Articles | Volume 15, issue 8
Geosci. Model Dev., 15, 3281–3313, 2022
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.
Geosci. Model Dev., 15, 3281–3313, 2022
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
Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16
Patrick C. Campbell et al.
Related authors
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
Short summary
Short summary
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2022-713, https://doi.org/10.5194/egusphere-2022-713, 2022
Short summary
Short summary
Plume height is important in wildfire smoke dispersion and the effects on 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, which affects pollution exceedance forecasts and early warning of extreme air pollution events.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Daiwen Kang, Nicholas K. Heath, Robert C. Gilliam, Tanya L. Spero, and Jonathan E. Pleim
Geosci. Model Dev., 15, 8561–8579, https://doi.org/10.5194/gmd-15-8561-2022, https://doi.org/10.5194/gmd-15-8561-2022, 2022
Short summary
Short summary
A lightning assimilation (LTA) technique implemented in the WRF model's Kain–Fritsch (KF) convective scheme is updated and applied to simulations from regional to hemispheric scales using observed lightning flashes from ground-based lightning detection networks. Different user-toggled options associated with the KF scheme on simulations with and without LTA are assessed. The model's performance is improved significantly by LTA, but it is sensitive to various factors.
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
Short summary
Short summary
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.
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
Short summary
Short summary
We assess Community Multiscale Air Quality (CMAQ) model simulations of nitrogen and sulfur deposition over US climate regions to evaluate the model ability to reproduce long-term deposition trends and total deposition budgets. A measurement–model fusion technique is found to improve estimates of wet deposition. Emission controls set by the Clean Air Act successfully decreased oxidized nitrogen deposition across the US; we find increasing amounts of reduced nitrogen to the total nitrogen budget.
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
EGUsphere, https://doi.org/10.5194/egusphere-2022-713, https://doi.org/10.5194/egusphere-2022-713, 2022
Short summary
Short summary
Plume height is important in wildfire smoke dispersion and the effects on 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, which affects pollution exceedance forecasts and 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. Discuss., https://doi.org/10.5194/acp-2022-435, https://doi.org/10.5194/acp-2022-435, 2022
Revised manuscript accepted for ACP
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Hyun Cheol Kim, Tianfeng Chai, Ariel Stein, and Shobha Kondragunta
Atmos. Chem. Phys., 20, 10259–10277, https://doi.org/10.5194/acp-20-10259-2020, https://doi.org/10.5194/acp-20-10259-2020, 2020
Short summary
Short summary
Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires, that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. Using NOAA HYSPLIT and GOES Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level and outperforms current operational system.
Li Pan, HyunCheol Kim, Pius Lee, Rick Saylor, YouHua Tang, Daniel Tong, Barry Baker, Shobha Kondragunta, Chuanyu Xu, Mark G. Ruminski, Weiwei Chen, Jeff Mcqueen, and Ivanka Stajner
Geosci. Model Dev., 13, 2169–2184, https://doi.org/10.5194/gmd-13-2169-2020, https://doi.org/10.5194/gmd-13-2169-2020, 2020
Short summary
Short summary
Compared to anthropogenic emissions, emissions from wildfires are largely uncontrolled and unpredictable. Quantitatively describing wildfire emissions and their contributions to air pollution remains a substantial challenge for air quality forecasting efforts. In this study, we test the wildfire calculation algorithm used by the National Air Quality Forecasting Capability (NAQFC) by comparison with ground, satellite and flight measurements during the Southeast Nexus (SENEX) field experiment.
Anna Karion, Thomas Lauvaux, Israel Lopez Coto, Colm Sweeney, Kimberly Mueller, Sharon Gourdji, Wayne Angevine, Zachary Barkley, Aijun Deng, Arlyn Andrews, Ariel Stein, and James Whetstone
Atmos. Chem. Phys., 19, 2561–2576, https://doi.org/10.5194/acp-19-2561-2019, https://doi.org/10.5194/acp-19-2561-2019, 2019
Short summary
Short summary
In this study, we use atmospheric methane concentration observations collected during an airborne campaign to compare different model-based emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We find that the tracer dispersion model has a significant impact on the results because the models differ in their simulation of vertical dispersion. Additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models.
Tianfeng Chai, Ariel Stein, and Fong Ngan
Geosci. Model Dev., 11, 5135–5148, https://doi.org/10.5194/gmd-11-5135-2018, https://doi.org/10.5194/gmd-11-5135-2018, 2018
Short summary
Short summary
While model predictions depend on release parameters, model uncertainties in inverse modeling should also vary with the source terms. In this paper, model uncertainties that will change with the source terms are introduced in a weak-constraint inverse modeling system. Tests using HYSPLIT model and CAPTEX observations show that adding such model uncertainty terms improves release rate estimates. A cost function normalization scheme introduced to avoid spurious solutions proves to be effective.
Peng Liu, Christian Hogrefe, Ulas Im, Jesper H. Christensen, Johannes Bieser, Uarporn Nopmongcol, Greg Yarwood, Rohit Mathur, Shawn Roselle, and Tanya Spero
Atmos. Chem. Phys., 18, 17157–17175, https://doi.org/10.5194/acp-18-17157-2018, https://doi.org/10.5194/acp-18-17157-2018, 2018
Short summary
Short summary
This study represents an intercomparison of four regional-scale air quality simulations in order to understand the model similarities and differences in estimating the impact of ozone imported from outside of the US on the surface ozone within the US at process level. Vertical turbulent mixing stands out as a primary contributor to the model differences in inert tracers.
