Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3281-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3281-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16
Patrick C. Campbell
CORRESPONDING AUTHOR
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Youhua Tang
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Pius Lee
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
retired
Barry Baker
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Daniel Tong
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Rick Saylor
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Ariel Stein
NOAA Air Resources Laboratory (ARL), College Park, MD, USA
Jianping Huang
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Ho-Chun Huang
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Edward Strobach
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Jeff McQueen
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
I.M. Systems Group Inc., Rockville, MD, USA
Ivanka Stajner
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
Jamese Sims
NOAA NWS/STI, College Park, MD, USA
Jose Tirado-Delgado
NOAA NWS/STI, College Park, MD, USA
Eastern Research Group, Inc. (ERG), College Park, MD, USA
Youngsun Jung
NOAA NWS/STI, College Park, MD, USA
Fanglin Yang
NOAA National Centers for Environmental Prediction (NCEP), College Park,
MD, USA
Tanya L. Spero
US Environmental Protection Agency, Research Triangle Park, NC, USA
Robert C. Gilliam
US Environmental Protection Agency, Research Triangle Park, NC, USA
Related authors
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, Raffaele Montuoro, and Robert C. Gilliam
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-107, https://doi.org/10.5194/gmd-2024-107, 2024
Preprint under review for GMD
Short summary
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during August 2023 shows that the updated model greatly improves the simulation of MDA8 O3 by reducing the bias by 72 % in the contiguous US. PM2.5 prediction is only enhanced in regions less affected by wildfire, highlighting the need for future refinements.
Yunyao Li, Daniel Tong, Siqi Ma, Saulo R. Freitas, Ravan Ahmadov, Mikhail Sofiev, Xiaoyang Zhang, Shobha Kondragunta, Ralph Kahn, Youhua Tang, Barry Baker, Patrick Campbell, Rick Saylor, Georg Grell, and Fangjun Li
Atmos. Chem. Phys., 23, 3083–3101, https://doi.org/10.5194/acp-23-3083-2023, https://doi.org/10.5194/acp-23-3083-2023, 2023
Short summary
Short summary
Plume height is important in wildfire smoke dispersion and affects air quality and human health. We assess the impact of plume height on wildfire smoke dispersion and the exceedances of the National Ambient Air Quality Standards. A higher plume height predicts lower pollution near the source region, but higher pollution in downwind regions, due to the faster spread of the smoke once ejected, affects pollution exceedance forecasts and the early warning of extreme air pollution events.
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.
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.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, Raffaele Montuoro, and Robert C. Gilliam
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-107, https://doi.org/10.5194/gmd-2024-107, 2024
Preprint under review for GMD
Short summary
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during August 2023 shows that the updated model greatly improves the simulation of MDA8 O3 by reducing the bias by 72 % in the contiguous US. PM2.5 prediction is only enhanced in regions less affected by wildfire, highlighting the need for future refinements.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-52, https://doi.org/10.5194/gmd-2024-52, 2024
Preprint under review for GMD
Short summary
Short summary
This work describe how we linked meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction in a global scale. This new model scales well on high performance computing environment and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Natalie Brett, Kathy S. Law, Steve R. Arnold, Javier G. Fochesatto, Jean-Christophe Raut, Tatsuo Onishi, Robert Gilliam, Kathleen Fahey, Deanna Huff, George Pouliot, Brice Barret, Elsa Dieudonne, Roman Pohorsky, Julia Schmale, Andrea Baccarini, Slimane Bekki, Gianluca Pappaccogli, Federico Scoto, Stefano Decesari, Antonio Donateo, Meeta Cesler-Maloney, William Simpson, Patrice Medina, Barbara D'Anna, Brice Temime-Roussel, Joel Savarino, Sarah Albertin, Jingqiu Mao, Becky Alexander, Allison Moon, Peter F. DeCarlo, Vanessa Selimovic, Robert Yokelson, and Ellis S. Robinson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1450, https://doi.org/10.5194/egusphere-2024-1450, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Processes influencing dispersion of local anthropogenic emissions in Arctic wintertime are investigated with dispersion model simulations. Modelled power plant plume rise that considers surface and elevated temperature inversions improves results compared to observations. Modelled near-surface concentrations are improved by representation of vertical mixing and emission estimates. Large increases in diesel vehicle emissions at temperatures reaching -35 °C are required to reproduce observed NOx.
Qindan Zhu, Rebecca H. Schwantes, Matthew Coggon, Colin Harkins, Jordan Schnell, Jian He, Havala O. T. Pye, Meng Li, Barry Baker, Zachary Moon, Ravan Ahmadov, Eva Y. Pfannerstill, Bryan Place, Paul Wooldridge, Benjamin C. Schulze, Caleb Arata, Anthony Bucholtz, John H. Seinfeld, Carsten Warneke, Chelsea E. Stockwell, Lu Xu, Kristen Zuraski, Michael A. Robinson, J. Andrew Neuman, Patrick R. Veres, Jeff Peischl, Steven S. Brown, Allen H. Goldstein, Ronald C. Cohen, and Brian C. McDonald
Atmos. Chem. Phys., 24, 5265–5286, https://doi.org/10.5194/acp-24-5265-2024, https://doi.org/10.5194/acp-24-5265-2024, 2024
Short summary
Short summary
Volatile organic compounds (VOCs) fuel the production of air pollutants like ozone and particulate matter. The representation of VOC chemistry remains challenging due to its complexity in speciation and reactions. Here, we develop a chemical mechanism, RACM2B-VCP, that better represents VOC chemistry in urban areas such as Los Angeles. We also discuss the contribution of VOCs emitted from volatile chemical products and other anthropogenic sources to total VOC reactivity and O3.
Edward J. Strobach, Sunil Baidar, Brian J. Carroll, Steven S. Brown, Kristen Zuraski, Matthew Coggon, Chelsea E. Stockwell, Lu Xu, Yelena L. Pichugina, Alan Brewer, Carsten Warneke, Jeff Peischl, Jessica Gilman, Brandi McCarty, Maxwell Holloway, and Richard Marchbanks
EGUsphere, https://doi.org/10.5194/egusphere-2024-447, https://doi.org/10.5194/egusphere-2024-447, 2024
Short summary
Short summary
Large-scale weather patterns are isolated from local patterns to study the impact that different weather scales have on air quality measurements. While impacts from large-scale meteorology were evaluated by separating ozone (O3) exceedance (>70 ppb) and non-exceedance (<70 ppb) days, we developed a technique a that allows direct comparisons of small temporal variations between chemical and dynamics measurements under rapid dynamical transitions.
Xiaodan Ma, Jianping Huang, Michaela Hegglin, Patrick Joeckel, and Tianliang Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2023-2411, https://doi.org/10.5194/egusphere-2023-2411, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Our study examines 30 years of tropospheric ozone changes in the Northwest Pacific region. We found a significant increase in ozone levels during spring and summer in the middle-upper troposphere. This change is driven by a complex interplay between stratospheric and tropospheric ozone, with implications for climate and air quality in East Asia. Further research into these mechanisms is needed.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
Short summary
Short summary
A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Christian Hogrefe, Jesse O. Bash, Jonathan E. Pleim, Donna B. Schwede, Robert C. Gilliam, Kristen M. Foley, K. Wyat Appel, and Rohit Mathur
Atmos. Chem. Phys., 23, 8119–8147, https://doi.org/10.5194/acp-23-8119-2023, https://doi.org/10.5194/acp-23-8119-2023, 2023
Short summary
Short summary
Under the umbrella of the fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4), this study applies AQMEII4 diagnostic tools to better characterize how dry deposition removes pollutants from the atmosphere in the widely used CMAQ model. The results illustrate how these tools can provide insights into similarities and differences between the two CMAQ dry deposition options that affect simulated pollutant budgets and ecosystem impacts from atmospheric pollution.
