed-Sphere Grid, Mon. Weather Rev., 141, 283–306, https://doi.org/10.1175/MWR-D-11-00201.1, 2013.
Heinzeller, D., Bernardet, L., Firl, G., Zhang, M., Sun, X., and Ek, M.: The Common Community Physics Package (CCPP) Framework v6, Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, 2023.
Helmig, D., Ganzeveld, L., Butler, T., and Oltmans, S. J.: The role of ozone atmosphere-snow gas exchange on polar, boundary-layer tropospheric ozone – a review and sensitivity analysis, Atmos. Chem. Phys., 7, 15–30, https://doi.org/10.5194/acp-7-15-2007, 2007.
Henze, D. K. and Seinfeld, J. H.: Global secondary organic aerosol from isoprene oxidation, Geophys. Res. Lett., 33, L09812, https://doi.org/10.1029/2006GL025976, 2006.
Hooper, L. G. and Kaufman, J. D.: Ambient Air Pollution and Clinical Implications for Susceptible Populations, Annals ATS, 15, S64–S68, https://doi.org/10.1513/AnnalsATS.201707-574MG, 2018.
Horowitz, L. W., Naik, V., Paulot, F., Ginoux, P. A., Dunne, J. P., Mao, J., Schnell, J., Chen, X., He, J., John, J. G., Lin, M., Lin, P., Malyshev, S., Paynter, D., Shevliakova, E., and Zhao, M.: The GFDL Global Atmospheric Chemistry-Climate Model AM4.1: Model Description and Simulation Characteristics, J. Adv. Model. Earth Sy., 12, e2019MS002032, https://doi.org/10.1029/2019MS002032, 2020.
Huang, J., McQueen, J., Wilczak, J., Djalalova, I., Stajner, I., Shafran, P., Allured, D., Lee, P., Pan, L., Tong, D., Huang, H.-C., DiMego, G., Upadhayay, S., and Monache, L. D.: Improving NOAA NAQFC PM2.5 Predictions with a Bias Correction Approach, Weather Forecast., 32, 407–421, https://doi.org/10.1175/WAF-D-16-0118.1, 2017.
Huang, J., McQueen, J., Yang, B., Shafran, P., Huang, H.-C., Bhattacharjee, P., Tang, Y., Campbell, P. C., Tong, D., Lee, P., Stajner, I., Kain, J. S., Tirado-Delgado, J., and Koch, D. M.: A comparison of global scale FV3 versus regional scale NAM meteorological drivers for regional air quality forecastin, The 100th AGU Fall Meeting, 9–13 December 2019, San Francisco, CA, 2019.
Huang, J., Stajner, I., Raffaele, M., Fanglin, Y., Kai, Y., Huang, H. C., Jeon, C. H., Curtis, B., McQueen, J., Haixia, L., Baker, B., Daniel, T., Tang, Y., Patrick, C., George, G., Frost, G., Rebecca, S., Wang, S., Kondragunta, S., Li, F., and Jung, Y.: Development of the next-generation air quality prediction system in the Unified Forecast System framework: Enhancing predictability of wildfire air quality impacts, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-23-0053.1, 2025 (code available at: (https://github.com/ufs-community/ufs-srweather-app/releases, last access: 15 March 2024).
Huang, M., Tong, D., Lee, P., Pan, L., Tang, Y., Stajner, I., Pierce, R. B., McQueen, J., and Wang, J.: Toward enhanced capability for detecting and predicting dust events in the western United States: the Arizona case study, Atmos. Chem. Phys., 15, 12595–12610, https://doi.org/10.5194/acp-15-12595-2015, 2015.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015.
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J., Montávez, J. P., and García-Bustamante, E.: A Revised Scheme for the WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012.
Kelly, J. T., Bhave, P. V., Nolte, C. G., Shankar, U., and Foley, K. M.: Simulating emission and chemical evolution of coarse sea-salt particles in the Community Multiscale Air Quality (CMAQ) model, Geosci. Model Dev., 3, 257–273, https://doi.org/10.5194/gmd-3-257-2010, 2010.
