Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6757-2023
© Author(s) 2023. 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-16-6757-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
22 Nov 2023
Model experiment description paper |
| 22 Nov 2023
| 22 Nov 2023
The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2
Zhongwei Luo
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Yan Han
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Kun Hua
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Yufen Zhang
CORRESPONDING AUTHOR
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Jianhui Wu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Xiaohui Bi
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Baoshuang Liu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
Yang Chen
Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
Xin Long
Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
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Atmos. Chem. Phys., 24, 1627–1639, https://doi.org/10.5194/acp-24-1627-2024, https://doi.org/10.5194/acp-24-1627-2024, 2024
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Taomou Zong, Zhijun Wu, Junrui Wang, Kai Bi, Wenxu Fang, Yanrong Yang, Xuena Yu, Zhier Bao, Xiangxinyue Meng, Yuheng Zhang, Song Guo, Yang Chen, Chunshan Liu, Yue Zhang, Shao-Meng Li, and Min Hu
Atmos. Meas. Tech., 16, 3679–3692, https://doi.org/10.5194/amt-16-3679-2023, https://doi.org/10.5194/amt-16-3679-2023, 2023
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Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-507, https://doi.org/10.5194/acp-2020-507, 2020
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Size distributions of inorganic and organic components in particulate matter (PM) can provide critical information on sources and pollution processes. Ions, elements, carbon fractions, n-alkanes, PAHs, hopanes and steranes in size-resolved PM were analyzed during one year in a northern Chinese megacity. Results reveal that size distributions of inorganic and organic aerosol components are dependent on seasons and pollution levels as a result of differing sources and physicochemical processes.
Yang Chen, Jing Cai, Zhichao Wang, Chao Peng, Xiaojiang Yao, Mi Tian, Yiqun Han, Guangming Shi, Zongbo Shi, Yue Liu, Xi Yang, Mei Zheng, Tong Zhu, Kebin He, Qiang Zhang, and Fumo Yang
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Patterns of particle transport, accumulation, and evolution in both urban and rural areas of Beijing are investigated. The two sites shared 17 common particle types in different stages of atmospheric processing.
Yang Chen, Guangming Shi, Jing Cai, Zongbo Shi, Zhichao Wang, Xiaojiang Yao, Mi Tian, Chao Peng, Yiqun Han, Tong Zhu, Yue Liu, Xi Yang, Mei Zheng, Fumo Yang, Qiang Zhang, and Kebin He
Atmos. Chem. Phys., 20, 9249–9263, https://doi.org/10.5194/acp-20-9249-2020, https://doi.org/10.5194/acp-20-9249-2020, 2020
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Individual particles were observed in two field studies during winter 2016 in the urban and rural areas of Beijing. An online single-particle chemical composition analysis was used as a tracing system to investigate the impact of heating activities and the formation of haze events. During the pollution events, a pattern of transport and accumulation was found with evidence of single particles. The transport from Pinggu to Peking University was significant but PKU to PG occurred occasionally.
Ru-Jin Huang, Yao He, Jing Duan, Yongjie Li, Qi Chen, Yan Zheng, Yang Chen, Weiwei Hu, Chunshui Lin, Haiyan Ni, Wenting Dai, Junji Cao, Yunfei Wu, Renjian Zhang, Wei Xu, Jurgita Ovadnevaite, Darius Ceburnis, Thorsten Hoffmann, and Colin D. O'Dowd
Atmos. Chem. Phys., 20, 9101–9114, https://doi.org/10.5194/acp-20-9101-2020, https://doi.org/10.5194/acp-20-9101-2020, 2020
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We systematically compared the submicron particle (PM1) processes in haze days with low and high relative humidity (RH) in wintertime Beijing. Nitrate had similar daytime growth rates in low-RH and high-RH pollution. OOA had a higher growth rate in low-RH pollution than in high-RH pollution. Sulfate had a decreasing trend in low-RH pollution, while it increased significantly in high-RH pollution. This distinction may be explained by the different processes affected by meteorological conditions.
