Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3617-2024
© Author(s) 2024. 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-17-3617-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 May 2024
Development and technical paper |
| 06 May 2024
| 06 May 2024
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Shuai Wang
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Mengyuan Zhang
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Yueqi Gao
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Peng Wang
Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
Qingyan Fu
Shanghai Environmental Monitoring Center, Shanghai 200235, China
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
Institute of Eco-Chongming (IEC), Shanghai 200062, China
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Ozone (O3) pollution has received extensive attention due to worsening air quality and rising health risks. The Chinese National Day holiday (CNDH), which is associated with intensive commercial and tourist activities, serves as a valuable experiment to evaluate the O3 response during the holiday. We find sharply increasing trends of observed O3 concentrations throughout China during the CNDH, leading to 33 % additional total daily deaths.
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In recent years, summer O3 pollution over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 pollution can co-occur. However, the synoptic weather pattern (SWP) related to this compound pollution remains unclear. Regional PM2.5 and O3 compound pollution is characterized by various SWPs with different dominant factors. Our findings provide insights into the regional co-occurring high PM2.5 and O3 levels via the effects of certain meteorological factors.
Jinlong Ma, Juanyong Shen, Peng Wang, Shengqiang Zhu, Yu Wang, Pengfei Wang, Gehui Wang, Jianmin Chen, and Hongliang Zhang
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Due to the reduced anthropogenic emissions during the COVID-19 lockdown, mainly from the transportation and industrial sectors, PM2.5 decreased significantly in the whole Yangtze River Delta (YRD) and its major cities. However, the contributions and relative importance of different source sectors and regions changed differently, indicating that control strategies should be adjusted accordingly for further pollution control.
Kun Zhang, Ling Huang, Qing Li, Juntao Huo, Yusen Duan, Yuhang Wang, Elly Yaluk, Yangjun Wang, Qingyan Fu, and Li Li
Atmos. Chem. Phys., 21, 5905–5917, https://doi.org/10.5194/acp-21-5905-2021, https://doi.org/10.5194/acp-21-5905-2021, 2021
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Recently, high O3 concentrations were frequently observed in rural areas of the Yangtze River Delta (YRD) region under stagnant conditions. Using an online measurement and observation-based model, we investigated the budget of ROx radicals and the influence of isoprene chemistry on O3 formation. Our results underline that isoprene chemistry in the rural atmosphere becomes important with the participation of anthropogenic NOx.
Mengyuan Zhang, Arpit Katiyar, Shengqiang Zhu, Juanyong Shen, Men Xia, Jinlong Ma, Sri Harsha Kota, Peng Wang, and Hongliang Zhang
Atmos. Chem. Phys., 21, 4025–4037, https://doi.org/10.5194/acp-21-4025-2021, https://doi.org/10.5194/acp-21-4025-2021, 2021
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We studied changes in air quality in India induced by the COVID-19 lockdown through both surface observations and the CMAQ model. Our results show that emission reductions improved the air quality across India during the lockdown. On average, the levels of PM2.5 and O3 decreased by 28 % and 15 %, indicating positive effects of lockdown measures. We suggest that more stringent and localized emission control strategies should be implemented in India to mitigate air pollutions.
Junjun Deng, Hao Guo, Hongliang Zhang, Jialei Zhu, Xin Wang, and Pingqing Fu
Atmos. Chem. Phys., 20, 14419–14435, https://doi.org/10.5194/acp-20-14419-2020, https://doi.org/10.5194/acp-20-14419-2020, 2020
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One-year source apportionment of BC aerosols in a coastal city in China was conducted with the light-absorption observation-based method and source-oriented model. Source contributions identified by the two source apportionment methods were compared. Temporal variability, potential sources and transport pathways of BC from fossil fuel and biomass burning were characterized. Significant influence of biomass burning in North and East–Central China on BC in the region was highlighted.
Zhihao Shi, Lin Huang, Jingyi Li, Qi Ying, Hongliang Zhang, and Jianlin Hu
Atmos. Chem. Phys., 20, 13455–13466, https://doi.org/10.5194/acp-20-13455-2020, https://doi.org/10.5194/acp-20-13455-2020, 2020
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Meteorological conditions play important roles in the formation of O3 and PM2.5 pollution in China. O3 is most sensitive to temperature and the sensitivity is dependent on the O3 chemistry formation or loss regime. PM2.5 is negatively sensitive to temperature, wind speed, and planetary boundary layer height and positively sensitive to humidity. The results imply that air quality in certain regions of China is sensitive to climate changes.
Rui Li, Qiongqiong Wang, Xiao He, Shuhui Zhu, Kun Zhang, Yusen Duan, Qingyan Fu, Liping Qiao, Yangjun Wang, Ling Huang, Li Li, and Jian Zhen Yu
Atmos. Chem. Phys., 20, 12047–12061, https://doi.org/10.5194/acp-20-12047-2020, https://doi.org/10.5194/acp-20-12047-2020, 2020
Xiaofei Qin, Leiming Zhang, Guochen Wang, Xiaohao Wang, Qingyan Fu, Jian Xu, Hao Li, Jia Chen, Qianbiao Zhao, Yanfen Lin, Juntao Huo, Fengwen Wang, Kan Huang, and Congrui Deng
Atmos. Chem. Phys., 20, 10985–10996, https://doi.org/10.5194/acp-20-10985-2020, https://doi.org/10.5194/acp-20-10985-2020, 2020
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The uncertainties in mercury emissions are much larger from natural sources than anthropogenic sources. A method was developed to quantify the contributions of natural surface emissions to ambient GEM based on PMF modeling. The annual GEM concentration in eastern China showed a decreasing trend from 2015 to 2018, while the relative contribution of natural surface emissions increased significantly from 41 % in 2015 to 57 % in 2018, gradually surpassing those from anthropogenic sources.