Yunyao Li, Daniel Tong, Siqi Ma, Saulo R. Freitas, Ravan Ahmadov, Mikhail Sofiev, Xiaoyang Zhang, Shobha Kondragunta, Ralph Kahn, Youhua Tang, Barry Baker, Patrick Campbell, Rick Saylor, Georg Grell, and Fangjun Li
Atmos. Chem. Phys., 23, 3083–3101, https://doi.org/10.5194/acp-23-3083-2023, https://doi.org/10.5194/acp-23-3083-2023, 2023
Short summary
Short summary
Plume height is important in wildfire smoke dispersion and affects air quality and human health. We assess the impact of plume height on wildfire smoke dispersion and the exceedances of the National Ambient Air Quality Standards. A higher plume height predicts lower pollution near the source region, but higher pollution in downwind regions, due to the faster spread of the smoke once ejected, affects pollution exceedance forecasts and the early warning of extreme air pollution events.
James D. East, Barron H. Henderson, Sergey L. Napelenok, Shannon N. Koplitz, Golam Sarwar, Robert Gilliam, Allen Lenzen, Daniel Q. Tong, R. Bradley Pierce, and Fernando Garcia-Menendez
Atmos. Chem. Phys., 22, 15981–16001, https://doi.org/10.5194/acp-22-15981-2022, https://doi.org/10.5194/acp-22-15981-2022, 2022
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.
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.
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
Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?
A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
RoadSurf 1.1: open-source road weather model library
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
The ddeq Python library for point source quantification from remote sensing images (version 1.0)
Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)
A general comprehensive evaluation method for cross-scale precipitation forecasts
Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
An objective identification technique for potential vorticity structures associated with African easterly waves
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
FUME 2.0 – Flexible Universal processor for Modeling Emissions
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands
Balloon drift estimation and improved position estimates for radiosondes
Emission ensemble approach to improve the development of multi-scale emission inventories
What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Assessing acetone for the GISS ModelE2.1 Earth system model
Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations
A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0
Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote sensing observations
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
Short summary
Short summary
Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
Short summary
Short summary
Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
Short summary
Short summary
Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
Short summary
Short summary
RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
Short summary
Short summary
The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
Short summary
Short summary
We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
Short summary
Short summary
The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
Short summary
Short summary
By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
Short summary
Short summary
We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
Short summary
Short summary
Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
Short summary
Short summary
The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
Short summary
Short summary
The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
Short summary
Short summary
There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
Short summary
Short summary
This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
Short summary
Short summary
This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
Short summary
Short summary
We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
Short summary
Short summary
This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
Short summary
Short summary
Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
Short summary
Short summary
In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
Short summary
Short summary
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
Short summary
Short summary
For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
Short summary
Short summary
Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
Short summary
Short summary
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
Short summary
Short summary
HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
Short summary
Short summary
This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
Short summary
Short summary
An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
Short summary
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
Short summary
Short summary
Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
Short summary
Short summary
Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
Short summary
Short summary
This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
Short summary
Short summary
Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
Short summary
Short summary
Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
Short summary
Short summary
Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Short summary
Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
Short summary
Short summary
An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
Short summary
Short summary
Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Short summary
In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
Short summary
Short summary
A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
Short summary
Short summary
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Short summary
The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
EGUsphere, https://doi.org/10.22541/essoar.169903618.82717612/v2, https://doi.org/10.22541/essoar.169903618.82717612/v2, 2024
Short summary
Short summary
Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate of the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
Short summary
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
Short summary
This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Short summary
PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Kelly M. Núñez Ocasio and Zachary L. Moon
EGUsphere, https://doi.org/10.5194/egusphere-2024-259, https://doi.org/10.5194/egusphere-2024-259, 2024
Short summary
Short summary
TAMS is an open-source mesoscale convective system tracking and classifying Python-based package that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Cited articles
Alexander, B., Park, R. J., Jacob, D. J., and Gong, S.: Transition
metal-catalyzed oxidation of atmospheric sulfur: global implications for the
sulfur budget, J. Geophys. Res., 114, D02309,
https://doi.org/10.1029/2008JD010486, 2009.
American Lung Association: Urban air pollution and health inequities: a
workshop report, Environ Health Perspect., 109 Suppl 3, 357–374, PMID: 11427385, PMCID:
PMC1240553, https://doi.org/10.2307/3434783, 2001.
Appel, K. W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S. C.:
Overview of the atmospheric model evaluation tool (amet) v1.1 for evaluating
meteorological and air quality models, Environ. Model. Softw., 26 434–443, https://doi.org/10.1016/j.envsoft.2010.09.007, 2011.
Appel, K. W., Bash, J. O., Fahey, K. M., Foley, K. M., Gilliam, R. C., Hogrefe, C., Hutzell, W. T., Kang, D., Mathur, R., Murphy, B. N., Napelenok, S. L., Nolte, C. G., Pleim, J. E., Pouliot, G. A., Pye, H. O. T., Ran, L., Roselle, S. J., Sarwar, G., Schwede, D. B., Sidi, F. I., Spero, T. L., and Wong, D. C.: The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation, Geosci. Model Dev., 14, 2867–2897, https://doi.org/10.5194/gmd-14-2867-2021, 2021.
Astitha, M., Luo, H., Rao, S. T., Hogrefe, C., Mathur, R., and Kumar, N.:
Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the
contiguous United States, Atmos. Environ., 164, 102–116,
https://doi.org/10.1016/j.atmosenv.2017.05.020, 2017.
Bai, L., Wang, J., Ma, X., and Lu, H.: Air Pollution Forecasts:
An Overview, Int. J. Env. Res. Pub. He., 15, 780, https://doi.org/10.3390/ijerph15040780, 2018.
Baker, B. and Pan, L.: Overview of the Model and Observation Evaluation
Toolkit (MONET) Version 1.0 for Evaluating Atmospheric Transport Models, Atmosphere, 8, 210, https://doi.org/10.3390/atmos8110210, 2017.
Bash, J. O., Walker, J. T., Katul, G. G., Jones, M. R., Nemitz, E., and
Robarge, W. P.: Estimation of In-Canopy Ammonia Sources and Sinks in a
Fertilized Zea mays Field, Environ. Sci. Technol., 44, 1683–1689, https://doi.org/10.1021/es9037269, 2010.
Bash, J. O., Cooter, E. J., Dennis, R. L., Walker, J. T., and Pleim, J. E.: Evaluation of a regional air-quality model with bidirectional NH3 exchange coupled to an agroecosystem model, Biogeosciences, 10, 1635–1645, https://doi.org/10.5194/bg-10-1635-2013, 2013.
Bash, J. O., Baker, K. R., and Beaver, M. R.: Evaluation of improved land use and canopy representation in BEIS v3.61 with biogenic VOC measurements in California, Geosci. Model Dev., 9, 2191–2207, https://doi.org/10.5194/gmd-9-2191-2016, 2016.