Wei Yuan, Ru-Jin Huang, Lu Yang, Jie Guo, Ziyi Chen, Jing Duan, Ting Wang, Haiyan Ni, Yongming Han, Yongjie Li, Qi Chen, Yang Chen, Thorsten Hoffmann, and Colin O'Dowd
Atmos. Chem. Phys., 20, 5129–5144, https://doi.org/10.5194/acp-20-5129-2020, https://doi.org/10.5194/acp-20-5129-2020, 2020
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We characterized light-absorbing properties, chromophore composition and sources of brown carbon (BrC) in Xi'an; identified three groups of light-absorbing organics; and quantified their contribution to overall BrC absorption. Our results showed that vehicle emissions and secondary formation are major sources of BrC in spring, coal combustion and vehicle emissions are major sources in fall, biomass burning and coal combustion become major sources in winter, and secondary BrC dominates in summer.
Jing Duan, Ru-Jin Huang, Yongjie Li, Qi Chen, Yan Zheng, Yang Chen, Chunshui Lin, Haiyan Ni, Meng Wang, Jurgita Ovadnevaite, Darius Ceburnis, Chunying Chen, Douglas R. Worsnop, Thorsten Hoffmann, Colin O'Dowd, and Junji Cao
Atmos. Chem. Phys., 20, 3793–3807, https://doi.org/10.5194/acp-20-3793-2020, https://doi.org/10.5194/acp-20-3793-2020, 2020
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We characterized secondary aerosol formation in Beijing. Our results showed that relative humidity (RH) and Ox have opposite effects on sulfate and nitrate formation in summer and winter. The wintertime more-oxidized OOA (MO-OOA) showed a good correlation with aerosol liquid water content (ALWC). Meanwhile, the dependence of less-oxidized OOA (LO-OOA) and the mass ratio of LO-OOA to MO-OOA in Ox both degraded when RH > 60 %, suggesting that RH or ALWC may also affect LO-OOA formation.
Meng Wang, Ru-Jin Huang, Junji Cao, Wenting Dai, Jiamao Zhou, Chunshui Lin, Haiyan Ni, Jing Duan, Ting Wang, Yang Chen, Yongjie Li, Qi Chen, Imad El Haddad, and Thorsten Hoffmann
Atmos. Meas. Tech., 12, 4779–4789, https://doi.org/10.5194/amt-12-4779-2019, https://doi.org/10.5194/amt-12-4779-2019, 2019
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The analytical performances of SE-GC-MS and TD-GC-MS for the determination of n-alkanes, PAHs and hopanes were evaluated and compared. The two methods show a good agreement with a high correlation efficient (R2 > 0.98) and a slope close to unity. The concentrations of n-alkanes, PAHs and hopanes are found to be much higher in Beijing than those in Chengdu, Shanghai and Guangzhou, most likely due to emissions from coal combustion for wintertime heating in Beijing.
Ruihe Lyu, Zongbo Shi, Mohammed Salim Alam, Xuefang Wu, Di Liu, Tuan V. Vu, Christopher Stark, Pingqing Fu, Yinchang Feng, and Roy M. Harrison
Atmos. Chem. Phys., 19, 10865–10881, https://doi.org/10.5194/acp-19-10865-2019, https://doi.org/10.5194/acp-19-10865-2019, 2019
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Severe pollution of the Beijing atmosphere is a frequent occurrence. The airborne particles which characterize the episodes of haze contain a wide range of chemical constituents but organic compounds make up a substantial proportion. In this study individual compounds are analysed under both haze and non-haze conditions, and the measurements are compared with samples collected in London, where the air pollution climate and sources are very different.