Krishnamurthy, V., Meixner, J., Stefanova, L., Wang, J., Worthen, D., Moorthi, S., Li, B., Sluka, T., and Stan, C.: Sources of Subseasonal Predictability over CONUS during Boreal Summer, J. Climate, 34, 3273–3294, https://doi.org/10.1175/JCLI-D-20-0586.1, 2021.
Krueger, S. K., Fu, Q., Liou, K. N., and Chin, H.-N. S.: Improvements of an Ice-Phase Microphysics Parameterization for Use in Numerical Simulations of Tropical Convection, J. Appl. Meteorol. Clim., 34, 281–287, https://doi.org/10.1175/1520-0450-34.1.281, 1995.
Kwok, R. H. F., Baker, K. R., Napelenok, S. L., and Tonnesen, G. S.: Photochemical grid model implementation and application of VOC, NOx, and O3 source apportionment, Geosci. Model Dev., 8, 99–114, https://doi.org/10.5194/gmd-8-99-2015, 2015.
Lee, B.-J., Kim, B., and Lee, K.: Air Pollution Exposure and Cardiovascular Disease, Toxicol Res., 30, 71–75, https://doi.org/10.5487/TR.2014.30.2.071, 2014.
Lee, P., McQueen, J., Stajner, I., Huang, J., Pan, L., Tong, D., Kim, H., Tang, Y., Kondragunta, S., Ruminski, M., Lu, S., Rogers, E., Saylor, R., Shafran, P., Huang, H.-C., Gorline, J., Upadhayay, S., and Artz, R.: NAQFC Developmental Forecast Guidance for Fine Particulate Matter (PM2.5), Weather Forecast., 32, 343–360, https://doi.org/10.1175/WAF-D-15-0163.1, 2017.
Li, F., Zhang, X., Kondragunta, S., Lu, X., Csiszar, I., and Schmidt, C. C.: Hourly biomass burning emissions product from blended geostationary and polar-orbiting satellites for air quality forecasting applications, Remote Sens. Environ., 281, 113237, https://doi.org/10.1016/j.rse.2022.113237, 2022.
Li, W., Wang, Y., Bernier, C., and Estes, M.: Identification of Sea Breeze Recirculation and Its Effects on Ozone in Houston, TX, During DISCOVER-AQ 2013, J. Geophys. Res.-Atmos., 125, e2020JD033165, https://doi.org/10.1029/2020JD033165, 2020.
Lin, H., Jacob, D. J., Lundgren, E. W., Sulprizio, M. P., Keller, C. A., Fritz, T. M., Eastham, S. D., Emmons, L. K., Campbell, P. C., Baker, B., Saylor, R. D., and Montuoro, R.: Harmonized Emissions Component (HEMCO) 3.0 as a versatile emissions component for atmospheric models: application in the GEOS-Chem, NASA GEOS, WRF-GC, CESM2, NOAA GEFS-Aerosol, and NOAA UFS models, Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021, 2021.
Lin, M., Horowitz, L. W., Xie, Y., Paulot, F., Malyshev, S., Shevliakova, E., Finco, A., Gerosa, G., Kubistin, D., and Pilegaard, K.: Vegetation feedbacks during drought exacerbate ozone air pollution extremes in Europe, Nat. Clim. Change, 10, 444–451, https://doi.org/10.1038/s41558-020-0743-y, 2020.
Lin, M., Horowitz, L. W., Zhao, M., Harris, L., Ginoux, P., Dunne, J., Malyshev, S., Shevliakova, E., Ahsan, H., Garner, S., Paulot, F., Pouyaei, A., Smith, S. J., Xie, Y., Zadeh, N., and Zhou, L.: The GFDL Variable-Resolution Global Chemistry-Climate Model for Research at the Nexus of US Climate and Air Quality Extremes, J. Adv. Model. Earth Sy., 16, e2023MS003984, https://doi.org/10.1029/2023MS003984, 2024.
Lin, Y.-L., Farley, R. D., and Orville, H. D.: Bulk Parameterization of the Snow Field in a Cloud Model, J. Appl. Meteorol. Climatol., 22, 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2, 1983.
