Articles | Volume 13, issue 1
https://doi.org/10.5194/gmd-13-23-2020
© Author(s) 2020. 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-13-23-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Jan 2020
Model description paper |
| 03 Jan 2020
Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data
Luolin Wu
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, P. R. China
Ming Chang
Institute for Environmental and Climate Research, Jinan University,
Guangzhou 510632, P. R. China
Xuemei Wang
CORRESPONDING AUTHOR
Institute for Environmental and Climate Research, Jinan University,
Guangzhou 510632, P. R. China
Jian Hang
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, P. R. China
Jinpu Zhang
Guangzhou Environmental Monitoring Center, Guangzhou 510030, P. R.
China
Liqing Wu
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, P. R. China
Min Shao
Institute for Environmental and Climate Research, Jinan University,
Guangzhou 510632, P. R. China
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Luolin Wu, Jian Hang, Xuemei Wang, Min Shao, and Cheng Gong
Geosci. Model Dev., 14, 4655–4681, https://doi.org/10.5194/gmd-14-4655-2021, https://doi.org/10.5194/gmd-14-4655-2021, 2021
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Benjamin A. Nault, Duseong S. Jo, Brian C. McDonald, Pedro Campuzano-Jost, Douglas A. Day, Weiwei Hu, Jason C. Schroder, James Allan, Donald R. Blake, Manjula R. Canagaratna, Hugh Coe, Matthew M. Coggon, Peter F. DeCarlo, Glenn S. Diskin, Rachel Dunmore, Frank Flocke, Alan Fried, Jessica B. Gilman, Georgios Gkatzelis, Jacqui F. Hamilton, Thomas F. Hanisco, Patrick L. Hayes, Daven K. Henze, Alma Hodzic, James Hopkins, Min Hu, L. Greggory Huey, B. Thomas Jobson, William C. Kuster, Alastair Lewis, Meng Li, Jin Liao, M. Omar Nawaz, Ilana B. Pollack, Jeffrey Peischl, Bernhard Rappenglück, Claire E. Reeves, Dirk Richter, James M. Roberts, Thomas B. Ryerson, Min Shao, Jacob M. Sommers, James Walega, Carsten Warneke, Petter Weibring, Glenn M. Wolfe, Dominique E. Young, Bin Yuan, Qiang Zhang, Joost A. de Gouw, and Jose L. Jimenez
Atmos. Chem. Phys., 21, 11201–11224, https://doi.org/10.5194/acp-21-11201-2021, https://doi.org/10.5194/acp-21-11201-2021, 2021
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Ye Kuang, Shan Huang, Biao Xue, Biao Luo, Qicong Song, Wei Chen, Weiwei Hu, Wei Li, Pusheng Zhao, Mingfu Cai, Yuwen Peng, Jipeng Qi, Tiange Li, Sihang Wang, Duohong Chen, Dingli Yue, Bin Yuan, and Min Shao
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Syuichi Itahashi, Baozhu Ge, Keiichi Sato, Zhe Wang, Junichi Kurokawa, Jiani Tan, Kan Huang, Joshua S. Fu, Xuemei Wang, Kazuyo Yamaji, Tatsuya Nagashima, Jie Li, Mizuo Kajino, Gregory R. Carmichael, and Zifa Wang
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Mingfu Cai, Baoling Liang, Qibin Sun, Li Liu, Bin Yuan, Min Shao, Shan Huang, Yuwen Peng, Zelong Wang, Haobo Tan, Fei Li, Hanbin Xu, Duohong Chen, and Jun Zhao
Atmos. Chem. Phys., 21, 8575–8592, https://doi.org/10.5194/acp-21-8575-2021, https://doi.org/10.5194/acp-21-8575-2021, 2021
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Atmos. Chem. Phys., 20, 15617–15633, https://doi.org/10.5194/acp-20-15617-2020, https://doi.org/10.5194/acp-20-15617-2020, 2020
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Caihong Wu, Chaomin Wang, Sihang Wang, Wenjie Wang, Bin Yuan, Jipeng Qi, Baolin Wang, Hongli Wang, Chen Wang, Wei Song, Xinming Wang, Weiwei Hu, Shengrong Lou, Chenshuo Ye, Yuwen Peng, Zelong Wang, Yibo Huangfu, Yan Xie, Manni Zhu, Junyu Zheng, Xuemei Wang, Bin Jiang, Zhanyi Zhang, and Min Shao
Atmos. Chem. Phys., 20, 14769–14785, https://doi.org/10.5194/acp-20-14769-2020, https://doi.org/10.5194/acp-20-14769-2020, 2020
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Sarah E. Benish, Hao He, Xinrong Ren, Sandra J. Roberts, Ross J. Salawitch, Zhanqing Li, Fei Wang, Yuying Wang, Fang Zhang, Min Shao, Sihua Lu, and Russell R. Dickerson
Atmos. Chem. Phys., 20, 14523–14545, https://doi.org/10.5194/acp-20-14523-2020, https://doi.org/10.5194/acp-20-14523-2020, 2020
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Chaomin Wang, Bin Yuan, Caihong Wu, Sihang Wang, Jipeng Qi, Baolin Wang, Zelong Wang, Weiwei Hu, Wei Chen, Chenshuo Ye, Wenjie Wang, Yele Sun, Chen Wang, Shan Huang, Wei Song, Xinming Wang, Suxia Yang, Shenyang Zhang, Wanyun Xu, Nan Ma, Zhanyi Zhang, Bin Jiang, Hang Su, Yafang Cheng, Xuemei Wang, and Min Shao
Atmos. Chem. Phys., 20, 14123–14138, https://doi.org/10.5194/acp-20-14123-2020, https://doi.org/10.5194/acp-20-14123-2020, 2020
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We utilized a novel online mass spectrometry method to measure the total concentration of higher alkanes at each carbon number at two different sites in China, allowing us to take into account SOA contributions from all isomers for higher alkanes. We found that higher alkanes account for significant fractions of SOA formation at the two sites. The contributions are comparable to or even higher than single-ring aromatics, the most-recognized SOA precursors in urban air.
Zhenhao Ling, Qianqian Xie, Min Shao, Zhe Wang, Tao Wang, Hai Guo, and Xuemei Wang
Atmos. Chem. Phys., 20, 11451–11467, https://doi.org/10.5194/acp-20-11451-2020, https://doi.org/10.5194/acp-20-11451-2020, 2020
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The observation data from a receptor site in the Pearl River Delta region were analyzed by a photochemical box model with near-explicit chemical mechanisms (i.e., the Master Chemical Mechanism, MCM), improvements with reversible and irreversible heterogeneous processes of glyoxal and methylglyoxal, and the gas-particle partitioning of oxidation products in the present study.