Jing Duan, Ru-Jin Huang, Chunshui Lin, Wenting Dai, Meng Wang, Yifang Gu, Ying Wang, Haobin Zhong, Yan Zheng, Haiyan Ni, Uli Dusek, Yang Chen, Yongjie Li, Qi Chen, Douglas R. Worsnop, Colin D. O'Dowd, and Junji Cao
Atmos. Chem. Phys., 19, 10319–10334, https://doi.org/10.5194/acp-19-10319-2019, https://doi.org/10.5194/acp-19-10319-2019, 2019
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We present the seasonal distinction of secondary aerosol formation in urban Beijing. Both photochemical oxidation and aqueous-phase processing played important roles in SOA (secondary organic aerosol) formation during all three seasons; while for sulfate formation, gas-phase photochemical oxidation was the major pathway in late summer, aqueous-phase reactions were more responsible during early winter, and both processes had contributions during autumn.
Qili Dai, Benjamin C. Schulze, Xiaohui Bi, Alexander A. T. Bui, Fangzhou Guo, Henry W. Wallace, Nancy P. Sanchez, James H. Flynn, Barry L. Lefer, Yinchang Feng, and Robert J. Griffin
Atmos. Chem. Phys., 19, 9641–9661, https://doi.org/10.5194/acp-19-9641-2019, https://doi.org/10.5194/acp-19-9641-2019, 2019
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The formation processes of secondary organic aerosol remain to be fully understood. We reported the measurement data from two field campaigns within Houston, TX, to investigate the effects of aqueous-phase chemistry and photochemistry in processing oxygenated organic aerosol (OOA) in winter and summer. Both photochemistry and aqueous-phase processing appear to facilitate more-oxidized OOA formation. The processing mechanism of less-oxidized OOA apparently depended on relative humidity.
Jing Ding, Pusheng Zhao, Jie Su, Qun Dong, Xiang Du, and Yufen Zhang
Atmos. Chem. Phys., 19, 7939–7954, https://doi.org/10.5194/acp-19-7939-2019, https://doi.org/10.5194/acp-19-7939-2019, 2019
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Aerosol acidity plays a key role in secondary aerosol formation. To provide a more comprehensive reference for aerosol pH and a basis for controlling secondary aerosol generation, this study used the latest data covering four seasons and different particle sizes to obtain the characteristics of aerosol pH and explore the main factors affecting aerosol pH and gas–particle partitioning in the Beijing area.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
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APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Yang Chen, Mi Tian, Ru-Jin Huang, Guangming Shi, Huanbo Wang, Chao Peng, Junji Cao, Qiyuan Wang, Shumin Zhang, Dongmei Guo, Leiming Zhang, and Fumo Yang
Atmos. Chem. Phys., 19, 3245–3255, https://doi.org/10.5194/acp-19-3245-2019, https://doi.org/10.5194/acp-19-3245-2019, 2019
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Amine-containing particles were characterized in an urban area of Chongqing during both summer and winter using a single-particle aerosol mass spectrometer (SPAMS). Amines were observed to internally mix with elemental carbon (EC), organic carbon (OC), sulfate, and nitrate. Diethylamine (DEA) was the most abundant in both number and peak area among amine-containing particles. Vegetation and traffic were the primary sources of particulate amines.
Xiaohui Bi, Qili Dai, Jianhui Wu, Qing Zhang, Wenhui Zhang, Ruixue Luo, Yuan Cheng, Jiaying Zhang, Lu Wang, Zhuojun Yu, Yufen Zhang, Yingze Tian, and Yinchang Feng
Atmos. Chem. Phys., 19, 3223–3243, https://doi.org/10.5194/acp-19-3223-2019, https://doi.org/10.5194/acp-19-3223-2019, 2019
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Source profiles are of great importance for the application of receptor models in source apportionment studies, as they characterize specific sources from a chemical point of view, revealing the signatures of source emissions. Here, a total of 3326 chemical profiles of the main primary sources across China from 1987 to 2017 are reviewed. The results highlight the urgent need for increased investigation of more specific markers beyond routinely measured components to better discriminate sources.
