Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3281-2022
© Author(s) 2022. 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-15-3281-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
21 Apr 2022
Model description paper |
| 21 Apr 2022
| 21 Apr 2022
Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16
Patrick C. Campbell
CORRESPONDING AUTHOR
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Youhua Tang
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Pius Lee
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
retired
Barry Baker
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Daniel Tong
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Rick Saylor
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Ariel Stein
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Jianping Huang
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Ho-Chun Huang
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Edward Strobach
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Jeff McQueen
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Ivanka Stajner
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
Jamese Sims
NOAA NWS/STI, College Park, MD, USA
Jose Tirado-Delgado
NOAA NWS/STI, College Park, MD, USA
Eastern Research Group, Inc. (ERG), College Park, MD, USA
Youngsun Jung
NOAA NWS/STI, College Park, MD, USA
Fanglin Yang
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
Tanya L. Spero
US Environmental Protection Agency, Research Triangle Park, NC, USA
Robert C. Gilliam
US Environmental Protection Agency, Research Triangle Park, NC, USA
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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
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Sarah E. Benish, Jesse O. Bash, Kristen M. Foley, K. Wyat Appel, Christian Hogrefe, Robert Gilliam, and George Pouliot
Atmos. Chem. Phys., 22, 12749–12767, https://doi.org/10.5194/acp-22-12749-2022, https://doi.org/10.5194/acp-22-12749-2022, 2022
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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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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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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.
Hyun Cheol Kim, Soontae Kim, Mark Cohen, Changhan Bae, Dasom Lee, Rick Saylor, Minah Bae, Eunhye Kim, Byeong-Uk Kim, Jin-Ho Yoon, and Ariel Stein
Atmos. Chem. Phys., 21, 10065–10080, https://doi.org/10.5194/acp-21-10065-2021, https://doi.org/10.5194/acp-21-10065-2021, 2021
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Global outbreaks of COVID-19 offer rare opportunities of natural experiments in emission control and corresponding responses of tropospheric chemistry. This study's novel approach investigates (1) isolating the pandemic's impact from natural and anthropogenic variations, (2) emission adjustment to reproduce real-time emissions, and (3) brute-force modeling to investigate Chinese economic activities. Results provide characteristics of the region's chemistry and emissions.
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.
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
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This paper details the scientific updates in the recently released CMAQ version 5.3 (and v5.3.1) and also includes operational and diagnostic evaluations of CMAQv5.3.1 against observations and the previous version of the CMAQ (v5.2.1). This work was done to improve the underlying science in CMAQ. This article is used to inform the CMAQ modeling community of the updates to the modeling system and the expected change in model performance from these updates (versus the previous model version).
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
Alexander, B., Park, R. J., Jacob, D. J., and Gong, S.: Transition
metal-catalyzed oxidation of atmospheric sulfur: global implications for the
sulfur budget, J. Geophys. Res., 114, D02309,
https://doi.org/10.1029/2008JD010486, 2009.
American Lung Association: Urban air pollution and health inequities: a
workshop report, Environ Health Perspect., 109 Suppl 3, 357–374, PMID: 11427385, PMCID:
PMC1240553, https://doi.org/10.2307/3434783, 2001.
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. Model. 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.
Astitha, M., Luo, H., Rao, S. T., Hogrefe, C., Mathur, R., and Kumar, N.:
Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the
contiguous United States, Atmos. Environ., 164, 102–116,
https://doi.org/10.1016/j.atmosenv.2017.05.020, 2017.
Bai, L., Wang, J., Ma, X., and Lu, H.: Air Pollution Forecasts:
An Overview, Int. J. Env. Res. Pub. He., 15, 780, https://doi.org/10.3390/ijerph15040780, 2018.
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.
Bash, J. O., Walker, J. T., Katul, G. G., Jones, M. R., Nemitz, E., and
Robarge, W. P.: Estimation of In-Canopy Ammonia Sources and Sinks in a
Fertilized Zea mays Field, Environ. Sci. Technol., 44, 1683–1689, https://doi.org/10.1021/es9037269, 2010.
Bash, J. O., Cooter, E. J., Dennis, R. L., Walker, J. T., and Pleim, J. E.: Evaluation of a regional air-quality model with bidirectional NH3 exchange coupled to an agroecosystem model, Biogeosciences, 10, 1635–1645, https://doi.org/10.5194/bg-10-1635-2013, 2013.
Bash, J. O., Baker, K. R., and Beaver, M. R.: Evaluation of improved land use and canopy representation in BEIS v3.61 with biogenic VOC measurements in California, Geosci. Model Dev., 9, 2191–2207, https://doi.org/10.5194/gmd-9-2191-2016, 2016.