Baozhu Ge, Syuichi Itahashi, Keiichi Sato, Danhui Xu, Junhua Wang, Fan Fan, Qixin Tan, Joshua S. Fu, Xuemei Wang, Kazuyo Yamaji, Tatsuya Nagashima, Jie Li, Mizuo Kajino, Hong Liao, Meigen Zhang, Zhe Wang, Meng Li, Jung-Hun Woo, Junichi Kurokawa, Yuepeng Pan, Qizhong Wu, Xuejun Liu, and Zifa Wang
Atmos. Chem. Phys., 20, 10587–10610, https://doi.org/10.5194/acp-20-10587-2020, https://doi.org/10.5194/acp-20-10587-2020, 2020
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Mingfu Cai, Baoling Liang, Qibin Sun, Shengzhen Zhou, Xiaoyang Chen, Bin Yuan, Min Shao, Haobo Tan, and Jun Zhao
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Cloud condensation nuclei activity in marine atmosphere affects cloud formation and the solar radiation balance over ocean. We employed advanced instruments to measure aerosol hygroscopicity and chemical composition in the northern South China Sea. Our results show that marine aerosols can be affected by local emissions or pollutants from long-range transport. Our study highlights dynamical variations in particle properties and the impact of long-range transport on this region during summertime.
Junchen Guo, Shengzhen Zhou, Mingfu Cai, Jun Zhao, Wei Song, Weixiong Zhao, Weiwei Hu, Yele Sun, Yao He, Chengqiang Yang, Xuezhe Xu, Zhisheng Zhang, Peng Cheng, Qi Fan, Jian Hang, Shaojia Fan, Xinming Wang, and Xuemei Wang
Atmos. Chem. Phys., 20, 7595–7615, https://doi.org/10.5194/acp-20-7595-2020, https://doi.org/10.5194/acp-20-7595-2020, 2020
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We characterized non-refractory submicron particulate matter (PM1.0) during winter in Guangzhou, south China. Chemical composition and key sources of ambient PM1.0 are revealed, highlighting the significant role of SOA. The relationship with SOA and peroxy radicals indicated gas-phase oxidation contributed predominantly to SOA formation during non-pollution periods, while heterogeneous/multiphase reactions played more important roles in SOA formation during pollution periods.
Jiani Tan, Joshua S. Fu, Gregory R. Carmichael, Syuichi Itahashi, Zhining Tao, Kan Huang, Xinyi Dong, Kazuyo Yamaji, Tatsuya Nagashima, Xuemei Wang, Yiming Liu, Hyo-Jung Lee, Chuan-Yao Lin, Baozhu Ge, Mizuo Kajino, Jia Zhu, Meigen Zhang, Hong Liao, and Zifa Wang
Atmos. Chem. Phys., 20, 7393–7410, https://doi.org/10.5194/acp-20-7393-2020, https://doi.org/10.5194/acp-20-7393-2020, 2020
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This study evaluated the performance of 12 chemical transport models from MICS-Asia III for predicting the particulate matter (PM) over East Asia. Four model processes were investigated as the possible reasons for model bias with measurements and the factors causing inconsistent predictions of PM from different models: (1) model inputs, (2) gas–particle conversion, (3) dust emission modules and (4) removal mechanisms (wet and dry depositions). The influence of each process was discussed.
Shengzhen Zhou, Luolin Wu, Junchen Guo, Weihua Chen, Xuemei Wang, Jun Zhao, Yafang Cheng, Zuzhao Huang, Jinpu Zhang, Yele Sun, Pingqing Fu, Shiguo Jia, Jun Tao, Yanning Chen, and Junxia Kuang
Atmos. Chem. Phys., 20, 6435–6453, https://doi.org/10.5194/acp-20-6435-2020, https://doi.org/10.5194/acp-20-6435-2020, 2020
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In this work, measurements of size-segregated aerosols were conducted at three altitudes (ground level, 118 m, and 488 m) on the 610 m high Canton Tower in southern China. Vertical variations of PM and size-segregated chemical compositions were investigated. The results indicated that meteorological parameters and atmospheric aqueous and heterogeneous reactions together led to aerosol formation and haze episodes in the Pearl River Delta region during the measurement periods.
Syuichi Itahashi, Baozhu Ge, Keiichi Sato, Joshua S. Fu, Xuemei Wang, Kazuyo Yamaji, Tatsuya Nagashima, Jie Li, Mizuo Kajino, Hong Liao, Meigen Zhang, Zhe Wang, Meng Li, Junichi Kurokawa, Gregory R. Carmichael, and Zifa Wang
Atmos. Chem. Phys., 20, 2667–2693, https://doi.org/10.5194/acp-20-2667-2020, https://doi.org/10.5194/acp-20-2667-2020, 2020
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This study gives an overview of acid deposition from the Model Inter-Comparison Study for Asia (MICS-Asia) phase III. Wet deposition simulated by a total of nine models is evaluated with observation data from the Acid Deposition Monitoring Network in East Asia (EANET). The total deposition maps comparing to emissions over Asia are presented. To seek a way to improve the model performance, ensemble approaches and the precipitation-adjusted method are discussed.
Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Joshua S. Fu, Xuemei Wang, Syuichi Itahashi, Kazuyo Yamaji, Tatsuya Nagashima, Hyo-Jung Lee, Cheol-Hee Kim, Chuan-Yao Lin, Lei Chen, Meigen Zhang, Zhining Tao, Jie Li, Mizuo Kajino, Hong Liao, Zhe Wang, Kengo Sudo, Yuesi Wang, Yuepeng Pan, Guiqian Tang, Meng Li, Qizhong Wu, Baozhu Ge, and Gregory R. Carmichael
Atmos. Chem. Phys., 20, 181–202, https://doi.org/10.5194/acp-20-181-2020, https://doi.org/10.5194/acp-20-181-2020, 2020
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Evaluation and uncertainty investigation of NO2, CO and NH3 modeling over China were conducted in this study using 14 chemical transport model results from MICS-Asia III. All models largely underestimated CO concentrations and showed very poor performance in reproducing the observed monthly variations of NH3 concentrations. Potential factors related to such deficiencies are investigated and discussed in this paper.
Jie Li, Tatsuya Nagashima, Lei Kong, Baozhu Ge, Kazuyo Yamaji, Joshua S. Fu, Xuemei Wang, Qi Fan, Syuichi Itahashi, Hyo-Jung Lee, Cheol-Hee Kim, Chuan-Yao Lin, Meigen Zhang, Zhining Tao, Mizuo Kajino, Hong Liao, Meng Li, Jung-Hun Woo, Jun-ichi Kurokawa, Zhe Wang, Qizhong Wu, Hajime Akimoto, Gregory R. Carmichael, and Zifa Wang
Atmos. Chem. Phys., 19, 12993–13015, https://doi.org/10.5194/acp-19-12993-2019, https://doi.org/10.5194/acp-19-12993-2019, 2019
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This study evaluated and intercompared 14 CTMs with ozone observations in East Asia, within the framework of the Model Inter-Comparison Study for ASIA Phase III (MICS-Asia III). Potential causes of the discrepancies between model results and observation were investigated by assessing the planetary boundary layer heights, emission fluxes, dry deposition, chemistry and vertical transport among models. Finally, a multi-model estimate of pollution distributions was provided.