Christopher G. Nolte, Tanya L. Spero, Jared H. Bowden, Megan S. Mallard, and Patrick D. Dolwick
Atmos. Chem. Phys., 18, 15471–15489, https://doi.org/10.5194/acp-18-15471-2018, https://doi.org/10.5194/acp-18-15471-2018, 2018
Short summary
Short summary
Changes in air pollution in the United States are simulated under three near-future climate scenarios. Widespread increases in average ozone levels are projected, with the largest increases during summer under the highest warming scenario. Increases are driven by higher temperatures and emissions from vegetation and are magnified at the upper end of the ozone distribution. The increases in ozone have potentially important implications for efforts to protect human health.
Orren Russell Bullock Jr., Hosein Foroutan, Robert C. Gilliam, and Jerold A. Herwehe
Geosci. Model Dev., 11, 2897–2922, https://doi.org/10.5194/gmd-11-2897-2018, https://doi.org/10.5194/gmd-11-2897-2018, 2018
Short summary
Short summary
The U.S. Environmental Protection Agency is developing a new modeling system to investigate air pollution pathways on a global scale. We plan to use the Model for Prediction Across Scales – Atmosphere (MPAS-A) to define the meteorology that affects air pollution transport and fate. In order to do so, MPAS-A must accurately reproduce historical weather conditions. This work demonstrates that our implementation of four-dimensional data assimilation by analysis nudging provides that capability.
Jun Wang, Partha S. Bhattacharjee, Vijay Tallapragada, Cheng-Hsuan Lu, Shobha Kondragunta, Arlindo da Silva, Xiaoyang Zhang, Sheng-Po Chen, Shih-Wei Wei, Anton S. Darmenov, Jeff McQueen, Pius Lee, Prabhat Koner, and Andy Harris
Geosci. Model Dev., 11, 2315–2332, https://doi.org/10.5194/gmd-11-2315-2018, https://doi.org/10.5194/gmd-11-2315-2018, 2018
Short summary
Short summary
The NEMS GFS Aerosol Component (NGAC) version 2.0 for global multispecies aerosol forecast was developed at NCEP. Additional sea salt, sulfate, organic carbon, and black carbon aerosol species were included. This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been provided to meet the stakeholder's needs.
Youhua Tang, Mariusz Pagowski, Tianfeng Chai, Li Pan, Pius Lee, Barry Baker, Rajesh Kumar, Luca Delle Monache, Daniel Tong, and Hyun-Cheol Kim
Geosci. Model Dev., 10, 4743–4758, https://doi.org/10.5194/gmd-10-4743-2017, https://doi.org/10.5194/gmd-10-4743-2017, 2017
Short summary
Short summary
In order to evaluate the data assimilation tools for regional real-time PM2.5 forecasts, we applied a 3D-Var assimilation tool to adjust the aerosol initial condition by assimilating satellite-retrieved aerosol optical depth and surface PM2.5 observations for a regional air quality model, which is compared to another assimilation method, optimal interpolation. We discuss the pros and cons of these two assimilation methods based on the comparison of their 1-month four-cycles-per-day runs.
Li Pan, Hyun Cheol Kim, Pius Lee, Rick Saylor, YouHua Tang, Daniel Tong, Barry Baker, Shobha Kondragunta, Chuanyu Xu, Mark G. Ruminski, Weiwei Chen, Jeff Mcqueen, and Ivanka Stajner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-207, https://doi.org/10.5194/gmd-2017-207, 2017
Revised manuscript not accepted
Short summary
Short summary
In this study, a system accounting for fire emissions in a chemical transport model is described. The focus of this work is to qualitatively evaluate the system's capability to capture fire signals identified by multiple observation data sets. We discuss how to use observational data correctly to filter out fire signals and synergistic use of multiple data sets together. We also address the limitations of each of the observation data sets and of the evaluation methods.
Rohit Mathur, Jia Xing, Robert Gilliam, Golam Sarwar, Christian Hogrefe, Jonathan Pleim, George Pouliot, Shawn Roselle, Tanya L. Spero, David C. Wong, and Jeffrey Young
Atmos. Chem. Phys., 17, 12449–12474, https://doi.org/10.5194/acp-17-12449-2017, https://doi.org/10.5194/acp-17-12449-2017, 2017
Short summary
Short summary
We extend CMAQ's applicability to the entire Northern Hemisphere to enable consistent examination of interactions between atmospheric processes occurring on various spatial and temporal scales. Improvements were made in model process representation, structure, and input data sets that enable a range of model applications including episodic intercontinental pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution–climate interactions.
Min Huang, Gregory R. Carmichael, James H. Crawford, Armin Wisthaler, Xiwu Zhan, Christopher R. Hain, Pius Lee, and Alex B. Guenther
Geosci. Model Dev., 10, 3085–3104, https://doi.org/10.5194/gmd-10-3085-2017, https://doi.org/10.5194/gmd-10-3085-2017, 2017
Short summary
Short summary
Various sensitivity simulations during two airborne campaigns were performed to assess the impact of different initialization methods and model resolutions on NUWRF-modeled weather states, heat fluxes, and the follow-on MEGAN isoprene emission calculations. Proper land initialization is shown to be important to the coupled weather modeling and the follow-on emission modeling, which is also critical to accurately representing other processes in air quality modeling and data assimilation.