Binkowski, F. S, Arunachalam, S., Adelman, Z., and Pinto, J.: Examining
photolysis rates with a prototype on-line photolysis module in
CMAQ, J. Appl. Meteorol. Clim., 46,
1252–1256, https://doi.org/10.1175/JAM2531.1, 2007.
Black, T. L.: The new NMC meso-scale Eta Model: description and forecast
examples, Weather Forecast., 9, 265–278,
https://doi.org/10.1175/1520-0434(1994)009<0265:TNNMEM>2.0.CO;2, 1994.
Bonan, G. B., Patton, E. G., Finnigan, J. J., Baldocchi, D. D., and Harman,
I. N.: Moving beyond the incorrect but useful paradigm: reevaluating
big-leaf and multilayer plant canopies to model biosphere-atmosphere fluxes
– a review, Agr. Forest Meteorol., 306, 108435,
https://doi.org/10.1016/j.agrformet.2021.108435, 2021.
Briggs, G. A.: A plume rise model compared with
observations, J. Air Pollut. Control Assoc., 15,
433–438, https://doi.org/10.1080/00022470.1965.10468404, 1965.
Byun, D. and Schere, K. L.: Review of the governing equations, computational
algorithms, and other components of the models-3 community multiscale air
quality (CMAQ) modeling system, Appl. Mech. Rev., 59, 51–77, https://doi.org/10.1115/1.2128636, 2006.
Byun, D. W. and Ching, J. K. S.: Science algorithms of the EPA Models-3
Community Multi-scale Air Quality (CMAQ) modeling system, EPA/600/R-99/030,
Office of Research and Development, US Environmental Protection Agency,
https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=63400&Lab=NERL (last access: 5 April 2022), 1999.
Campbell, G. S. and Norman J. M.: An introduction to environmental
biophysics, Springer, 5, ISBN 978-0-387-94937-6, 1998.
Campbell, P., Zhang, Y., Yahya, K., Wang, K., Hogrefe, C., Pouliot, G., Knote, C., Hodzic, A., San Jose, R., Perez, J., Guerrero, P., Baro, R., and Makar, P.: A Multi-Model Assessment for the 2006 and 2010 Simulations under the
Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over
North America: Part I. Indicators of the Sensitivity of O3 and PM2.5
Formation Regimes, Atmos. Environ., 115, 569–586, https://doi.org/10.1016/j.atmosenv.2014.12.026, 2015.
Campbell, P. C.: The NOAA-EPA Atmosphere-Chemistry Coupler (NACC), Zenodo [code], https://doi.org/10.5281/zenodo.5507489, 2021a.
Campbell, P. C.: The Advanced National Air Quality Forecast Capability (NAQFC), Zenodo [code], https://doi.org/10.5281/zenodo.5507511, 2021b.
Campbell, P. C., Bash, J. O., and Spero, T. L.: Updates to the Noah land
surface model in WRF-CMAQ to improve simulated meteorology, air quality, and
deposition, J. Adv. Model. Earth Sy., 11, 231–256.
https://doi.org/10.1029/2018MS001422 2019.
Campbell, P. C., Tong, D., Tang, Y., Baker, B., Lee, P., Saylor, R., Stein, A., Ma, S., and Qu, Z.:
Impacts of the COVID-19 Economic Slowdown on Ozone Pollution in the U.S., Atmos. Environ., 264, 118713, https://doi.org/10.1016/j.atmosenv.2021.118713,
2021.
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. L., 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., Andronova, N., Van Leer, B., Penner, J. E., Boyd, J. P.,
Jablonowski, C., and Lin, S.: A Control-Volume Model of the Compressible
Euler Equations with a Vertical Lagrangian Coordinate, Mon. Weather Rev., 141, 2526–2544,
https://doi.org/10.1175/MWR-D-12-00129.1, 2013.
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.
Chin, M., Rood, R. B., Lin, S.-J., Muller, J. F., and Thomspon, A. M.:
Atmospheric sulfur cycle in the global model GOCART: Model description and
global properties, J. Geophys. Res., 105, 24671–24687,
https://doi.org/10.1029/2000JD900384, 2000.
Chin, M., Ginoux, P., Kinne, S., Holben, B. N., Duncan,
B. N., Martin, R. V., Logan, J. A., Akiko, H., and Nakajima, T.: Tropospheric aerosol optical thickness from the GOCART model and
comparisons with satellite and sun photometer measurements, J. Atmos. Sci., 59, 461–483,
https://doi.org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2, 2002.
Chinese State Council: Air Pollution Prevention and Control Action Plan (Guo
Fa [2013] No. 37,
http://en.cleanairchina.org/file/loadFile/26.html (last access: 5 April 2022), 2013.