Lei Chen, Yi Gao, Meigen Zhang, Joshua S. Fu, Jia Zhu, Hong Liao, Jialin Li, Kan Huang, Baozhu Ge, Xuemei Wang, Yun Fat Lam, Chuan-Yao Lin, Syuichi Itahashi, Tatsuya Nagashima, Mizuo Kajino, Kazuyo Yamaji, Zifa Wang, and Jun-ichi Kurokawa
Atmos. Chem. Phys., 19, 11911–11937, https://doi.org/10.5194/acp-19-11911-2019, https://doi.org/10.5194/acp-19-11911-2019, 2019
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Simulated aerosol concentrations from 14 CTMs within the framework of MICS-Asia III are detailedly evaluated with an extensive set of measurements in East Asia. Similarities and differences among model performances are also analyzed. Although more considerable capacities for reproducing the aerosol concentrations and their variations are shown in current CTMs than those in MICS-Asia II, more efforts are needed to reduce diversities of simulated aerosol concentrations among air quality models.
Yahui Bian, Zhijiong Huang, Jiamin Ou, Zhuangmin Zhong, Yuanqian Xu, Zhiwei Zhang, Xiao Xiao, Xiao Ye, Yuqi Wu, Xiaohong Yin, Cheng Li, Liangfu Chen, Min Shao, and Junyu Zheng
Atmos. Chem. Phys., 19, 11701–11719, https://doi.org/10.5194/acp-19-11701-2019, https://doi.org/10.5194/acp-19-11701-2019, 2019
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During 2006–2015, emissions of SO2, NOx, PM2.5 and PM10 saw an obvious downtrend. However, most emissions still have large reduction potential. On-road mobile sources and solvent use are the two key sources that should receive more effective control measures in GD. Also, controls measures on VOC and NH3 should be weighted since they still increased in 2006–2015. Since most control measures focused on PRD rather than non-PRD in GD, emissions in non-PRD were increasingly important.
Wenjie Wang, Xin Li, Min Shao, Min Hu, Limin Zeng, Yusheng Wu, and Tianyi Tan
Atmos. Chem. Phys., 19, 9413–9429, https://doi.org/10.5194/acp-19-9413-2019, https://doi.org/10.5194/acp-19-9413-2019, 2019
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We quantitatively evaluated the relationship between photolysis frequencies and AOD based on 4 years of observational data in Beijing. This study concludes that the influence of aerosol on photolysis frequencies and thus on the rate of oxidation of VOCs and NOx to ozone is important for determining the atmospheric effects of controlling the precursor emissions of these two important air pollutants (aerosols and ozone).
Zhuoran He, Xuemei Wang, Zhenhao Ling, Jun Zhao, Hai Guo, Min Shao, and Zhe Wang
Atmos. Chem. Phys., 19, 8801–8816, https://doi.org/10.5194/acp-19-8801-2019, https://doi.org/10.5194/acp-19-8801-2019, 2019
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In this study, source apportionment of volatile organic compounds (VOCs) and their contributions to photochemical O3 formation were analyzed by the positive matrix factorization model and an observation-based model using data collected at a receptor site in the Pearl River Delta (PRD) region. Furthermore, the policies for controlling VOCs are briefly reviewed. The findings could provide quantitative information for devising appropriate measures against VOCs, NOx and O3 pollution in the PRD.
Liqing Wu, Xuemei Wang, Sihua Lu, Min Shao, and Zhenhao Ling
Atmos. Chem. Phys., 19, 8141–8161, https://doi.org/10.5194/acp-19-8141-2019, https://doi.org/10.5194/acp-19-8141-2019, 2019
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Semi-volatile and intermediate-volatility organic compounds (S–IVOCs) are considered critical precursors of secondary organic aerosol (SOA), which is an important component of fine particulate matter (PM2.5). In this study, an emission inventory of S–IVOCs in the Pearl River Delta (PRD) region was developed for the first time for the year 2010, while the contributions of S–IVOCs to SOA formation was evaluated by the WRF-Chem model.
Zhaofeng Tan, Keding Lu, Andreas Hofzumahaus, Hendrik Fuchs, Birger Bohn, Frank Holland, Yuhan Liu, Franz Rohrer, Min Shao, Kang Sun, Yusheng Wu, Limin Zeng, Yinsong Zhang, Qi Zou, Astrid Kiendler-Scharr, Andreas Wahner, and Yuanhang Zhang
Atmos. Chem. Phys., 19, 7129–7150, https://doi.org/10.5194/acp-19-7129-2019, https://doi.org/10.5194/acp-19-7129-2019, 2019
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Atmospheric OH, HO2, and RO2 radicals; OH reactivity; and trace gases measured in the Pearl River Delta in autumn 2014 are used for radical budget analyses. The RO2 budget suggests that unexplained OH reactivity is due to unmeasured volatile organic compounds. The OH budget points to a missing OH source and that of RO2 to a missing RO2 sink at low NO. This could indicate a common, unknown process that converts RO2 to OH without the involvement of NO, which would reduce ozone production by 30 %.
Guo Li, Yafang Cheng, Uwe Kuhn, Rongjuan Xu, Yudong Yang, Hannah Meusel, Zhibin Wang, Nan Ma, Yusheng Wu, Meng Li, Jonathan Williams, Thorsten Hoffmann, Markus Ammann, Ulrich Pöschl, Min Shao, and Hang Su
Atmos. Chem. Phys., 19, 2209–2232, https://doi.org/10.5194/acp-19-2209-2019, https://doi.org/10.5194/acp-19-2209-2019, 2019
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VOCs play a key role in atmospheric chemistry. Emission and deposition on soil have been suggested as important sources and sinks of atmospheric trace gases. The exchange characteristics and heterogeneous chemistry of VOCs on soil, however, are not well understood. We used a newly designed differential coated-wall flow tube system to investigate the long-term variability of bidirectional air–soil exchange of 13 VOCs at ambient air conditions of an urban background site in Beijing.
Daocheng Gong, Hao Wang, Shenyang Zhang, Yu Wang, Shaw Chen Liu, Hai Guo, Min Shao, Congrong He, Duohong Chen, Lingyan He, Lei Zhou, Lidia Morawska, Yuanhang Zhang, and Boguang Wang
Atmos. Chem. Phys., 18, 14417–14432, https://doi.org/10.5194/acp-18-14417-2018, https://doi.org/10.5194/acp-18-14417-2018, 2018
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The complex air pollution in the air-polluted Pearl River Delta (PRD) region in southern China has significantly elevated the background atmospheric oxidative capacity of the adjacent forests and subsequently lowered the levels of important biogenic volatile organic compounds, such as isoprene, which probably affect the regional air quality and ecological environment in the long term.