Chaopeng Hong, Qiang Zhang, Yang Zhang, Youhua Tang, Daniel Tong, and Kebin He
Geosci. Model Dev., 10, 2447–2470, https://doi.org/10.5194/gmd-10-2447-2017, https://doi.org/10.5194/gmd-10-2447-2017, 2017
Short summary
Short summary
A regional coupled climate–chemistry modeling system using the dynamical downscaling technique was established and evaluated. The modeling system performed well for both the climatological and the short-term air quality applications over east Asia. Regional models outperformed global models in regional climate and air quality predictions. The coupled modeling system improved the model performance, although some biases remained in the aerosol–cloud–radiation variables.
K. Wyat Appel, Sergey L. Napelenok, Kristen M. Foley, Havala O. T. Pye, Christian Hogrefe, Deborah J. Luecken, Jesse O. Bash, Shawn J. Roselle, Jonathan E. Pleim, Hosein Foroutan, William T. Hutzell, George A. Pouliot, Golam Sarwar, Kathleen M. Fahey, Brett Gantt, Robert C. Gilliam, Nicholas K. Heath, Daiwen Kang, Rohit Mathur, Donna B. Schwede, Tanya L. Spero, David C. Wong, and Jeffrey O. Young
Geosci. Model Dev., 10, 1703–1732, https://doi.org/10.5194/gmd-10-1703-2017, https://doi.org/10.5194/gmd-10-1703-2017, 2017
Short summary
Short summary
The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system. The CMAQ model is used extensively throughout the world to simulate air pollutants for many purposes, including regulatory and air quality forecasting applications. This work describes the scientific updates made to the latest version of the CMAQ modeling system (CMAQv5.1) and presents an evaluation of the new model against observations and results from the previous model version.
Tianfeng Chai, Alice Crawford, Barbara Stunder, Michael J. Pavolonis, Roland Draxler, and Ariel Stein
Atmos. Chem. Phys., 17, 2865–2879, https://doi.org/10.5194/acp-17-2865-2017, https://doi.org/10.5194/acp-17-2865-2017, 2017
Short summary
Short summary
An inverse system based on the HYSPLIT dispersion model has been built to estimate volcanic ash source strengths, vertical distribution, and temporal variations. Using MODIS retrievals from the 2008 Kasatochi volcanic ash clouds, three options for matching model results to satellite mass loadings are tested. They all show decent skill. It is also found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.
Hyun Cheol Kim, Soontae Kim, Seok-Woo Son, Pius Lee, Chun-Sil Jin, Eunhye Kim, Byeong-Uk Kim, Fong Ngan, Changhan Bae, Chang-Keun Song, and Ariel Stein
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-673, https://doi.org/10.5194/acp-2016-673, 2016
Revised manuscript not accepted
Short summary
Short summary
In recent years, frequent occurrence of severe haze events in East Asia is one of the most serious public concerns in this region. We demonstrate that daily pollutant transport patterns in East Asia are visible from satellite images when inspected with corresponding synoptic weather analyses. Our manuscript focuses on the possible role of meteorology, especially by the routine passages of synoptic systems, on the production and removal of regional pollution in East Asia.
Cheng-Hsuan Lu, Arlindo da Silva, Jun Wang, Shrinivas Moorthi, Mian Chin, Peter Colarco, Youhua Tang, Partha S. Bhattacharjee, Shen-Po Chen, Hui-Ya Chuang, Hann-Ming Henry Juang, Jeffery McQueen, and Mark Iredell
Geosci. Model Dev., 9, 1905–1919, https://doi.org/10.5194/gmd-9-1905-2016, https://doi.org/10.5194/gmd-9-1905-2016, 2016
Short summary
Short summary
Aerosols have an important effect on the Earth's climate and implications for public health. NASA has partnered with NOAA to transfer GOCART aerosol model to NCEP, enabling the first global aerosol forecasting system at NOAA/NCEP. This collaboration reflects an effective research-to-operation transition, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders and to allow the effects of aerosols on weather and climate prediction to be considered.
Hyun Cheol Kim, Pius Lee, Laura Judd, Li Pan, and Barry Lefer
Geosci. Model Dev., 9, 1111–1123, https://doi.org/10.5194/gmd-9-1111-2016, https://doi.org/10.5194/gmd-9-1111-2016, 2016
Short summary
Short summary
Fair comparison between satellite- and modeled urban NO2 column densities is important in emission inventory evaluation and regulation policy making. This study focuses on the impact of satellite footprint resolution geometry. Since OMI NO2 pixels are too coarse to resolve fine-scale urban plumes, it may cause 20–30 % bias over major cities. We introduce approaches to adjust spatial and vertical structure (downscaling & averaging kernel), and demonstrate improved agreement between sat. and model.
M. Huang, D. Tong, P. Lee, L. Pan, Y. Tang, I. Stajner, R. B. Pierce, J. McQueen, and J. Wang
Atmos. Chem. Phys., 15, 12595–12610, https://doi.org/10.5194/acp-15-12595-2015, https://doi.org/10.5194/acp-15-12595-2015, 2015
Short summary
Short summary
We developed Arizona dust records in 2005-2013 using multiple surface and remote sensing observation data sets. The inter-annual variability of dust events was anticorrelated with three drought indicators (PDSI, satellite NDVI and soil moisture), and stronger dust activity was found in the afternoon than in the morning due to stronger winds and drier soil. Impact of a recent dust event accompanied by a stratospheric ozone intrusion was evaluated with various observational and modeling data sets.
