Articles | Volume 10, issue 12
https://doi.org/10.5194/gmd-10-4743-2017
© Author(s) 2017. 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-10-4743-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Dec 2017
Development and technical paper |
| 22 Dec 2017
| 22 Dec 2017
A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods
NOAA Air Resources Laboratory, College Park, MD, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA
Mariusz Pagowski
NOAA Earth System Research Laboratory, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
Tianfeng Chai
NOAA Air Resources Laboratory, College Park, MD, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA
NOAA Air Resources Laboratory, College Park, MD, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA
Pius Lee
NOAA Air Resources Laboratory, College Park, MD, USA
Barry Baker
NOAA Air Resources Laboratory, College Park, MD, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA
Rajesh Kumar
National Center for Atmospheric Research, Boulder, CO, USA
Luca Delle Monache
National Center for Atmospheric Research, Boulder, CO, USA
Daniel Tong
NOAA Air Resources Laboratory, College Park, MD, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA, USA
Hyun-Cheol Kim
NOAA Air Resources Laboratory, College Park, MD, USA
Cooperative Institute for Climate and Satellites, University of Maryland, College Park, MD, USA
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Connor J. Clayton, Daniel R. Marsh, Steven T. Turnock, Ailish M. Graham, Kirsty J. Pringle, Carly L. Reddington, Rajesh Kumar, and James B. McQuaid
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Andrew O. Langford, Raul J. Alvarez II, Kenneth C. Aikin, Sunil Baidar, W. Alan Brewer, Steven S. Brown, Matthew M. Coggan, Patrick D. Cullis, Jessica Gilman, Georgios I. Gkatzelis, Detlev Helmig, Bryan J. Johnson, K. Emma Knowland, Rajesh Kumar, Aaron D. Lamplugh, Audra McClure-Begley, Brandi J. McCarty, Ann M. Middlebrook, Gabriele Pfister, Jeff Peischl, Irina Petropavlovskikh, Pamela S. Rickley, Andrew W. Rollins, Scott P. Sandberg, Christoph J. Senff, and Carsten Warneke
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Preprint withdrawn
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Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
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Atmos. Chem. Phys., 24, 5265–5286, https://doi.org/10.5194/acp-24-5265-2024, https://doi.org/10.5194/acp-24-5265-2024, 2024
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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
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Fei Liu, Steffen Beirle, Joanna Joiner, Sungyeon Choi, Zhining Tao, K. Emma Knowland, Steven J. Smith, Daniel Q. Tong, Siqi Ma, Zachary T. Fasnacht, and Thomas Wagner
Atmos. Chem. Phys., 24, 3717–3728, https://doi.org/10.5194/acp-24-3717-2024, https://doi.org/10.5194/acp-24-3717-2024, 2024
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Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
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Leon Kuhn, Steffen Beirle, Vinod Kumar, Sergey Osipov, Andrea Pozzer, Tim Bösch, Rajesh Kumar, and Thomas Wagner
Atmos. Chem. Phys., 24, 185–217, https://doi.org/10.5194/acp-24-185-2024, https://doi.org/10.5194/acp-24-185-2024, 2024
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NO₂ is an important air pollutant. It was observed that the WRF-Chem model shows significant deviations in NO₂ abundance when compared to measurements. We use a 1-month simulation over central Europe to show that these deviations can be mostly resolved by reparameterization of the vertical mixing routine. In order to validate our results, they are compared to in situ, satellite, and MAX-DOAS measurements.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Tianfeng Chai, Xinrong Ren, Fong Ngan, Mark Cohen, and Alice Crawford
Atmos. Chem. Phys., 23, 12907–12933, https://doi.org/10.5194/acp-23-12907-2023, https://doi.org/10.5194/acp-23-12907-2023, 2023
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The SO2 emissions of three power plants are estimated using aircraft observations and an ensemble of HYSPLIT dispersion simulations with different plume rise parameters. The emission estimates using the runs with the lowest root mean square errors (RMSEs) and the runs with the best correlation coefficients between the predicted and observed mixing ratios both agree well with the Continuous Emissions Monitoring Systems (CEMS) data. The RMSE-based plume rise appears to be more reasonable.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
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A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
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To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Matthew S. Johnson, Amir H. Souri, Sajeev Philip, Rajesh Kumar, Aaron Naeger, Jeffrey Geddes, Laura Judd, Scott Janz, Heesung Chong, and John Sullivan
Atmos. Meas. Tech., 16, 2431–2454, https://doi.org/10.5194/amt-16-2431-2023, https://doi.org/10.5194/amt-16-2431-2023, 2023
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Satellites provide vital information for studying the processes controlling ozone formation. Based on the abundance of particular gases in the atmosphere, ozone formation is sensitive to specific human-induced and natural emission sources. However, errors and biases in satellite retrievals hinder this data source’s application for studying ozone formation sensitivity. We conducted a thorough statistical evaluation of two commonly applied satellites for investigating ozone formation sensitivity.
