Articles | Volume 18, issue 15
https://doi.org/10.5194/gmd-18-4855-2025
© Author(s) 2025. 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-18-4855-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0 – Part 1: System description and preliminary experimental results
Yongzhu Liu
State Key Laboratory of Severe Weather Meteorological Science and Technology, Beijing, 10081, China
CMA Earth System Modeling and Prediction Centre (CEMC), Beijing, 10081, China
Key Laboratory of Earth System Modeling and Prediction, China Meteorological Administration, Beijing, 10081, China
Xiaoye Zhang
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather Meteorological Science and Technology, Beijing, 10081, China
Chinese Academy of Meteorological Sciences, Beijing, 10081, China
State Key Laboratory of Severe Weather Meteorological Science and Technology, Beijing, 10081, China
CMA Earth System Modeling and Prediction Centre (CEMC), Beijing, 10081, China
Key Laboratory of Earth System Modeling and Prediction, China Meteorological Administration, Beijing, 10081, China
Chao Wang
State Key Laboratory of Severe Weather Meteorological Science and Technology, Beijing, 10081, China
CMA Earth System Modeling and Prediction Centre (CEMC), Beijing, 10081, China
Key Laboratory of Earth System Modeling and Prediction, China Meteorological Administration, Beijing, 10081, China
Wenxing Jia
State Key Laboratory of Severe Weather Meteorological Science and Technology, Beijing, 10081, China
Chinese Academy of Meteorological Sciences, Beijing, 10081, China
Deying Wang
State Key Laboratory of Severe Weather Meteorological Science and Technology, Beijing, 10081, China
Chinese Academy of Meteorological Sciences, Beijing, 10081, China
Zhaorong Zhuang
State Key Laboratory of Severe Weather Meteorological Science and Technology, Beijing, 10081, China
CMA Earth System Modeling and Prediction Centre (CEMC), Beijing, 10081, China
Key Laboratory of Earth System Modeling and Prediction, China Meteorological Administration, Beijing, 10081, China
Xueshun Shen
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather Meteorological Science and Technology, Beijing, 10081, China
CMA Earth System Modeling and Prediction Centre (CEMC), Beijing, 10081, China
Key Laboratory of Earth System Modeling and Prediction, China Meteorological Administration, Beijing, 10081, China
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Xiaojing Shen, Junying Sun, Huizheng Che, Yangmei Zhang, Chunhong Zhou, Ke Gui, Wanyun Xu, Quan Liu, Junting Zhong, Can Xia, Xinyao Hu, Sinan Zhang, Jialing Wang, Shuo Liu, Jiayuan Lu, Aoyuan Yu, and Xiaoye Zhang
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Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
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Wenjie Zhang, Hong Wang, Xiaoye Zhang, Liping Huang, Yue Peng, Zhaodong Liu, Xiao Zhang, and Huizheng Che
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Mengying Li, Shaocai Yu, Xue Chen, Zhen Li, Yibo Zhang, Zhe Song, Weiping Liu, Pengfei Li, Xiaoye Zhang, Meigen Zhang, Yele Sun, Zirui Liu, Caiping Sun, Jingkun Jiang, Shuxiao Wang, Benjamin N. Murphy, Kiran Alapaty, Rohit Mathur, Daniel Rosenfeld, and John H. Seinfeld
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Lei Li, Yevgeny Derimian, Cheng Chen, Xindan Zhang, Huizheng Che, Gregory L. Schuster, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Christian Matar, Fabrice Ducos, Yana Karol, Benjamin Torres, Ke Gui, Yu Zheng, Yuanxin Liang, Yadong Lei, Jibiao Zhu, Lei Zhang, Junting Zhong, Xiaoye Zhang, and Oleg Dubovik
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Junting Zhong, Xiaoye Zhang, Ke Gui, Jie Liao, Ye Fei, Lipeng Jiang, Lifeng Guo, Liangke Liu, Huizheng Che, Yaqiang Wang, Deying Wang, and Zijiang Zhou
Earth Syst. Sci. Data, 14, 3197–3211, https://doi.org/10.5194/essd-14-3197-2022, https://doi.org/10.5194/essd-14-3197-2022, 2022
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Historical long-term PM2.5 records with high temporal resolution are essential but lacking for research and environmental management. Here, we reconstruct site-based and gridded PM2.5 datasets at 6-hour intervals from 1960 to 2020 that combine visibility, meteorological data, and emissions based on a machine learning model with extracted spatial features. These two PM2.5 datasets will lay the foundation of research studies associated with air pollution, climate change, and aerosol reanalysis.