Clough, S. A., Shephard, M. W., Mlawer, J. E., 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.
Cooter, E. J., Bash, J. O., Walker, J. T., Jones, M. R., and Robarge, W.:
Estimation of NH3 bi-directional flux from managed agricultural soils, Atmos. Environ.,
44, 2107–2115,
https://doi.org/10.1016/j.atmosenv.2010.02.044, 2010.
Cooter, E. J., Bash, J. O., Benson, V., and Ran, L.: Linking agricultural crop management and air quality models for regional to national-scale nitrogen assessments, Biogeosciences, 9, 4023–4035, https://doi.org/10.5194/bg-9-4023-2012, 2012.
Demetriou, C. A. and Vineis, P.: Carcinogenicity of ambient air pollution:
use of biomarkers, lessons learnt and future directions, J. Thorac. Dis.,
7, 67–95, https://doi.org/10.3978/j.issn.2072-1439.2014.12.31, 2015.
Ding, H. and Zhu, Y.: NDE Vegetation Products System Algorithm Theoretical
Basis Document, Version 4.0. NOAA/NESDIS/OSPO, https://www.ospo.noaa.gov/Products/documents/GVF_ATBD_V4.0.pdf (last access: 2 February 2021), 2018.
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.
Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G.: Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017), Earth Syst. Sci. Data, 10, 1491–1501, https://doi.org/10.5194/essd-10-1491-2018, 2018.
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.
Ek, M., Mitchell, B. 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.,
108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
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.
Finkelstein, M. M., Jerrett, M., DeLuca, P., Finkelstein, N., Verma, D. K.,
Chapman, K., and Sears, M. R.: Relation between income, air pollution and
mortality: A cohort study, Can. Med. Assoc. J., 169, 397-402, 2003.
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.
Ginoux, P., Chin, M., Tegen, I., Prospero, J., Holben, B., Dubovik, O., and
Lin, S.-J.: Sources and global distributions of dust aerosols simulated with
the GOCART model, J. Geophys. Res., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
Grell, G. A., Dudhia, J., and Stauffer, D. R.: 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.
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. 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-17-0046.1, 2019.
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.: 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.
Harris, L. M., Lin, S., and Tu, C.: High-Resolution Climate Simulations Using
GFDL HiRAM with a Stretched Global Grid, J. Climate, 29, 4293–4314,
https://doi.org/10.1175/JCLI-D-15-0389.1, 2016.
Huang, J. and McQueen, J.: Development and evaluation of offline
coupling of FV3-based GFS with CMAQ at NOAA, The 17th CMAS Conference,
22–24 October 2018, UNC-Chapel Hill, NC, 2018.
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, 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. B.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103,
https://doi.org/10.1029/2008JD009944, 2008.
Institute of Medicine: Toward Environmental Justice: Research, Education,
and Health Policy Needs, Washington, DC, National Academy Press, https://doi.org/10.17226/6034, 1999.
Janjic, Z. and Gall, R. L.: Scientific documentation of the NCEP
nonhydrostatic multiscale model on the B grid (NMMB), Part 1 Dynamics (No.
NCAR/TN-489+STR), University Corporation for Atmospheric Research,
https://doi.org/10.5065/D6WH2MZX, 2012.
Jimenez, P. A., Dudhia, J., Gonzalez-Rouco, J. F., Navarro, J., Montavez, J.
P., and Garcia-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.
Kang, D., Eder, B. K., Stein, A. F., Grell, G. A., Peckham, S. E., and
McHenry, J.: The New England Air Quality Forecasting Pilot Program:
Development of an Evaluation Protocol and Performance Benchmark, J. Air Waste Manage. Assoc., 55,
1782–1796, https://doi.org/10.1080/10473289.2005.10464775,
2005.
Karamchandani, P., Long, Y., Pirovano, G., Balzarini, A., and Yarwood, G.: Source-sector contributions to European ozone and fine PM in 2010 using AQMEII modeling data, Atmos. Chem. Phys., 17, 5643–5664, https://doi.org/10.5194/acp-17-5643-2017, 2017.
Kar Kurt, O., Zhang, J., and Pinkerton, K. E.: Pulmonary Health Effects of
Air Pollution, Curr. Opin. Pulm. Med., 22, 138–143,
https://doi.org/10.1097/MCP.0000000000000248, 2016.
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.
Kim, Y., Sartelet, K., and Seigneur, C.: Formation of secondary aerosols over Europe: comparison of two gas-phase chemical mechanisms, Atmos. Chem. Phys., 11, 583–598, https://doi.org/10.5194/acp-11-583-2011, 2011a.