Yunyao Li, Daniel Tong, Siqi Ma, Saulo R. Freitas, Ravan Ahmadov, Mikhail Sofiev, Xiaoyang Zhang, Shobha Kondragunta, Ralph Kahn, Youhua Tang, Barry Baker, Patrick Campbell, Rick Saylor, Georg Grell, and Fangjun Li
Atmos. Chem. Phys., 23, 3083–3101, https://doi.org/10.5194/acp-23-3083-2023, https://doi.org/10.5194/acp-23-3083-2023, 2023
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Plume height is important in wildfire smoke dispersion and affects air quality and human health. We assess the impact of plume height on wildfire smoke dispersion and the exceedances of the National Ambient Air Quality Standards. A higher plume height predicts lower pollution near the source region, but higher pollution in downwind regions, due to the faster spread of the smoke once ejected, affects pollution exceedance forecasts and the early warning of extreme air pollution events.
Prajjwal Rawat, Manish Naja, Evan Fishbein, Pradeep K. Thapliyal, Rajesh Kumar, Piyush Bhardwaj, Aditya Jaiswal, Sugriva N. Tiwari, Sethuraman Venkataramani, and Shyam Lal
Atmos. Meas. Tech., 16, 889–909, https://doi.org/10.5194/amt-16-889-2023, https://doi.org/10.5194/amt-16-889-2023, 2023
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Satellite-based ozone observations have gained importance due to their global coverage. However, satellite-retrieved products are indirect and need to be validated, particularly over mountains. Ozonesondes launched from a Himalayan site are used to assess the Atmospheric Infrared Sounder (AIRS) ozone retrieval. AIRS is shown to overestimate ozone in the upper troposphere and lower stratosphere, while the differences from ozonesondes are more minor in the middle troposphere and stratosphere.
Pooja V. Pawar, Sachin D. Ghude, Gaurav Govardhan, Prodip Acharja, Rachana Kulkarni, Rajesh Kumar, Baerbel Sinha, Vinayak Sinha, Chinmay Jena, Preeti Gunwani, Tapan Kumar Adhya, Eiko Nemitz, and Mark A. Sutton
Atmos. Chem. Phys., 23, 41–59, https://doi.org/10.5194/acp-23-41-2023, https://doi.org/10.5194/acp-23-41-2023, 2023
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In this study, for the first time in South Asia we compare simulated ammonia, ammonium, and total ammonia using the WRF-Chem model and MARGA measurements during winter in the Indo-Gangetic Plain region. Since observations show HCl promotes the fraction of high chlorides in Delhi, we added HCl / Cl emissions to the model. We conducted three sensitivity experiments with changes in HCl emissions, and improvements are reported in accurately simulating ammonia, ammonium, and total ammonia.
James D. East, Barron H. Henderson, Sergey L. Napelenok, Shannon N. Koplitz, Golam Sarwar, Robert Gilliam, Allen Lenzen, Daniel Q. Tong, R. Bradley Pierce, and Fernando Garcia-Menendez
Atmos. Chem. Phys., 22, 15981–16001, https://doi.org/10.5194/acp-22-15981-2022, https://doi.org/10.5194/acp-22-15981-2022, 2022
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We present a framework that uses a computer model of air quality, along with air pollution data from satellite instruments, to estimate emissions of nitrogen oxides (NOx) across the Northern Hemisphere. The framework, which advances current methods to infer emissions from satellite observations, provides observationally constrained NOx estimates, including in regions of the world where emissions are highly uncertain, and can improve simulations of air pollutants relevant for health and policy.