Jian Xu, Jia Chen, Na Zhao, Guochen Wang, Guangyuan Yu, Hao Li, Juntao Huo, Yanfen Lin, Qingyan Fu, Hongyu Guo, Congrui Deng, Shan-Hu Lee, Jianmin Chen, and Kan Huang
Atmos. Chem. Phys., 20, 7259–7269, https://doi.org/10.5194/acp-20-7259-2020, https://doi.org/10.5194/acp-20-7259-2020, 2020
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This study provided evidence that gas-particle partitioning of ammonia, as opposed to ammonia concentration, plays a critical role in the haze formation. A reduction in ammonia emissions alone may not reduce air pollution effectively, at least at rural agricultural sites in China.
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.
Jianming Xu, Xuexi Tie, Wei Gao, Yanfen Lin, and Qingyan Fu
Atmos. Chem. Phys., 19, 9017–9035, https://doi.org/10.5194/acp-19-9017-2019, https://doi.org/10.5194/acp-19-9017-2019, 2019
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The PM2.5 in China has decreased significantly in recent years as a result of the implementation of the Chinese Clean Air Action Plan in 2013, while the O3 pollution is getting worse, especially in megacities. The work aims to better understand the elevated O3 pollution in the megacity of Shanghai, China, and its response to emission changes, which is important for developing an effective emission control strategy in the future.
Xinning Wang, Yin Shen, Yanfen Lin, Jun Pan, Yan Zhang, Peter K. K. Louie, Mei Li, and Qingyan Fu
Atmos. Chem. Phys., 19, 6315–6330, https://doi.org/10.5194/acp-19-6315-2019, https://doi.org/10.5194/acp-19-6315-2019, 2019
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Shipping emissions were measured online at Shanghai Port, and their impacts on local air quality at the port and in the surrounding area were quantitatively assessed. Ship emission plumes were readily detectable before they dissipated. We captured ship emission plumes using synchronized peaks of SO2 and vanadium particles. By measuring the pollutant concentrations during plumes and their occurrence frequency, we made quantitative estimations of ship emission impacts on port air quality.
Junlan Feng, Yan Zhang, Shanshan Li, Jingbo Mao, Allison P. Patton, Yuyan Zhou, Weichun Ma, Cong Liu, Haidong Kan, Cheng Huang, Jingyu An, Li Li, Yin Shen, Qingyan Fu, Xinning Wang, Juan Liu, Shuxiao Wang, Dian Ding, Jie Cheng, Wangqi Ge, Hong Zhu, and Katherine Walker
Atmos. Chem. Phys., 19, 6167–6183, https://doi.org/10.5194/acp-19-6167-2019, https://doi.org/10.5194/acp-19-6167-2019, 2019
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This study aims to estimate the emissions, air quality and population exposure impacts of shipping in 2015, prior to the implementation of the DECAs. It shows that ship emissions within 12 NM of the shore could account for over 55 % of the shipping impact on air pollution in the YRD in summer. Ships entering the Yangtze River and other inland waterways of Shanghai contribute 40–80 % of the ship-related air pollution and population exposure，which both have important implications regarding policy.
Xiaofei Qin, Xiaohao Wang, Yijie Shi, Guangyuan Yu, Na Zhao, Yanfen Lin, Qingyan Fu, Dongfang Wang, Zhouqing Xie, Congrui Deng, and Kan Huang
Atmos. Chem. Phys., 19, 5923–5940, https://doi.org/10.5194/acp-19-5923-2019, https://doi.org/10.5194/acp-19-5923-2019, 2019
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The seasonal pattern of atmospheric mercury species over a regional transport intersection zone in east China indicated impacts from both natural re-emissions and anthropogenic emissions. Quasi-local sources were more important than long-range transport for mercury, opposite from particles. Shipping activities were especially outstanding emissions. Abnormally high GOM was ascribed to the high oxidant levels. The gas–particle partition inhibited the formation of GOM under high particle levels.
Xue Qiao, Hao Guo, Ya Tang, Pengfei Wang, Wenye Deng, Xing Zhao, Jianlin Hu, Qi Ying, and Hongliang Zhang
Atmos. Chem. Phys., 19, 5791–5803, https://doi.org/10.5194/acp-19-5791-2019, https://doi.org/10.5194/acp-19-5791-2019, 2019
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A source-oriented version of the CMAQ model was used to quantify contributions from nine regions to PM2.5 and its components in the 18 cities within Sichuan Basin. Nonlocal emissions contribute 39–66 % and 25–52 % to the citywide average PM2.5 concentrations of 45–126 and 14–31 µg m3 in the winter and summer, respectively. This study demonstrates the importance of joint emission control efforts among cities within the SCB and neighboring regions to the east.