Binkowski, F. S, Arunachalam, S., Adelman, Z., and Pinto, J.: Examining
photolysis rates with a prototype on-line photolysis module in
CMAQ, J. Appl. Meteorol. Clim., 46,
1252–1256, https://doi.org/10.1175/JAM2531.1, 2007.
Black, T. L.: The new NMC meso-scale Eta Model: description and forecast
examples, Weather Forecast., 9, 265–278,
https://doi.org/10.1175/1520-0434(1994)009<0265:TNNMEM>2.0.CO;2, 1994.
Bonan, G. B., Patton, E. G., Finnigan, J. J., Baldocchi, D. D., and Harman,
I. N.: Moving beyond the incorrect but useful paradigm: reevaluating
big-leaf and multilayer plant canopies to model biosphere-atmosphere fluxes
– a review, Agr. Forest Meteorol., 306, 108435,
https://doi.org/10.1016/j.agrformet.2021.108435, 2021.
Briggs, G. A.: A plume rise model compared with
observations, J. Air Pollut. Control Assoc., 15,
433–438, https://doi.org/10.1080/00022470.1965.10468404, 1965.
Byun, D. and Schere, K. L.: Review of the governing equations, computational
algorithms, and other components of the models-3 community multiscale air
quality (CMAQ) modeling system, Appl. Mech. Rev., 59, 51–77, https://doi.org/10.1115/1.2128636, 2006.
Byun, D. W. and Ching, J. K. S.: Science algorithms of the EPA Models-3
Community Multi-scale Air Quality (CMAQ) modeling system, EPA/600/R-99/030,
Office of Research and Development, US Environmental Protection Agency,
https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=63400&Lab=NERL (last access: 5 April 2022), 1999.
Campbell, G. S. and Norman J. M.: An introduction to environmental
biophysics, Springer, 5, ISBN 978-0-387-94937-6, 1998.
Campbell, P., Zhang, Y., Yahya, K., Wang, K., Hogrefe, C., Pouliot, G., Knote, C., Hodzic, A., San Jose, R., Perez, J., Guerrero, P., Baro, R., and Makar, P.: A Multi-Model Assessment for the 2006 and 2010 Simulations under the
Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over
North America: Part I. Indicators of the Sensitivity of O3 and PM2.5
Formation Regimes, Atmos. Environ., 115, 569–586, https://doi.org/10.1016/j.atmosenv.2014.12.026, 2015.
Campbell, P. C.: The NOAA-EPA Atmosphere-Chemistry Coupler (NACC), Zenodo [code], https://doi.org/10.5281/zenodo.5507489, 2021a.
Campbell, P. C.: The Advanced National Air Quality Forecast Capability (NAQFC), Zenodo [code], https://doi.org/10.5281/zenodo.5507511, 2021b.
Campbell, P. C., Bash, J. O., and Spero, T. L.: Updates to the Noah land
surface model in WRF-CMAQ to improve simulated meteorology, air quality, and
deposition, J. Adv. Model. Earth Sy., 11, 231–256.
https://doi.org/10.1029/2018MS001422 2019.
Campbell, P. C., Tong, D., Tang, Y., Baker, B., Lee, P., Saylor, R., Stein, A., Ma, S., and Qu, Z.:
Impacts of the COVID-19 Economic Slowdown on Ozone Pollution in the U.S., Atmos. Environ., 264, 118713, https://doi.org/10.1016/j.atmosenv.2021.118713,
2021.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model
with the Penn State-NCAR MM5 modeling system. Part I: Model implementation
and sensitivity, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chen, J.-H. and Lin, S.-J.: The remarkable predictability of inter-annual
variability of Atlantic hurricanes during the past decade, Geophys. Res. Lett., 38, L11804,
https://doi.org/10.1029/2011GL047629, 2011.
Chen, J.-H. and Lin, S.-J.: Seasonal predictions of tropical cyclones using a
25 km-resolution general circulation model, J. Climate, 26, 380–398,
https://doi.org/10.1175/JCLI-D-12-00061.1, 2013.
Chen, J.-H., Lin, S.-J., Zhou, L., Chen, X., Rees, S. L., Bender, M., and
Morin, M.: Evaluation of Tropical Cyclone Forecasts in the Next Generation
Global Prediction System, Mon. Weather Rev., 147, 3409–3428,
https://doi.org/10.1175/MWR-D-18-0227.1, 2019.
Chen, X., Andronova, N., Van Leer, B., Penner, J. E., Boyd, J. P.,
Jablonowski, C., and Lin, S.: A Control-Volume Model of the Compressible
Euler Equations with a Vertical Lagrangian Coordinate, Mon. Weather Rev., 141, 2526–2544,
https://doi.org/10.1175/MWR-D-12-00129.1, 2013.
Chen, X., Zhang, Y., Wang, K., Tong, D., Lee, P., Tang, Y., Huang, J., Campbell, P. C., Mcqueen, J., Pye, H. O. T., Murphy, B. N., and Kang, D.: Evaluation of the offline-coupled GFSv15–FV3–CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States, Geosci. Model Dev., 14, 3969–3993, https://doi.org/10.5194/gmd-14-3969-2021, 2021.
Chin, M., Rood, R. B., Lin, S.-J., Muller, J. F., and Thomspon, A. M.:
Atmospheric sulfur cycle in the global model GOCART: Model description and
global properties, J. Geophys. Res., 105, 24671–24687,
https://doi.org/10.1029/2000JD900384, 2000.
Chin, M., Ginoux, P., Kinne, S., Holben, B. N., Duncan,
B. N., Martin, R. V., Logan, J. A., Akiko, H., and Nakajima, T.: Tropospheric aerosol optical thickness from the GOCART model and
comparisons with satellite and sun photometer measurements, J. Atmos. Sci., 59, 461–483,
https://doi.org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2, 2002.
Chinese State Council: Air Pollution Prevention and Control Action Plan (Guo
Fa [2013] No. 37,
http://en.cleanairchina.org/file/loadFile/26.html (last access: 5 April 2022), 2013.
Clough, S. A., Shephard, M. W., Mlawer, J. E., Delamere,
J. S., Iacono, M. J., Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative transfer modeling: A
summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005.
Cooter, E. J., Bash, J. O., Walker, J. T., Jones, M. R., and Robarge, W.:
Estimation of NH3 bi-directional flux from managed agricultural soils, Atmos. Environ.,
44, 2107–2115,
https://doi.org/10.1016/j.atmosenv.2010.02.044, 2010.
Cooter, E. J., Bash, J. O., Benson, V., and Ran, L.: Linking agricultural crop management and air quality models for regional to national-scale nitrogen assessments, Biogeosciences, 9, 4023–4035, https://doi.org/10.5194/bg-9-4023-2012, 2012.
Demetriou, C. A. and Vineis, P.: Carcinogenicity of ambient air pollution:
use of biomarkers, lessons learnt and future directions, J. Thorac. Dis.,
7, 67–95, https://doi.org/10.3978/j.issn.2072-1439.2014.12.31, 2015.