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022, https://doi.org/10.5194/gmd-15-7977-2022, 2022
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This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
Alice Crawford, Tianfeng Chai, Binyu Wang, Allison Ring, Barbara Stunder, Christopher P. Loughner, Michael Pavolonis, and Justin Sieglaff
Atmos. Chem. Phys., 22, 13967–13996, https://doi.org/10.5194/acp-22-13967-2022, https://doi.org/10.5194/acp-22-13967-2022, 2022
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This study describes the development of a workflow which produces probabilistic and quantitative forecasts of volcanic ash in the atmosphere. The workflow includes methods of incorporating satellite observations of the ash cloud into a modeling framework as well as verification statistics that can be used to guide further model development and provide information for risk-based approaches to flight planning.
Li Zhang, Raffaele Montuoro, Stuart A. McKeen, Barry Baker, Partha S. Bhattacharjee, Georg A. Grell, Judy Henderson, Li Pan, Gregory J. Frost, Jeff McQueen, Rick Saylor, Haiqin Li, Ravan Ahmadov, Jun Wang, Ivanka Stajner, Shobha Kondragunta, Xiaoyang Zhang, and Fangjun Li
Geosci. Model Dev., 15, 5337–5369, https://doi.org/10.5194/gmd-15-5337-2022, https://doi.org/10.5194/gmd-15-5337-2022, 2022
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The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
Atmos. Chem. Phys., 22, 8725–8737, https://doi.org/10.5194/acp-22-8725-2022, https://doi.org/10.5194/acp-22-8725-2022, 2022
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This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Patrick C. Campbell, Youhua Tang, Pius Lee, Barry Baker, Daniel Tong, Rick Saylor, Ariel Stein, Jianping Huang, Ho-Chun Huang, Edward Strobach, Jeff McQueen, Li Pan, Ivanka Stajner, Jamese Sims, Jose Tirado-Delgado, Youngsun Jung, Fanglin Yang, Tanya L. Spero, and Robert C. Gilliam
Geosci. Model Dev., 15, 3281–3313, https://doi.org/10.5194/gmd-15-3281-2022, https://doi.org/10.5194/gmd-15-3281-2022, 2022
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NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the harmful effects of air pollution, while saving billions of dollars per year. Here we describe and evaluate the development of the most advanced version of the NAQFC to date, which became operational at NOAA on 20 July 2021. The new NAQFC is based on a coupling of NOAA's operational Global Forecast System (GFS) version 16 with the Community Multiscale Air Quality (CMAQ) model version 5.3.1.
Andrew O. Langford, Christoph J. Senff, Raul J. Alvarez II, Ken C. Aikin, Sunil Baidar, Timothy A. Bonin, W. Alan Brewer, Jerome Brioude, Steven S. Brown, Joel D. Burley, Dani J. Caputi, Stephen A. Conley, Patrick D. Cullis, Zachary C. J. Decker, Stéphanie Evan, Guillaume Kirgis, Meiyun Lin, Mariusz Pagowski, Jeff Peischl, Irina Petropavlovskikh, R. Bradley Pierce, Thomas B. Ryerson, Scott P. Sandberg, Chance W. Sterling, Ann M. Weickmann, and Li Zhang
Atmos. Chem. Phys., 22, 1707–1737, https://doi.org/10.5194/acp-22-1707-2022, https://doi.org/10.5194/acp-22-1707-2022, 2022
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The Fires, Asian, and Stratospheric Transport–Las Vegas Ozone Study (FAST-LVOS) combined lidar, aircraft, and in situ measurements with global models to investigate the contributions of stratospheric intrusions, regional and Asian pollution, and wildfires to background ozone in the southwestern US during May and June 2017 and demonstrated that these processes contributed to background ozone levels that exceeded 70 % of the US National Ambient Air Quality Standard during the 6-week campaign.
Siqi Ma, Daniel Tong, Lok Lamsal, Julian Wang, Xuelei Zhang, Youhua Tang, Rick Saylor, Tianfeng Chai, Pius Lee, Patrick Campbell, Barry Baker, Shobha Kondragunta, Laura Judd, Timothy A. Berkoff, Scott J. Janz, and Ivanka Stajner
Atmos. Chem. Phys., 21, 16531–16553, https://doi.org/10.5194/acp-21-16531-2021, https://doi.org/10.5194/acp-21-16531-2021, 2021
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Predicting high ozone gets more challenging as urban emissions decrease. How can different techniques be used to foretell the quality of air to better protect human health? We tested four techniques with the CMAQ model against observations during a field campaign over New York City. The new system proves to better predict the magnitude and timing of high ozone. These approaches can be extended to other regions to improve the predictability of high-O3 episodes in contemporary urban environments.