Liu, F., Choi, S., Li, C., Fioletov, V. E., McLinden, C. A., Joiner, J., Krotkov, N. A., Bian, H., Janssens-Maenhout, G., Darmenov, A. S., and da Silva, A. M.: A new global anthropogenic SO2 emission inventory for the last decade: a mosaic of satellite-derived and bottom-up emissions, Atmos. Chem. Phys., 18, 16571–16586, https://doi.org/10.5194/acp-18-16571-2018, 2018.
Lord, S. J., Willoughby, H. E., and Piotrowicz, J. M.: Role of a Parameterized Ice-Phase Microphysics in an Axisymmetric, Nonhydrostatic Tropical Cyclone Model, J. Atmos. Sci., 41, 2836–2848, https://doi.org/10.1175/1520-0469(1984)041<2836:ROAPIP>2.0.CO;2, 1984.
Lovett, G. M., Tear, T. H., Evers, D. C., Findlay, S. E. G., Cosby, B. J., Dunscomb, J. K., Driscoll, C. T., and Weathers, K. C.: Effects of Air Pollution on Ecosystems and Biological Diversity in the Eastern United States, Ann. NY Acad. Sci., 1162, 99–135, https://doi.org/10.1111/j.1749-6632.2009.04153.x, 2009.
Luecken, D. J., Yarwood, G., and Hutzell, W. T.: Multipollutant modeling of ozone, reactive nitrogen and HAPs across the continental US with CMAQ-CB6, Atmos. Environ., 201, 62–72, https://doi.org/10.1016/j.atmosenv.2018.11.060, 2019.
Mathur, R., Yu, S., Kang, D., and Schere, K. L.: Assessment of the wintertime performance of developmental particulate matter forecasts with the Eta-Community Multiscale Air Quality modeling system, J. Geophys. Res.-Atmos., 113, D02303, https://doi.org/10.1029/2007JD008580, 2008.
McKeen, S., Grell, G., Peckham, S., Wilczak, J., Djalalova, I., Hsie, E.-Y., Frost, G., Peischl, J., Schwarz, J., Spackman, R., Holloway, J., de Gouw, J., Warneke, C., Gong, W., Bouchet, V., Gaudreault, S., Racine, J., McHenry, J., McQueen, J., Lee, P., Tang, Y., Carmichael, G. R., and Mathur, R.: An evaluation of real-time air quality forecasts and their urban emissions over eastern Texas during the summer of 2006 Second Texas Air Quality Study field study, J. Geophys. Res.-Atmos., 114, D00F11, https://doi.org/10.1029/2008JD011697, 2009.
Mészáros, R., Horváth, L., Weidinger, T., Neftel, A., Nemitz, E., Dämmgen, U., Cellier, P., and Loubet, B.: Measurement and modelling ozone fluxes over a cut and fertilized grassland, Biogeosciences, 6, 1987–1999, https://doi.org/10.5194/bg-6-1987-2009, 2009.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Tr. Akad. Nauk SSSR Geophiz. Inst., 24, 163–187, 1954.
Murphy, B. N., Nolte, C. G., Sidi, F., Bash, J. O., Appel, K. W., Jang, C., Kang, D., Kelly, J., Mathur, R., Napelenok, S., Pouliot, G., and Pye, H. O. T.: The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2, Geosci. Model Dev., 14, 3407–3420, https://doi.org/10.5194/gmd-14-3407-2021, 2021.
NEI (National Emissions Inventory Collaborative): 2016v1 Emissions Modeling Platform [data set], http://views.cira.colostate.edu/wiki/wiki/10202 (last access: 20 February 2024), 2019.
Odum, J. R., Hoffmann, T., Bowman, F., Collins, D., Flagan, R. C., and Seinfeld, J. H.: Gas/Particle Partitioning and Secondary Organic Aerosol Yields, Environ. Sci. Technol., 30, 2580–2585, https://doi.org/10.1021/es950943+, 1996.
O'Rourke, P. R., Smith, S. J., Mott, A., Ahsan, H., McDuffie, E. E., Crippa, M., Klimont, Z., McDonald, B., Wang, S., Nicholson, M. B., Feng, L., and Hoesly, R. M.: CEDS v_2021_02_05 Release Emission Data (v_2021_02_05), Zenodo [data set], https://doi.org/10.5281/zenodo.4509372, 2021.