Weiqiang Yang, Yanli Zhang, Xinming Wang, Sheng Li, Ming Zhu, Qingqing Yu, Guanghui Li, Zhonghui Huang, Huina Zhang, Zhenfeng Wu, Wei Song, Jihua Tan, and Min Shao
Atmos. Chem. Phys., 18, 12663–12682, https://doi.org/10.5194/acp-18-12663-2018, https://doi.org/10.5194/acp-18-12663-2018, 2018
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We present observation-based evaluations of the reduction of ambient VOCs under intervention control measures during APEC China 2014 in Beijing and the contributions of emissions from domestic solid fuel burning to ambient VOCs during winter heating. Controlling vehicle exhaust and solvent use was found to be effective in reducing ambient VOCs in non-heating periods, and controlling emissions from residential burning of solid fuels became much more important during winter heating.
Zhaofeng Tan, Franz Rohrer, Keding Lu, Xuefei Ma, Birger Bohn, Sebastian Broch, Huabin Dong, Hendrik Fuchs, Georgios I. Gkatzelis, Andreas Hofzumahaus, Frank Holland, Xin Li, Ying Liu, Yuhan Liu, Anna Novelli, Min Shao, Haichao Wang, Yusheng Wu, Limin Zeng, Min Hu, Astrid Kiendler-Scharr, Andreas Wahner, and Yuanhang Zhang
Atmos. Chem. Phys., 18, 12391–12411, https://doi.org/10.5194/acp-18-12391-2018, https://doi.org/10.5194/acp-18-12391-2018, 2018
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We present the first wintertime OH, HO2, and RO2 measurements in Beijing, China. OH concentrations are nearly 2-fold larger than those observed in foreign cities during wintertime. The high OH and large OH reactivities indicate photochemical processes can be effective even during wintertime. A box model largely underestimated HO2 and RO2 concentrations during pollution episodes correlated with high NOx, indicating a deficit current chemistry in the high NOx regime.
Shiguo Jia, Xuemei Wang, Qi Zhang, Sayantan Sarkar, Luolin Wu, Minjuan Huang, Jinpu Zhang, and Liming Yang
Atmos. Chem. Phys., 18, 11125–11133, https://doi.org/10.5194/acp-18-11125-2018, https://doi.org/10.5194/acp-18-11125-2018, 2018
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Aerosol pH are often directly compared across studies while ignoring the inconsistency in standard states. This study attempts to address this issue by comparing aerosol pH with different standard states on the basis of theoretical considerations followed with a set of field data as an example. Application of a pH standardization protocol including a precise statement of thermodynamic model parameters is recommended to avoid biases in cross-comparison.
Zhuofei Du, Min Hu, Jianfei Peng, Wenbin Zhang, Jing Zheng, Fangting Gu, Yanhong Qin, Yudong Yang, Mengren Li, Yusheng Wu, Min Shao, and Shijin Shuai
Atmos. Chem. Phys., 18, 9011–9023, https://doi.org/10.5194/acp-18-9011-2018, https://doi.org/10.5194/acp-18-9011-2018, 2018
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By combining approaches involving chassis dynamometer measurements and environmental chamber simulations, we find that gasoline direct injection (GDI) vehicles contribute more primary aerosol and secondary organic aerosol than port fuel injection (PFI) vehicles. Our results highlight the considerable potential contribution of GDI vehicles to urban air pollution, since the market share of GDI vehicles will dominate over that of PFI vehicles in the future.
Guo Li, Hang Su, Uwe Kuhn, Hannah Meusel, Markus Ammann, Min Shao, Ulrich Pöschl, and Yafang Cheng
Atmos. Chem. Phys., 18, 2669–2686, https://doi.org/10.5194/acp-18-2669-2018, https://doi.org/10.5194/acp-18-2669-2018, 2018
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Coated-wall flow tube reactors are frequently used to investigate gas uptake and heterogeneous or multiphase reaction kinetics under laminar flow conditions. In previous applications, the effects of coating surface roughness on flow conditions were not well quantified. In this study, a criterion is proposed to eliminate/minimize the potential effects of coating surface roughness on laminar flow in coated-wall flow tube experiments and validate the applications of diffusion correction methods.
Shengzhen Zhou, Perry K. Davy, Minjuan Huang, Jingbo Duan, Xuemei Wang, Qi Fan, Ming Chang, Yiming Liu, Weihua Chen, Shanju Xie, Travis Ancelet, and William J. Trompetter
Atmos. Chem. Phys., 18, 2049–2064, https://doi.org/10.5194/acp-18-2049-2018, https://doi.org/10.5194/acp-18-2049-2018, 2018
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We collected hourly samples of PM2.5 and PM2.5–10 at an industrial city in the PRD, China. The samples were analyzed for black carbon and elemental compositions. Receptor modeling of the dataset by positive matrix factorization was used to identify PM sources. Human health exposure risks to the selected trace elements in PM released from the specific sources were estimated. The source–risk apportionment method helps decision makers to manage air quality more effectively.
Jianfei Peng, Min Hu, Song Guo, Zhuofei Du, Dongjie Shang, Jing Zheng, Jun Zheng, Limin Zeng, Min Shao, Yusheng Wu, Don Collins, and Renyi Zhang
Atmos. Chem. Phys., 17, 10333–10348, https://doi.org/10.5194/acp-17-10333-2017, https://doi.org/10.5194/acp-17-10333-2017, 2017
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Rapid growth of BC particles was observed in Beijing using a new outdoor chamber, with an average growth rate of 26 ± 11 nm h−1. Secondary organic aerosol (SOA) accounted for more than 90 % of the coating mass. The hygroscopic growth factor of BC particles increased to 1.06–1.08 upon ageing. The κ (kappa) values for BC particles were calculated as only 0.035, indicating that initial photochemical ageing of BC particles does not appreciably alter the particle hygroscopicity in Beijing.
Yudong Yang, Min Shao, Stephan Keßel, Yue Li, Keding Lu, Sihua Lu, Jonathan Williams, Yuanhang Zhang, Liming Zeng, Anke C. Nölscher, Yusheng Wu, Xuemei Wang, and Junyu Zheng
Atmos. Chem. Phys., 17, 7127–7142, https://doi.org/10.5194/acp-17-7127-2017, https://doi.org/10.5194/acp-17-7127-2017, 2017
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Total OH reactivity is an important parameter to evaluate understanding of atmospheric chemistry, especially the VOC contribution to air pollution. Measured by comparative reactivity methods, total OH reactivity in Beijing and Heshan revealed significant differences between measured and calculated results, such as missing reactivity, which were related to unmeasured primary or secondary species. This missing reactivity would introduce a 21–30 % underestimation for ozone production efficiency.