Zhaofeng Tan, Keding Lu, Meiqing Jiang, Rong Su, Hongli Wang, Shengrong Lou, Qingyan Fu, Chongzhi Zhai, Qinwen Tan, Dingli Yue, Duohong Chen, Zhanshan Wang, Shaodong Xie, Limin Zeng, and Yuanhang Zhang
Atmos. Chem. Phys., 19, 3493–3513, https://doi.org/10.5194/acp-19-3493-2019, https://doi.org/10.5194/acp-19-3493-2019, 2019
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We evaluated the atmospheric oxidation capacity (AOC) in four Chinese megacities during photochemically polluted seasons. The chemical production of ozone and particle nitrate was diagnosed through a box model, which can be attributed to daytime radical chemistry. Our work highlights that the formation of both ozone and fine particles is largely driven by the atmospheric radical chemistry in China. Consequently, we suggest future pollution mitigation strategies should consider the role of AOC.
Hao Guo, Sri Harsha Kota, Kaiyu Chen, Shovan Kumar Sahu, Jianlin Hu, Qi Ying, Yuan Wang, and Hongliang Zhang
Atmos. Chem. Phys., 18, 15219–15229, https://doi.org/10.5194/acp-18-15219-2018, https://doi.org/10.5194/acp-18-15219-2018, 2018
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A total of 1.04 million premature mortalities and up to 2 years of life lost (YLL) per person were estimated in India in 2015 due to PM2.5. Premature mortality due to cerebrovascular disease (CEVD) was the highest (0.44 million), followed by ischaemic heart disease (IHD, 0.40 million). The residential sector was the largest contributor, followed by industry, agriculture and energy. Reducing PM2.5 concentrations would lead to a significant reduction in premature mortality and YLL.
Mingjie Kang, Pingqing Fu, Kimitaka Kawamura, Fan Yang, Hongliang Zhang, Zhengchen Zang, Hong Ren, Lujie Ren, Ye Zhao, Yele Sun, and Zifa Wang
Atmos. Chem. Phys., 18, 13947–13967, https://doi.org/10.5194/acp-18-13947-2018, https://doi.org/10.5194/acp-18-13947-2018, 2018
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Molecular characterization and spatial distribution of biogenic primary organic aerosol (POA) and secondary organic aerosol (SOA) in the marine atmosphere are not well known. Here, we analysed the organic molecular composition of marine aerosols collected during a marine cruise in the East China Sea during May–June 2014. Our results suggest that the Asian continent can be a natural emitter of biogenic POA and SOA, which can be transported to the downwind marine atmosphere.
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.
Qiongzhen Wang, Xinyi Dong, Joshua S. Fu, Jian Xu, Congrui Deng, Yilun Jiang, Qingyan Fu, Yanfen Lin, Kan Huang, and Guoshun Zhuang
Atmos. Chem. Phys., 18, 3505–3521, https://doi.org/10.5194/acp-18-3505-2018, https://doi.org/10.5194/acp-18-3505-2018, 2018
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A synergy of ground-based atmospheric chemistry observation, lidar, and numerical modeling was used to investigate a super dust event passing over Shanghai. The degree of dust that was modified by anthropogenic sources highly depended on the transport pathways. A community regional air quality model with improved dust scheme reproduced reasonable dust chemistry results. The chemical and optical properties of evolving dust are crucial for evaluating the climatic effects of dust.
Jianlin Hu, Xun Li, Lin Huang, Qi Ying, Qiang Zhang, Bin Zhao, Shuxiao Wang, and Hongliang Zhang
Atmos. Chem. Phys., 17, 13103–13118, https://doi.org/10.5194/acp-17-13103-2017, https://doi.org/10.5194/acp-17-13103-2017, 2017
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The model performance of CMAQ with WRF using four different emission inventories in China was validated and compared to obtain the best air pollutants prediction for health effect studies of severe air pollution. The differences in performance of chemical transport model were analyzed for different months and regions in the vast part of China and ensemble predictions were firstly obtained from different inventories for health analysis with minimized errors for pollutants including PM2.5 and O3.
Jianlin Hu, Shantanu Jathar, Hongliang Zhang, Qi Ying, Shu-Hua Chen, Christopher D. Cappa, and Michael J. Kleeman
Atmos. Chem. Phys., 17, 5379–5391, https://doi.org/10.5194/acp-17-5379-2017, https://doi.org/10.5194/acp-17-5379-2017, 2017
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Organic aerosol is a major constituent of ultrafine particulate matter (PM0.1). In this study, a source-oriented air quality model was used to simulate the concentrations and sources of primary and secondary organic aerosols in PM0.1 in California for a 9-year modeling period to provide useful information for epidemiological studies to further investigate the associations with health outcomes.