Haochi Che, Michal Segal-Rozenhaimer, Lu Zhang, Caroline Dang, Paquita Zuidema, Arthur J. Sedlacek III, Xiaoye Zhang, and Connor Flynn
Atmos. Chem. Phys., 22, 8767–8785, https://doi.org/10.5194/acp-22-8767-2022, https://doi.org/10.5194/acp-22-8767-2022, 2022
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A 17-month in situ study on Ascension Island found low single-scattering albedo and strong absorption enhancement of the marine boundary layer aerosols during biomass burnings on the African continent, along with apparent patterns of regular monthly variability. We further discuss the characteristics and drivers behind these changes and find that biomass burning conditions in Africa may be the main factor influencing the optical properties of marine boundary aerosols.
Ke Gui, Wenrui Yao, Huizheng Che, Linchang An, Yu Zheng, Lei Li, Hujia Zhao, Lei Zhang, Junting Zhong, Yaqiang Wang, and Xiaoye Zhang
Atmos. Chem. Phys., 22, 7905–7932, https://doi.org/10.5194/acp-22-7905-2022, https://doi.org/10.5194/acp-22-7905-2022, 2022
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This study investigates the aerosol optical and radiative properties and meteorological drivers during two mega SDS events over Northern China in March 2021. The MODIS-retrieved DOD data registered these two events as the most intense episode in the same period in history over the past 20 years. These two extreme SDS events were associated with both atmospheric circulation extremes and local meteorological anomalies that favor enhanced dust emissions in the Gobi Desert.
Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao
Atmos. Chem. Phys., 22, 6393–6410, https://doi.org/10.5194/acp-22-6393-2022, https://doi.org/10.5194/acp-22-6393-2022, 2022
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Super dust storms reappeared in East Asia last spring after being absent for one and a half decades. Accurate simulation of such super sandstorms is valuable, but challenging due to imperfect emissions. In this study, the emissions of these dust storms are estimated by assimilating multiple observations. The results reveal that emissions originated from both China and Mongolia. However, for northern China, long-distance transport from Mongolia contributes much more dust than Chinese deserts.
Yu Zheng, Huizheng Che, Yupeng Wang, Xiangao Xia, Xiuqing Hu, Xiaochun Zhang, Jun Zhu, Jibiao Zhu, Hujia Zhao, Lei Li, Ke Gui, and Xiaoye Zhang
Atmos. Meas. Tech., 15, 2139–2158, https://doi.org/10.5194/amt-15-2139-2022, https://doi.org/10.5194/amt-15-2139-2022, 2022
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Ground-based observations of aerosols and aerosol data verification is important for satellite and climate model modification. Here we present an evaluation of aerosol microphysical, optical and radiative properties measured using a multiwavelength photometer with a highly integrated design and smart control performance. The validation of this product is discussed in detail using AERONET as a reference. This work contributes to reducing AOD uncertainties in China and combating climate change.
Wenxing Jia and Xiaoye Zhang
Atmos. Chem. Phys., 21, 16827–16841, https://doi.org/10.5194/acp-21-16827-2021, https://doi.org/10.5194/acp-21-16827-2021, 2021
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Heavy aerosol pollution incidents have attracted much attention since 2013, but the temporal and spatial limitations of observations and the inaccuracy of simulation are a stumbling block to assessing pollution mechanisms. The correct simulation of boundary layer mixing process of pollutant is a challenge for mesoscale numerical models. We add the turbulent diffusion term of aerosol to the WRF-Chem model to prove the impact of turbulent diffusion on pollutant concentration.
Ke Gui, Huizheng Che, Yu Zheng, Hujia Zhao, Wenrui Yao, Lei Li, Lei Zhang, Hong Wang, Yaqiang Wang, and Xiaoye Zhang
Atmos. Chem. Phys., 21, 15309–15336, https://doi.org/10.5194/acp-21-15309-2021, https://doi.org/10.5194/acp-21-15309-2021, 2021
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This study utilized the globally gridded aerosol extinction data from CALIOP during 2007–2019 to investigate the 3D climatology, trends, and meteorological drivers of tropospheric type-dependent aerosols. Results revealed that the planetary boundary layer (PBL) and the free troposphere contribute 62.08 % and 37.92 %, respectively, of the global tropospheric TAOD. Trends in
CALIOP-derived aerosol loading, in particular those partitioned in the PBL, can be explained to a large extent by meteorology.