Ding, H. and Zhu, Y.: NDE Vegetation Products System Algorithm Theoretical
Basis Document, Version 4.0. NOAA/NESDIS/OSPO, https://www.ospo.noaa.gov/Products/documents/GVF_ATBD_V4.0.pdf (last access: 2 February 2021), 2018.
Dong, X., Fu, J. S., Huang, K., Tong, D., and Zhuang, G.: Model development of dust emission and heterogeneous chemistry within the Community Multiscale Air Quality modeling system and its application over East Asia, Atmos. Chem. Phys., 16, 8157–8180, https://doi.org/10.5194/acp-16-8157-2016, 2016.
Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G.: Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017), Earth Syst. Sci. Data, 10, 1491–1501, https://doi.org/10.5194/essd-10-1491-2018, 2018.
Eder, B., Kang, D., Mathur, R., Yu, S., and Schere, K.: An operational
evaluation of the Eta-CMAQ air quality forecast
model, Atmos. Environ., 40, 4894–4905,
https://doi.org/10.1016/j.atmosenv.2005.12.062, 2006.
Eder, B., Kang, D., Mathur, R., Pleim, J., Yu, S., Otte, T., and Pouliot,
G.: A performance evaluation of the National Air Quality Forecast Capability
for the summer of 2007, Atmos. Environ., 43, 2312–2320,
https://doi.org/10.1016/j.atmosenv.2009.01.033, 2009.
Ek, M., Mitchell, B. K. E., Lin, Y., Rogers, E., Grunmann, P., Koren,
V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National
Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.,
108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Emery, C., Liu, Z., Russell, A. G., Odman, M. T., Yarwood, G., and Kumar,
N.: Recommendations on statistics and benchmarks to assess photochemical
model performance, J. Air Waste Manage. Assoc., 67, 582–598,
https://doi.org/10.1080/10962247.2016.1265027, 2017.
Finkelstein, M. M., Jerrett, M., DeLuca, P., Finkelstein, N., Verma, D. K.,
Chapman, K., and Sears, M. R.: Relation between income, air pollution and
mortality: A cohort study, Can. Med. Assoc. J., 169, 397-402, 2003.
Fu, X., Wang, S. X., Cheng, Z., Xing, J., Zhao, B., Wang, J. D., and Hao, J. M.: Source, transport and impacts of a heavy dust event in the Yangtze River Delta, China, in 2011, Atmos. Chem. Phys., 14, 1239–1254, https://doi.org/10.5194/acp-14-1239-2014, 2014.
Gantt, B., Kelly, J. T., and Bash, J. O.: Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2, Geosci. Model Dev., 8, 3733–3746, https://doi.org/10.5194/gmd-8-3733-2015, 2015.
Ginoux, P., Chin, M., Tegen, I., Prospero, J., Holben, B., Dubovik, O., and
Lin, S.-J.: Sources and global distributions of dust aerosols simulated with
the GOCART model, J. Geophys. Res., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
Grell, G. A., Dudhia, J., and Stauffer, D. R.: A description of the
fifth-generation Penn State/NCAR Mesoscale Model (MM5), NCAR tech. Note NCAR
TN-398-1-STR, 117 pp., https://doi.org/10.5065/D60Z716B, 1994.
Han, J. and Pan, H.-L.: Revision of Convection and Vertical Diffusion
Schemes in the NCEP Global Forecast System, Weather Forecast., 26, 520–533, https://doi.org/10.1175/WAF-D-10-05038.1, 2011.
Han, J. and Bretherton, C. S.: TKE-Based Moist Eddy-Diffusivity Mass-Flux
(EDMF) Parameterization for Vertical Turbulent Mixing, Weather Forecast., 34, 869–886,
https://doi.org/10.1175/WAF-D-17-0046.1, 2019.
Han, J., Wang, W., Kwon, Y. C., Hong, S.-Y., Tallapragada, V., and Yang,
F.: Updates in the NCEP GFS Cumulus Convection Schemes with Scale and
Aerosol Awareness, Weather Forecast., 32, 2005–2017,
https://doi.org/10.1175/WAF-D-17-0046.1, 2017.
Harris, L. M. and Lin, S.: A Two-Way Nested Global-Regional Dynamical Core on
the Cubed-Sphere Grid, Mon. Weather Rev., 141, 283–306,
https://doi.org/10.1175/MWR-D-11-00201.1, 2013.
Harris, L. M., Lin, S., and Tu, C.: High-Resolution Climate Simulations Using
GFDL HiRAM with a Stretched Global Grid, J. Climate, 29, 4293–4314,
https://doi.org/10.1175/JCLI-D-15-0389.1, 2016.
Huang, J. and McQueen, J.: Development and evaluation of offline
coupling of FV3-based GFS with CMAQ at NOAA, The 17th CMAS Conference,
22–24 October 2018, UNC-Chapel Hill, NC, 2018.
Huang, J., McQueen, J., Yang, B., Shafran, P., Huang, H.-C., Bhattacharjee,
P., Tang, Y., Campbell, P. C., Tong, D., Lee, P., Stajner, I., Kain, J. S., Tirado-Delgado, J., and Koch, D. M.: A comparison of global scale FV3 versus regional scale NAM
meteorological drivers for regional air quality forecastin, The 100th AGU
Fall Meeting, 9–13 December 2019, San Francisco, CA, 2019.
Huang, M., Tong, D., Lee, P., Pan, L., Tang, Y., Stajner, I., Pierce, R. B., McQueen, J., and Wang, J.: Toward enhanced capability for detecting and predicting dust events in the western United States: the Arizona case study, Atmos. Chem. Phys., 15, 12595–12610, https://doi.org/10.5194/acp-15-12595-2015, 2015.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. B.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103,
https://doi.org/10.1029/2008JD009944, 2008.
Institute of Medicine: Toward Environmental Justice: Research, Education,
and Health Policy Needs, Washington, DC, National Academy Press, https://doi.org/10.17226/6034, 1999.
Janjic, Z. and Gall, R. L.: Scientific documentation of the NCEP
nonhydrostatic multiscale model on the B grid (NMMB), Part 1 Dynamics (No.
NCAR/TN-489+STR), University Corporation for Atmospheric Research,
https://doi.org/10.5065/D6WH2MZX, 2012.
Jimenez, P. A., Dudhia, J., Gonzalez-Rouco, J. F., Navarro, J., Montavez, J.
P., and Garcia-Bustamante, E.: A revised scheme for the WRF surface layer
formulation, Mon. Weather Rev., 140, 898–918,
https://doi.org/10.1175/MWR-D-11-00056.1, 2012.
Kang, D., Eder, B. K., Stein, A. F., Grell, G. A., Peckham, S. E., and
McHenry, J.: The New England Air Quality Forecasting Pilot Program:
Development of an Evaluation Protocol and Performance Benchmark, J. Air Waste Manage. Assoc., 55,
1782–1796, https://doi.org/10.1080/10473289.2005.10464775,
2005.
Karamchandani, P., Long, Y., Pirovano, G., Balzarini, A., and Yarwood, G.: Source-sector contributions to European ozone and fine PM in 2010 using AQMEII modeling data, Atmos. Chem. Phys., 17, 5643–5664, https://doi.org/10.5194/acp-17-5643-2017, 2017.
Kar Kurt, O., Zhang, J., and Pinkerton, K. E.: Pulmonary Health Effects of
Air Pollution, Curr. Opin. Pulm. Med., 22, 138–143,
https://doi.org/10.1097/MCP.0000000000000248, 2016.