H. C. Kim, P. Lee, F. Ngan, Y. Tang, H. L. Yoo, and L. Pan
Geosci. Model Dev., 8, 2959–2965, https://doi.org/10.5194/gmd-8-2959-2015, https://doi.org/10.5194/gmd-8-2959-2015, 2015
Short summary
Short summary
This study focuses on the evaluation of regional air quality model's performance based on the cloud information from satellites. While cloud information is crucial in photochemistry model, the definitions of cloud fraction from model and satellite are not physically consistent. We demonstrate that improper modeling of cloud fraction is correlated with surface ozone bias, and we also show that current model cloud field might be too bright, causing an overestimation of surface ozone level.
P. A. Cleary, N. Fuhrman, L. Schulz, J. Schafer, J. Fillingham, H. Bootsma, J. McQueen, Y. Tang, T. Langel, S. McKeen, E. J. Williams, and S. S. Brown
Atmos. Chem. Phys., 15, 5109–5122, https://doi.org/10.5194/acp-15-5109-2015, https://doi.org/10.5194/acp-15-5109-2015, 2015
Short summary
Short summary
This study examines ozone mixing ratios over Lake Michigan as measured on the Lake Express ferry, by shoreline differential optical absorption spectroscopy (DOAS) observations in southeastern Wisconsin, and as predicted by the Community Multiscale Air Quality (CMAQ) model. Over water, ozone was determined to be an average of 3.8ppb higher than shoreline observations but overpredicted by the CMAQ model by as much as 11-16ppb midday.
M. S. Mallard, C. G. Nolte, T. L. Spero, O. R. Bullock, K. Alapaty, J. A. Herwehe, J. Gula, and J. H. Bowden
Geosci. Model Dev., 8, 1085–1096, https://doi.org/10.5194/gmd-8-1085-2015, https://doi.org/10.5194/gmd-8-1085-2015, 2015
Short summary
Short summary
Because global climate models (GCMs) are typically run at coarse spatial resolution, lakes are often poorly resolved in their global fields. When downscaling such GCMs using the Weather Research & Forecasting (WRF) model, use of WRF’s default interpolation methods can result in unrealistic lake temperatures and ice cover, which can impact simulated air temperatures and precipitation. Here, alternative methods for setting lake variables in WRF downscaling applications are presented and compared.
T. Chai, H.-C. Kim, P. Lee, D. Tong, L. Pan, Y. Tang, J. Huang, J. McQueen, M. Tsidulko, and I. Stajner
Geosci. Model Dev., 6, 1831–1850, https://doi.org/10.5194/gmd-6-1831-2013, https://doi.org/10.5194/gmd-6-1831-2013, 2013
R. D. Saylor
Atmos. Chem. Phys., 13, 693–715, https://doi.org/10.5194/acp-13-693-2013, https://doi.org/10.5194/acp-13-693-2013, 2013
Related subject area
Atmospheric sciences
Lightning assimilation in the WRF model (Version 4.1.1): technique updates and assessment of the applications from regional to hemispheric scales
Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)
Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement
A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Importance of different parameterization changes for the updated dust cycle modeling in the Community Atmosphere Model (version 6.1)
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
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
A lumped species approach for the simulation of secondary organic aerosol production from intermediate-volatility organic compounds (IVOCs): application to road transport in PMCAMx-iv (v1.0)
TrackMatcher – a tool for finding intercepts in tracks of geographical positions
Recovery of sparse urban greenhouse gas emissions
Tropospheric transport and unresolved convection: numerical experiments with CLaMS 2.0/MESSy
MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling
A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case
Repeatable high-resolution statistical downscaling through deep learning
Atmospherically Relevant Chemistry and Aerosol box model – ARCA box (version 1.2)
MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Introduction of the DISAMAR radiative transfer model: determining instrument specifications and analysing methods for atmospheric retrieval (version 4.1.5)
Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study
Downscaling atmospheric chemistry simulations with physically consistent deep learning
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs
Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework
OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3
Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes
A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018
TransClim (v1.0): a chemistry–climate response model for assessing the effect of mitigation strategies for road traffic on ozone
A description of the first open-source community release of MISTRA-v9.0: a 0D/1D atmospheric boundary layer chemistry model
Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations
Computationally efficient methods for large-scale atmospheric inverse modeling
Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)
Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0
RAP-Net: Region Attention Predictive Network for precipitation nowcasting
Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models
uDALES 1.0: a large-eddy simulation model for urban environments
Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1)
Assimilation of GPM-retrieved ocean surface meteorology data for two snowstorm events during ICE-POP 2018
A multi-pollutant and multi-sectorial approach to screening the consistency of emission inventories
Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain. A case study using WRF-LES V4.0.1
Transporting CRM Variance in a Multiscale Modelling Framework
Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): a protocol for investigating the role of stratospheric polar vortex disturbances in subseasonal to seasonal forecasts
The Mission Support System (MSS v7.0.0) and its use in planning for the SouthTRAC aircraft campaign
Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate δ13C(CH4) and CH4: a case study with model LMDz-SACS
The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model
Validation of turbulent heat transfer models against eddy covariance flux measurements over a seasonally ice-covered lake
Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1)
Order of magnitude wall time improvement of variational methane inversions by physical parallelization: a demonstration using TM5-4DVAR
Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the Weather Research and Forecasting (v4.1.3) model during the ICE-POP 2018 field campaign
GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): An automatic generation tool of semi-explicit mechanisms
Daiwen Kang, Nicholas K. Heath, Robert C. Gilliam, Tanya L. Spero, and Jonathan E. Pleim
Geosci. Model Dev., 15, 8561–8579, https://doi.org/10.5194/gmd-15-8561-2022, https://doi.org/10.5194/gmd-15-8561-2022, 2022
Short summary
Short summary
A lightning assimilation (LTA) technique implemented in the WRF model's Kain–Fritsch (KF) convective scheme is updated and applied to simulations from regional to hemispheric scales using observed lightning flashes from ground-based lightning detection networks. Different user-toggled options associated with the KF scheme on simulations with and without LTA are assessed. The model's performance is improved significantly by LTA, but it is sensitive to various factors.
