Articles | Volume 9, issue 6
https://doi.org/10.5194/gmd-9-2153-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-2153-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
14 Jun 2016
Development and technical paper |
| 14 Jun 2016
Development of an adjoint model of GRAPES–CUACE and its application in tracking influential haze source areas in north China
Xing Qin An
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather, Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Shi Xian Zhai
State Key Laboratory of Severe Weather, Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Key Laboratory for Aerosol-Cloud-Precipitation of China
Meteorological Administration, Collaborative Innovation Center on Forecast
and Evaluation of Meteorological Disasters, School of Atmospheric Physics,
Nanjing University of Information Science & Technology, Nanjing 210044,
China
Min Jin
Wuhan Meteorological Observatory, Wuhan 430040,
China
Sunling Gong
State Key Laboratory of Severe Weather, Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Yu Wang
State Key Laboratory of Severe Weather, Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
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- Mapping ozone source-receptor relationship and apportioning the health impact in the Pearl River Delta region using adjoint sensitivity analysis M. Wang et al. 10.1016/j.atmosenv.2019.117026
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- Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China J. Jin et al. 10.5194/acp-20-15207-2020
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- Progress in quantitative research on the relationship between atmospheric oxidation and air quality Y. Wang et al. 10.1016/j.jes.2022.06.029
- Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model C. Liu et al. 10.1016/j.scitotenv.2021.145580
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28 citations as recorded by crossref.
- Application of Machine-Learning-Based Fusion Model in Visibility Forecast: A Case Study of Shanghai, China Z. Yu et al. 10.3390/rs13112096
- The Different Impacts of Emissions and Meteorology on PM2.5 Changes in Various Regions in China: A Case Study W. Zhang et al. 10.3390/atmos13020222
- Variational Quality Control of Non-Gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments J. He et al. 10.1007/s00376-021-0336-3
- An improved four-dimensional variation source term inversion model with observation error regularization C. Han et al. 10.1016/j.dt.2022.03.012
- Release estimation of pollutants in river by the variational analysis approach J. Pingfei et al. 10.1016/j.jconhyd.2022.103999
- Development of GRAPES-CUACE adjoint model version 2.0 and its application in sensitivity analysis of ozone pollution in north China C. Wang et al. 10.1016/j.scitotenv.2022.153879
- The combined effects of heterogeneous chemistry and aerosol-radiation interaction on severe haze simulation by atmospheric chemistry model in Middle-Eastern China Z. Liu et al. 10.1016/j.atmosenv.2023.119729
- Tracking sensitive source areas of different weather pollution types using GRAPES-CUACE adjoint model C. Wang et al. 10.1016/j.atmosenv.2017.11.041
- Sensitivities of Ozone Air Pollution in the Beijing–Tianjin–Hebei Area to Local and Upwind Precursor Emissions Using Adjoint Modeling X. Wang et al. 10.1021/acs.est.1c00131
- Application of a Fusion Model Based on Machine Learning in Visibility Prediction M. Zhen et al. 10.3390/rs15051450
- Sensitivity analysis of atmospheric oxidation capacity in Beijing based on the GRAPES-CUACE adjoint model C. Wang et al. 10.1016/j.atmosenv.2023.119641
- Mapping ozone source-receptor relationship and apportioning the health impact in the Pearl River Delta region using adjoint sensitivity analysis M. Wang et al. 10.1016/j.atmosenv.2019.117026
- Development of four-dimensional variational assimilation system based on the GRAPES–CUACE adjoint model (GRAPES–CUACE-4D-Var V1.0) and its application in emission inversion C. Wang et al. 10.5194/gmd-14-337-2021
- Sensitivity analysis and precursor emission sources reduction strategies of O3 for different pollution weather types based on the GRAPES-CUACE adjoint model C. Wang et al. 10.1016/j.atmosenv.2024.120632
- Incorporation and improvement of a heterogeneous chemistry mechanism in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on secondary inorganic aerosol and PM2.5 simulations in Middle-Eastern China Z. Liu et al. 10.1016/j.scitotenv.2022.157530
- Tracking a Severe Pollution Event in Beijing in December 2016 with the GRAPES–CUACE Adjoint Model C. Wang et al. 10.1007/s13351-018-7062-5
- Evaluating the contributions of changed meteorological conditions and emission to substantial reductions of PM2.5 concentration from winter 2016 to 2017 in Central and Eastern China W. Zhang et al. 10.1016/j.scitotenv.2020.136892
- Detection of critical PM2.5 emission sources and their contributions to a heavy haze episode in Beijing, China, using an adjoint model S. Zhai et al. 10.5194/acp-18-6241-2018
- Sensitivity analysis of PM2.5 and O3 co-pollution in Beijing based on GRAPES-CUACE adjoint model Z. Liu et al. 10.1016/j.jes.2024.11.020
- Source contributions of surface ozone in China using an adjoint sensitivity analysis M. Wang et al. 10.1016/j.scitotenv.2019.01.116
- Assessing the nonlinearity of wintertime PM2.5 formation in response to precursor emission changes in North China with the adjoint method N. Lu et al. 10.1088/1748-9326/ad60df
- Inverse estimation of finite-duration source release mass in river pollution accidents based on adjoint equation method P. Jing et al. 10.1007/s11356-020-07841-1
- Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0) H. Zhu et al. 10.5194/gmd-17-6337-2024
- Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China J. Jin et al. 10.5194/acp-20-15207-2020
- Development of WRF/CUACE v1.0 model and its preliminary application in simulating air quality in China L. Zhang et al. 10.5194/gmd-14-703-2021
- Comparing the impact of strong and weak East Asian winter monsoon on PM2.5 concentration in Beijing C. Wang et al. 10.1016/j.atmosres.2018.08.022
- Progress in quantitative research on the relationship between atmospheric oxidation and air quality Y. Wang et al. 10.1016/j.jes.2022.06.029
- Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model C. Liu et al. 10.1016/j.scitotenv.2021.145580
Latest update: 24 Apr 2025
Short summary
The aerosol adjoint module of the atmospheric chemical modeling system GRAPES–CUACE was developed, tested for its correctness, and used in a receptor–source sensitivity test. The results showed that controlling critical emission sources during critical time intervals on the basis of adjoint sensitivity analysis is much more efficient than controlling administrative specified regions during an experiential time period.
The aerosol adjoint module of the atmospheric chemical modeling system GRAPES–CUACE was...
