Articles | Volume 9, issue 8
https://doi.org/10.5194/gmd-9-2623-2016
© 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-9-2623-2016
© 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
| 
10 Aug 2016
Development and technical paper |
| 10 Aug 2016
Background error covariance with balance constraints for aerosol species and applications in variational data assimilation
Zengliang Zang
CORRESPONDING AUTHOR
College of Meteorology and Oceanography, PLA University of
Science and Technology, Nanjing 211101, China
Zilong Hao
College of Meteorology and Oceanography, PLA University of
Science and Technology, Nanjing 211101, China
Yi Li
College of Meteorology and Oceanography, PLA University of
Science and Technology, Nanjing 211101, China
Xiaobin Pan
College of Meteorology and Oceanography, PLA University of
Science and Technology, Nanjing 211101, China
Wei You
College of Meteorology and Oceanography, PLA University of
Science and Technology, Nanjing 211101, China
Zhijin Li
Joint Institute For Regional Earth System Science and Engineering,
University of California, Los Angeles, California 90095, USA
National Center for Atmospheric Research, Boulder, Colorado 80305, USA
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Cited
10 citations as recorded by crossref.
- The impacts of background error covariance on particulate matter assimilation and forecast: An ideal case study with a modal aerosol model over China J. Pang & X. Wang https://doi.org/10.1016/j.scitotenv.2021.147417
- Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM2.5 and PM10 Forecasts in the Sichuan Basin Z. Zhang et al. https://doi.org/10.1029/2020EA001614
- Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime S. Feng et al. https://doi.org/10.1016/j.atmosenv.2018.05.049
- The use of forecast gradients in 3DVar data assimilation Z. Zhu et al. https://doi.org/10.1016/j.apm.2019.04.038
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. https://doi.org/10.3390/rs14010220
- Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods U. Dash et al. https://doi.org/10.1016/j.envpol.2023.121099
- Development of a three-dimensional variational data assimilation system for 137Cs based on WRF-Chem model and applied to the Fukushima nuclear accident Y. Hu et al. https://doi.org/10.1088/2515-7620/ad7a5f
- Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland M. Werner et al. https://doi.org/10.3390/rs11202364
- 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. https://doi.org/10.3390/rs14164009
- Assimilation of PM2.5 ground base observations to two chemical schemes in WRF-Chem – The results for the winter and summer period M. Werner et al. https://doi.org/10.1016/j.atmosenv.2018.12.016
10 citations as recorded by crossref.
- The impacts of background error covariance on particulate matter assimilation and forecast: An ideal case study with a modal aerosol model over China J. Pang & X. Wang https://doi.org/10.1016/j.scitotenv.2021.147417
- Development of Three‐Dimensional Variational Data Assimilation Method of Aerosol for the CMAQ Model: An Application for PM2.5 and PM10 Forecasts in the Sichuan Basin Z. Zhang et al. https://doi.org/10.1029/2020EA001614
- Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime S. Feng et al. https://doi.org/10.1016/j.atmosenv.2018.05.049
- The use of forecast gradients in 3DVar data assimilation Z. Zhu et al. https://doi.org/10.1016/j.apm.2019.04.038
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. https://doi.org/10.3390/rs14010220
- Performance comparisons of the three data assimilation methods for improved predictability of PM2·5: Ensemble Kalman filter, ensemble square root filter, and three-dimensional variational methods U. Dash et al. https://doi.org/10.1016/j.envpol.2023.121099
- Development of a three-dimensional variational data assimilation system for 137Cs based on WRF-Chem model and applied to the Fukushima nuclear accident Y. Hu et al. https://doi.org/10.1088/2515-7620/ad7a5f
- Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland M. Werner et al. https://doi.org/10.3390/rs11202364
- 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. https://doi.org/10.3390/rs14164009
- Assimilation of PM2.5 ground base observations to two chemical schemes in WRF-Chem – The results for the winter and summer period M. Werner et al. https://doi.org/10.1016/j.atmosenv.2018.12.016
Saved (final revised paper)
Latest update: 14 Jun 2026
Short summary
The aerosol data assimilation and forecasts can be improved by adopting balance constraints that spread observation information across variables, thus producing balanced initial distributions. Surface and aircraft aerosol observations were assimilated to demonstrate the impact of the balance constraints. The results showed that the forecasting experiment with balance constraints is more skillful and durable than the experiment without balance constraints.
The aerosol data assimilation and forecasts can be improved by adopting balance constraints that...