Adrian Chappell, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-337, https://doi.org/10.5194/gmd-2021-337, 2021
Revised manuscript not accepted
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Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Our results indicate that tuning dust emission models to dust in the atmosphere has hidden dust emission modelling weaknesses and its poor performance. Our new approach will reduce uncertainty and driven by prognostic albedo improve Earth System Models of aerosol effects on future environmental change.
Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
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Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Haipeng Lin, Daniel J. Jacob, Elizabeth W. Lundgren, Melissa P. Sulprizio, Christoph A. Keller, Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Patrick C. Campbell, Barry Baker, Rick D. Saylor, and Raffaele Montuoro
Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021, https://doi.org/10.5194/gmd-14-5487-2021, 2021
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Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to select, add, and scale emissions from different sources through a configuration file with no change to the model source code. We demonstrate the implementation of HEMCO in several models, all sharing the same HEMCO core code and database library.
Hyun Cheol Kim, Soontae Kim, Mark Cohen, Changhan Bae, Dasom Lee, Rick Saylor, Minah Bae, Eunhye Kim, Byeong-Uk Kim, Jin-Ho Yoon, and Ariel Stein
Atmos. Chem. Phys., 21, 10065–10080, https://doi.org/10.5194/acp-21-10065-2021, https://doi.org/10.5194/acp-21-10065-2021, 2021
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Global outbreaks of COVID-19 offer rare opportunities of natural experiments in emission control and corresponding responses of tropospheric chemistry. This study's novel approach investigates (1) isolating the pandemic's impact from natural and anthropogenic variations, (2) emission adjustment to reproduce real-time emissions, and (3) brute-force modeling to investigate Chinese economic activities. Results provide characteristics of the region's chemistry and emissions.
Xiaoyang Chen, Yang Zhang, Kai Wang, Daniel Tong, Pius Lee, Youhua Tang, Jianping Huang, Patrick C. Campbell, Jeff Mcqueen, Havala O. T. Pye, Benjamin N. Murphy, and Daiwen Kang
Geosci. Model Dev., 14, 3969–3993, https://doi.org/10.5194/gmd-14-3969-2021, https://doi.org/10.5194/gmd-14-3969-2021, 2021
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The continuously updated National Air Quality Forecast Capability (NAQFC) provides air quality forecasts. To support the development of the next-generation NAQFC, we evaluate a prototype of GFSv15-CMAQv5.0.2. The performance and the potential improvements for the system are discussed. This study can provide a scientific basis for further development of NAQFC and help it to provide more accurate air quality forecasts to the public over the contiguous United States.
Fernando Chouza, Thierry Leblanc, Mark Brewer, Patrick Wang, Sabino Piazzolla, Gabriele Pfister, Rajesh Kumar, Carl Drews, Simone Tilmes, Louisa Emmons, and Matthew Johnson
Atmos. Chem. Phys., 21, 6129–6153, https://doi.org/10.5194/acp-21-6129-2021, https://doi.org/10.5194/acp-21-6129-2021, 2021
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The tropospheric ozone lidar at the JPL Table Mountain Facility (TMF) was used to investigate the impact of Los Angeles (LA) Basin pollution transport and stratospheric intrusions in the planetary boundary layer on the San Gabriel Mountains. The results of this study indicate a dominant role of the LA Basin pollution on days when high ozone levels were observed at TMF (March–October period).
Youhua Tang, Huisheng Bian, Zhining Tao, Luke D. Oman, Daniel Tong, Pius Lee, Patrick C. Campbell, Barry Baker, Cheng-Hsuan Lu, Li Pan, Jun Wang, Jeffery McQueen, and Ivanka Stajner
Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, https://doi.org/10.5194/acp-21-2527-2021, 2021
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Chemical lateral boundary condition (CLBC) impact is essential for regional air quality prediction during intrusion events. We present a model mapping Goddard Earth Observing System (GEOS) to Community Multi-scale Air Quality (CMAQ) CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species. Influence depends on distance from the inflow boundary and species and their regional characteristics. We use aerosol optical thickness to derive CLBCs, achieving reasonable prediction.