Hongyi Ding, Le Cao, Haimei Jiang, Wenxing Jia, Yong Chen, and Junling An
Geosci. Model Dev., 14, 6135–6153, https://doi.org/10.5194/gmd-14-6135-2021, https://doi.org/10.5194/gmd-14-6135-2021, 2021
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We performed a WRF model study to figure out the mechanism of how the change in minimum eddy diffusivity (Kzmin) in the planetary boundary layer (PBL) closure scheme (ACM2) affects the simulated near-surface temperature in Beijing, China. Moreover, the influence of changing Kzmin on the temperature prediction in areas with different land-use categories was studied. The model performance using a functional-type Kzmin for capturing the temperature change in this area was also clarified.
Qingyang Xiao, Yixuan Zheng, Guannan Geng, Cuihong Chen, Xiaomeng Huang, Huizheng Che, Xiaoye Zhang, Kebin He, and Qiang Zhang
Atmos. Chem. Phys., 21, 9475–9496, https://doi.org/10.5194/acp-21-9475-2021, https://doi.org/10.5194/acp-21-9475-2021, 2021
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We used both statistical methods and a chemical transport model to assess the contribution of meteorology and emissions to PM2.5 during 2000–2018. Both methods revealed that emissions dominated the long-term PM2.5 trend with notable meteorological effects ranged up to 37.9 % of regional annual average PM2.5. The meteorological contribution became more beneficial to PM2.5 control in southern China but more unfavorable in northern China during the studied period.
Xiaojing Shen, Junying Sun, Fangqun Yu, Ying Wang, Junting Zhong, Yangmei Zhang, Xinyao Hu, Can Xia, Sinan Zhang, and Xiaoye Zhang
Atmos. Chem. Phys., 21, 7039–7052, https://doi.org/10.5194/acp-21-7039-2021, https://doi.org/10.5194/acp-21-7039-2021, 2021
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In this work, we revealed the changes of PNSD and NPF events during the COVID-19 lockdown period in Beijing, China, to illustrate the impact of reduced primary emission and elavated atmospheric oxidized capicity on the nucleation and growth processes. The subsequent growth of nucleated particles and their contribution to the aerosol pollution formation were also explored, to highlight the necessity of controlling the nanoparticles in the future air quality management.
Linlin Liang, Guenter Engling, Chang Liu, Wanyun Xu, Xuyan Liu, Yuan Cheng, Zhenyu Du, Gen Zhang, Junying Sun, and Xiaoye Zhang
Atmos. Chem. Phys., 21, 3181–3192, https://doi.org/10.5194/acp-21-3181-2021, https://doi.org/10.5194/acp-21-3181-2021, 2021
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A unique episode with extreme biomass burning (BB) impact, with daily concentration of levoglucosan as high as 4.37 µg m-3, was captured at an area upwind of Beijing. How this extreme BB pollution event was generated and what were the chemical properties of PM2.5 under this kind severe BB pollution level in the real atmospheric environment were both presented in this measurement report. Moreover, the variation of the ratios of BB tracers during different BB pollution periods was also exhibited.
Lei Zhang, Sunling Gong, Tianliang Zhao, Chunhong Zhou, Yuesi Wang, Jiawei Li, Dongsheng Ji, Jianjun He, Hongli Liu, Ke Gui, Xiaomei Guo, Jinhui Gao, Yunpeng Shan, Hong Wang, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 14, 703–718, https://doi.org/10.5194/gmd-14-703-2021, https://doi.org/10.5194/gmd-14-703-2021, 2021
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Development of chemical transport models with advanced physics and chemical schemes is important for improving air-quality forecasts. This study develops the chemical module CUACE by updating with a new particle dry deposition scheme and adding heterogenous chemical reactions and couples it with the WRF model. The coupled model (WRF/CUACE) was able to capture well the variations of PM2.5, O3, NO2, and secondary inorganic aerosols in eastern China.
Zhanshan Ma, Chuanfeng Zhao, Jiandong Gong, Jin Zhang, Zhe Li, Jian Sun, Yongzhu Liu, Jiong Chen, and Qingu Jiang
Geosci. Model Dev., 14, 205–221, https://doi.org/10.5194/gmd-14-205-2021, https://doi.org/10.5194/gmd-14-205-2021, 2021
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The spin-up in GRAPES_GFS, under different initial fields, goes through a dramatic adjustment in the first half-hour of integration and slow dynamic and thermal adjustments afterwards. It lasts for at least 6 h, with model adjustment gradually completed from lower to upper layers in the model. Thus, the forecast results, at least in the first 6 h, should be avoided when used. In addition, the spin-up process should repeat when the model simulation is interrupted.
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Short summary
In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled aerosol–meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the China Meteorological Administration Global Forecasting System (CMA-GFS) operational global numerical weather model. The results show that assimilating BC (black carbon) observations can generate analysis increments not only for BC but also for atmospheric variables.
In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts,...