Articles | Volume 20, issue 15
https://doi.org/10.5194/acp-20-9311-2020
© 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/acp-20-9311-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Aug 2020
Research article |
| 07 Aug 2020
| 07 Aug 2020
Development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: aiming to improve air quality forecasting and diagnose model deficiencies
Wei Sun
National Center for Atmospheric Research, Boulder, CO 80301, USA
National Space Science Center, Chinese Academy of Sciences, Beijing,
100190, China
National Center for Atmospheric Research, Boulder, CO 80301, USA
Institute of Urban Meteorology, China Meteorology Administration,
Beijing, 100089, China
Pusheng Zhao
Institute of Urban Meteorology, China Meteorology Administration,
Beijing, 100089, China
Min Chen
Institute of Urban Meteorology, China Meteorology Administration,
Beijing, 100089, China
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- Observationally constrained global NOx and CO emissions variability reveals sources which contribute significantly to CO2 emissions S. Wang et al. 10.1038/s41612-025-00977-2
- Regional aerosol forecasts based on deep learning and numerical weather prediction Y. Qiu et al. 10.1038/s41612-023-00397-0
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- 3DVAR meteorological data assimilation and aerosol impact on the simulation of heat wave 2022 over Haryana using WRF-Chem P. Deb et al. 10.1016/j.apr.2025.102440
- Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities Q. Zhu et al. 10.1021/acs.est.1c05636
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- How frequent and which variables of automatic weather station data should be assimilated into WRF-3DVar model? A case study of a squall line event in Beijing Z. Qiao et al. 10.1016/j.atmosres.2024.107460
15 citations as recorded by crossref.
- Turbulence-permitting air pollution simulation for the Stuttgart metropolitan area T. Schwitalla et al. 10.5194/acp-21-4575-2021
- 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
- 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
- Observationally constrained global NOx and CO emissions variability reveals sources which contribute significantly to CO2 emissions S. Wang et al. 10.1038/s41612-025-00977-2
- Regional aerosol forecasts based on deep learning and numerical weather prediction Y. Qiu et al. 10.1038/s41612-023-00397-0
- Impact of model resolution and its representativeness consistency with observations on operational prediction of PM2.5 with 3D-VAR data assimilation Y. Wei et al. 10.1016/j.apr.2024.102141
- Implementation of aerosol data assimilation in WRFDA (v4.0.3) for WRF-Chem (v3.9.1) using the RACM/MADE-VBS scheme S. Ha 10.5194/gmd-15-1769-2022
- Development and application of a hybrid long-short term memory – three dimensional variational technique for the improvement of PM2.5 forecasting X. Lu et al. 10.1016/j.scitotenv.2020.144221
- 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
- Development of the CMA-ChemRA: China Regional Weakly Coupled Chemical-Weather Reanalysis System with product since 2007 T. Zhang et al. 10.1016/j.scitotenv.2024.177552
- 3DVAR meteorological data assimilation and aerosol impact on the simulation of heat wave 2022 over Haryana using WRF-Chem P. Deb et al. 10.1016/j.apr.2025.102440
- Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities Q. Zhu et al. 10.1021/acs.est.1c05636
- 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
- Further development and application of the WRFDA-Chem three-dimensional variational (3DVAR) system: Joint assimilation of satellite AOD retrievals and surface observations Y. Zhou et al. 10.1016/j.atmosres.2025.107942
- How frequent and which variables of automatic weather station data should be assimilated into WRF-3DVar model? A case study of a squall line event in Beijing Z. Qiao et al. 10.1016/j.atmosres.2024.107460
Latest update: 28 Mar 2025
Short summary
A new aerosol and gas pollutant assimilation capability is developed within the WRFDA system with the 3D variational algorithm and MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) aerosol scheme. By assimilating surface PM2.5, PM10, SO2, NO2, O3, and CO, it improves 24 h air quality forecasting. Based on this system, model deficiencies are explored. Parameterization in the newly added inorganic aerosol heterogeneous reactions should be adjusted and verified by data assimilation.
A new aerosol and gas pollutant assimilation capability is developed within the WRFDA system...
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