Kim, Y., Couvidat, F., Sartelet, K., and Seigneur, C.: Comparison of
different gas phase mechanisms and aerosol modules for simulating
particulate matter
formation, J. Air Waste Manage., 61, 1218e1226,
https://doi.org/10.1080/10473289.2011.603999, 2011b.
Krueger, S. K., Fu, Q., Liou, K. N., and Chin, H.-N. S.: Improvement of an
ice-phase microphysics parameterization for use in numerical simulations of
tropical convection, J. Appl. Meteorol., 34, 281–287, https://doi.org/10.1175/1520-0450-34.1.281, 1995.
Landrigan, P. J., Fuller, R., Acosta, N. J., Adeyi, O., Arnold, R.,
Basu, N., Bibi Balde, A., Bertollini, R. Bose-O'Reilly, S., Boufford, J. I.., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., Hanrahan, D., Hunter, D., Khare, M., Krupnick, A., Lanphear, B., Lohani, B., Martin, K., Mathiasen, K., McTeer, M. A., Murray, C. J. L., Ndahimananjara, J. D., Perera, F., Potocnik, J., Preker, A. S., Ramesh, J., Rockstrom, J., Salinas, C., Samson, L. D., Sandilya, K., Sly, P. D., Smith, K. R., Steiner, A., Stewart, R. B., Suk, W. A., van Schayck, O. C. P., Yadama, G. N., Yumkella, K., and Zhong, M.: The Lancet Commission on pollution and health, Lancet, 391, 462–512,
https://doi.org/10.1016/S0140-6736(17)32345-0, 2018.
Lee, B.-J., Kim, B., and Lee, K.: Air Pollution Exposure and Cardiovascular
Disease, Toxicol Res.-UK, 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.
Lin, S.: A “Vertically Lagrangian” Finite-Volume Dynamical Core for
Global Models, Mon. Weather Rev., 132, 2293–2307,
https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2, 2004.
Lin, S. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian
Transport Schemes, Mon. Weather Rev., 124, 2046–2070,
https://doi.org/10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2, 1996.
Lin, S., Chao, W. C., Sud, Y. C., and Walker, G. K.: A Class of the van
Leer-type Transport Schemes and Its Application to the Moisture Transport in
a General Circulation Model, Mon. Weather Rev., 122, 1575–1593,
https://doi.org/10.1175/1520-0493(1994)122<1575:ACOTVL>2.0.CO;2, 1994.
Lin, Y.-L., Farley, R. D., and Orville, H. D.: Bulk parameterization of the
snow field in a cloud model, J. Clim. Appl. Meteorol., 22, 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2, 1983.
Liu, Y. and Wang, T.: Worsening urban ozone pollution in China from 2013 to 2017 – Part 1: The complex and varying roles of meteorology, Atmos. Chem. Phys., 20, 6305–6321, https://doi.org/10.5194/acp-20-6305-2020, 2020.
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.
Makar, P. A., Staebler, R., Akingunola, A., Zhang, J., McLinden, C.,
Kharol, S. K., Pabla, B., Cheung, P., and Zheng, Q.: The effects of forest canopy shading and turbulence on
boundary layer ozone, Nat Commun., 8, 15243,
https://doi.org/10.1038/ncomms15243, 2017.
Makar, P. A., Stroud, C., Akingunola, A., Zhang, J., Ren, S., Cheung, P., and Zheng, Q.: Vehicle-induced turbulence and atmospheric pollution, Atmos. Chem. Phys., 21, 12291–12316, https://doi.org/10.5194/acp-21-12291-2021, 2021.
Marlier, M. E., Jina, A. S., Kinney, P. L., and DeFries, R. S.: Extreme Air
Pollution in Global Megacities, Curr Clim Change Rep., 2, 15–27, https://doi.org/10.1007/s40641-016-0032-z, 2016.
Martin, R. L. and Good, T. W.: Catalyzed oxidation of sulfur dioxide in
solution: the iron-manganese synergism, Atmos. Environ., 25A, 2395–2399, https://doi.org/10.1016/0960-1686(91)90113-L, 1991.
Massad, R.-S., Nemitz, E., and Sutton, M. A.: Review and parameterisation of bi-directional ammonia exchange between vegetation and the atmosphere, Atmos. Chem. Phys., 10, 10359–10386, https://doi.org/10.5194/acp-10-10359-2010, 2010.
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., 113, D02303,
https://doi.org/10.1029/2007JD008580, 2008.
Matthias, V., Arndt, J. A., Aulinger, A., Bieser, J., Denier
van der Gon, H., Kranenburg, R., Kuenen, J., Neumann, D., Pouliot, G., and Quante, M.: Modeling emissions for three-dimensional
atmospheric chemistry transport models, J. Air Waste Manage., 68, 763–800, https://doi.org/10.1080/10962247.2018.1424057, 2018.