Jianlin Hu, Peng Wang, Qi Ying, Hongliang Zhang, Jianjun Chen, Xinlei Ge, Xinghua Li, Jingkun Jiang, Shuxiao Wang, Jie Zhang, Yu Zhao, and Yingyi Zhang
Atmos. Chem. Phys., 17, 77–92, https://doi.org/10.5194/acp-17-77-2017, https://doi.org/10.5194/acp-17-77-2017, 2017
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An annual simulation of secondary organic aerosol (SOA) concentrations in China with updated SOA formation pathways reveals that SOA can be a significant contributor to PM2.5 in major urban areas. Summer SOA is dominated by emissions from biogenic sources, while winter SOA is dominated by anthropogenic emissions such as alkanes and aromatic compounds. Reactive surface uptake of dicarbonyls throughout the year and isoprene epoxides in summer is the most important contributor.
Jianlin Hu, Jianjun Chen, Qi Ying, and Hongliang Zhang
Atmos. Chem. Phys., 16, 10333–10350, https://doi.org/10.5194/acp-16-10333-2016, https://doi.org/10.5194/acp-16-10333-2016, 2016
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A yearlong (2013) air-quality simulation was conducted to provide detailed temporal and spatial information of ozone, PM2.5 total and chemical components. The paper firstly compared the simulated air pollutants in China with country-wide public available observations for a whole year. It proves the ability of CMAQ in reproducing severe air pollution in China, shows directions that need to be improved, and benefits future source apportionment and human exposure studies.
Hsiang-He Lee, Shu-Hua Chen, Michael J. Kleeman, Hongliang Zhang, Steven P. DeNero, and David K. Joe
Atmos. Chem. Phys., 16, 8353–8374, https://doi.org/10.5194/acp-16-8353-2016, https://doi.org/10.5194/acp-16-8353-2016, 2016
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A source-oriented CCN module was implemented in a source-oriented chemistry model to study the effect of aerosol mixing state on fog formation. The fraction of aerosols activating into CCN at a supersaturation of 0.5 % in the Central Valley decreased from 94 % in the internal mixture model to 80 % in the source-oriented model. The internal mixture model predicted greater CCN activation than the source-oriented model due to artificial coating of hydrophobic particles with hygroscopic components.
J. Hu, H. Zhang, Q. Ying, S.-H. Chen, F. Vandenberghe, and M. J. Kleeman
Atmos. Chem. Phys., 15, 3445–3461, https://doi.org/10.5194/acp-15-3445-2015, https://doi.org/10.5194/acp-15-3445-2015, 2015
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Air quality model simulations have been conducted for California from 2000 to 2009 with 4km spatial resolution to provide exposure data for health effect studies. Comprehensive analysis shows that predicted concentrations for many pollutants are in agreement with measurements at monitoring stations, building confidence that the fields may be useful at times and locations where measurements are not available. Data can be downloaded for free at http://faculty.engineering.ucdavis.edu/kleeman/.
S. Xiao, M. Y. Wang, L. Yao, M. Kulmala, B. Zhou, X. Yang, J. M. Chen, D. F. Wang, Q. Y. Fu, D. R. Worsnop, and L. Wang
Atmos. Chem. Phys., 15, 1769–1781, https://doi.org/10.5194/acp-15-1769-2015, https://doi.org/10.5194/acp-15-1769-2015, 2015
H. Zhang, S. P. DeNero, D. K. Joe, H.-H. Lee, S.-H. Chen, J. Michalakes, and M. J. Kleeman
Atmos. Chem. Phys., 14, 485–503, https://doi.org/10.5194/acp-14-485-2014, https://doi.org/10.5194/acp-14-485-2014, 2014
Related subject area
Atmospheric sciences
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
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
Assessment of object-based indices to identify convective organization
The Global Forest Fire Emissions Prediction System version 1.0
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Orbital-Radar v1.0.0: A tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
TAMS: a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems in simulated and satellite-derived datasets
Development of the adjoint of the unified tropospheric–stratospheric chemistry extension (UCX) in GEOS-Chem adjoint v36
New explicit formulae for the settling speed of prolate spheroids in the atmosphere: theoretical background and implementation in AerSett v2.0.2
ZJU-AERO V0.5: an Accurate and Efficient Radar Operator designed for CMA-GFS/MESO with the capability to simulate non-spherical hydrometeors
The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations
Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3
The CHIMERE chemistry-transport model v2023r1
tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics
Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1
Modeling of PAHs From Global to Regional Scales: Model Development and Investigation of Health Risks from 2013 to 2018 in China
WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model
Representing effects of surface heterogeneity in a multi-plume eddy diffusivity mass flux boundary layer parameterization
Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?
A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
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.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
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Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-129, https://doi.org/10.5194/gmd-2024-129, 2024
Revised manuscript accepted for GMD
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Orbital-radar is a Python tool transferring sub-orbital radar data (ground-based, airborne, and forward-simulated NWP) into synthetical space-borne cloud profiling radar data mimicking the platform characteristics, e.g. EarthCARE or CloudSat CPR. The novelty of orbital-radar is the simulation platform characteristic noise floors and errors. By this long time data sets can be transformed into synthetic observations for Cal/Valor sensitivity studies for new or future satellite missions.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
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In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
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A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
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The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
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Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
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This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
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Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
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This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
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Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
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TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
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Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
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We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
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A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 2024
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Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
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In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
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A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-1437, https://doi.org/10.5194/egusphere-2024-1437, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can well reproduce the distribution of PAHs. 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 of BaP is less than PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although "the Action Plan" has been implemented.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
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Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Nathan P. Arnold
Geosci. Model Dev., 17, 5041–5056, https://doi.org/10.5194/gmd-17-5041-2024, https://doi.org/10.5194/gmd-17-5041-2024, 2024
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Earth system models often represent the land surface at smaller scales than the atmosphere, but surface–atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
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Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
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Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
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Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Cited articles
Aleksankina, K., Reis, S., Vieno, M., and Heal, M. R.: Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model, Atmos. Chem. Phys., 19, 2881–2898, https://doi.org/10.5194/acp-19-2881-2019, 2019.