Zhaofeng Tan, Hendrik Fuchs, Keding Lu, Andreas Hofzumahaus, Birger Bohn, Sebastian Broch, Huabin Dong, Sebastian Gomm, Rolf Häseler, Lingyan He, Frank Holland, Xin Li, Ying Liu, Sihua Lu, Franz Rohrer, Min Shao, Baolin Wang, Ming Wang, Yusheng Wu, Limin Zeng, Yinsong Zhang, Andreas Wahner, and Yuanhang Zhang
Atmos. Chem. Phys., 17, 663–690, https://doi.org/10.5194/acp-17-663-2017, https://doi.org/10.5194/acp-17-663-2017, 2017
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In this study, we performed accurate OH measurements as well as selective HO2 and RO2 measurements at a rural site in North China Plain with state-of-the-art instruments newly developed. We confirmed the previous discovery on the enhancement of the OH in low NOx with which little O3 production was associated, and we found a missing RO2 source in high NOx which promoted higher O3 production. Our results are of vital importance for ozone abatement strategies currently under discussion for China.
Hendrik Fuchs, Zhaofeng Tan, Keding Lu, Birger Bohn, Sebastian Broch, Steven S. Brown, Huabin Dong, Sebastian Gomm, Rolf Häseler, Lingyan He, Andreas Hofzumahaus, Frank Holland, Xin Li, Ying Liu, Sihua Lu, Kyung-Eun Min, Franz Rohrer, Min Shao, Baolin Wang, Ming Wang, Yusheng Wu, Limin Zeng, Yinson Zhang, Andreas Wahner, and Yuanhang Zhang
Atmos. Chem. Phys., 17, 645–661, https://doi.org/10.5194/acp-17-645-2017, https://doi.org/10.5194/acp-17-645-2017, 2017
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OH reactivity was measured during a 1-month long campaign at a rural site in the North China Plain in 2014. OH reactivity measurements are compared to calculations using OH reactant measurements. Good agreement is found indicating that all important OH reactants were measured. In addition, the chemical OH budget is analyzed. In contrast to previous campaigns in China in 2006, no significant imbalance between OH production and destruction is found.
Yee Jun Tham, Zhe Wang, Qinyi Li, Hui Yun, Weihao Wang, Xinfeng Wang, Likun Xue, Keding Lu, Nan Ma, Birger Bohn, Xin Li, Simonas Kecorius, Johannes Größ, Min Shao, Alfred Wiedensohler, Yuanhang Zhang, and Tao Wang
Atmos. Chem. Phys., 16, 14959–14977, https://doi.org/10.5194/acp-16-14959-2016, https://doi.org/10.5194/acp-16-14959-2016, 2016
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This work addresses the unclear global significance of chlorine activation processes in the troposphere. The first high-quality measurement data set of ClNO2 in northern China revealed strong ClNO2 production in the residual layers, and demonstrated its significant effects on radical budget and ozone production. Our findings imply the widespread effects of ClNO2 over the polluted regions of northern China, which may increase photochemical and haze pollution.
Weihua Chen, Xuemei Wang, Jason Blake Cohen, Shengzhen Zhou, Zhisheng Zhang, Ming Chang, and Chuen-Yu Chan
Atmos. Chem. Phys., 16, 13271–13289, https://doi.org/10.5194/acp-16-13271-2016, https://doi.org/10.5194/acp-16-13271-2016, 2016
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Measurements of size-resolved aerosols (0.25–18 μm) were conducted at three sites (urban, suburban and background sites) in southern China during monsoon season (May–June) in 2010 aqueous-phase reaction was the main formation pathway of droplet mode sulfate. New particle formation, chemical aging, and long-range transport from upwind urban or biomass burning regions were also found to be important in at least some of the sights on some of the sampling days.
Wei Hu, Min Hu, Wei-Wei Hu, Hongya Niu, Jing Zheng, Yusheng Wu, Wentai Chen, Chen Chen, Lingyu Li, Min Shao, Shaodong Xie, and Yuanhang Zhang
Atmos. Chem. Phys., 16, 13213–13230, https://doi.org/10.5194/acp-16-13213-2016, https://doi.org/10.5194/acp-16-13213-2016, 2016
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An Aerodyne high-resolution time-of-flight AMS was deployed at a suburban site in the Sichuan Basin, southwestern China, under high emission intensity, and unique geographical and adverse meteorological conditions. OA was the most abundant component (36 %) in PM1, characterized by a relatively high oxidation state. The contributions of BBOA and BC to PM1 were high in primary emission episodes, highlighting the critical influence of biomass burning.
Guo Li, Hang Su, Xin Li, Uwe Kuhn, Hannah Meusel, Thorsten Hoffmann, Markus Ammann, Ulrich Pöschl, Min Shao, and Yafang Cheng
Atmos. Chem. Phys., 16, 10299–10311, https://doi.org/10.5194/acp-16-10299-2016, https://doi.org/10.5194/acp-16-10299-2016, 2016
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Indoor and outdoor formaldehyde (HCHO) are both of considerable concern because of its health effects and its role in atmospheric chemistry. The heterogeneous reactions between gaseous HCHO with soils can pose important impact on both HCHO budget and soil ecosystem. Our results confirms that HCHO uptake by soil is a complex process involving both adsorption/desorption and chemical reactions. Soil and soil-derived airborne particles can either act as a source or a sink for HCHO.
Xuekun Fang, Min Shao, Andreas Stohl, Qiang Zhang, Junyu Zheng, Hai Guo, Chen Wang, Ming Wang, Jiamin Ou, Rona L. Thompson, and Ronald G. Prinn
Atmos. Chem. Phys., 16, 3369–3382, https://doi.org/10.5194/acp-16-3369-2016, https://doi.org/10.5194/acp-16-3369-2016, 2016
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This is the first study reporting top-down estimates of benzene and toluene emissions in southern China using atmospheric measurement data from a rural site in the area, an atmospheric transport model and an inverse modeling method. This study shows in detail the temporal and spatial differences between the inversion estimate and four different bottom-up emission inventories (RCP, REAS, MEIC; Yin et al., 2015). We propose that more observations are urgently needed in future.
Yaning Kang, Mingxu Liu, Yu Song, Xin Huang, Huan Yao, Xuhui Cai, Hongsheng Zhang, Ling Kang, Xuejun Liu, Xiaoyuan Yan, Hong He, Qiang Zhang, Min Shao, and Tong Zhu
Atmos. Chem. Phys., 16, 2043–2058, https://doi.org/10.5194/acp-16-2043-2016, https://doi.org/10.5194/acp-16-2043-2016, 2016
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The multi-year (1980–2012) comprehensive ammonia emissions inventories were compiled for China on 1 km × 1 km grid.
Various realistic parameters (ambient temperature, wind speed, soil acidity, synthetic fertilizer types, etc.) were considered in these inventories to synthetically refine the emission factors of ammonia volatilization according to local agricultural practice.
This paper shows the interannual trend and spatial distribution of ammonia emissions in details over recent decades.
Z. Y. Wu, L. Zhang, X. M. Wang, and J. W. Munger
Atmos. Chem. Phys., 15, 7487–7496, https://doi.org/10.5194/acp-15-7487-2015, https://doi.org/10.5194/acp-15-7487-2015, 2015
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In this study, we have developed a modified micrometeorological gradient method (MGM), although based on existing micrometeorological theory, to estimate O3 dry deposition fluxes over a forest canopy using concentration gradients between a level above and a level below the canopy top. The new method provides an alternative approach in monitoring/estimating long-term deposition fluxes of similar pollutants over tall canopies and is expected to be useful for the scientific community.