McKeen, S., Wilczak, J., Grell, G., Djalova, I., Peckham, S.,
Hsie, E.-Y., Gong, W., Bouchet, V., Menard, S., Moffet, R., McHenry, J., McQueen, J., Tang, Y., Carmichael, G. R., Pagowski, M., Chan, A., Dye, T., Frost, G., Lee, P., and Mathur, R.: Assessment of an ensemble of seven real-time ozone forecasts over
eastern North America during the summer of 2004, J. Geophys. Res., 110, D21307,
https://doi.org/10.1029/2005JD005858, 2005.
McKeen, S., Chung, S. H., Wilczak, J., Grell, G., Djalalova,
I., Peckham, S., Gong, W., Bouchet, V., Moffet, R., Tang, Y., Carmichael, G. R., Mathur, R., and Yu, S.: Evaluation of several PM2.5 forecast models
using data collected during the
ICARTT/NEAQS 2004 field study, J. Geophys. Res., 112, D10S20,
https://doi.org/10.1029/2006JD007608, 2007.
McKeen, S., Grell, G., Peckham, S., Wilczak, J., Djalalova,
I., Hsie, E., Frost, G., Peischl, J., Schwartz, 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., 114, D00F11,
https://doi.org/10.1029/2008JD011697, 2009.
Miller, J., Safford, H., Crimmins, M., and Thode, A.: Quantitative evidence
for increasing forest fire severity in the Sierra Nevada and Southern
Cascade Mountains, California and Nevada, USA, Ecosystems, 12, 16–32,
https://doi.org/10.1007/s10021-008-9201-9, 2009.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: Radiative transfer for inhomogeneous atmosphere: RTTM, a validated
correlated-k model for the longwave, J. Geophys. Res., 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 (in Russian).
Myneni, R. and Knyazikhin, Y.: VIIRS/NPP Leaf Area Index/FPAR 8-Day L4
Global 500m SIN Grid V001, NASA EOSDIS Land Processes DAAC [data set],
https://doi.org/10.5067/VIIRS/VNP15A2H.001, 2018.
National Emissions Inventory (NEIC): NEI 2014v2 Emissions, U.S. EPA Repository [data set],
https://edap.epa.gov/public/extensions/nei_report_2014/dashboard.html#sector-db (last access: 26 February 2020), 2014.
National Emissions Inventory Collaborative (NEI): 2016v1 Emissions
Modeling Platform [data set], http://views.cira.colostate.edu/wiki/wiki/10202 (last access: 5 April 2022), 2019.
Nemitz, E., Sutton, M. A., Schjoerring, J. K., Husted, S., and Wyers, G. P.:
Resistance modelling of ammonia exchange over oilseed rape, Agr. Forest Meteorol., 105,
405–425, https://doi.org/10.1016/S0168-1923(00)00206-9, 2000.
Niinemets, Ü., Arneth, A., Kuhn, U., Monson, R. K., Peñuelas, J., and Staudt, M.: The emission factor of volatile isoprenoids: stress, acclimation, and developmental responses, Biogeosciences, 7, 2203–2223, https://doi.org/10.5194/bg-7-2203-2010, 2010.
NOAA/NWS: Global Forecast System (GFS) Version 16 [data set], https://www.nco.ncep.noaa.gov/pmb/products/gfs/, last access: 5 April 2022a.
NOAA/NWS: Air Quality Forecast Guidance – Operational [data set], https://airquality.weather.gov/, last access: 5 April 2022b.
NOAA/NWS: Air Quality Forecast Guidance – Experimental [data set], https://digital.mdl.nws.noaa.gov/airquality/, last access: 5 April 2022c.
NOAA/NWS: Operational CMAQ Verification – Experimental [data set], https://www.emc.ncep.noaa.gov/mmb/aq/verification_diagnostics/cmaq_verf/ last access: 5 April 2022d.
O’Neill, M. S., Jerrett, M., Kawachi, I., Levy, J. I., Cohen, A. J.,
Gouveia, N., Wilkinson, P., Fletcher, T., Cifuentes, L., and Schwartz, J.: Health, wealth, and air pollution: Advancing theory and methods,
Environ. Health Persp., 111, 1861–1870, https://doi.org/10.1289/ehp.6334, 2003.
Otte, T. L. and Pleim, J. E.: The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1, Geosci. Model Dev., 3, 243–256, https://doi.org/10.5194/gmd-3-243-2010, 2010.
Otte, T. L., Pouliot, G., Pleim, J. E., Young, J. O., Schere, K. L.,
Wong, D. C., Lee, P., Tsidulko, M., McQueen, J., 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.