Bai, K., Li, K., Ma, M., Li, K., Li, Z., Guo, J., Chang, N.-B., Tan, Z., and Han, D.: LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion, Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, 2022.
Beekmann, M. and Derognat, C.: Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the atmospheric pollution over the Paris area (ESQUIF) campaign, J. Geophys. Res.-Atmos., 108, 8859, 10.1029/2003JD003391, 2003.
Bei, N., Wu, J., Elser, M., Feng, T., Cao, J., El-Haddad, I., Li, X., Huang, R., Li, Z., Long, X., Xing, L., Zhao, S., Tie, X., Prévôt, A. S. H., and Li, G.: Impacts of meteorological uncertainties on the haze formation in Beijing–Tianjin–Hebei (BTH) during wintertime: a case study, Atmos. Chem. Phys., 17, 14579–14591, https://doi.org/10.5194/acp-17-14579-2017, 2017.
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, 2016.
Binkowski, F. S. and Roselle, S. J.: Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description, J. Geophys. Res.-Atmos., 108, 4183, https://doi.org/10.1029/2001JD001409, 2003.
Carlton, A. G., Turpin, B. J., Altieri, K. E., Seitzinger, S., Reff, A., Lim, H.-J., and Ervens, B.: Atmospheric oxalic acid and SOA production from glyoxal: Results of aqueous photooxidation experiments, Atmos. Environ., 41, 7588–7602, https://doi.org/10.1016/j.atmosenv.2007.05.035, 2007.
Carter, W. P. and Heo, G.: Development of revised SAPRC aromatics mechanisms, Atmos. Environ., 77, 404–414, 2013.
Chen, S., Wang, H., Lu, K., Zeng, L., Hu, M., and Zhang, Y.: The trend of surface ozone in Beijing from 2013 to 2019: Indications of the persisting strong atmospheric oxidation capacity, Atmos. Environ., 242, 117801, https://doi.org/10.1016/j.atmosenv.2020.117801, 2020.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, 13–17 August 2016, WOS:000485529800092, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Chen, Z. Y., Chen, D. L., Zhao, C. F., Kwan, M. P., Cai, J., Zhuang, Y., Zhao, B., Wang, X. Y., Chen, B., Yang, J., Li, R. Y., He, B., Gao, B. B., Wang, K. C., 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, Y., He, K. B., Du, Z. Y., Zheng, M., Duan, F. K., and Ma, Y. L.: Humidity plays an important role in the PM2.5 pollution in Beijing, Environ. Pollut., 197, 68–75, https://doi.org/10.1016/j.envpol.2014.11.028, 2015.
Fry, J. L., Draper, D. C., Barsanti, K. C., Smith, J. N., Ortega, J., Winkler, P. M., Lawler, M. J., Brown, S. S., Edwards, P. M., Cohen, R. C., and Lee, L.: Secondary Organic Aerosol Formation and Organic Nitrate Yield from NO3 Oxidation of Biogenic Hydrocarbons, Environ. Sci. Technol., 48, 11944–11953, https://doi.org/10.1021/es502204x, 2014.
Grinsztajn, L., Oyallon, E., and Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data?, Adv. Neur. In., 35, 507–520, 2022.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Hanna, S., Russell, A., Wilkinson, J., Vukovich, J., and Hansen, D.: Monte Carlo estimation of uncertainties in BEIS3 emission outputs and their effects on uncertainties in chemical transport model predictions, J. Geophys. Res.-Atmos., 110, D01302, https://doi.org/10.1029/2004JD004986, 2005.
Hou, L. L., Dai, Q. L., Song, C. B., Liu, B. W., Guo, F. Z., Dai, T. J., Li, L. X., Liu, B. S., Bi, X. H., Zhang, Y. F., and Feng, Y. C.: Revealing Drivers of Haze Pollution by Explainable Machine Learning, Environ. Sci. Tech. Let., 9, 112–119, https://doi.org/10.1021/acs.estlett.1c00865, 2022.
Hu, J., Wang, Y., Ying, Q., and Zhang, H.: Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China, Atmos. Environ., 95, 598–609, 2014.
Hu, J., Chen, J., Ying, Q., and Zhang, H.: One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system, Atmos. Chem. Phys., 16, 10333–10350, https://doi.org/10.5194/acp-16-10333-2016, 2016.
Hu, J., Huang, L., Chen, M., Liao, H., Zhang, H., Wang, S., Zhang, Q., and Ying, Q.: Premature mortality attributable to particulate matter in China: source contributions and responses to reductions, Environ. Sci. Technol., 51, 9950–9959, 2017a.
