Articles | Volume 16, issue 10
https://doi.org/10.5194/acp-16-6395-2016
© 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/acp-16-6395-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
25 May 2016
Research article |
| 25 May 2016
Limitations of ozone data assimilation with adjustment of NOx emissions: mixed effects on NO2 forecasts over Beijing and surrounding areas
Xiao Tang
CORRESPONDING AUTHOR
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Jiang Zhu
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
ZiFa Wang
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Alex Gbaguidi
AECOM Asia, Hong Kong, China
CaiYan Lin
Aviation Meteorological Center, Air Traffic Management Bureau, Civil
Aviation Administration of China, Beijing, China
JinYuan Xin
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Tao Song
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Bo Hu
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
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Cited
22 citations as recorded by crossref.
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- Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. 10.1029/2019JD030421
- The optimization of SO2 emissions by the 4DVAR and EnKF methods and its application in WRF-Chem Y. Hu et al. 10.1016/j.scitotenv.2023.163796
- Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network H. Wu et al. 10.1007/s00376-018-8067-9
- A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC L. Kong et al. 10.5194/essd-13-529-2021
- A novel approach for monitoring vertical profiles of boundary-layer pollutants: Utilizing routine news helicopter flights E. Crosman et al. 10.1016/j.apr.2017.01.013
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
- Estimating Ground-Level Ozone Concentrations in Eastern China Using Satellite-Based Precursors X. Zhang et al. 10.1109/TGRS.2020.2966780
- Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0 E. Emili et al. 10.5194/gmd-9-3933-2016
- 基于高分辨率气溶胶观测资料的多尺度三维变分同化及预报 增. 臧 et al. 10.1360/SSTe-2022-0026
- A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region X. Cheng et al. 10.5194/acp-21-13747-2021
- The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system D. Chen et al. 10.5194/acp-19-8619-2019
- Air Quality Forecasting with Inversely Updated Emissions for China H. Wu et al. 10.1021/acs.estlett.3c00266
- The impact of multi-species surface chemical observation assimilation on air quality forecasts in China Z. Peng et al. 10.5194/acp-18-17387-2018
- A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ C. Huang et al. 10.3390/rs15061711
- Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application Z. Zang et al. 10.1007/s11430-022-9974-4
- Source tagging modeling study of regional contributions to acid rain in summer over Liaoning Province, Northeastern China A. Gbaguidi et al. 10.1016/j.envpol.2017.12.076
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China Y. Hu et al. 10.5194/acp-22-13183-2022
- Assessment of the Meteorological Impact on Improved PM2.5 Air Quality Over North China During 2016–2019 Based on a Regional Joint Atmospheric Composition Reanalysis Data‐Set X. Kou et al. 10.1029/2020JD034382
- A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
- Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts C. Ma et al. 10.1007/s00376-017-7179-y
22 citations as recorded by crossref.
- High-spatiotemporal-resolution inverse estimation of CO and NOx emission reductions during emission control periods with a modified ensemble Kalman filter H. Wu et al. 10.1016/j.atmosenv.2020.117631
- Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. 10.1029/2019JD030421
- The optimization of SO2 emissions by the 4DVAR and EnKF methods and its application in WRF-Chem Y. Hu et al. 10.1016/j.scitotenv.2023.163796
- Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network H. Wu et al. 10.1007/s00376-018-8067-9
- A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC L. Kong et al. 10.5194/essd-13-529-2021
- A novel approach for monitoring vertical profiles of boundary-layer pollutants: Utilizing routine news helicopter flights E. Crosman et al. 10.1016/j.apr.2017.01.013
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
- Estimating Ground-Level Ozone Concentrations in Eastern China Using Satellite-Based Precursors X. Zhang et al. 10.1109/TGRS.2020.2966780
- Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0 E. Emili et al. 10.5194/gmd-9-3933-2016
- 基于高分辨率气溶胶观测资料的多尺度三维变分同化及预报 增. 臧 et al. 10.1360/SSTe-2022-0026
- A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region X. Cheng et al. 10.5194/acp-21-13747-2021
- The 2015 and 2016 wintertime air pollution in China: SO<sub>2</sub> emission changes derived from a WRF-Chem/EnKF coupled data assimilation system D. Chen et al. 10.5194/acp-19-8619-2019
- Air Quality Forecasting with Inversely Updated Emissions for China H. Wu et al. 10.1021/acs.estlett.3c00266
- The impact of multi-species surface chemical observation assimilation on air quality forecasts in China Z. Peng et al. 10.5194/acp-18-17387-2018
- A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ C. Huang et al. 10.3390/rs15061711
- Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application Z. Zang et al. 10.1007/s11430-022-9974-4
- Source tagging modeling study of regional contributions to acid rain in summer over Liaoning Province, Northeastern China A. Gbaguidi et al. 10.1016/j.envpol.2017.12.076
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China Y. Hu et al. 10.5194/acp-22-13183-2022
- Assessment of the Meteorological Impact on Improved PM2.5 Air Quality Over North China During 2016–2019 Based on a Regional Joint Atmospheric Composition Reanalysis Data‐Set X. Kou et al. 10.1029/2020JD034382
- A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China G. Jia et al. 10.1016/j.jes.2021.08.048
- Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts C. Ma et al. 10.1007/s00376-017-7179-y
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Latest update: 24 Apr 2024
Short summary
Chemical data assimilation through adjusting precursor emissions has brought out notable impacts on improving ozone forecasts in previous studies. This paper, from another point of view, investigated in detail the impacts of adjusting nitrogen oxide emissions on the forecasts of nitrogen dioxide through assimilating ozone observations. Limitations of the existing chemical data assimilation methods in a highly nonlinear system were identified and highlighted.
Chemical data assimilation through adjusting precursor emissions has brought out notable impacts...
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