10 citations as recorded by crossref.

1. 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 10.1016/j.scitotenv.2021.147417
2. 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. 10.1029/2020EA001614
3. Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime S. Feng et al. 10.1016/j.atmosenv.2018.05.049
4. The use of forecast gradients in 3DVar data assimilation Z. Zhu et al. 10.1016/j.apm.2019.04.038
5. Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
6. 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. 10.1016/j.envpol.2023.121099
7. 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. 10.1088/2515-7620/ad7a5f
8. 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. 10.3390/rs11202364
9. 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
10. 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. 10.1016/j.atmosenv.2018.12.016