Kelly, J. T., Bhave, P. V., Nolte, C. G., Shankar, U., and Foley, K. M.: Simulating emission and chemical evolution of coarse sea-salt particles in the Community Multiscale Air Quality (CMAQ) model, Geosci. Model Dev., 3, 257–273, https://doi.org/10.5194/gmd-3-257-2010, 2010.
Kim, Y., Sartelet, K., and Seigneur, C.: Formation of secondary aerosols over Europe: comparison of two gas-phase chemical mechanisms, Atmos. Chem. Phys., 11, 583–598, https://doi.org/10.5194/acp-11-583-2011, 2011a.
Kim, Y., Couvidat, F., Sartelet, K., and Seigneur, C.: Comparison of
different gas phase mechanisms and aerosol modules for simulating
particulate matter
formation, J. Air Waste Manage., 61, 1218e1226,
https://doi.org/10.1080/10473289.2011.603999, 2011b.
Krueger, S. K., Fu, Q., Liou, K. N., and Chin, H.-N. S.: Improvement of an
ice-phase microphysics parameterization for use in numerical simulations of
tropical convection, J. Appl. Meteorol., 34, 281–287, https://doi.org/10.1175/1520-0450-34.1.281, 1995.
Landrigan, P. J., Fuller, R., Acosta, N. J., Adeyi, O., Arnold, R.,
Basu, N., Bibi Balde, A., Bertollini, R. Bose-O'Reilly, S., Boufford, J. I.., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., Hanrahan, D., Hunter, D., Khare, M., Krupnick, A., Lanphear, B., Lohani, B., Martin, K., Mathiasen, K., McTeer, M. A., Murray, C. J. L., Ndahimananjara, J. D., Perera, F., Potocnik, J., Preker, A. S., Ramesh, J., Rockstrom, J., Salinas, C., Samson, L. D., Sandilya, K., Sly, P. D., Smith, K. R., Steiner, A., Stewart, R. B., Suk, W. A., van Schayck, O. C. P., Yadama, G. N., Yumkella, K., and Zhong, M.: The Lancet Commission on pollution and health, Lancet, 391, 462–512,
https://doi.org/10.1016/S0140-6736(17)32345-0, 2018.
Lee, B.-J., Kim, B., and Lee, K.: Air Pollution Exposure and Cardiovascular
Disease, Toxicol Res.-UK, 30, 71–75, https://doi.org/10.5487/TR.2014.30.2.071, 2014.
Lee, P., McQueen, J., Stajner, I., Huang, J., Pan, L., Tong, D., Kim, H., Tang, Y., Kondragunta, S., Ruminski, M., Lu, S., Rogers, E., Saylor, R., Shafran, P., Huang, H.-C., Gorline, J., Upadhayay, S., and Artz, R.:
NAQFC Developmental Forecast Guidance for Fine Particulate Matter (PM2.5),
Weather Forecast., 32, 343–360, https://doi.org/10.1175/waf-d-15-0163.1,
2017.
Lin, S.: A “Vertically Lagrangian” Finite-Volume Dynamical Core for
Global Models, Mon. Weather Rev., 132, 2293–2307,
https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2, 2004.
Lin, S. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian
Transport Schemes, Mon. Weather Rev., 124, 2046–2070,
https://doi.org/10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2, 1996.
Lin, S., Chao, W. C., Sud, Y. C., and Walker, G. K.: A Class of the van
Leer-type Transport Schemes and Its Application to the Moisture Transport in
a General Circulation Model, Mon. Weather Rev., 122, 1575–1593,
https://doi.org/10.1175/1520-0493(1994)122<1575:ACOTVL>2.0.CO;2, 1994.
Lin, Y.-L., Farley, R. D., and Orville, H. D.: Bulk parameterization of the
snow field in a cloud model, J. Clim. Appl. Meteorol., 22, 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2, 1983.
Liu, Y. and Wang, T.: Worsening urban ozone pollution in China from 2013 to 2017 – Part 1: The complex and varying roles of meteorology, Atmos. Chem. Phys., 20, 6305–6321, https://doi.org/10.5194/acp-20-6305-2020, 2020.
Lord, S. J., Willoughby, H. E., and Piotrowicz, J. M.: Role of a
parameterized ice-phase microphysics in an axisymmetric, nonhydrostatic
tropical cyclone model, J. Atmos. Sci., 41, 2836–2848,
https://doi.org/10.1175/1520-0469(1984)041<2836:ROAPIP>2.0.CO;2, 1984.
Makar, P. A., Staebler, R., Akingunola, A., Zhang, J., McLinden, C.,
Kharol, S. K., Pabla, B., Cheung, P., and Zheng, Q.: The effects of forest canopy shading and turbulence on
boundary layer ozone, Nat Commun., 8, 15243,
https://doi.org/10.1038/ncomms15243, 2017.
Makar, P. A., Stroud, C., Akingunola, A., Zhang, J., Ren, S., Cheung, P., and Zheng, Q.: Vehicle-induced turbulence and atmospheric pollution, Atmos. Chem. Phys., 21, 12291–12316, https://doi.org/10.5194/acp-21-12291-2021, 2021.
Marlier, M. E., Jina, A. S., Kinney, P. L., and DeFries, R. S.: Extreme Air
Pollution in Global Megacities, Curr Clim Change Rep., 2, 15–27, https://doi.org/10.1007/s40641-016-0032-z, 2016.
Martin, R. L. and Good, T. W.: Catalyzed oxidation of sulfur dioxide in
solution: the iron-manganese synergism, Atmos. Environ., 25A, 2395–2399, https://doi.org/10.1016/0960-1686(91)90113-L, 1991.
Massad, R.-S., Nemitz, E., and Sutton, M. A.: Review and parameterisation of bi-directional ammonia exchange between vegetation and the atmosphere, Atmos. Chem. Phys., 10, 10359–10386, https://doi.org/10.5194/acp-10-10359-2010, 2010.
Mathur, R., Yu, S., Kang, D., and Schere, K. L.: Assessment of the
wintertime performance of developmental particulate matter forecasts with
the Eta-Community Multiscale Air Quality modeling system, J. Geophys. Res., 113, D02303,
https://doi.org/10.1029/2007JD008580, 2008.
Matthias, V., Arndt, J. A., Aulinger, A., Bieser, J., Denier
van der Gon, H., Kranenburg, R., Kuenen, J., Neumann, D., Pouliot, G., and Quante, M.: Modeling emissions for three-dimensional
atmospheric chemistry transport models, J. Air Waste Manage., 68, 763–800, https://doi.org/10.1080/10962247.2018.1424057, 2018.
McKeen, S., Wilczak, J., Grell, G., Djalova, I., Peckham, S.,
Hsie, E.-Y., Gong, W., Bouchet, V., Menard, S., Moffet, R., McHenry, J., McQueen, J., Tang, Y., Carmichael, G. R., Pagowski, M., Chan, A., Dye, T., Frost, G., Lee, P., and Mathur, R.: Assessment of an ensemble of seven real-time ozone forecasts over
eastern North America during the summer of 2004, J. Geophys. Res., 110, D21307,
https://doi.org/10.1029/2005JD005858, 2005.