Otte, T. L., Pouliot, G., Pleim, J. E., Young, J. O., Schere, K. L., Wong, D. C., Lee, P. C. S., Tsidulko, M., McQueen, J. T., Davidson, P., Mathur, R., Chuang, H.-Y., DiMego, G., and Seaman, N. L.: Linking the Eta Model with the Community Multiscale Air Quality (CMAQ) Modeling System to Build a National Air Quality Forecasting System, Weather Forecast., 20, 367–384, https://doi.org/10.1175/WAF855.1, 2005.
Pleim, J. E., Ran, L., Saylor, R. D., Willison, J., and Binkowski, F. S.: A New Aerosol Dry Deposition Model for Air Quality and Climate Modeling, J. Adv. Model. Earth Sy., 14, e2022MS003050, https://doi.org/10.1029/2022MS003050, 2022.
Pye, H. O. T., Chan, A. W. H., Barkley, M. P., and Seinfeld, J. H.: Global modeling of organic aerosol: the importance of reactive nitrogen (NOx and NO3), Atmos. Chem. Phys., 10, 11261–11276, https://doi.org/10.5194/acp-10-11261-2010, 2010.
Pye, H. O. T., Pinder, R. W., Piletic, I. R., Xie, Y., Capps, S. L., Lin, Y.-H., Surratt, J. D., Zhang, Z., Gold, A., Luecken, D. J., Hutzell, W. T., Jaoui, M., Offenberg, J. H., Kleindienst, T. E., Lewandowski, M., and Edney, E. O.: Epoxide Pathways Improve Model Predictions of Isoprene Markers and Reveal Key Role of Acidity in Aerosol Formation, Environ. Sci. Technol., 47, 11056–11064, https://doi.org/10.1021/es402106h, 2013.
Pye, H. O. T., Luecken, D. J., Xu, L., Boyd, C. M., Ng, N. L., Baker, K. R., Ayres, B. R., Bash, J. O., Baumann, K., Carter, W. P. L., Edgerton, E., Fry, J. L., Hutzell, W. T., Schwede, D. B., and Shepson, P. B.: Modeling the Current and Future Roles of Particulate Organic Nitrates in the Southeastern United States, Environ. Sci. Technol., 49, 14195–14203, https://doi.org/10.1021/acs.est.5b03738, 2015.
Pye, H. O. T., Murphy, B. N., Xu, L., Ng, N. L., Carlton, A. G., Guo, H., Weber, R., Vasilakos, P., Appel, K. W., Budisulistiorini, S. H., Surratt, J. D., Nenes, A., Hu, W., Jimenez, J. L., Isaacman-VanWertz, G., Misztal, P. K., and Goldstein, A. H.: On the implications of aerosol liquid water and phase separation for organic aerosol mass, Atmos. Chem. Phys., 17, 343–369, https://doi.org/10.5194/acp-17-343-2017, 2017.
Pye, H. O. T., D'Ambro, E. L., Lee, B. H., Schobesberger, S., Takeuchi, M., Zhao, Y., Lopez-Hilfiker, F., Liu, J., Shilling, J. E., Xing, J., Mathur, R., Middlebrook, A. M., Liao, J., Welti, A., Graus, M., Warneke, C., de Gouw, J. A., Holloway, J. S., Ryerson, T. B., Pollack, I. B., and Thornton, J. A.: Anthropogenic enhancements to production of highly oxygenated molecules from autoxidation, P. Natl. Acad. Sci., 116, 6641–6646, https://doi.org/10.1073/pnas.1810774116, 2019.
Saha, P. K. and Grieshop, A. P.: Exploring Divergent Volatility Properties from Yield and Thermodenuder Measurements of Secondary Organic Aerosol from á-Pinene Ozonolysis, Environ. Sci. Technol., 50, 5740–5749, https://doi.org/10.1021/acs.est.6b00303, 2016.
Sarwar, G., Simon, H., Bhave, P., and Yarwood, G.: Examining the impact of heterogeneous nitryl chloride production on air quality across the United States, Atmos. Chem. Phys., 12, 6455–6473, https://doi.org/10.5194/acp-12-6455-2012, 2012.