Sujeong Lim, Hyeon-Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, and Seon Ki Park
Geosci. Model Dev., 15, 8541–8559, https://doi.org/10.5194/gmd-15-8541-2022, https://doi.org/10.5194/gmd-15-8541-2022, 2022
Short summary
Short summary
The land surface model (LSM) contains various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of the Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve snowfall prediction.
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu
Geosci. Model Dev., 15, 8439–8452, https://doi.org/10.5194/gmd-15-8439-2022, https://doi.org/10.5194/gmd-15-8439-2022, 2022
Short summary
Short summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
Short summary
Short summary
Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
Geosci. Model Dev., 15, 8295–8323, https://doi.org/10.5194/gmd-15-8295-2022, https://doi.org/10.5194/gmd-15-8295-2022, 2022
Short summary
Short summary
In light of recent global warming, we aim to improve methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.
Longlei Li, Natalie M. Mahowald, Jasper F. Kok, Xiaohong Liu, Mingxuan Wu, Danny M. Leung, Douglas S. Hamilton, Louisa K. Emmons, Yue Huang, Neil Sexton, Jun Meng, and Jessica Wan
Geosci. Model Dev., 15, 8181–8219, https://doi.org/10.5194/gmd-15-8181-2022, https://doi.org/10.5194/gmd-15-8181-2022, 2022
Short summary
Short summary
This study advances mineral dust parameterizations in the Community Atmospheric Model (CAM; version 6.1). Efforts include 1) incorporating a more physically based dust emission scheme; 2) updating the dry deposition scheme; and 3) revising the gravitational settling velocity to account for dust asphericity. Substantial improvements achieved with these updates can help accurately quantify dust–climate interactions using CAM, such as the dust-radiation and dust–cloud interactions.
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
Short summary
Short summary
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.
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, https://doi.org/10.5194/gmd-15-7859-2022, 2022
Short summary
Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807, https://doi.org/10.5194/gmd-15-7791-2022, https://doi.org/10.5194/gmd-15-7791-2022, 2022
Short summary
Short summary
This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
Stella E. I. Manavi and Spyros N. Pandis
Geosci. Model Dev., 15, 7731–7749, https://doi.org/10.5194/gmd-15-7731-2022, https://doi.org/10.5194/gmd-15-7731-2022, 2022
Short summary
Short summary
The paper describes the first step towards the development of a simulation framework for the chemistry and secondary organic aerosol production of intermediate-volatility organic compounds (IVOCs). These compounds can be a significant source of organic particulate matter. Our approach treats IVOCs as lumped compounds that retain their chemical characteristics. Estimated IVOC emissions from road transport were a factor of 8 higher than emissions used in previous applications.
Peter Bräuer and Matthias Tesche
Geosci. Model Dev., 15, 7557–7572, https://doi.org/10.5194/gmd-15-7557-2022, https://doi.org/10.5194/gmd-15-7557-2022, 2022
Short summary
Short summary
This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting of data in the vicinity of intersections to achieve the optimal combination of various data sets.
Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich
Geosci. Model Dev., 15, 7533–7556, https://doi.org/10.5194/gmd-15-7533-2022, https://doi.org/10.5194/gmd-15-7533-2022, 2022
Short summary
Short summary
Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Paul Konopka, Mengchu Tao, Marc von Hobe, Lars Hoffmann, Corinna Kloss, Fabrizio Ravegnani, C. Michael Volk, Valentin Lauther, Andreas Zahn, Peter Hoor, and Felix Ploeger
Geosci. Model Dev., 15, 7471–7487, https://doi.org/10.5194/gmd-15-7471-2022, https://doi.org/10.5194/gmd-15-7471-2022, 2022
Short summary
Short summary
Pure trajectory-based transport models driven by meteorology derived from reanalysis products (ERA5) take into account only the resolved, advective part of transport. That means neither mixing processes nor unresolved subgrid-scale advective processes like convection are included. The Chemical Lagrangian Model of the Stratosphere (CLaMS) includes these processes. We show that isentropic mixing dominates unresolved transport. The second most important transport process is unresolved convection.
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396, https://doi.org/10.5194/gmd-15-7371-2022, https://doi.org/10.5194/gmd-15-7371-2022, 2022
Short summary
Short summary
This paper presents the latest version of the street-network model MUNICH, v2.0. The description of MUNICH v1.0, which models gas-phase pollutants in a street network, was published in GMD in 2018. Since then, major modifications have been made to MUNICH. The comprehensive aerosol model SSH-aerosol is now coupled to MUNICH to simulate primary and secondary aerosol concentrations. New parameterisations have also been introduced. Test cases are defined to illustrate the new model functionalities.