Ruihe Lyu, Mohammed S. Alam, Christopher Stark, Ruixin Xu, Zongbo Shi, Yinchang Feng, and Roy M. Harrison
Atmos. Chem. Phys., 19, 2233–2246, https://doi.org/10.5194/acp-19-2233-2019, https://doi.org/10.5194/acp-19-2233-2019, 2019
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Organic matter comprises a substantial proportion of the mass of toxic airborne particles which cause poor health and premature death. In this paper, new measurements of three important groups of organic compounds are reported and are analysed to infer their sources and their contributions to airborne particle concentrations.
Qiyuan Wang, Suixin Liu, Nan Li, Wenting Dai, Yunfei Wu, Jie Tian, Yaqing Zhou, Meng Wang, Steven Sai Hang Ho, Yang Chen, Renjian Zhang, Shuyu Zhao, Chongshu Zhu, Yongming Han, Xuexi Tie, and Junji Cao
Atmos. Chem. Phys., 19, 1881–1899, https://doi.org/10.5194/acp-19-1881-2019, https://doi.org/10.5194/acp-19-1881-2019, 2019
Benjamin C. Schulze, Henry W. Wallace, Alexander T. Bui, James H. Flynn, Matt H. Erickson, Sergio Alvarez, Qili Dai, Sascha Usenko, Rebecca J. Sheesley, and Robert J. Griffin
Atmos. Chem. Phys., 18, 14217–14241, https://doi.org/10.5194/acp-18-14217-2018, https://doi.org/10.5194/acp-18-14217-2018, 2018
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Atmospheric field measurements at a coastal site near Houston, TX, were used to investigate the influence of shipping vessel emissions on aerosol mass and composition over the Gulf of Mexico. Results suggest that, despite recent regulations, these vessels still produce a considerable fraction of inorganic and organic aerosol mass in the region. Secondary effects of shipping emissions on organic aerosol composition, such as influences on aerosol aging, were also identified.
Ru-Jin Huang, Junji Cao, Yang Chen, Lu Yang, Jincan Shen, Qihua You, Kai Wang, Chunshui Lin, Wei Xu, Bo Gao, Yongjie Li, Qi Chen, Thorsten Hoffmann, Colin D. O'Dowd, Merete Bilde, and Marianne Glasius
Atmos. Meas. Tech., 11, 3447–3456, https://doi.org/10.5194/amt-11-3447-2018, https://doi.org/10.5194/amt-11-3447-2018, 2018
Congbo Song, Yan Liu, Shida Sun, Luna Sun, Yanjie Zhang, Chao Ma, Jianfei Peng, Qian Li, Jinsheng Zhang, Qili Dai, Baoshuang Liu, Peng Wang, Yi Zhang, Ting Wang, Lin Wu, Min Hu, and Hongjun Mao
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-387, https://doi.org/10.5194/acp-2018-387, 2018
Revised manuscript not accepted
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Vehicular emission is a key contributor to ambient volatile organic compounds (VOCs) and NOx in Chinese megacities. Information on real-world emission factors (EFs) for a typical urban fleet is still limited. We found that improvement of fuel quality can significantly reduce feet-average EFs of VOCs (especially for BTEX). Our study provided implications for O3 control in China from the view of primary emission, and highlighted the importance of further control of evaporative emissions.
Baoshuang Liu, Yuan Cheng, Ming Zhou, Danni Liang, Qili Dai, Lu Wang, Wei Jin, Lingzhi Zhang, Yibin Ren, Jingbo Zhou, Chunling Dai, Jiao Xu, Jiao Wang, Yinchang Feng, and Yufen Zhang
Atmos. Chem. Phys., 18, 7019–7039, https://doi.org/10.5194/acp-18-7019-2018, https://doi.org/10.5194/acp-18-7019-2018, 2018
Xing Peng, Jian Gao, Guoliang Shi, Xurong Shi, Yanqi Huangfu, Jiayuan Liu, Yuechong Zhang, Yinchang Feng, Wei Wang, Ruoyu Ma, Cesunica E. Ivey, and Yi Deng
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-997, https://doi.org/10.5194/acp-2017-997, 2018
Preprint withdrawn
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A finding here is that source emission dominates the level of pollutants and short-term meteorological condition determines the variation of pollutants. Primary source impact levels are mainly influenced by source emissions, and secondary source impact levels are mainly influenced by synoptic scale fluctuations and source emissions. The implications of results are for source apportionment analyses conducted with data from different geographical locations and under various weather conditions.