Yilin Chen, Huizhong Shen, Jennifer Kaiser, Yongtao Hu, Shannon L. Capps, Shunliu Zhao, Amir Hakami, Jhih-Shyang Shih, Gertrude K. Pavur, Matthew D. Turner, Daven K. Henze, Jaroslav Resler, Athanasios Nenes, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Tianfeng Chai, Lieven Clarisse, Pierre-François Coheur, Martin Van Damme, and Armistead G. Russell
Atmos. Chem. Phys., 21, 2067–2082, https://doi.org/10.5194/acp-21-2067-2021, https://doi.org/10.5194/acp-21-2067-2021, 2021
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Ammonia (NH3) emissions can exert adverse impacts on air quality and ecosystem well-being. NH3 emission inventories are viewed as highly uncertain. Here we optimize the NH3 emission estimates in the US using an air quality model and NH3 measurements from the IASI satellite instruments. The optimized NH3 emissions are much higher than the National Emissions Inventory estimates in April. The optimized NH3 emissions improved model performance when evaluated against independent observation.
Liqiang Wang, Shaocai Yu, Pengfei Li, Xue Chen, Zhen Li, Yibo Zhang, Mengying Li, Khalid Mehmood, Weiping Liu, Tianfeng Chai, Yannian Zhu, Daniel Rosenfeld, and John H. Seinfeld
Atmos. Chem. Phys., 20, 14787–14800, https://doi.org/10.5194/acp-20-14787-2020, https://doi.org/10.5194/acp-20-14787-2020, 2020
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The Chinese government has made major strides in curbing anthropogenic emissions. In this study, we constrain a state-of-the-art CTM by a reliable data assimilation method with extensive chemical and meteorological observations. This comprehensive technical design provides a crucial advance in isolating the influences of emission changes and meteorological perturbations over the Yangtze River Delta (YRD) from 2016 to 2019, thus establishing the first map of the PM2.5 mitigation across the YRD.
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
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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.
Chinmay Jena, Sachin D. Ghude, Rachana Kulkarni, Sreyashi Debnath, Rajesh Kumar, Vijay Kumar Soni, Prodip Acharja, Santosh H. Kulkarni, Manoj Khare, Akshara J. Kaginalkar, Dilip M. Chate, Kaushar Ali, Ravi S. Nanjundiah, and Madhavan N. Rajeevan
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-673, https://doi.org/10.5194/acp-2020-673, 2020
Publication in ACP not foreseen
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Simulations of atmospheric particulate matter (PM2.5) with WRF-Chem model with three different aerosol mechanisms coupled with gas-phase chemical schemes are compared to understand the spatial and temporal variability of PM2.5 over the Indo-Gangetic Plain (IGP) in the winter season. All three chemical schemes underestimate the observed concentrations of major aerosol composition and precursor gases over IGP which in turn affect the optical depth and overall performance of the model for PM2.5.
Shunliu Zhao, Matthew G. Russell, Amir Hakami, Shannon L. Capps, Matthew D. Turner, Daven K. Henze, Peter B. Percell, Jaroslav Resler, Huizhong Shen, Armistead G. Russell, Athanasios Nenes, Amanda J. Pappin, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Charles O. Stanier, and Tianfeng Chai
Geosci. Model Dev., 13, 2925–2944, https://doi.org/10.5194/gmd-13-2925-2020, https://doi.org/10.5194/gmd-13-2925-2020, 2020
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
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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.
Meng Gao, Jinhui Gao, Bin Zhu, Rajesh Kumar, Xiao Lu, Shaojie Song, Yuzhong Zhang, Beixi Jia, Peng Wang, Gufran Beig, Jianlin Hu, Qi Ying, Hongliang Zhang, Peter Sherman, and Michael B. McElroy
Atmos. Chem. Phys., 20, 4399–4414, https://doi.org/10.5194/acp-20-4399-2020, https://doi.org/10.5194/acp-20-4399-2020, 2020
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A regional fully coupled meteorology–chemistry model, Weather Research and Forecasting model with Chemistry (WRF-Chem), was employed to study the seasonality of ozone (O3) pollution and its sources in both China and India.
Hao He, Xin-Zhong Liang, Chao Sun, Zhining Tao, and Daniel Q. Tong
Atmos. Chem. Phys., 20, 3191–3208, https://doi.org/10.5194/acp-20-3191-2020, https://doi.org/10.5194/acp-20-3191-2020, 2020
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We studied the trend of US ozone pollution from 1990 to 2015 using EPA observations and computer simulations. Observations indicated a decrease in peak ozone at noon due to regulations and a slight increase in ozone in early morning and late afternoon possibly. Our modeling system confirmed these findings and provided detailed information about ozone photochemistry. These results revealed the success of previous control measures and provide scientific evidence for the future regulations.