Yongbo Zhou, Yubao Liu, Zhaoyang Huo, and Yang Li
Geosci. Model Dev., 15, 7397–7420, https://doi.org/10.5194/gmd-15-7397-2022, https://doi.org/10.5194/gmd-15-7397-2022, 2022
Short summary
Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Dánnell Quesada-Chacón, Klemens Barfus, and Christian Bernhofer
Geosci. Model Dev., 15, 7353–7370, https://doi.org/10.5194/gmd-15-7353-2022, https://doi.org/10.5194/gmd-15-7353-2022, 2022
Short summary
Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
Petri Clusius, Carlton Xavier, Lukas Pichelstorfer, Putian Zhou, Tinja Olenius, Pontus Roldin, and Michael Boy
Geosci. Model Dev., 15, 7257–7286, https://doi.org/10.5194/gmd-15-7257-2022, https://doi.org/10.5194/gmd-15-7257-2022, 2022
Short summary
Short summary
Atmospheric chemistry and aerosol processes form a dynamic and sensitively balanced system, and solving problems regarding air quality or climate requires detailed modelling and coupling of the processes. The models involved are often very complex to use. We have addressed this problem with the new ARCA box model. It puts much of the current knowledge of the nano- and microscale aerosol dynamics and chemistry into usable software and has the potential to become a valuable tool in the community.
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
Geosci. Model Dev., 15, 7139–7151, https://doi.org/10.5194/gmd-15-7139-2022, https://doi.org/10.5194/gmd-15-7139-2022, 2022
Short summary
Short summary
MultilayerPy is a Python-based framework facilitating the creation, running and optimisation of state-of-the-art kinetic multi-layer models of aerosol and film processes. Models can be fit to data with local and global optimisation algorithms along with a statistical sampling algorithm, which quantifies the uncertainty in optimised model parameters. This “modelling study in a box” enables more reproducible and reliable results, with model code and outputs produced in a human-readable way.
Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes
Geosci. Model Dev., 15, 7031–7050, https://doi.org/10.5194/gmd-15-7031-2022, https://doi.org/10.5194/gmd-15-7031-2022, 2022
Short summary
Short summary
We present an overview of the DISAMAR radiative transfer code, highlighting the novel semi-analytical derivatives for the doubling–adding formulae and the new DISMAS technique for weak absorbers. DISAMAR includes forward simulations and retrievals for satellite spectral measurements from 270 to 2400 nm to determine instrument specifications for passive remote sensing. It has been used in various Sentinel-4/5P/5 projects and in the TROPOMI aerosol layer height and ozone profile products.
Ivette H. Banos, Will D. Mayfield, Guoqing Ge, Luiz F. Sapucci, Jacob R. Carley, and Louisa Nance
Geosci. Model Dev., 15, 6891–6917, https://doi.org/10.5194/gmd-15-6891-2022, https://doi.org/10.5194/gmd-15-6891-2022, 2022
Short summary
Short summary
A prototype data assimilation system for NOAA’s next-generation rapidly updated, convection-allowing forecast system, or Rapid Refresh Forecast System (RRFS) v0.1, is tested and evaluated. The impact of using data assimilation with a convective storm case study is examined. Although the convection in RRFS tends to be overestimated in intensity and underestimated in extent, the use of data assimilation proves to be crucial to improve short-term forecasts of storms and precipitation.
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, https://doi.org/10.5194/gmd-15-6677-2022, 2022
Short summary
Short summary
This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022, https://doi.org/10.5194/gmd-15-6341-2022, 2022
Short summary
Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022, https://doi.org/10.5194/gmd-15-6259-2022, 2022
Short summary
Short summary
ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219, https://doi.org/10.5194/gmd-15-6197-2022, https://doi.org/10.5194/gmd-15-6197-2022, 2022
Short summary
Short summary
In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
Vincent Huijnen, Philippe Le Sager, Marcus O. Köhler, Glenn Carver, Samuel Rémy, Johannes Flemming, Simon Chabrillat, Quentin Errera, and Twan van Noije
Geosci. Model Dev., 15, 6221–6241, https://doi.org/10.5194/gmd-15-6221-2022, https://doi.org/10.5194/gmd-15-6221-2022, 2022
Short summary
Short summary
We report on the first implementation of atmospheric chemistry and aerosol as part of the OpenIFS model, based on the CAMS global model. We give an overview of the model and evaluate two reference model configurations, with and without the stratospheric chemistry extension, against a variety of observational datasets. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022, https://doi.org/10.5194/gmd-15-6143-2022, 2022
Short summary
Short summary
Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
Ping Wang, Kebiao Mao, Fei Meng, Zhihao Qin, Shu Fang, and Sayed M. Bateni
Geosci. Model Dev., 15, 6059–6083, https://doi.org/10.5194/gmd-15-6059-2022, https://doi.org/10.5194/gmd-15-6059-2022, 2022
Short summary
Short summary
In order to obtain the key parameters of high-temperature spatial–temporal variation analysis, this study proposed a daily highest air temperature (Tmax) estimation frame to build a Tmax dataset in China from 1979 to 2018. We found that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. The abnormal temperature changes mainly occurred in El Nin~o years or La Nin~a years. IOBW had a stronger influence on China's warming events than other factors.