Huanbo Wang, Mi Tian, Yang Chen, Guangming Shi, Yuan Liu, Fumo Yang, Leiming Zhang, Liqun Deng, Jiayan Yu, Chao Peng, and Xuyao Cao
Atmos. Chem. Phys., 18, 865–881, https://doi.org/10.5194/acp-18-865-2018, https://doi.org/10.5194/acp-18-865-2018, 2018
Mi Tian, Huanbo Wang, Yang Chen, Fumo Yang, Xiaohua Zhang, Qiang Zou, Renquan Zhang, Yongliang Ma, and Kebin He
Atmos. Chem. Phys., 16, 7357–7371, https://doi.org/10.5194/acp-16-7357-2016, https://doi.org/10.5194/acp-16-7357-2016, 2016
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The discussion was based on high time resolution data which could provide detailed insight into short haze periods. The dominant species in PM2.5 and which were responsible for the visibility reduction were identified in Suzhou.
The formation mechanisms of sulfate and nitrate were explored as high secondary aerosol contributions to particulate pollution during haze events. The impact of local and transport sources on the origin of aerosol pollution in Suzhou was discussed.
S. Han, Y. Zhang, J. Wu, X. Zhang, Y. Tian, Y. Wang, J. Ding, W. Yan, X. Bi, G. Shi, Z. Cai, Q. Yao, H. Huang, and Y. Feng
Atmos. Chem. Phys., 15, 11165–11177, https://doi.org/10.5194/acp-15-11165-2015, https://doi.org/10.5194/acp-15-11165-2015, 2015
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It is crucial for studying regional-scale PM pollution and for the development of efficient joint control policy to improve understanding of the regional background PM concentration. Based on the vertical variation periodic characteristics of particle mass concentration, the atmospheric boundary layer structure, as well as the vertical distribution of chemical composition and pollution source apportionment, a method to estimate regional background PM concentration is proposed.
Y. Z. Tian, J. Wang, X. Peng, G. L. Shi, and Y. C. Feng
Atmos. Chem. Phys., 14, 9469–9479, https://doi.org/10.5194/acp-14-9469-2014, https://doi.org/10.5194/acp-14-9469-2014, 2014
Related subject area
Atmospheric sciences
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
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)
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
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
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
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
A new set of indicators for model evaluation complementing to FAIRMODE’s MQO
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
Carbon dioxide plume dispersion simulated at hectometer scale using DALES: model formulation and observational evaluation
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0 – Part 1: System description
The third Met Office Unified Model-JULES Regional Atmosphere and Land Configuration, RAL3
Atmospheric moisture tracking with WAM2layers v3
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
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.
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.
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.
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.
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.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
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We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and Winter-Summer gradients reveal issues. O3 evaluation shows that e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
EGUsphere, https://doi.org/10.5194/egusphere-2024-3721, https://doi.org/10.5194/egusphere-2024-3721, 2024
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We introduce a new simulation platform based on the Dutch Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in the turbulent environments with hectometer resolution. This model incorporates both anthropogenic emission inventory and ecosystem exchanges. Simulation results for the main urban area in the Netherlands demonstrate a strong potential of DALES to enhance CO2 emission modeling, which is important for refining their reduction strategies.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-148, https://doi.org/10.5194/gmd-2024-148, 2024
Revised manuscript accepted for GMD
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In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled chemistry meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the CMA-GFS operational global numerical weather model. The results show that assimilating BC observations can generate analysis increments not only for BC but also for atmospheric variables.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
EGUsphere, https://doi.org/10.5194/egusphere-2024-3401, https://doi.org/10.5194/egusphere-2024-3401, 2024
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We introduce a new version of WAM2layers, a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data became a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent and reliable, and easier to maintain.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Cited articles
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Bi, X., Dai, Q., Wu, J., Zhang, Q., Zhang, W., Luo, R., Cheng, Y., Zhang, J., Wang, L., Yu, Z., Zhang, Y., Tian, Y., and Feng, Y.: Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017, Atmos. Chem. Phys., 19, 3223–3243, https://doi.org/10.5194/acp-19-3223-2019, 2019a.