Siqi Ma, Xuelei Zhang, Chao Gao, Daniel Q. Tong, Aijun Xiu, Guangjian Wu, Xinyuan Cao, Ling Huang, Hongmei Zhao, Shichun Zhang, Sergio Ibarra-Espinosa, Xin Wang, Xiaolan Li, and Mo Dan
Geosci. Model Dev., 12, 4603–4625, https://doi.org/10.5194/gmd-12-4603-2019, https://doi.org/10.5194/gmd-12-4603-2019, 2019
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Dust storms are thought to be a worldwide societal issue, and numerical modeling is an effective way to help us to predict dust events. Here we present the first comprehensive evaluation of dust emission modules in four commonly used air quality models for northeastern China. The results showed that most of these models were able to capture this dust event and indicated the dust source maps should be carefully selected or replaced with a new one that is constructed with local data.
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
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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.
Marianne Tronstad Lund, Gunnar Myhre, Amund Søvde Haslerud, Ragnhild Bieltvedt Skeie, Jan Griesfeller, Stephen Matthew Platt, Rajesh Kumar, Cathrine Lund Myhre, and Michael Schulz
Geosci. Model Dev., 11, 4909–4931, https://doi.org/10.5194/gmd-11-4909-2018, https://doi.org/10.5194/gmd-11-4909-2018, 2018
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Atmospheric aerosols play a key role in the climate system, but their exact impact on the energy balance remains uncertain. Accurate representation of the geographical distribution and properties of aerosols in global models is key to reduce this uncertainty. Here we use a new emission inventory and a range of observations to carefully validate a state-of-the-art model and present an updated estimate of the net direct effect of anthropogenic aerosols since the preindustrial era.
Piyush Bhardwaj, Manish Naja, Maheswar Rupakheti, Aurelia Lupascu, Andrea Mues, Arnico Kumar Panday, Rajesh Kumar, Khadak Singh Mahata, Shyam Lal, Harish C. Chandola, and Mark G. Lawrence
Atmos. Chem. Phys., 18, 11949–11971, https://doi.org/10.5194/acp-18-11949-2018, https://doi.org/10.5194/acp-18-11949-2018, 2018
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This study provides information about the regional variabilities in some of the pollutants using observations in Nepal and India. It is shown that agricultural crop residue burning leads to a significant enhancement in ozone and CO over a wider region. Further, the wintertime higher ozone levels are shown to be largely due to local emissions, while regional transport could be important in spring and hence shows the role of regional sources versus local sources in the Kathmandu Valley.
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
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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.
Luca Delle Monache, Stefano Alessandrini, Irina Djalalova, James Wilczak, and Jason C. Knievel
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-1214, https://doi.org/10.5194/acp-2017-1214, 2018
Preprint withdrawn
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The authors demonstrate how the analog ensemble (AnEn) can efficiently generate deterministic and probabilistic forecasts of air quality. The method avoids the complexity and real-time computational expense of dynamical (i.e., model-based) ensembles. AnEn deterministic predictions have lower errors and are better correlated with observations. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.
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
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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.
Hyun Cheol Kim, Eunhye Kim, Changhan Bae, Jeong Hoon Cho, Byeong-Uk Kim, and Soontae Kim
Atmos. Chem. Phys., 17, 10315–10332, https://doi.org/10.5194/acp-17-10315-2017, https://doi.org/10.5194/acp-17-10315-2017, 2017
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This study is intended to provide reference information regarding quantitative estimation of regional contributions and model uncertainty in meteorology simulations and emissions inventories. Simulated PM concentration is sensitive to meteorology, but estimated contributions are mostly robust. Regional contributions are also found to be sensitive to the choice of emissions inventories. The modeled results should be considered with caution when interpreted for emission regulation policy making.
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
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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
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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.
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
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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
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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.
Xinyi Dong, Joshua S. Fu, Kan Huang, Daniel Tong, and Guoshun Zhuang
Atmos. Chem. Phys., 16, 8157–8180, https://doi.org/10.5194/acp-16-8157-2016, https://doi.org/10.5194/acp-16-8157-2016, 2016
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The Community Multiscale Air Quality (CMAQ) model has been further developed in terms of simulating natural wind-blown dust in this study, with a series of modifications aimed at improving the model's capability to predict the emission, transport, and chemical reactions of dust aerosols. Evaluation with observations suggested improved model performance by correcting the double counting of soil moisture impact, applying source-dependent speciation profile, and implementing heterogeneous chemitry.