Vanessa Simone Rieger and Volker Grewe
Geosci. Model Dev., 15, 5883–5903, https://doi.org/10.5194/gmd-15-5883-2022, https://doi.org/10.5194/gmd-15-5883-2022, 2022
Short summary
Short summary
Road traffic emissions of nitrogen oxides, volatile organic compounds and carbon monoxide produce ozone in the troposphere and thus influence Earth's climate. To assess the ozone response to a broad range of mitigation strategies for road traffic, we developed a new chemistry–climate response model called TransClim. It is based on lookup tables containing climate–response relations and thus is able to quickly determine the climate response of a mitigation option.
Josué Bock, Jan Kaiser, Max Thomas, Andreas Bott, and Roland von Glasow
Geosci. Model Dev., 15, 5807–5828, https://doi.org/10.5194/gmd-15-5807-2022, https://doi.org/10.5194/gmd-15-5807-2022, 2022
Short summary
Short summary
MISTRA-v9.0 is an atmospheric boundary layer chemistry model. The model includes a detailed particle description with regards to the microphysics, gas–particle interactions, and liquid phase chemistry within particles. Version 9.0 is the first release of MISTRA as an open-source community model. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA with good consistency.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 15, 5547–5565, https://doi.org/10.5194/gmd-15-5547-2022, https://doi.org/10.5194/gmd-15-5547-2022, 2022
Short summary
Short summary
Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges. This article describes computationally efficient methods for large-scale atmospheric inverse modeling.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Bo Wu, Qixiang Cai, Di Liu, and Pengfei Han
Geosci. Model Dev., 15, 5511–5528, https://doi.org/10.5194/gmd-15-5511-2022, https://doi.org/10.5194/gmd-15-5511-2022, 2022
Short summary
Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
Brian T. Dinkelacker, Pablo Garcia Rivera, Ioannis Kioutsioukis, Peter J. Adams, and Spyros N. Pandis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-145, https://doi.org/10.5194/gmd-2022-145, 2022
Preprint under review for GMD
Short summary
Short summary
The performance of a chemical transport model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Total PM2.5 mass is reproduced well by the model during the winter when compared to regulatory measurements, but in the summer PM2.5 is underpredicted mainly due to difficulties in reproducing the regional secondary organic aerosol levels.
Zheng Zhang, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li
Geosci. Model Dev., 15, 5407–5419, https://doi.org/10.5194/gmd-15-5407-2022, https://doi.org/10.5194/gmd-15-5407-2022, 2022
Short summary
Short summary
In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existing models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models, especially in those regions with moderate and heavy rainfall. Considering that these regions cause more threats to human activities, the research in our work is significant for preventing natural disasters caused by heavy rainfall.
Fabian Maier, Christoph Gerbig, Ingeborg Levin, Ingrid Super, Julia Marshall, and Samuel Hammer
Geosci. Model Dev., 15, 5391–5406, https://doi.org/10.5194/gmd-15-5391-2022, https://doi.org/10.5194/gmd-15-5391-2022, 2022
Short summary
Short summary
We show that the default representation of point source emissions in WRF–STILT leads to large overestimations when modelling fossil fuel CO2 concentrations for a 30 m high observation site during stable atmospheric conditions. We therefore introduce a novel point source modelling approach in WRF-STILT that takes into account their effective emission heights and results in a much better agreement with observations.
Ivo Suter, Tom Grylls, Birgit S. Sützl, Sam O. Owens, Chris E. Wilson, and Maarten van Reeuwijk
Geosci. Model Dev., 15, 5309–5335, https://doi.org/10.5194/gmd-15-5309-2022, https://doi.org/10.5194/gmd-15-5309-2022, 2022
Short summary
Short summary
Cities are increasingly moving to the fore of climate and air quality research due to their central role in the population’s health and well-being, while suitable models remain scarce. This article describes the development of a new urban LES model, which allows examining the effects of various processes, infrastructure and vegetation on the local climate and air quality. Possible applications are demonstrated and a comparison to an experiment is shown.
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
Short summary
Short summary
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.
Xuanli Li, Jason B. Roberts, Jayanthi Srikishen, Jonathan L. Case, Walter A. Petersen, Gyuwon Lee, and Christopher R. Hain
Geosci. Model Dev., 15, 5287–5308, https://doi.org/10.5194/gmd-15-5287-2022, https://doi.org/10.5194/gmd-15-5287-2022, 2022
Short summary
Short summary
This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system. This was for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field experiments. The results indicated a positive impact of the data for short-term forecasts for heavy snowfall.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev., 15, 5271–5286, https://doi.org/10.5194/gmd-15-5271-2022, https://doi.org/10.5194/gmd-15-5271-2022, 2022
Short summary
Short summary
In this work, we propose a screening method to improve the quality of emission inventories, which are responsible for large uncertainties in air-quality modeling. The first step of screening consists of keeping only emission contributions that are relevant enough. In a second step, the method identifies large differences that provide evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European-scale inventory over 150 European cities.
Julian Quimbayo-Duarte, Johannes Wagner, Norman Wildmann, Thomas Gerz, and Juerg Schmidli
Geosci. Model Dev., 15, 5195–5209, https://doi.org/10.5194/gmd-15-5195-2022, https://doi.org/10.5194/gmd-15-5195-2022, 2022
Short summary
Short summary
The ultimate objective of this model evaluation is to improve boundary layer flow representation over complex terrain. The numerical model is tested against observations retrieved during the Perdigão 2017 field campaign (moderate complex terrain). We observed that the inclusion of a forest parameterization in the numerical model significantly improves the representation of the wind field in the atmospheric boundary layer.