Y. Liu, B. Yuan, X. Li, M. Shao, S. Lu, Y. Li, C.-C. Chang, Z. Wang, W. Hu, X. Huang, L. He, L. Zeng, M. Hu, and T. Zhu
Atmos. Chem. Phys., 15, 3045–3062, https://doi.org/10.5194/acp-15-3045-2015, https://doi.org/10.5194/acp-15-3045-2015, 2015
M. Wang, M. Shao, W. Chen, S. Lu, Y. Liu, B. Yuan, Q. Zhang, Q. Zhang, C.-C. Chang, B. Wang, L. Zeng, M. Hu, Y. Yang, and Y. Li
Atmos. Chem. Phys., 15, 1489–1502, https://doi.org/10.5194/acp-15-1489-2015, https://doi.org/10.5194/acp-15-1489-2015, 2015
X. Li, F. Rohrer, T. Brauers, A. Hofzumahaus, K. Lu, M. Shao, Y. H. Zhang, and A. Wahner
Atmos. Chem. Phys., 14, 12291–12305, https://doi.org/10.5194/acp-14-12291-2014, https://doi.org/10.5194/acp-14-12291-2014, 2014
Q. Zhang, B. Yuan, M. Shao, X. Wang, S. Lu, K. Lu, M. Wang, L. Chen, C.-C. Chang, and S. C. Liu
Atmos. Chem. Phys., 14, 6089–6101, https://doi.org/10.5194/acp-14-6089-2014, https://doi.org/10.5194/acp-14-6089-2014, 2014
M. Wang, M. Shao, W. Chen, B. Yuan, S. Lu, Q. Zhang, L. Zeng, and Q. Wang
Atmos. Chem. Phys., 14, 5871–5891, https://doi.org/10.5194/acp-14-5871-2014, https://doi.org/10.5194/acp-14-5871-2014, 2014
M. Li, Q. Zhang, D. G. Streets, K. B. He, Y. F. Cheng, L. K. Emmons, H. Huo, S. C. Kang, Z. Lu, M. Shao, H. Su, X. Yu, and Y. Zhang
Atmos. Chem. Phys., 14, 5617–5638, https://doi.org/10.5194/acp-14-5617-2014, https://doi.org/10.5194/acp-14-5617-2014, 2014
K. D. Lu, F. Rohrer, F. Holland, H. Fuchs, T. Brauers, A. Oebel, R. Dlugi, M. Hu, X. Li, S. R. Lou, M. Shao, T. Zhu, A. Wahner, Y. H. Zhang, and A. Hofzumahaus
Atmos. Chem. Phys., 14, 4979–4999, https://doi.org/10.5194/acp-14-4979-2014, https://doi.org/10.5194/acp-14-4979-2014, 2014
W. T. Chen, M. Shao, S. H. Lu, M. Wang, L. M. Zeng, B. Yuan, and Y. Liu
Atmos. Chem. Phys., 14, 3047–3062, https://doi.org/10.5194/acp-14-3047-2014, https://doi.org/10.5194/acp-14-3047-2014, 2014
H. Liu, X. M. Wang, J. M. Pang, and K. B. He
Atmos. Chem. Phys., 13, 12013–12027, https://doi.org/10.5194/acp-13-12013-2013, https://doi.org/10.5194/acp-13-12013-2013, 2013
S. Situ, A. Guenther, X. Wang, X. Jiang, A. Turnipseed, Z. Wu, J. Bai, and X. Wang
Atmos. Chem. Phys., 13, 11803–11817, https://doi.org/10.5194/acp-13-11803-2013, https://doi.org/10.5194/acp-13-11803-2013, 2013
Z. B. Wang, M. Hu, D. Mogensen, D. L. Yue, J. Zheng, R. Y. Zhang, Y. Liu, B. Yuan, X. Li, M. Shao, L. Zhou, Z. J. Wu, A. Wiedensohler, and M. Boy
Atmos. Chem. Phys., 13, 11157–11167, https://doi.org/10.5194/acp-13-11157-2013, https://doi.org/10.5194/acp-13-11157-2013, 2013
W. W. Hu, M. Hu, B. Yuan, J. L. Jimenez, Q. Tang, J. F. Peng, W. Hu, M. Shao, M. Wang, L. M. Zeng, Y. S. Wu, Z. H. Gong, X. F. Huang, and L. Y. He
Atmos. Chem. Phys., 13, 10095–10112, https://doi.org/10.5194/acp-13-10095-2013, https://doi.org/10.5194/acp-13-10095-2013, 2013
C.-C. Chang, M. Shao, C. C. K. Chou, S.-C. Liu, J.-L. Wang, K.-Z. Lee, C.-H. Lai, T. Zhu, and P.-H. Lin
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-25939-2013, https://doi.org/10.5194/acpd-13-25939-2013, 2013
Revised manuscript not accepted
B. Yuan, W. W. Hu, M. Shao, M. Wang, W. T. Chen, S. H. Lu, L. M. Zeng, and M. Hu
Atmos. Chem. Phys., 13, 8815–8832, https://doi.org/10.5194/acp-13-8815-2013, https://doi.org/10.5194/acp-13-8815-2013, 2013
Related subject area
Atmospheric sciences
Use of threshold parameter variation for tropical cyclone tracking
Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes
The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale
A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter
Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1
CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
A method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)
Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis
Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)
Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance
A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0
The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models
The development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)
GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)
Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models
Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method
An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters
Sensitivity of tropospheric ozone to halogen chemistry in the chemistry–climate model LMDZ-INCA vNMHC
Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants
Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models
The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications
An approach to refining the ground meteorological observation stations for improving PM2.5 forecasts in the Beijing–Tianjin–Hebei region
Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS
Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm
Self-nested large-eddy simulations in PALM model system v21.10 for offshore wind prediction under different atmospheric stability conditions
How does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00?
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
Updated isoprene and terpene emission factors for the Interactive BVOC (iBVOC) emission scheme in the United Kingdom Earth System Model (UKESM1.0)
Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model
Evaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, Texas
Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling
Halogen chemistry in volcanic plumes: a 1D framework based on MOCAGE 1D (version R1.18.1) preparing 3D global chemistry modelling
PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting
Long-term evaluation of surface air pollution in CAMSRA and MERRA-2 global reanalyses over Europe (2003–2020)
A simplified non-linear chemistry-transport model for analyzing NO2 column observations
Evaluating Three Decades of Precipitation in the Upper Colorado River Basin from a High-Resolution Regional Climate Model
Emulating aerosol optics with randomly generated neural networks
Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality
Comparison of ozone formation attribution techniques in the northeastern United States
Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution
Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0
Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model
Emulating lateral gravity wave propagation in a global chemistry-climate model (EMAC v2.55.2) through horizontal flux redistribution
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME
An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Improving the representation of shallow cumulus convection with the simplified-higher-order-closure–mass-flux (SHOC+MF v1.0) approach
ISAT v2.0: an integrated tool for nested-domain configurations and model-ready emission inventories for WRF-AQM
Bernhard M. Enz, Jan P. Engelmann, and Ulrike Lohmann
Geosci. Model Dev., 16, 5093–5112, https://doi.org/10.5194/gmd-16-5093-2023, https://doi.org/10.5194/gmd-16-5093-2023, 2023
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An algorithm to track tropical cyclones in model simulation data has been developed. The algorithm uses many combinations of varying parameter thresholds to detect weaker phases of tropical cyclones while still being resilient to false positives. It is shown that the algorithm performs well and adequately represents the tropical cyclone activity of the underlying simulation data. The impact of false positives on overall tropical cyclone activity is shown to be insignificant.