Hu, J., Wang, P., Ying, Q., Zhang, H., Chen, J., Ge, X., Li, X., Jiang, J., Wang, S., Zhang, J., Zhao, Y., and Zhang, Y.: Modeling biogenic and anthropogenic secondary organic aerosol in China, Atmos. Chem. Phys., 17, 77–92, https://doi.org/10.5194/acp-17-77-2017, 2017b.
Huang, Z., Hu, Y., Zheng, J., Yuan, Z., Russell, A. G., Ou, J., and Zhong, Z.: A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM(2.5) Simulations, Environ. Sci. Technol., 51, 3852–3859, https://doi.org/10.1021/acs.est.6b05479, 2017.
Huang, Z., Zheng, J., Ou, J., Zhong, Z., Wu, Y., 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.
Ke, G. L., Meng, Q., Finley, T., Wang, T. F., Chen, W., Ma, W. D., Ye, Q. W., and Liu, T. Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree, 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, 4–9 December, WOS:000452649403021, 2017.
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/nwx150, 2017.
Li, T., Zhang, Q., Peng, Y., Guan, X., Li, L., Mu, J., Wang, X., Yin, X., and Wang, Q.: Contributions of Various Driving Factors to Air Pollution Events: Interpretability Analysis from Machine Learning Perspective, Environ. Int., 107861, https://doi.org/10.1016/j.envint.2023.107861, 2023.
Liang, F., Xiao, Q., Huang, K., 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, 2020.
Liang, W., Luo, S., Zhao, G., and Wu, H.: Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms, Mathematics, 8, 765, https://doi.org/10.3390/math8050765, 2020.
Liu, J. and Xing, J.: Identifying Contributors to PM2.5 Simulation Biases of Chemical Transport Model Using Fully Connected Neural Networks, J. Adv. Model. Earth Sy., 15, e2021MS002898, https://doi.org/10.1029/2021MS002898, 2022.
Liu, J., Ding, J., Rexiding, M., Li, X., Zhang, J., Ran, S., Bao, Q., and Ge, X.: Characteristics of dust aerosols and identification of dust sources in Xinjiang, China, Atmos. Environ., 262, 118651, https://doi.org/10.1016/j.atmosenv.2021.118651, 2021a.
Liu, J., Chu, B., Chen, T., Zhong, C., Liu, C., Ma, Q., Ma, J., Zhang, P., and He, H.: Secondary organic aerosol formation potential from ambient air in Beijing: effects of atmospheric oxidation capacity at different pollution levels, Environ. Sci. Technol., 55, 4565–4572, 2021b.
Liu, S., Xing, J., Sahu, S. K., Liu, X., Liu, S., Jiang, Y., Zhang, H., Li, S., Ding, D., Chang, X., and Wang, S.: Wind-blown dust and its impacts on particulate matter pollution in Northern China: current and future scenarios, Environ. Res. Lett., 16, 114041, https://doi.org/10.1088/1748-9326/ac31ec, 2021.
Liu, X., Shen, G., Chen, L., Qian, Z., Zhang, N., Chen, Y., Chen, Y., Cao, J., Cheng, H., Du, W., Li, B., Li, G., Li, Y., Liang, X., Liu, M., Lu, H., Luo, Z., Ren, Y., Zhang, Y., Zhu, D., and Tao, S.: Spatially Resolved Emission Factors to Reduce Uncertainties in Air Pollutant Emission Estimates from the Residential Sector, Environ. Sci. Technol., 55, 4483–4493, https://doi.org/10.1021/acs.est.0c08568, 2021.
Liu, X., Lu, D., Zhang, A., Liu, Q., and Jiang, G.: Data-driven machine learning in environmental pollution: Gains and problems, Environ. Sci. Technol., 56, 2124–2133, 2022.
Loyola-González, O., Ramírez-Sáyago, E., and Medina-Pérez, M. A.: Towards improving decision tree induction by combining split evaluation measures, Knowl.-Based Syst., 277, 110832, https://doi.org/10.1016/j.knosys.2023.110832, 2023.
Ma, J., Shen, J., Wang, P., Zhu, S., Wang, Y., Wang, P., Wang, G., Chen, J., and Zhang, H.: Modeled changes in source contributions of particulate matter during the COVID-19 pandemic in the Yangtze River Delta, China, Atmos. Chem. Phys., 21, 7343–7355, https://doi.org/10.5194/acp-21-7343-2021, 2021.
Meng, C., Cheng, T. H., Gu, X. F., Shi, S. Y., Wang, W. N., Wu, Y., and Bao, F. W.: Contribution of meteorological factors to particulate pollution during winters in Beijing, Sci. Total Environ., 656, 977–985, https://doi.org/10.1016/j.scitotenv.2018.11.365, 2019.
NCEP, F.: National Centers for Environmental Prediction/National Weather Service/NOAA/US Department of Commerce, NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 2000, updated daily.
Pleim, J. and Ran, L.: Surface flux modeling for air quality applications, Atmosphere, 2, 271–302, 2011.
Qiao, X., Ying, Q., Li, X., Zhang, H., Hu, J., Tang, Y., and Chen, X.: Source apportionment of PM2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented Community Multiscale Air Quality model, Sci. Total Environ., 612, 462–471, https://doi.org/10.1016/j.scitotenv.2017.08.272, 2018.