McKeen, S., Chung, S. H., Wilczak, J., Grell, G., Djalalova,
I., Peckham, S., Gong, W., Bouchet, V., Moffet, R., Tang, Y., Carmichael, G. R., Mathur, R., and Yu, S.: Evaluation of several PM2.5 forecast models
using data collected during the
ICARTT/NEAQS 2004 field study, J. Geophys. Res., 112, D10S20,
https://doi.org/10.1029/2006JD007608, 2007.
McKeen, S., Grell, G., Peckham, S., Wilczak, J., Djalalova,
I., Hsie, E., Frost, G., Peischl, J., Schwartz, J., Spackman, R., Holloway, J., de Gouw, J., Warneke, C., Gong, W., Bouchet, V., Gaudreault, S., Racine, J., McHenry, J., McQueen, J., Lee, P., Tang, Y., Carmichael, G. R., and Mathur, R.: An evaluation of real-time air quality forecasts and their urban
emissions over eastern Texas during the summer of 2006 Second Texas Air
Quality Study field study, J. Geophys. Res., 114, D00F11,
https://doi.org/10.1029/2008JD011697, 2009.
Miller, J., Safford, H., Crimmins, M., and Thode, A.: Quantitative evidence
for increasing forest fire severity in the Sierra Nevada and Southern
Cascade Mountains, California and Nevada, USA, Ecosystems, 12, 16–32,
https://doi.org/10.1007/s10021-008-9201-9, 2009.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: Radiative transfer for inhomogeneous atmosphere: RTTM, a validated
correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682,
https://doi.org/10.1029/97JD00237, 1997.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the
surface layer of the atmosphere, Tr. Akad. Nauk SSSR Geophiz. Inst., 24, 163–187, 1954 (in Russian).
Myneni, R. and Knyazikhin, Y.: VIIRS/NPP Leaf Area Index/FPAR 8-Day L4
Global 500m SIN Grid V001, NASA EOSDIS Land Processes DAAC [data set],
https://doi.org/10.5067/VIIRS/VNP15A2H.001, 2018.
National Emissions Inventory (NEIC): NEI 2014v2 Emissions, U.S. EPA Repository [data set],
https://edap.epa.gov/public/extensions/nei_report_2014/dashboard.html#sector-db (last access: 26 February 2020), 2014.
National Emissions Inventory Collaborative (NEI): 2016v1 Emissions
Modeling Platform [data set], http://views.cira.colostate.edu/wiki/wiki/10202 (last access: 5 April 2022), 2019.
Nemitz, E., Sutton, M. A., Schjoerring, J. K., Husted, S., and Wyers, G. P.:
Resistance modelling of ammonia exchange over oilseed rape, Agr. Forest Meteorol., 105,
405–425, https://doi.org/10.1016/S0168-1923(00)00206-9, 2000.
Niinemets, Ü., Arneth, A., Kuhn, U., Monson, R. K., Peñuelas, J., and Staudt, M.: The emission factor of volatile isoprenoids: stress, acclimation, and developmental responses, Biogeosciences, 7, 2203–2223, https://doi.org/10.5194/bg-7-2203-2010, 2010.
NOAA/NWS: Global Forecast System (GFS) Version 16 [data set], https://www.nco.ncep.noaa.gov/pmb/products/gfs/, last access: 5 April 2022a.
NOAA/NWS: Air Quality Forecast Guidance – Operational [data set], https://airquality.weather.gov/, last access: 5 April 2022b.
NOAA/NWS: Air Quality Forecast Guidance – Experimental [data set], https://digital.mdl.nws.noaa.gov/airquality/, last access: 5 April 2022c.
NOAA/NWS: Operational CMAQ Verification – Experimental [data set], https://www.emc.ncep.noaa.gov/mmb/aq/verification_diagnostics/cmaq_verf/ last access: 5 April 2022d.
O’Neill, M. S., Jerrett, M., Kawachi, I., Levy, J. I., Cohen, A. J.,
Gouveia, N., Wilkinson, P., Fletcher, T., Cifuentes, L., and Schwartz, J.: Health, wealth, and air pollution: Advancing theory and methods,
Environ. Health Persp., 111, 1861–1870, https://doi.org/10.1289/ehp.6334, 2003.
Otte, T. L. and Pleim, J. E.: The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1, Geosci. Model Dev., 3, 243–256, https://doi.org/10.5194/gmd-3-243-2010, 2010.
Otte, T. L., Pouliot, G., Pleim, J. E., Young, J. O., Schere, K. L.,
Wong, D. C., Lee, P., Tsidulko, M., McQueen, J., Davidson, P., Mathur, R., Chuang, H.-Y., DiMego, G., and Seaman, N. L.: Linking the Eta Model with the Community Multiscale Air
Quality (CMAQ) Modeling System to Build a National Air Quality Forecasting
System, Weather Forecast., 20, 367–384,
https://doi.org/10.1175/WAF855.1, 2005.
Pinder, R. W., Dennis, R. L., and Bhave, P. V.: Observable indicators of the
sensitivity of PM2.5 nitrate to emission reductions: part I. Derivation of
the adjusted gas
ratio and applicability at regulatory-relevant time scales, Atmos. Environ., 42,
1275e1286, https://doi.org/10.1016/j.atmosenv.2007.10.039,
2008.
Pleim, J. and Ran, L.: Surface flux modeling for air quality applications,
Atmosphere, 2, 271–302, https://doi.org/10.3390/atmos2030271, 2011.
Pleim, J. E.: A combined local and nonlocal closure model for the
atmospheric boundary layer. Part I: Model description and testing, J. Appl. Meteor. Climatol., 46,
1383–1395, https://doi.org/10.1175/JAM2539.1, 2007a.
Pleim, J. E.: A combined local and nonlocal closure model for the
atmospheric boundary layer. Part II: Application and evaluation in a
mesoscale meteorological model, J. Appl. Meteorol. Clim., 46, 1396–1409,
https://doi.org/10.1175/JAM2534.1, 2007b.
Pleim, J. E., Bash, J. O., Walker, J. T., and Cooter, E. J.: Development and
evaluation of an ammonia bidirectional flux parameterization for air quality
models, J. Geophys. Res.-Atmos., 118, 3794–3806, https://doi.org/10.1002/jgrd.50262,
2013.
Pleim, J. E., Ran, L., Appel, W., Shephard, M. W., and Cady-Pereira, K.: New
bidirectional ammonia flux model in an air quality model coupled with an
agricultural model, J. Adv. Model. Earth Sy., 11, 2934–2957,
https://doi.org/10.1029/2019MS001728, 2019.
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C.
A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen, F., Barlarge, M., Yu, M., and Duda, M.: The weather research and forecasting model: Overview,
system efforts, and future directions, B. Am. Meteorol. Soc., 98, 1717–1737,
https://doi.org/10.1175/BAMS-D-15-00308.1, 2017.
Putman, W. M. and Lin, S.-J.: Finite-volume transport on various
cubed-sphere grids, J. Comput. Phys., 227, 55–78,
https://doi.org/10.1016/j.jcp.2007.07.022, 2007.