Pinder, R. W., Dennis, R. L., and Bhave, P. V.: Observable indicators of the
sensitivity of PM2.5 nitrate to emission reductions: part I. Derivation of
the adjusted gas
ratio and applicability at regulatory-relevant time scales, Atmos. Environ., 42,
1275e1286, https://doi.org/10.1016/j.atmosenv.2007.10.039,
2008.
Pleim, J. and Ran, L.: Surface flux modeling for air quality applications,
Atmosphere, 2, 271–302, https://doi.org/10.3390/atmos2030271, 2011.
Pleim, J. E.: A combined local and nonlocal closure model for the
atmospheric boundary layer. Part I: Model description and testing, J. Appl. Meteor. Climatol., 46,
1383–1395, https://doi.org/10.1175/JAM2539.1, 2007a.
Pleim, J. E.: A combined local and nonlocal closure model for the
atmospheric boundary layer. Part II: Application and evaluation in a
mesoscale meteorological model, J. Appl. Meteorol. Clim., 46, 1396–1409,
https://doi.org/10.1175/JAM2534.1, 2007b.
Pleim, J. E., Bash, J. O., Walker, J. T., and Cooter, E. J.: Development and
evaluation of an ammonia bidirectional flux parameterization for air quality
models, J. Geophys. Res.-Atmos., 118, 3794–3806, https://doi.org/10.1002/jgrd.50262,
2013.
Pleim, J. E., Ran, L., Appel, W., Shephard, M. W., and Cady-Pereira, K.: New
bidirectional ammonia flux model in an air quality model coupled with an
agricultural model, J. Adv. Model. Earth Sy., 11, 2934–2957,
https://doi.org/10.1029/2019MS001728, 2019.
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C.
A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen, F., Barlarge, M., Yu, M., and Duda, M.: The weather research and forecasting model: Overview,
system efforts, and future directions, B. Am. Meteorol. Soc., 98, 1717–1737,
https://doi.org/10.1175/BAMS-D-15-00308.1, 2017.
Putman, W. M. and Lin, S.-J.: Finite-volume transport on various
cubed-sphere grids, J. Comput. Phys., 227, 55–78,
https://doi.org/10.1016/j.jcp.2007.07.022, 2007.
Pye, H. O. T., Pinder, R. W., Piletic, I., 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., 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.
Ran, L., Cooter, E., Benson, V., and He, Q.: Development of an agricultural
fertilizer modeling system for bi-directional ammonia fluxes in the CMAQ
model, edited by: Steyn, D. G. and Castelli, S. T., Air Pollution Modeling
and its Application XXI, Chapter 36, Dordrecht, Springer,
213–219, https://doi.org/10.1007/978-94-007-1359-8_36, 2011.
Ran, L., Pleim, J., Gilliam, R., Binkowski, F. S., Hogrefe, C., and Band,
L.: Improved meteorology from an updated WRF/CMAQ modeling system with MODIS
vegetation and albedo, J. Geophys. Res.-Atmos., 121, 2393–2415, https://doi.org/10.1002/2015JD024406, 2016.
Rogers, E., Black, T., Deaven, D., DiMego, G., Zhao,
Q., Baldwin, M., Junker, N. W., and Lin, Y.:
Changes to the operational “early” Eta Analysis/Forecast System at the
National Centers for Environmental Prediction, Weather Forecast., 11 391–413, https://doi.org/10.1175/1520-0434(1996)011<0391:CTTOEA>2.0.CO;2, 1996.
Sarwar, G., Fahey, K., Napelenok, S., Roselle, S., and Mathur, R.: Examining
the impact of CMAQ model updates on aerosol sulfate predictions, The 10th
Annual CMAS Models-3 User's Conference, 24–26 October 2011, Chapel Hill, NC, 2011.
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.
Schwede, D., Pouliot, G. A., and Pierce, T.: Changes to the Biogenic
Emissions Inventory System Version 3 (BEIS3), in: Proceedings of the 4th CMAS
Models-3 Users' Conference, 26–28 September 2005, Chapel Hill, NC, 2005.
Sillman, S.: The use of NOy, H2O2, and HNO3 as indicators for
ozone-NOx-hydrocarbon sensitivity in urban locations, J. Geophys. Res.-Atmos., 100, 14175–14188,
https://doi.org/10.1029/94JD02953, 1995.
Sillman, S.: The relation between ozone, NOx and hydrocarbons in urban and
polluted rural environments, Atmos. Environ., 33, 1821–1845,
https://doi.org/10.1016/S1352-2310(98)00345-8, 1999.