Qu, Y., Voulgarakis, A., Wang, T., Kasoar, M., Wells, C., Yuan, C., Varma, S., and Mansfield, L.: A study of the effect of aerosols on surface ozone through meteorology feedbacks over China, Atmos. Chem. Phys., 21, 5705–5718, https://doi.org/10.5194/acp-21-5705-2021, 2021.
Ryu, Y.-H. and Min, S.-K.: Improving Wet and Dry Deposition of Aerosols in WRF-Chem: Updates to Below-Cloud Scavenging and Coarse-Particle Dry Deposition, J. Adv. Model. Earth Sy., 14, e2021MS002792, https://doi.org/10.1029/2021MS002792, 2022.
Shang, D., Peng, J., Guo, S., Wu, Z., and Hu, M.: Secondary aerosol formation in winter haze over the Beijing-Tianjin-Hebei Region, China, Front. Env. Sci. Eng., 15, 1–13, 2021.
Shen, H., Luo, Z., Xiong, R., Liu, X., Zhang, L., Li, Y., Du, W., Chen, Y., Cheng, H., Shen, G., and Tao, S.: A critical review of pollutant emission factors from fuel combustion in home stoves, Environ. Int., 157, 106841, https://doi.org/10.1016/j.envint.2021.106841, 2021.
Shu, Q., Murphy, B., Schwede, D., Henderson, B. H., Pye, H. O. T., Appel, K. W., Khan, T. R., and Perlinger, J. A.: Improving the particle dry deposition scheme in the CMAQ photochemical modeling system, Atmos. Environ., 289, 119343, https://doi.org/10.1016/j.atmosenv.2022.119343, 2022.
Stirnberg, R., Cermak, J., Kotthaus, S., Haeffelin, M., Andersen, H., Fuchs, J., Kim, M., Petit, J.-E., and Favez, O.: Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning, Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021, 2021.
Sun, H., Shin, Y. M., Xia, M., Ke, S., Wan, M., Yuan, L., Guo, Y., and Archibald, A. T.: Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990–2019: A Space–Time Bayesian Neural Network Downscaler, Environ. Sci. Technol., 56, 7337–7349, 2021.
Sun, X., Liu, M., and Sima, Z.: A novel cryptocurrency price trend forecasting model based on LightGBM, Financ. Res. Lett., 32, 101084, https://doi.org/10.1016/j.frl.2018.12.032, 2020.
US EPA Office of Research and Development: CMAQv5.0.2 (5.0.2), Zenodo [code], https://doi.org/10.5281/zenodo.1079898, 2014.
Wang, P., Qiao, X., and Zhang, H.: Modeling PM2.5 and O3 with aerosol feedbacks using WRF/Chem over the Sichuan Basin, southwestern China, Chemosphere, 254, 126735, https://doi.org/10.1016/j.chemosphere.2020.126735, 2020.
Wang, S., Wang, P., Qi, Q., Wang, S., Meng, X., Kan, H., Zhu, S., and Zhang, H.: Improved estimation of particulate matter in China based on multisource data fusion, Sci. Total Environ., 161552, https://doi.org/10.1016/j.scitotenv.2023.161552, 2023a.
Wang, S., Wang, P., Zhang, R., Meng, X., Kan, H., and Zhang, H.: Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020, Chemosphere, 330, 138742, https://doi.org/10.1016/j.chemosphere.2023.138742, 2023b.
Wang, S., Zhang, M., Gao, Y., Wang, P., and Zhang, H.: Diagnosing drivers of PM2.5 simulation biases from meteorology, chemical composition, and emission sources using an efficient machine learning method, Zenodo [code and data set], https://doi.org/10.5281/zenodo.10283739, 2023c.
Wang, S. Y., Zhang, Y. L., Ma, J. L., Zhu, S. Q., Shen, J. Y., Wang, P., and Zhang, H. L.: Responses of decline in air pollution and recovery associated with COVID-19 lockdown in the Pearl River Delta, Sci. Total Environ., 756, 143868, https://doi.org/10.1016/j.scitotenv.2020.143868, 2021.
Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y.: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees, Atmos. Chem. Phys., 20, 3273–3289, https://doi.org/10.5194/acp-20-3273-2020, 2020.
Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Remote Sens. Environ., 252, 112136, https://doi.org/10.1016/j.rse.2020.112136, 2021a.
Wei, J., Li, Z., Pinker, R. T., Wang, J., Sun, L., Xue, W., Li, R., and Cribb, M.: Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM), Atmos. Chem. Phys., 21, 7863–7880, https://doi.org/10.5194/acp-21-7863-2021, 2021b.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
World Health Organization: WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide: executive summary, World Health Organization, Geneva, PMID: 3466200, 2021.
Wu, K., Yang, X. Y., Chen, D., Gu, S., Lu, Y. Q., Jiang, Q., Wang, K., Ou, Y. H., Qian, Y., Shao, P., and Lu, S. H.: Estimation of biogenic VOC emissions and their corresponding impact on ozone and secondary organic aerosol formation in China, Atmos. Res., 231, 104656, https://doi.org/10.1016/j.atmosres.2019.104656, 2020.