Bi, X., Dai, Q., Wu, J., Zhang, Q., Zhang, W., Luo, R., Cheng, Y., Zhang, J., Wang, L., Yu, Z., Zhang, Y., Tian, Y., and Feng, Y.: Data for: chemical compositions of the main source profiles of ambient particulate matter across China, Mendeley Data [data set], https://doi.org/10.17632/x8dfshjt9j.2, 2019b.
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Chen, Z., Chen, D., Zhao, C., Kwan, M., Cai, J., Zhuang, Y., Zhao, B., Wang, X., Chen, B., Yang, J., Li, R., He, B., Gao, B., Wang, K., and Xu, B.: Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism, Environ. Int., 139, 105558, https://doi.org/10.1016/j.envint.2020.105558, 2020.
Cheng, N. L., Meng, F., Wang, J. K., Chen, Y. B., Wei, X., and Han, H.: Numerical simulation of the spatial distribution and deposition of PM2.5 in East China coastal area in 2010, J. Safety Environ., 15, 305–310, https://doi.org/10.13637/j.issn.1009-6094.2015.06.063, 2015 (in Chinese).
Eder, B. K. and Yu, S. C.: A performance evaluation of the 2004 release of Models-3 CMAQ, Atmos. Environ., 40, 4811–4824, https://doi.org/10.1007/978-0-387-68854-1_57, 2006.
Feng, Y., Bi, X., Wu, J., and Zhang, Y.: The state environment protection key laboratory of urban particulate air pollution prevention, Database of Source Profiles of Air pollution (SPAP) [data set], http://www.nkspap.com:9091/ (last access: 14 November 2023), 2020.
Foley, K. M., Roselle, S. J., Appel, K. W., Bhave, P. V., Pleim, J. E., Otte, T. L., Mathur, R., Sarwar, G., Young, J. O., Gilliam, R. C., Nolte, C. G., Kelly, J. T., Gilliland, A. B., and Bash, J. O.: Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7, Geosci. Model Dev., 3, 205–226, https://doi.org/10.5194/gmd-3-205-2010, 2010.
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–
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Gao, S., Zhang, S., Che, X., Ma, Y., Chen, X., Duan, Y., Fu, Q., Wang, S., Zhou, B., Wei, C., and Jiao, Z.: New understanding of source profiles: Example of the coating industry, J. Clean. Prod., 357, 132025, https://doi.org/10.1016/j.jclepro.2022.132025, 2022.
Guo, R., Yang, J., and Liu, Z.: Influence of heat treatment conditions on release of chlorine from Datong coal, J. Anal. Appl. Pyrol., 71, 179–186, https://doi.org/10.1016/S0165-2370(03)00086-X, 2004.
Guo, Y. Y., Gao, X., Zhu, T. Y., Luo, L., and Zheng, Y.: Chemical profiles of PM emitted from the iron and steel industry in northern China, Atmos. Environ., 150, 187–197, https://doi.org/10.1016/j.atmosenv.2016.11.055, 2017.
Guo, Z., Hao, Y., Tian, H., Bai, X., Wu, B., Liu, S., Luo, L., Liu, W., Zhao, S., Lin, S., Lv, Y., Yang, J., and Xiao, Y.: Field measurements on emission characteristics, chemical profiles, and emission factors of size-segregated PM from cement plants in China, Sci. Total Environ., 818, 151822, https://doi.org/10.1016/j.scitotenv.2021.151822, 2021.