Sepehr Fathi, Mark Gordon, and Yongsheng Chen
Geosci. Model Dev., 16, 5069–5091, https://doi.org/10.5194/gmd-16-5069-2023, https://doi.org/10.5194/gmd-16-5069-2023, 2023
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We have combined various capabilities within a WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The study objective was to resolve transport processes at the scale of measurements to assess and optimize aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological and within 1–2 standard deviations of observed wind fields. Mass was conserved in the model within 5 % of input emissions.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
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The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
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Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
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A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
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We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Yosuke Yamazaki
Geosci. Model Dev., 16, 4749–4766, https://doi.org/10.5194/gmd-16-4749-2023, https://doi.org/10.5194/gmd-16-4749-2023, 2023
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The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
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To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev., 16, 4617–4638, https://doi.org/10.5194/gmd-16-4617-2023, https://doi.org/10.5194/gmd-16-4617-2023, 2023
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Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, https://doi.org/10.5194/gmd-16-4551-2023, 2023
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The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev., 16, 4405–4425, https://doi.org/10.5194/gmd-16-4405-2023, https://doi.org/10.5194/gmd-16-4405-2023, 2023
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Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, https://doi.org/10.5194/gmd-16-4427-2023, 2023
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We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403, https://doi.org/10.5194/gmd-16-4385-2023, https://doi.org/10.5194/gmd-16-4385-2023, 2023
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The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383, https://doi.org/10.5194/gmd-16-4367-2023, https://doi.org/10.5194/gmd-16-4367-2023, 2023
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Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Laurent Menut
Geosci. Model Dev., 16, 4265–4281, https://doi.org/10.5194/gmd-16-4265-2023, https://doi.org/10.5194/gmd-16-4265-2023, 2023
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This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211, https://doi.org/10.5194/gmd-16-4193-2023, https://doi.org/10.5194/gmd-16-4193-2023, 2023
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In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
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The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062, https://doi.org/10.5194/gmd-16-4041-2023, https://doi.org/10.5194/gmd-16-4041-2023, 2023
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We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
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Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
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We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, https://doi.org/10.5194/gmd-16-3873-2023, 2023
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The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev., 16, 3827–3848, https://doi.org/10.5194/gmd-16-3827-2023, https://doi.org/10.5194/gmd-16-3827-2023, 2023
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An approach is proposed to refine a ground meteorological observation network to improve the PM2.5 forecasts in the Beijing–Tianjin–Hebei region. A cost-effective observation network is obtained and makes the relevant PM2.5 forecasts assimilate fewer observations but achieve the forecasting skill comparable to or higher than that obtained by assimilating all ground station observations, suggesting that many of the current ground stations can be greatly scattered to avoid much unnecessary work.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, https://doi.org/10.5194/gmd-16-3699-2023, 2023
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Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao
Geosci. Model Dev., 16, 3611–3628, https://doi.org/10.5194/gmd-16-3611-2023, https://doi.org/10.5194/gmd-16-3611-2023, 2023
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Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Geosci. Model Dev., 16, 3553–3564, https://doi.org/10.5194/gmd-16-3553-2023, https://doi.org/10.5194/gmd-16-3553-2023, 2023
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Local refinement of the grid is a powerful method allowing us to reduce the computational time while preserving the accuracy in the area of interest. Depending on the implementation, the local refinement may introduce unwanted numerical effects into the results. We study the wind speed common to the wind turbine operational speeds and confirm strong alteration of the result when the heat fluxes are present, except for the specific refinement scheme used.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551, https://doi.org/10.5194/gmd-16-3535-2023, https://doi.org/10.5194/gmd-16-3535-2023, 2023
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Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavcic, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben J. Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
EGUsphere, https://doi.org/10.5194/egusphere-2023-647, https://doi.org/10.5194/egusphere-2023-647, 2023
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3D climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
James Weber, James A. King, Katerina Sindelarova, and Maria Val Martin
Geosci. Model Dev., 16, 3083–3101, https://doi.org/10.5194/gmd-16-3083-2023, https://doi.org/10.5194/gmd-16-3083-2023, 2023
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The emissions of volatile organic compounds from vegetation (BVOCs) influence atmospheric composition and contribute to certain gases and aerosols (tiny airborne particles) which play a role in climate change. BVOC emissions are likely to change in the future due to changes in climate and land use. Therefore, accurate simulation of BVOC emission is important, and this study describes an update to the simulation of BVOC emissions in the United Kingdom Earth System Model (UKESM).
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
EGUsphere, https://doi.org/10.5194/egusphere-2023-892, https://doi.org/10.5194/egusphere-2023-892, 2023
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With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 Tracking Aerosol Convection Experiment Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993, https://doi.org/10.5194/gmd-16-2975-2023, https://doi.org/10.5194/gmd-16-2975-2023, 2023
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The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
Virginie Marécal, Ronan Voisin-Plessis, Tjarda Jane Roberts, Alessandro Aiuppa, Herizo Narivelo, Paul David Hamer, Béatrice Josse, Jonathan Guth, Luke Surl, and Lisa Grellier
Geosci. Model Dev., 16, 2873–2898, https://doi.org/10.5194/gmd-16-2873-2023, https://doi.org/10.5194/gmd-16-2873-2023, 2023
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We implemented a halogen volcanic chemistry scheme in a one-dimensional modelling framework preparing for further use in a three-dimensional global chemistry-transport model. The results of the simulations for an eruption of Mt Etna in 2008, including various sensitivity tests, show a good consistency with previous modelling studies.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
Geosci. Model Dev., 16, 2737–2752, https://doi.org/10.5194/gmd-16-2737-2023, https://doi.org/10.5194/gmd-16-2737-2023, 2023
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Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Aleksander Lacima, Hervé Petetin, Albert Soret, Dene Bowdalo, Oriol Jorba, Zhaoyue Chen, Raúl F. Méndez Turrubiates, Hicham Achebak, Joan Ballester, and Carlos Pérez García-Pando
Geosci. Model Dev., 16, 2689–2718, https://doi.org/10.5194/gmd-16-2689-2023, https://doi.org/10.5194/gmd-16-2689-2023, 2023
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Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Supercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modeled surface pollution.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
EGUsphere, https://doi.org/10.5194/egusphere-2023-876, https://doi.org/10.5194/egusphere-2023-876, 2023
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To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and evaluate modeled results against TROPOMI v2 over multiple power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind direction and prior emissions.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-69, https://doi.org/10.5194/gmd-2023-69, 2023
Preprint under review for GMD
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It's important to know how well atmospheric models do in the mountains, but there aren't very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado river basin against the data that's available. The model works pretty well but, there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we couldn't before.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
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Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Joey C. Y. Lam, Amos P. K. Tai, Jason A. Ducker, and Christopher D. Holmes
Geosci. Model Dev., 16, 2323–2342, https://doi.org/10.5194/gmd-16-2323-2023, https://doi.org/10.5194/gmd-16-2323-2023, 2023
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We developed a new component within an atmospheric chemistry model to better simulate plant ecophysiological processes relevant for ozone air quality. We showed that it reduces simulated biases in plant uptake of ozone in prior models. The new model enables us to explore how future climatic changes affect air quality via affecting plants, examine ozone–vegetation interactions and feedbacks, and evaluate the impacts of changing atmospheric chemistry and climate on vegetation productivity.