Sillman, S., Logan, J. A., and Wofsy, S. C.: The sensitivity of ozone to
nitrogen oxides and hydrocarbons in regional ozone episodes, J. Geophys. Res., 95,
1837–1852, https://doi.org/10.1029/JD095iD02p01837, 1990.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric
model for weather research and forecasting applications, J. Computat. Phys., 227,
3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner,
J., and Huang, X.: A Description of the Advanced Research WRF Model Version
4 (No. NCAR/TN-556+STR), https://doi.org/10.5065/1dfh-6p97,
2019.
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, Springer, Dordrecht,
379–384, https://doi.org/10.1007/978-94-007-1359-8_64,
2011.
Sun, J., Fu, J. S., Huang, K., and Gao, Y.: Estimation of future PM2.5- and
ozone-related mortality over the continental United States in a changing
climate: An application of high-resolution dynamical downscaling technique,
J. Air Waste Manage., 65, 611–623, https://doi.org/10.1080/10962247.2015.1033068,
2015.
Tang, Y., Lee, P., Tsidulko, M., Huang, H.-C., Mcqueen, J., DiMego, G. J., Emmons, L. K., Pierce, R. B., Thompson, A. M., Lin, H.-M., Kang, D., Tong, D., Yu, S., Mathur, R., Pleim, J. E., Otte, T. L., Pouliot, G., Young, J. O., Schere, K. L., Davidson, P. M., and Stajner, I.:
The impact of chemical lateral boundary conditions on CMAQ predictions of
tropospheric ozone over the continental United States, Environ. Fluid Mech., 9, 43–58,
https://doi.org/10.1007/s10652-008-9092-5, 2009.
Tang, Y., Chai, T., Pan, L., Lee, P., Tong, D., Kim, H.-C., and Chen, W.: Using
optimal interpolation to assimilate surface measurements and satellite AOD
for ozone and PM2.5: A case study for July 2011, J. Air Waste Manage., 65, 1206–1216,
https://doi.org/10.1080/10962247.2015.1062439, 2015.
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.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. H.:
Implementation and verification of the unified NOAH land surface model in
the WRF model, 20th Conference on Weather Analysis
and Forecasting/16th Conference on Numerical Weather Prediction,
Seattle, WA, 14 January 2004, https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm (last access: 6 April 2022) 2004.
Tong, D. Q., Lee, P., and Saylor, R. D.: New Direction: The need to develop
process-based emission forecasting models, Atmos. Environ., 47, 560–561,
https://doi.org/10.1016/j.atmosenv.2011.10.070, 2012.
Tong, D. Q., Wang, J. X. L., Gill, T. E., Lei, H., and Wang, B.: Intensified
dust storm activity and Valley fever infection in the southwestern United
States, Geophys. Res. Lett., 44, 4304–4312,
https://doi.org/10.1002/2017GL073524, 2017.
Troen, I. and Mahrt, L.: A simple model of the atmospheric boundary layer:
Sensitivity to surface evaporation, Bound.-Lay. Meteorol., 37, 129–148,
https://doi.org/10.1007/BF00122760, 1986.
US EPA Office of Research and Development: CMAQv5.0.2 (Version 5.0.2),
Zenodo [data set], https://doi.org/10.5281/zenodo.1079898, 2014.
US EPA Office of Research and Development: CMAQ (Version 5.3.1), Zenodo [data set],
https://doi.org/10.5281/zenodo.3585898, 2019.
Vukovich, J. M. and Pierce, T.: The Implementation of BEIS3 within the SMOKE
modeling framework, Environ. Sci., 2002.
Weiss, A. and Norman, J.: Partitioning solar radiation into direct and
diffuse, visible and nearinfrared components, Agr. Forest Meteorol., 34, 205–213,
https://doi.org/10.1016/0168-1923(85)90020-6, 1985.
Westerling A. L., Hidalgo, H. G., Cayan, D. R., and Swetnam, T. W.: Warming and
earlier spring increase western US forest wildfire activity, Science, 313, 940–943,
https://doi.org/10.1126/science.1128834, 2006.
Wilkins, J., Pouliot, G., Pierce, T., and Beidler, J.: Exploring the
Vertical Distribution of Wildland Fire Smoke in CMAQ, 2019 International
Emissions Inventory Conference, 28 July–2 August 2019, Dallas, Texas,
https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NERL&dirEntryId=346294 (last access: 6 April 2022), 2019.
Williams, A. P., Cook, E. R., Smerdon, J. E., Cook, B. I.,
Abatzoglou, J. T., Bolles, K., Baek, S. H., Badger, A. M., and Livneh, B.: Large contribution from anthropogenic warming to an
emerging North American megadrought, Science, 368, 314–318,
https://doi.org/10.1126/science.aaz9600, 2020.