Han, Y., Xu, H., Bi, X. H., Lin, F. M., Li, J., Zhang, Y. F., and Feng, Y. C.: The effect of atmospheric particulates on the rainwater chemistry in the Yangtze River Delta, China, J. Air Waste Manage., 69, 1452–1466, https://doi.org/10.1080/10962247.2019.1674750, 2019.
Hopke, P. K., Dai, Q., Li, L., Feng, Y.: Global review of recent source apportionments for airborne particulate matter, Sci. Total Environ., 740, 140091, https://doi.org/10.1016/j.scitotenv.2020.140091, 2020.
Hopke, P. K., Feng, Y. C., and Dai, Q.: Source apportionment of particle number concentrations: A global review, Sci. Total Environ., 819, 153104, https://doi.org/10.1016/j.scitotenv.2022.153104, 2022.
Hsu, Y., Divita, F., and Dorn, J.: SPECIATE 5.0 – Speciation Database Development Documentation, Final Report, Abt Associates Inc./Office of Research and Development/U.S. Environmental Protection Agency Research Triangle Park, NC27711, [data set], https://www.epa.gov/sites/default/files/2019-07/speciate_5.0_0.zip (last access: 14 November 2023), 2019.
Huang, C. H., Hu, J. L., Xue, T., Xu, H., and Wang, M.: High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019, Environ. Sci. Technol., 55, 2152–2162, https://doi.org/10.1021/acs.est.0c05815, 2021.
Huang, Z. J., Zheng, J. Y., Qu, J. M., Zhong, Z. M., Wu, Y. Q., and Shao, M.: A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models, Environ. Sci. Technol., 53, 3110–3118, https://doi.org/10.1021/acs.est.8b06326, 2019.
Ji, Z., Gan, M., Fan, X., Chen, X., Li, Q., Lv, W., Tian, Y., Zhou, Y., and Jiang, T.: Characteristics of PM2.5 from iron ore sintering process: Influences of raw materials and controlling methods, J. Clean. Prod., 148, 12–22, https://doi.org/10.1016/j.jclepro.2017.01.103, 2017.
Li, J., Wu, Y., Ren, L., Wang, W., Tao, J., Gao, Y., Li, G., Yang, X., Han, Z., and Zhang, R.: Variation in PM2.5 sources in central North China Plain during 2017–2019: Response to mitigation strategies, J. Environ. Manage., 28, 112370, https://doi.org/10.1016/j.jenvman.2021.112370, 2021.
Li, M., Hu, M., Du, B., Guo, Q., Tan, T., Zheng, J., Huang, X., He, L., Wu, Z., and Guo, S.: Temporal and spatial distribution of PM2.5 chemical composition in a coastal city of Southeast China, Sci. Total Environ., 605–606, 337–346, https://doi.org/10.1016/j.scitotenv.2017.03.260, 2017a.
Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B., Cui, H., Man, H., Zhang, Q., and He, K.: Anthropogenic emission inventories in China: a review, Natl. Sci. Rev., 4, 834–866, https://doi.org/10.1093/nsr/nwy044, 2017b.
Li, X., He, K., Li, C., Yang, F., Zhao, Q., Ma, Y., Chen, Y., Ouyang, W., and Chen, G.: PM2.5 mass, chemical composition, and light extinction before and during the 2008 Beijing Olympics, J. Geophys. Res., 118, 12158–12167, https://doi.org/10.1002/2013JD020106, 2013.
Liang, F., Xiao, Q., Yang, X., Liu, F., Li, J., Lu, X., Liu, Y., and Gu, D.: The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China, P. Natl. Acad. Sci. USA, 117, 25601–25608, https://doi.org/10.1073/pnas.1919641117, 2020.
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Short summary
This study explores how the variation in the source profiles adopted in chemical transport models (CTMs) impacts the simulated results of chemical components in PM2.5 based on sensitivity analysis. The impact on PM2.5 components cannot be ignored, and its influence can be transmitted and linked between components. The representativeness and timeliness of the source profile should be paid adequate attention in air quality simulation.
This study explores how the variation in the source profiles adopted in chemical transport...