Qian Shu, Sergey L. Napelenok, William T. Hutzell, Kirk R. Baker, Barron H. Henderson, Benjamin N. Murphy, and Christian Hogrefe
Geosci. Model Dev., 16, 2303–2322, https://doi.org/10.5194/gmd-16-2303-2023, https://doi.org/10.5194/gmd-16-2303-2023, 2023
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Source attribution methods are generally used to determine culpability of precursor emission sources to ambient pollutant concentrations. However, source attribution of secondarily formed pollutants such as ozone and its precursors cannot be explicitly measured, making evaluation of source apportionment methods challenging. In this study, multiple apportionment approach comparisons show common features but still reveal wide variations in predicted sector contribution and species dependency.
Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl
Geosci. Model Dev., 16, 2181–2192, https://doi.org/10.5194/gmd-16-2181-2023, https://doi.org/10.5194/gmd-16-2181-2023, 2023
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We use neural-network-based single-image super-resolution to improve the upscaling of meteorological wind fields to be used for particle dispersion models. This deep-learning-based methodology improves the standard linear interpolation typically used in particle dispersion models. The improvement of wind fields leads to substantial improvement in the computed trajectories of the particles.
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodriguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
Geosci. Model Dev., 16, 2193–2213, https://doi.org/10.5194/gmd-16-2193-2023, https://doi.org/10.5194/gmd-16-2193-2023, 2023
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This work aims to derive and evaluate a general statistical post-processing tool specifically designed for the street scale that can be applied to any urban air quality system. Our data fusion methodology corrects NO2 fields based on continuous hourly observations and experimental campaigns. This study enables us to obtain exceedance probability maps of air quality standards. In 2019, 13 % of the Barcelona area had a 70 % or higher probability of exceeding the annual legal NO2 limit of 40 µg/m3.
Liang Wang, Bingcheng Wan, Shaohui Zhou, Haofei Sun, and Zhiqiu Gao
Geosci. Model Dev., 16, 2167–2179, https://doi.org/10.5194/gmd-16-2167-2023, https://doi.org/10.5194/gmd-16-2167-2023, 2023
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The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in the northwestern Pacific from hours 6–72 based on GRU_CNN, which we proposed in this paper and which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchar, Patrick Jöckel, Astrid Kerkweg, and Bastian Kern
EGUsphere, https://doi.org/10.5194/egusphere-2023-270, https://doi.org/10.5194/egusphere-2023-270, 2023
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Dynamical model biases result from the columnar approach of gravity wave (GW) schemes, but parallel decomposition makes horizontal GW propagation computationally unfeasible. In the global model EMAC, we approximate it by GW redistribution at one altitude using tailor-made redistribution maps generated with a ray-tracer. More spread-out GW drag helps reconciling the model with observations and closing the 60S GW gap. Polar vortex dynamics are improved, enhancing climate model credibility.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-70, https://doi.org/10.5194/gmd-2023-70, 2023
Revised manuscript accepted for GMD
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A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger
Geosci. Model Dev., 16, 2037–2054, https://doi.org/10.5194/gmd-16-2037-2023, https://doi.org/10.5194/gmd-16-2037-2023, 2023
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Kinetic multi-layer models (KMs) successfully describe heterogeneous and multiphase atmospheric chemistry. In applications requiring repeated execution, however, these models can be too expensive. We trained machine learning surrogate models on output of the model KM-SUB and achieved high correlations. The surrogate models run orders of magnitude faster, which suggests potential applicability in global optimization tasks and as sub-modules in large-scale atmospheric models.
Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty, and Matt Rigby
Geosci. Model Dev., 16, 1997–2009, https://doi.org/10.5194/gmd-16-1997-2023, https://doi.org/10.5194/gmd-16-1997-2023, 2023
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Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50 000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
EGUsphere, https://doi.org/10.5194/egusphere-2023-357, https://doi.org/10.5194/egusphere-2023-357, 2023
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Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidental) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011), and show that it improves the simulation of wet deposition.
Maria J. Chinita, Mikael Witte, Marcin J. Kurowski, Joao Teixeira, Kay Suselj, Georgios Matheou, and Peter Bogenschutz
Geosci. Model Dev., 16, 1909–1924, https://doi.org/10.5194/gmd-16-1909-2023, https://doi.org/10.5194/gmd-16-1909-2023, 2023
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Low clouds are one of the largest sources of uncertainty in climate prediction. In this paper, we introduce the first version of the unified turbulence and shallow convection parameterization named SHOC+MF developed to improve the representation of shallow cumulus clouds in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). Here, we also show promising preliminary results in a single-column model framework for two benchmark cases of shallow cumulus convection.
Kun Wang, Chao Gao, Kai Wu, Kaiyun Liu, Haofan Wang, Mo Dan, Xiaohui Ji, and Qingqing Tong
Geosci. Model Dev., 16, 1961–1973, https://doi.org/10.5194/gmd-16-1961-2023, https://doi.org/10.5194/gmd-16-1961-2023, 2023
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This study establishes an easy-to-use and integrated framework for a model-ready emission inventory for the Weather Research and Forecasting (WRF)–Air Quality Numerical Model (AQM). A free tool called the ISAT (Inventory Spatial Allocation Tool) was developed based on this framework. ISAT helps users complete the workflow from the WRF nested-domain configuration to a model-ready emission inventory for AQM with a regional emission inventory and a shapefile for the target region.
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Short summary
We developed the Real-time On-road Emission (ROE v1.0) model to obtain the street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode Map navigation application. The results are close to other emission inventories. Meanwhile, we applied our results to a street-level air quality model for studying the impact of the national holiday traffic volume change on air quality. The model can be further extended to more districts in China or other countries.
We developed the Real-time On-road Emission (ROE v1.0) model to obtain the street-scale on-road...