Sarwar, G., Gantt, B., Schwede, D., Foley, K., Mathur, R., and Saiz-Lopez, A.: Impact of Enhanced Ozone Deposition and Halogen Chemistry on Tropospheric Ozone over the Northern Hemisphere, Environ. Sci. Technol., 49, 9203–9211, https://doi.org/10.1021/acs.est.5b01657, 2015.
Sarwar, G., Gantt, B., Foley, K., Fahey, K., Spero, T. L., Kang, D., Mathur, R., Foroutan, H., Xing, J., Sherwen, T., and Saiz-Lopez, A.: Influence of bromine and iodine chemistry on annual, seasonal, diurnal, and background ozone: CMAQ simulations over the Northern Hemisphere, Atmos. Environ., 213, 395–404, https://doi.org/10.1016/j.atmosenv.2019.06.020, 2019.
Shu, Q., Murphy, B., Schwede, D., Henderson, B. H., Pye, H. O. T., Appel, K. W., Khan, T. R., and Perlinger, J. A.: Improving the particle dry deposition scheme in the CMAQ photochemical modeling system, Atmos. Environ., 289, 119343, https://doi.org/10.1016/j.atmosenv.2022.119343, 2022.
Sofiev, M., Ermakova, T., and Vankevich, R.: Evaluation of the smoke-injection height from wild-land fires using remote-sensing data, Atmos. Chem. Phys., 12, 1995–2006, https://doi.org/10.5194/acp-12-1995-2012, 2012.
Stajner, I., Davidson, P., Byun, D., McQueen, J., Draxler, R., Dickerson, P., and Meagher, J.: US National Air Quality Forecast Capability: Expanding Coverage to Include Particulate Matter, in: Air Pollution Modeling and its Application XXI, Dordrecht, 379–384, https://doi.org/10.1007/978-94-007-1359-8_64, 2012.
Tai, A. P. K., Martin, M. V., and Heald, C. L.: Threat to future global food security from climate change and ozone air pollution, Nat. Clim. Change, 4, 817–821, https://doi.org/10.1038/nclimate2317, 2014.
Tang, Y., Bian, H., Tao, Z., Oman, L. D., Tong, D., Lee, P., Campbell, P. C., Baker, B., Lu, C.-H., Pan, L., Wang, J., McQueen, J., and Stajner, I.: Comparison of chemical lateral boundary conditions for air quality predictions over the contiguous United States during pollutant intrusion events, Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, 2021.
Tang, Y., Campbell, P. C., Lee, P., Saylor, R., Yang, F., Baker, B., Tong, D., Stein, A., Huang, J., Huang, H.-C., Pan, L., McQueen, J., Stajner, I., Tirado-Delgado, J., Jung, Y., Yang, M., Bourgeois, I., Peischl, J., Ryerson, T., Blake, D., Schwarz, J., Jimenez, J.-L., Crawford, J., Diskin, G., Moore, R., Hair, J., Huey, G., Rollins, A., Dibb, J., and Zhang, X.: Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign, Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022, 2022.
Taylor, G. E., Johnson, D. W., and Andersen, C. P.: Air Pollution and Forest Ecosystems: A Regional to Global Perspective, Ecol. Appl., 4, 662–689, https://doi.org/10.2307/1941999, 1994.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M., Mitchell, K., Ek, M., Gayno, G., and Wegiel, J.: Implementation and verification of the unified NOAH land surface model in the WRF model (Formerly Paper Number 17.5), in: Proceedings of the 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, Seattle, WA, USA, https://n2t.org/ark:/85065/d7fb523p (last access: 5 March 2025), 2004.
UNEP (United Nations EnvironmentProgramme): Actions on Air Quality: A Global Summary of Policies and Programmes to Reduce Air Pollution, ISBN 978-92-807-3880-3, 2021.
United States Environmental Protection Agency: AirNow Data Manage Center, AirNow [data set], https://files.airnowtech.org/ (last access: 10 March 2025), 2023.
Van Dingenen, R., Dentener, F. J., Raes, F., Krol, M. C., Emberson, L., and Cofala, J.: The global impact of ozone on agricultural crop yields under current and future air quality legislation, Atmos. Environ., 43, 604–618, https://doi.org/10.1016/j.atmosenv.2008.10.033, 2009.