Pye, H. O. T., Pinder, R. W., Piletic, I., Xie, Y., Capps,
S. L., Lin, Y.-H., Surratt, J. D., Zhang, Z., Gold, A., Luecken, D. J., Hutzell, W. T., Jaoui, M., Offenberg, J. H., Kleindienst, T. E., Lewandowski, M., and Edney, E. O.: Epoxide pathways improve model predictions of isoprene markers and
reveal key role of acidity in aerosol formation, Environ. Sci. Technol., 47, 11056–11064,
https://doi.org/10.1021/es402106h, 2013.
Pye, H. O. T., Murphy, B. N., Xu, L., Ng, N. L., Carlton, A. G., Guo, H., Weber, R., Vasilakos, P., Appel, K. W., Budisulistiorini, S. H., Surratt, J. D., Nenes, A., Hu, W., Jimenez, J. L., Isaacman-VanWertz, G., Misztal, P. K., and Goldstein, A. H.: On the implications of aerosol liquid water and phase separation for organic aerosol mass, Atmos. Chem. Phys., 17, 343–369, https://doi.org/10.5194/acp-17-343-2017, 2017.
Ran, L., Cooter, E., Benson, V., and He, Q.: Development of an agricultural
fertilizer modeling system for bi-directional ammonia fluxes in the CMAQ
model, edited by: Steyn, D. G. and Castelli, S. T., Air Pollution Modeling
and its Application XXI, Chapter 36, Dordrecht, Springer,
213–219, https://doi.org/10.1007/978-94-007-1359-8_36, 2011.
Ran, L., Pleim, J., Gilliam, R., Binkowski, F. S., Hogrefe, C., and Band,
L.: Improved meteorology from an updated WRF/CMAQ modeling system with MODIS
vegetation and albedo, J. Geophys. Res.-Atmos., 121, 2393–2415, https://doi.org/10.1002/2015JD024406, 2016.
Rogers, E., Black, T., Deaven, D., DiMego, G., Zhao,
Q., Baldwin, M., Junker, N. W., and Lin, Y.:
Changes to the operational “early” Eta Analysis/Forecast System at the
National Centers for Environmental Prediction, Weather Forecast., 11 391–413, https://doi.org/10.1175/1520-0434(1996)011<0391:CTTOEA>2.0.CO;2, 1996.
Sarwar, G., Fahey, K., Napelenok, S., Roselle, S., and Mathur, R.: Examining
the impact of CMAQ model updates on aerosol sulfate predictions, The 10th
Annual CMAS Models-3 User's Conference, 24–26 October 2011, Chapel Hill, NC, 2011.
Sarwar, G., Gantt, B., Foley, K., Fahey, K., Spero, T. L., Kang, D., Mathur, R., Foroutan, H., Xing, J., Sherwen, T., and Saiz-Lopez, A.:
Influence of bromine and iodine chemistry on annual, seasonal, diurnal, and
background ozone: CMAQ simulations over the Northern Hemisphere, Atmos. Environ., 213,
395–404, https://doi.org/10.1016/j.atmosenv.2019.06.020, 2019.
Schwede, D., Pouliot, G. A., and Pierce, T.: Changes to the Biogenic
Emissions Inventory System Version 3 (BEIS3), in: Proceedings of the 4th CMAS
Models-3 Users' Conference, 26–28 September 2005, Chapel Hill, NC, 2005.
Sillman, S.: The use of NOy, H2O2, and HNO3 as indicators for
ozone-NOx-hydrocarbon sensitivity in urban locations, J. Geophys. Res.-Atmos., 100, 14175–14188,
https://doi.org/10.1029/94JD02953, 1995.
Sillman, S.: The relation between ozone, NOx and hydrocarbons in urban and
polluted rural environments, Atmos. Environ., 33, 1821–1845,
https://doi.org/10.1016/S1352-2310(98)00345-8, 1999.
Sillman, S., Logan, J. A., and Wofsy, S. C.: The sensitivity of ozone to
nitrogen oxides and hydrocarbons in regional ozone episodes, J. Geophys. Res., 95,
1837–1852, https://doi.org/10.1029/JD095iD02p01837, 1990.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric
model for weather research and forecasting applications, J. Computat. Phys., 227,
3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner,
J., and Huang, X.: A Description of the Advanced Research WRF Model Version
4 (No. NCAR/TN-556+STR), https://doi.org/10.5065/1dfh-6p97,
2019.
Sofiev, M., Ermakova, T., and Vankevich, R.: Evaluation of the smoke-injection height from wild-land fires using remote-sensing data, Atmos. Chem. Phys., 12, 1995–2006, https://doi.org/10.5194/acp-12-1995-2012, 2012.
Stajner, I., Davidson, P., Byun, D., McQueen, J., Draxler, R., Dickerson,
P., and Meagher, J.: US National Air Quality Forecast Capability: Expanding
Coverage to Include Particulate Matter, Springer, Dordrecht,
379–384, https://doi.org/10.1007/978-94-007-1359-8_64,
2011.
Sun, J., Fu, J. S., Huang, K., and Gao, Y.: Estimation of future PM2.5- and
ozone-related mortality over the continental United States in a changing
climate: An application of high-resolution dynamical downscaling technique,
J. Air Waste Manage., 65, 611–623, https://doi.org/10.1080/10962247.2015.1033068,
2015.
Tang, Y., Lee, P., Tsidulko, M., Huang, H.-C., Mcqueen, J., DiMego, G. J., Emmons, L. K., Pierce, R. B., Thompson, A. M., Lin, H.-M., Kang, D., Tong, D., Yu, S., Mathur, R., Pleim, J. E., Otte, T. L., Pouliot, G., Young, J. O., Schere, K. L., Davidson, P. M., and Stajner, I.:
The impact of chemical lateral boundary conditions on CMAQ predictions of
tropospheric ozone over the continental United States, Environ. Fluid Mech., 9, 43–58,
https://doi.org/10.1007/s10652-008-9092-5, 2009.
Tang, Y., Chai, T., Pan, L., Lee, P., Tong, D., Kim, H.-C., and Chen, W.: Using
optimal interpolation to assimilate surface measurements and satellite AOD
for ozone and PM2.5: A case study for July 2011, J. Air Waste Manage., 65, 1206–1216,
https://doi.org/10.1080/10962247.2015.1062439, 2015.
Tang, Y., Bian, H., Tao, Z., Oman, L. D., Tong, D., Lee, P., Campbell, P. C., Baker, B., Lu, C.-H., Pan, L., Wang, J., McQueen, J., and Stajner, I.: Comparison of chemical lateral boundary conditions for air quality predictions over the contiguous United States during pollutant intrusion events, Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, 2021.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. H.:
Implementation and verification of the unified NOAH land surface model in
the WRF model, 20th Conference on Weather Analysis
and Forecasting/16th Conference on Numerical Weather Prediction,
Seattle, WA, 14 January 2004, https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm (last access: 6 April 2022) 2004.
Tong, D. Q., Lee, P., and Saylor, R. D.: New Direction: The need to develop
process-based emission forecasting models, Atmos. Environ., 47, 560–561,
https://doi.org/10.1016/j.atmosenv.2011.10.070, 2012.
Tong, D. Q., Wang, J. X. L., Gill, T. E., Lei, H., and Wang, B.: Intensified
dust storm activity and Valley fever infection in the southwestern United
States, Geophys. Res. Lett., 44, 4304–4312,
https://doi.org/10.1002/2017GL073524, 2017.