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
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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
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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.
Xiangshang Li, Yunsoo Choi, Beata Czader, Anirban Roy, Hyuncheol Kim, Barry Lefer, and Shuai Pan
Atmos. Chem. Phys., 16, 3127–3144, https://doi.org/10.5194/acp-16-3127-2016, https://doi.org/10.5194/acp-16-3127-2016, 2016
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We performed a a month-long sensitivity study of obs-nudging on meteorology and ozone chemistry. Notable increases in temperature and wind performance were observed after obs-nudging with hourly nudging files. PBL height also matched better in the sensitivity case. The model ozone improved at ground level and aloft but to a lesser degree. An examination of a high ozone episode showed that the current nudging process does not perform consistently – quite well at times while poor at other times.
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
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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
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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.
R. Kumar, M. C. Barth, V. S. Nair, G. G. Pfister, S. Suresh Babu, S. K. Satheesh, K. Krishna Moorthy, G. R. Carmichael, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 15, 5415–5428, https://doi.org/10.5194/acp-15-5415-2015, https://doi.org/10.5194/acp-15-5415-2015, 2015
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We examine differences in the surface BC between the Bay of Bengal (BoB) and the Arabian Sea (AS) and identify dominant sources of BC in South Asia during ICARB. Anthropogenic emissions were the main source of BC during ICARB and had about 5 times stronger influence on the BoB compared to the AS. Regional-scale transport contributes up to 25% of BC mass concentrations in western and eastern India, suggesting that surface BC mass concentrations cannot be linked directly to the local emissions.
M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Žabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur
Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, https://doi.org/10.5194/acp-15-5325-2015, 2015
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Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
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
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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. Pagowski, Z. Liu, G. A. Grell, M. Hu, H.-C. Lin, and C. S. Schwartz
Geosci. Model Dev., 7, 1621–1627, https://doi.org/10.5194/gmd-7-1621-2014, https://doi.org/10.5194/gmd-7-1621-2014, 2014
R. Kumar, M. C. Barth, S. Madronich, M. Naja, G. R. Carmichael, G. G. Pfister, C. Knote, G. P. Brasseur, N. Ojha, and T. Sarangi
Atmos. Chem. Phys., 14, 6813–6834, https://doi.org/10.5194/acp-14-6813-2014, https://doi.org/10.5194/acp-14-6813-2014, 2014
S. Alessandrini, F. Davò, S. Sperati, M. Benini, and L. Delle Monache
Adv. Sci. Res., 11, 49–53, https://doi.org/10.5194/asr-11-49-2014, https://doi.org/10.5194/asr-11-49-2014, 2014
R. Kumar, M. C. Barth, G. G. Pfister, M. Naja, and G. P. Brasseur
Atmos. Chem. Phys., 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, https://doi.org/10.5194/acp-14-2431-2014, 2014
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
M. Huang, G. R. Carmichael, T. Chai, R. B. Pierce, S. J. Oltmans, D. A. Jaffe, K. W. Bowman, A. Kaduwela, C. Cai, S. N. Spak, A. J. Weinheimer, L. G. Huey, and G. S. Diskin
Atmos. Chem. Phys., 13, 359–391, https://doi.org/10.5194/acp-13-359-2013, https://doi.org/10.5194/acp-13-359-2013, 2013
Related subject area
Atmospheric sciences
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models
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PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Comprehensive evaluation of iAMAS (v1.0) in simulating Antarctic meteorological fields with observations and reanalysis
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
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This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
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In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-229, https://doi.org/10.5194/gmd-2024-229, 2025
Revised manuscript accepted for GMD
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This study presents the first comprehensive evaluation of unstructured meshes using the iAMAS model over Antarctica, encompassing both surface and upper-level meteorological fields. Comparison with ERA5 and observational data reveals that the iAMAS model performs well in simulating the Antarctic atmosphere; iAMAS demonstrates comparable, and in some cases superior, performance in simulating temperature and wind speed in East Antarctica when compared to ERA5.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Cited articles
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Appel, K. W., Roselle, S. J., Gilliam, R. C., and Pleim, J. E.: Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers, Geosci. Model Dev., 3, 169–188, https://doi.org/10.5194/gmd-3-169-2010, 2010.
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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.
In order to evaluate the data assimilation tools for regional real-time PM2.5 forecasts, we...