WHO (World Health Organization): WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, WHO, Geneva, Switzerland, ISBN 978-92-4-003422-8, 2021.
WHO: Ambient (outdoor) air pollution, https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health, last access: 10 May 2023.
Willmott, C. J.: On the Validation of Models, Phys. Geogr., 2, 184–194, https://doi.org/10.1080/02723646.1981.10642213, 1981.
Xu, L., Pye, H. O. T., He, J., Chen, Y., Murphy, B. N., and Ng, N. L.: Experimental and model estimates of the contributions from biogenic monoterpenes and sesquiterpenes to secondary organic aerosol in the southeastern United States, Atmos. Chem. Phys., 18, 12613–12637, https://doi.org/10.5194/acp-18-12613-2018, 2018.
Yarwood, G., Whitten, G. Z., Jung, J., Heo, G., and Allen, D. T.: Development, evaluation and testing of version 6 of the carbon Bond chemical mechanism (CB6), Final report to the Texas Commission on Environmental Quality, Work Order No. 582-7-84005-FY10-26, 2010.
Yarwood, G., Shi, Y., and Beardsley, R.: Impact of CB6r5 Mechanism Changes on Air Pollutant Modeling in Texas, Texas Commission on Environmental Quality, Austin, Texas, USA 2020.
Zhang, H., Yee, L. D., Lee, B. H., Curtis, M. P., Worton, D. R., Isaacman-VanWertz, G., Offenberg, J. H., Lewandowski, M., Kleindienst, T. E., Beaver, M. R., Holder, A. L., Lonneman, W. A., Docherty, K. S., Jaoui, M., Pye, H. O. T., Hu, W., Day, D. A., Campuzano-Jost, P., Jimenez, J. L., Guo, H., Weber, R. J., de Gouw, J., Koss, A. R., Edgerton, E. S., Brune, W., Mohr, C., Lopez-Hilfiker, F. D., Lutz, A., Kreisberg, N. M., Spielman, S. R., Hering, S. V., Wilson, K. R., Thornton, J. A., and Goldstein, A. H.: Monoterpenes are the largest source of summertime organic aerosol in the southeastern United States, P. Natl. Acad. Sci., 115, 2038–2043, https://doi.org/10.1073/pnas.1717513115, 2018a.
Zhang, W., Capps, S. L., Hu, Y., Nenes, A., Napelenok, S. L., and Russell, A. G.: Development of the high-order decoupled direct method in three dimensions for particulate matter: enabling advanced sensitivity analysis in air quality models, Geosci. Model Dev., 5, 355–368, https://doi.org/10.5194/gmd-5-355-2012, 2012.
Zhang, Y., Chen, Y., Lambe, A. T., Olson, N. E., Lei, Z., Craig, R. L., Zhang, Z., Gold, A., Onasch, T. B., Jayne, J. T., Worsnop, D. R., Gaston, C. J., Thornton, J. A., Vizuete, W., Ault, A. P., and Surratt, J. D.: Effect of the Aerosol-Phase State on Secondary Organic Aerosol Formation from the Reactive Uptake of Isoprene-Derived Epoxydiols (IEPOX), Environ. Sci. Tech. Let., 5, 167–174, https://doi.org/10.1021/acs.estlett.8b00044, 2018b.
Zhou, L., Lin, S.-J., Chen, J.-H., Harris, L. M., Chen, X., and Rees, S. L.: Toward Convective-Scale Prediction within the Next Generation Global Prediction System, B. Am. Meteorol. Soc., 100, 1225–1243, https://doi.org/10.1175/BAMS-D-17-0246.1, 2019.
Zhu, J. and Liang, X.-Z.: Impacts of the Bermuda High on Regional Climate and Ozone over the United States, J. Climate, 26, 1018–1032, https://doi.org/10.1175/JCLI-D-12-00168.1, 2013.
Zhu, J., Wang, W., Liu, Y., Kumar, A., and DeWitt, D.: Advances in Seasonal Predictions of Arctic Sea Ice With NOAA UFS, Geophys. Res. Lett., 50, e2022GL102392, https://doi.org/10.1029/2022GL102392, 2023.
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by...