Troen, I. and Mahrt, L.: A simple model of the atmospheric boundary layer:
Sensitivity to surface evaporation, Bound.-Lay. Meteorol., 37, 129–148,
https://doi.org/10.1007/BF00122760, 1986.
US EPA Office of Research and Development: CMAQv5.0.2 (Version 5.0.2),
Zenodo [data set], https://doi.org/10.5281/zenodo.1079898, 2014.
US EPA Office of Research and Development: CMAQ (Version 5.3.1), Zenodo [data set],
https://doi.org/10.5281/zenodo.3585898, 2019.
Vukovich, J. M. and Pierce, T.: The Implementation of BEIS3 within the SMOKE
modeling framework, Environ. Sci., 2002.
Weiss, A. and Norman, J.: Partitioning solar radiation into direct and
diffuse, visible and nearinfrared components, Agr. Forest Meteorol., 34, 205–213,
https://doi.org/10.1016/0168-1923(85)90020-6, 1985.
Westerling A. L., Hidalgo, H. G., Cayan, D. R., and Swetnam, T. W.: Warming and
earlier spring increase western US forest wildfire activity, Science, 313, 940–943,
https://doi.org/10.1126/science.1128834, 2006.
Wilkins, J., Pouliot, G., Pierce, T., and Beidler, J.: Exploring the
Vertical Distribution of Wildland Fire Smoke in CMAQ, 2019 International
Emissions Inventory Conference, 28 July–2 August 2019, Dallas, Texas,
https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NERL&dirEntryId=346294 (last access: 6 April 2022), 2019.
Williams, A. P., Cook, E. R., Smerdon, J. E., Cook, B. I.,
Abatzoglou, J. T., Bolles, K., Baek, S. H., Badger, A. M., and Livneh, B.: Large contribution from anthropogenic warming to an
emerging North American megadrought, Science, 368, 314–318,
https://doi.org/10.1126/science.aaz9600, 2020.
Williams, J. R.: The EPIC model, in: Computer models in
watershed hydrology, edited by: Singh, V. P., Chapter 25, 909–1000, Littleton, CO, Water
Resources Publications, ISBN-13 978-0-918334-91-6, 1995.
World Health Organization (WHO): Air Quality Guidelines, Global update 2005,
Particulate matter, ozone, nitrogen dioxide and sulfur dioxide, ISBN 92-890-2192-6, 2006.
World Health Organization (WHO): WHO Guidelines for Indoor Air Quality:
Selected Pollutants, World Health Organization, Regional Office for Europe
Scherfigsvej 8, 2100 Copenhagen, Denmark, ISBN 9789289002134, 2010.
Yang, F., Tallapragada, V., Kain, J. S., Wei, H., Yang, R., Yudin,
V. A., Moorthi, S., Han, J., Hou, Y. T., Wang, J., Treadon, R., and Kleist, D. T.: Model Upgrade Plan and Initial Results from a Prototype NCEP Global
Forecast System Version 16, 2020 AMS Conference, Boston, MA, 15 January 2020,
https://ams.confex.com/ams/2020Annual/webprogram/Paper362797.html (last access: 6 April 2022), 2020.
Yarwood, G., Whitten, G. Z., and Jung, J.: Final Report. Development,
Evaluation and Testing of Version 6 of the Carbon Bond Chemical Mechanism
(CB6), 22 September 2010, ENVIRON International Corporation, 06-17477Y,
https://www.tceq.texas.gov/assets/public/implementation/air/am/contracts/reports/pm/5820784005FY1026-20100922-environ-cb6.pdf (last access: 6 April 2022), 2010.
Zeka A., Zanobetti, A., and Schwartz, J.: Short term effects of particulate
matter on cause specific mortality: effects of lags and modification by city
characteristics, Occup. Environ. Med., 62, 718–725,
https://doi.org/10.1136/oem.2004.017012, 2006.
Zhang, X., Kondragunta, S., Ram, J., Schmidt, C., and Huang, H.-C.:
Near-real-time global biomass burning emissions product from geostationary
satellite constellation, J. Geophys. Res.-Atmos., 117, D14201,
https://doi.org/10.1029/2012JD017459, 2012.
Zhang, X., Kondragunta, S., and Roy, D. P.: Interannual variation in biomass
burning and fire seasonality derived from geostationary satellite data
across the contiguous United States from 1995 to 2011, J. Geophys. Res.-Biogeo., 119, 1147–1162,
https://doi.org/10.1002/2013JG002518, 2014.
Zhang, Y., Vijayaraghavan, K., Wen, X.-Y., Snell, H. E., and Jacobson, M. Z.:
Probing into regional ozone and particulate matter pollution in the United
States: 1. A 1
year CMAQ simulation and evaluation using surface and satellite data,
J. Geophys. Res., 114, D22304, https://doi.org/10.1029/2009JD011898, 2009a.
Zhang, Y., Wen, X.-Y., Wang, K., Vijayaraghavan, K., and Jacobson, M. Z.:
Probing
into regional ozone and particulate matter pollution in the United States:
2. An
examination of formation mechanisms through a process analysis technique
and sensitivity study, J. Geophys. Res., 114, D22304, https://doi.org/10.1029/2009JD011898, 2009b.
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.:
Real-time air quality forecasting, part I: History, techniques, and current
status, Atmos. Environ., 60, 632–655,
https://doi.org/10.1016/j.atmosenv.2012.06.031, 2012a.
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.:
Real-time air quality forecasting, part II: State of the science, current
research needs, and future prospects, Atmos. Environ., 60, 656–676, https://doi.org/10.1016/j.atmosenv.2012.02.041, 2012b.
Zhang, Y., West, J. J., Mathur, R., Xing, J., Hogrefe, C., Roselle, S. J., Bash, J. O., Pleim, J. E., Gan, C.-M., and Wong, D. C.: Long-term trends in the ambient PM2.5- and O3-related mortality burdens in the United States under emission reductions from 1990 to 2010, Atmos. Chem. Phys., 18, 15003–15016, https://doi.org/10.5194/acp-18-15003-2018, 2018.
Zhao, H., Zheng, Y., and Li, T.: Air Quality and Control Measures Evaluation
during the 2014 Youth Olympic Games in Nanjing and its Surrounding Cities,
Atmosphere, 8, 8060100, https://doi.org/10.3390/atmos8060100, 2017.
Zhou, L., Lin, S., Chen, J., Harris, L. M., Chen, X., and Rees, S. L.:
Toward Convective-Scale Prediction within the Next Generation Global
Prediction System, B. Am. Meteorol. Soc., 100, 1225–1243, https://doi.org/10.1175/BAMS-D-17-0246.1, 2019.
Zhou, T., Sun, J., and Yu, H.: Temporal and Spatial Patterns of China's Main
Air Pollutants: Years 2014 and 2015, Atmosphere, 8, 8080137,
https://doi.org/10.3390/atmos8080137, 2017.
Short summary
NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the harmful effects of air pollution, while saving billions of dollars per year. Here we describe and evaluate the development of the most advanced version of the NAQFC to date, which became operational at NOAA on 20 July 2021. The new NAQFC is based on a coupling of NOAA's operational Global Forecast System (GFS) version 16 with the Community Multiscale Air Quality (CMAQ) model version 5.3.1.
NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the...