Walter Hannah and Kyle Pressel
EGUsphere, https://doi.org/10.5194/egusphere-2022-397, https://doi.org/10.5194/egusphere-2022-397, 2022
Short summary
Short summary
A multiscale modelling framework couples two models of the atmosphere that each cover different scale ranges. Traditionally, fluctuations in the small-scale model are not transported by the flow on the large-scale model grid, but this is hypothesized to be responsible for a persistent, unphysical checkerboard pattern. A method is presented to facilitate the transport of these small-scale fluctuations, analogous to how small-scale clouds and turbulence are transported in the real atmosphere.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
Short summary
Short summary
This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Reimar Bauer, Jens-Uwe Grooß, Jörn Ungermann, May Bär, Markus Geldenhuys, and Lars Hoffmann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-155, https://doi.org/10.5194/gmd-2022-155, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurements campaigns during the last decade. Here, we describe the MSS software and its use during the SouthTRAC measurement campaign in 2019. As example for how the MSS software is used in conjunction with many data sets, we describe the planning of a single flight probing orographic gravity waves propagating up into the lower mesosphere.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, Bruce H. Vaughn, Sylvia Englund Michel, and Philippe Bousquet
Geosci. Model Dev., 15, 4831–4851, https://doi.org/10.5194/gmd-15-4831-2022, https://doi.org/10.5194/gmd-15-4831-2022, 2022
Short summary
Short summary
Estimating CH4 sources by exploiting observations within an inverse modeling framework is a powerful approach. Here, a new system designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. By optimizing both the emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate the co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.
Bok H. Baek, Rizzieri Pedruzzi, Minwoo Park, Chi-Tsan Wang, Younha Kim, Chul-Han Song, and Jung-Hun Woo
Geosci. Model Dev., 15, 4757–4781, https://doi.org/10.5194/gmd-15-4757-2022, https://doi.org/10.5194/gmd-15-4757-2022, 2022
Short summary
Short summary
The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road-link-level network information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced inventory for policymakers, stakeholders, and the air quality modeling community.
Joonatan Ala-Könni, Kukka-Maaria Kohonen, Matti Leppäranta, and Ivan Mammarella
Geosci. Model Dev., 15, 4739–4755, https://doi.org/10.5194/gmd-15-4739-2022, https://doi.org/10.5194/gmd-15-4739-2022, 2022
Short summary
Short summary
Properties of seasonally ice-covered lakes are not currently sufficiently included in global climate models. To fill this gap, this study evaluates three models that could be used to quantify the amount of heat that moves from and into the lake by the air above it and through evaporation of the ice cover. The results show that the complex nature of the surrounding environment as well as difficulties in accurately measuring the surface temperature of ice introduce errors to these models.
Jason E. Williams, Vincent Huijnen, Idir Bouarar, Mehdi Meziane, Timo Schreurs, Sophie Pelletier, Virginie Marécal, Beatrice Josse, and Johannes Flemming
Geosci. Model Dev., 15, 4657–4687, https://doi.org/10.5194/gmd-15-4657-2022, https://doi.org/10.5194/gmd-15-4657-2022, 2022
Short summary
Short summary
The global CAMS air quality model is used for providing tropospheric ozone information to end users. This paper updates the chemical mechanism employed (CBA) and compares it against two other mechanisms (MOCAGE, MOZART) and a multi-decadal dataset based on a previous version of CBA. We perform extensive validation for the US using multiple surface and aircraft datasets, providing an assessment of biases and the extent of correlation across different seasons during 2014.
Sudhanshu Pandey, Sander Houweling, and Arjo Segers
Geosci. Model Dev., 15, 4555–4567, https://doi.org/10.5194/gmd-15-4555-2022, https://doi.org/10.5194/gmd-15-4555-2022, 2022
Short summary
Short summary
Inversions are used to calculate methane emissions using atmospheric mole-fraction measurements. Multidecadal inversions are needed to extract information from the long measurement records of methane. However, multidecadal inversion computations can take months to finish. Here, we demonstrate an order of magnitude improvement in wall clock time for an iterative multidecadal inversion by physical parallelization of chemical transport model.
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev., 15, 4529–4553, https://doi.org/10.5194/gmd-15-4529-2022, https://doi.org/10.5194/gmd-15-4529-2022, 2022
Short summary
Short summary
This study evaluates the performance of the four microphysics parameterizations, the WDM6, WDM7, Thompson, and Morrison schemes, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events are selected and classified into three categories (cold-low, warm-low, and air–sea interaction cases). The evaluation focuses on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.
Zhizhao Wang, Florian Couvidat, and Karine Sartelet
EGUsphere, https://doi.org/10.5194/egusphere-2022-245, https://doi.org/10.5194/egusphere-2022-245, 2022
Short summary
Short summary
3D air-quality models need to represent the complexity of SOA formation with a manageable computational cost. Therefore, we developed GENOA v1.0, an algorithm to generate semi-explicit SOA mechanisms preserving the accuracy of the explicit VOC chemical mechanisms on SOA formation. When applying to the MCM degradation scheme of beta-caryophyllene, GENOA managed to reduce the mechanism from 1626 to 23 reactions and from 579 to 15 species with an average error lower than 3 %.
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., McQueen, J., Wilczak, J., Djalalova, I., Stajner, I., 60
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. 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...