Williams, J. R.: The EPIC model, in: Computer models in
watershed hydrology, edited by: Singh, V. P., Chapter 25, 909–1000, Littleton, CO, Water
Resources Publications, ISBN-13 978-0-918334-91-6, 1995.
World Health Organization (WHO): Air Quality Guidelines, Global update 2005,
Particulate matter, ozone, nitrogen dioxide and sulfur dioxide, ISBN 92-890-2192-6, 2006.
World Health Organization (WHO): WHO Guidelines for Indoor Air Quality:
Selected Pollutants, World Health Organization, Regional Office for Europe
Scherfigsvej 8, 2100 Copenhagen, Denmark, ISBN 9789289002134, 2010.
Yang, F., Tallapragada, V., Kain, J. S., Wei, H., Yang, R., Yudin,
V. A., Moorthi, S., Han, J., Hou, Y. T., Wang, J., Treadon, R., and Kleist, D. T.: Model Upgrade Plan and Initial Results from a Prototype NCEP Global
Forecast System Version 16, 2020 AMS Conference, Boston, MA, 15 January 2020,
https://ams.confex.com/ams/2020Annual/webprogram/Paper362797.html (last access: 6 April 2022), 2020.
Yarwood, G., Whitten, G. Z., and Jung, J.: Final Report. Development,
Evaluation and Testing of Version 6 of the Carbon Bond Chemical Mechanism
(CB6), 22 September 2010, ENVIRON International Corporation, 06-17477Y,
https://www.tceq.texas.gov/assets/public/implementation/air/am/contracts/reports/pm/5820784005FY1026-20100922-environ-cb6.pdf (last access: 6 April 2022), 2010.
Zeka A., Zanobetti, A., and Schwartz, J.: Short term effects of particulate
matter on cause specific mortality: effects of lags and modification by city
characteristics, Occup. Environ. Med., 62, 718–725,
https://doi.org/10.1136/oem.2004.017012, 2006.
Zhang, X., Kondragunta, S., Ram, J., Schmidt, C., and Huang, H.-C.:
Near-real-time global biomass burning emissions product from geostationary
satellite constellation, J. Geophys. Res.-Atmos., 117, D14201,
https://doi.org/10.1029/2012JD017459, 2012.
Zhang, X., Kondragunta, S., and Roy, D. P.: Interannual variation in biomass
burning and fire seasonality derived from geostationary satellite data
across the contiguous United States from 1995 to 2011, J. Geophys. Res.-Biogeo., 119, 1147–1162,
https://doi.org/10.1002/2013JG002518, 2014.
Zhang, Y., Vijayaraghavan, K., Wen, X.-Y., Snell, H. E., and Jacobson, M. Z.:
Probing into regional ozone and particulate matter pollution in the United
States: 1. A 1
year CMAQ simulation and evaluation using surface and satellite data,
J. Geophys. Res., 114, D22304, https://doi.org/10.1029/2009JD011898, 2009a.
Zhang, Y., Wen, X.-Y., Wang, K., Vijayaraghavan, K., and Jacobson, M. Z.:
Probing
into regional ozone and particulate matter pollution in the United States:
2. An
examination of formation mechanisms through a process analysis technique
and sensitivity study, J. Geophys. Res., 114, D22304, https://doi.org/10.1029/2009JD011898, 2009b.
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.:
Real-time air quality forecasting, part I: History, techniques, and current
status, Atmos. Environ., 60, 632–655,
https://doi.org/10.1016/j.atmosenv.2012.06.031, 2012a.
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.:
Real-time air quality forecasting, part II: State of the science, current
research needs, and future prospects, Atmos. Environ., 60, 656–676, https://doi.org/10.1016/j.atmosenv.2012.02.041, 2012b.
Zhang, Y., West, J. J., Mathur, R., Xing, J., Hogrefe, C., Roselle, S. J., Bash, J. O., Pleim, J. E., Gan, C.-M., and Wong, D. C.: Long-term trends in the ambient PM2.5- and O3-related mortality burdens in the United States under emission reductions from 1990 to 2010, Atmos. Chem. Phys., 18, 15003–15016, https://doi.org/10.5194/acp-18-15003-2018, 2018.
Zhao, H., Zheng, Y., and Li, T.: Air Quality and Control Measures Evaluation
during the 2014 Youth Olympic Games in Nanjing and its Surrounding Cities,
Atmosphere, 8, 8060100, https://doi.org/10.3390/atmos8060100, 2017.
Zhou, L., Lin, S., Chen, J., 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.
Zhou, T., Sun, J., and Yu, H.: Temporal and Spatial Patterns of China's Main
Air Pollutants: Years 2014 and 2015, Atmosphere, 8, 8080137,
https://doi.org/10.3390/atmos8080137, 2017.
Short summary
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.
NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the...