Wu, Y., Liu, J., Zhai, J., Cong, L., Wang, Y., Ma, W., Zhang, Z., and Li, C.: Comparison of dry and wet deposition of particulate matter in near-surface waters during summer, PloS one, 13, e0199241, https://doi.org/10.1371/journal.pone.0199241, 2018.
Xiao, Q., Zheng, Y., Geng, G., Chen, C., Huang, X., Che, H., Zhang, X., He, K., and Zhang, Q.: Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000–2018, Atmos. Chem. Phys., 21, 9475–9496, https://doi.org/10.5194/acp-21-9475-2021, 2021.
Xiao, Q., Geng, G., Xue, T., Liu, S., Cai, C., He, K., and Zhang, Q.: Tracking PM2.5 and O3 Pollution and the Related Health Burden in China 20132020, Environ. Sci. Technol., 56, 6922–6932, https://doi.org/10.1021/acs.est.1c04548, 2022.
Xu, Q., Wang, S., Jiang, J., Bhattarai, N., Li, X., Chang, X., Qiu, X., Zheng, M., Hua, Y., and Hao, J.: Nitrate dominates the chemical composition of PM2.5 during haze event in Beijing, China, Sci. Total Environ., 689, 1293–1303, 2019.
Xue, T., Zheng, Y., Tong, D., Zheng, B., Li, X., Zhu, T., and Zhang, Q.: Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000–2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations, Environ. Int., 123, 345–357, 2019.
Yan, J., Xu, Y., Cheng, Q., Jiang, S., Wang, Q., Xiao, Y., Ma, C., Yan, J., and Wang, X.: LightGBM: accelerated genomically designed crop breeding through ensemble learning, Genome Biol., 22, 1–24, 2021.
Yang, W., Li, J., Wang, W., Li, J., Ge, M., Sun, Y., Chen, X., Ge, B., Tong, S., Wang, Q., and Wang, Z.: Investigating secondary organic aerosol formation pathways in China during 2014, Atmos. Environ., 213, 133–147, https://doi.org/10.1016/j.atmosenv.2019.05.057, 2019.
Yang, X., Zhao, C. F., Guo, J. P., and Wang, Y.: Intensification of aerosol pollution associated with its feedback with surface solar radiation and winds in Beijing, J. Geophys. Res.-Atmos., 121, 4093–4099, https://doi.org/10.1002/2015jd024645, 2016.
Ye, X., Wang, X., and Zhang, L.: Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth, Environ. Sci. Technol., 56, 16665–16675, https://doi.org/10.1021/acs.est.2c05712, 2022.
Ying, Q., Wu, L., and Zhang, H.: Local and inter-regional contributions to PM2.5 nitrate and sulfate in China, Atmos. Environ., 94, 582–592, 2014.
Ying, Q., Li, J., and Kota, S. H.: Significant contributions of isoprene to summertime secondary organic aerosol in eastern United States, Environ. Sci. Technol., 49, 7834–7842, 2015.
Zhai, S., Jacob, D. J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T., and Liao, H.: Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmos. Chem. Phys., 19, 11031–11041, https://doi.org/10.5194/acp-19-11031-2019, 2019.
Zhang, H., Surratt, J. D., Lin, Y. H., Bapat, J., and Kamens, R. M.: Effect of relative humidity on SOA formation from isoprene/NO photooxidation: enhancement of 2-methylglyceric acid and its corresponding oligoesters under dry conditions, Atmos. Chem. Phys., 11, 6411–6424, https://doi.org/10.5194/acp-11-6411-2011, 2011.
Zhang, H., Li, J., Ying, Q., Yu, J. Z., Wu, D., Cheng, Y., He, K., and Jiang, J.: Source apportionment of PM2.5 nitrate and sulfate in China using a source-oriented chemical transport model, Atmos. Environ., 62, 228–242, 2012.
Zhang, Q., Quan, J., Tie, X., Li, X., Liu, Q., Gao, Y., and Zhao, D.: Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China, Sci. Total Environ., 502, 578–584, 2015.
Zhang, R., Sun, X. S., Shi, A. J., Huang, Y. H., Yan, J., Nie, T., Yan, X., and Li, X.: Secondary inorganic aerosols formation during haze episodes at an urban site in Beijing, China, Atmos. Environ., 177, 275–282, https://doi.org/10.1016/j.atmosenv.2017.12.031, 2018.
Zhao, B., Liou, K.-N., Gu, Y., Li, Q., Jiang, J. H., Su, H., He, C., Tseng, H.-L. R., Wang, S., Liu, R., Qi, L., Lee, W.-L., and Hao, J.: Enhanced PM2.5 pollution in China due to aerosol-cloud interactions, Sci. Rep.-UK, 7, 4453, https://doi.org/10.1038/s41598-017-04096-8, 2017.
Zhu, Q., Bi, J., Liu, X., Li, S., Wang, W., Zhao, Y., and Liu, Y.: Satellite-based long-term spatiotemporal patterns of surface ozone concentrations in China: 2005–2019, Environ. Health Persp., 130, 027004, https://doi.org/10.1289/EHP9406, 2022.
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Numerical models are widely used in air pollution modeling but suffer from significant biases....
