Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-6285-2020
© 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-13-6285-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Dec 2020
Development and technical paper |
| 10 Dec 2020
| 10 Dec 2020
Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM2.5 forecasts across China
Yanfei Liang
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
PLA Unit 32145, People's Liberation Army, Xinxiang, China
Zengliang Zang
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and
Fine Mechanics, Chinese Academy of Sciences, Hefei, China
Peng Yan
Meteorological Observation Center, Chinese Meteorological
Administration, Beijing, China
Yiwen Hu
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing,
China
Yan Zhou
PLA Unit 78127, People's Liberation Army, Beijing, China
Wei You
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
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Cited
19 citations as recorded by crossref.
- Optimizing the Numerical Simulation of the Dust Event of March 2021: Integrating Aerosol Observations through Multi-Scale 3D Variational Assimilation in the WRF-Chem Model S. Mei et al. 10.3390/rs16111852
- 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
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
- Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model H. Ye et al. 10.1016/j.atmosres.2021.105877
- 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
- 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
- Three-dimensional variational assimilation of Lidar extinction profiles: Application to PM2.5 prediction in north China L. Gao et al. 10.1016/j.atmosenv.2021.118828
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- 3DVAR Aerosol Data Assimilation and Evaluation Using Surface PM2.5, Himawari-8 AOD and CALIPSO Profile Observations in the North China Z. Zang et al. 10.3390/rs14164009
- Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem T. Dai et al. 10.5194/acp-21-4357-2021
- Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe M. Chrit & M. Majdi 10.3390/atmos13050763
- Assimilating GNSS Tropospheric Products and Quantitative Evaluation of Their Contributions to Numerical Weather Prediction Y. Ma et al. 10.1109/TGRS.2025.3559789
- Harnessing AI and advanced modeling for precision ozone control: A data-driven approach to air quality management G. Lin et al. 10.1016/j.jhazmat.2025.138709
- Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. 10.1016/j.jes.2022.04.012
- Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China H. Ye et al. 10.1016/j.atmosres.2022.106069
- Scattering and absorbing aerosols in the climate system J. Li et al. 10.1038/s43017-022-00296-7
- A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations D. Wang et al. 10.5194/gmd-15-1821-2022
- A Review of Data Assimilation on Aerosol Optical, Radiative, and Climatic Effects Study Y. Cheng et al. 10.1007/s41810-022-00142-9
- Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast J. Bi et al. 10.1021/acs.est.1c05578
19 citations as recorded by crossref.
- Optimizing the Numerical Simulation of the Dust Event of March 2021: Integrating Aerosol Observations through Multi-Scale 3D Variational Assimilation in the WRF-Chem Model S. Mei et al. 10.3390/rs16111852
- 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
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
- Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model H. Ye et al. 10.1016/j.atmosres.2021.105877
- 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
- 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
- Three-dimensional variational assimilation of Lidar extinction profiles: Application to PM2.5 prediction in north China L. Gao et al. 10.1016/j.atmosenv.2021.118828
- An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application H. Wang et al. 10.5194/gmd-15-3555-2022
- 3DVAR Aerosol Data Assimilation and Evaluation Using Surface PM2.5, Himawari-8 AOD and CALIPSO Profile Observations in the North China Z. Zang et al. 10.3390/rs14164009
- Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem T. Dai et al. 10.5194/acp-21-4357-2021
- Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe M. Chrit & M. Majdi 10.3390/atmos13050763
- Assimilating GNSS Tropospheric Products and Quantitative Evaluation of Their Contributions to Numerical Weather Prediction Y. Ma et al. 10.1109/TGRS.2025.3559789
- Harnessing AI and advanced modeling for precision ozone control: A data-driven approach to air quality management G. Lin et al. 10.1016/j.jhazmat.2025.138709
- Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. 10.1016/j.jes.2022.04.012
- Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China H. Ye et al. 10.1016/j.atmosres.2022.106069
- Scattering and absorbing aerosols in the climate system J. Li et al. 10.1038/s43017-022-00296-7
- A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations D. Wang et al. 10.5194/gmd-15-1821-2022
- A Review of Data Assimilation on Aerosol Optical, Radiative, and Climatic Effects Study Y. Cheng et al. 10.1007/s41810-022-00142-9
- Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast J. Bi et al. 10.1021/acs.est.1c05578
Latest update: 30 Jun 2025
