41 citations as recorded by crossref.

1. 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
2. An evaluation of the impact of aerosol particles on weather forecasts from a biomass burning aerosol event over the Midwestern United States: observational-based analysis of surface temperature J. Zhang et al. 10.5194/acp-16-6475-2016
3. Inverse modeling of black carbon emissions over China using ensemble data assimilation P. Wang et al. 10.5194/acp-16-989-2016
4. Ensemble filter based estimation of spatially distributed parameters in a mesoscale dust model: experiments with simulated and real data V. Khade et al. 10.5194/acp-13-3481-2013
5. Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. 10.1029/2018MS001475
6. Machine learning for observation bias correction with application to dust storm data assimilation J. Jin et al. 10.5194/acp-19-10009-2019
7. An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model W. Zhuo et al. 10.3390/rs15071825
8. The value of satellite observations in the analysis and short-range prediction of Asian dust A. Benedetti et al. 10.5194/acp-19-987-2019
9. Inverse modeling of the 2021 spring super dust storms in East Asia J. Jin et al. 10.5194/acp-22-6393-2022
10. Estimation of aerosol particle number distributions with Kalman Filtering – Part 1: Theory, general aspects and statistical validity T. Viskari et al. 10.5194/acp-12-11767-2012
11. Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing X. Cheng et al. 10.1016/j.scitotenv.2019.05.186
12. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation J. Huang et al. 10.1016/j.agrformet.2015.10.013
13. Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0 J. Jin et al. 10.5194/gmd-14-5607-2021
14. Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model T. Dai et al. 10.1016/j.envpol.2014.06.021
15. Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition? M. Kacenelenbogen et al. 10.5194/acp-22-3713-2022
16. Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM2.5 Predictions M. Gao et al. 10.1021/acs.est.6b03745
17. PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method E. Lee et al. 10.1007/s13143-013-0009-y
18. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models M. Bocquet et al. 10.5194/acp-15-5325-2015
19. 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
20. Model bias correction for dust storm forecast using ensemble Kalman filter C. Lin et al. 10.1029/2007JD009498
21. Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST–ACRM model with Ensemble Kalman Filter H. Ma et al. 10.1016/j.mcm.2012.12.028
22. Data assimilation of CALIPSO aerosol observations T. Sekiyama et al. 10.5194/acp-10-39-2010
23. Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model N. Schutgens et al. 10.5194/acp-10-2561-2010
24. Sensitivity tests for an ensemble Kalman filter for aerosol assimilation N. Schutgens et al. 10.5194/acp-10-6583-2010
25. Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar J. Jin et al. 10.1016/j.atmosenv.2018.05.060
26. Dust Emission Estimated with an Assimilated Dust Transport Model Using Lidar Network Data and Vegetation Growth in the Gobi Desert in Mongolia N. Sugimoto et al. 10.2151/sola.2010-032
27. Assimilation of remote sensing into crop growth models: Current status and perspectives J. Huang et al. 10.1016/j.agrformet.2019.06.008
28. Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation W. Zhuo et al. 10.1016/j.eja.2022.126556
29. Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM<sub>2.5</sub> S. Park et al. 10.5194/gmd-15-2773-2022
30. Development of a coupled aerosol lidar data quality assurance and control scheme with Monte Carlo analysis and bilateral filtering H. Wang et al. 10.1016/j.scitotenv.2020.138844
31. How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent J. Jin et al. 10.5194/acp-23-1641-2023
32. Combined effect of surface PM2.5 assimilation and aerosol-radiation interaction on winter severe haze prediction in central and eastern China Y. Peng et al. 10.1016/j.apr.2023.101802
33. Long‐term inverse modeling of Asian dust: Interannual variations of its emission, transport, deposition, and radiative forcing K. Yumimoto & T. Takemura 10.1002/2014JD022390
34. Impact of the OMI aerosol optical depth on analysis increments through coupled meteorology–aerosol data assimilation for an Asian dust storm E. Lee et al. 10.1016/j.rse.2017.02.013
35. Dust Emission Inversion Using Himawari‐8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al. 10.1029/2018MS001491
36. A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China D. Wang et al. 10.1007/s11430-019-9601-4
37. Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions S. Li et al. 10.5194/gmd-16-4171-2023
38. Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al. 10.5194/acp-14-12031-2014
39. 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
40. Dust storm ensemble forecast experiments in East Asia J. Zhu et al. 10.1007/s00376-009-8218-0
41. Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble data assimilation system A. Tsikerdekis et al. 10.5194/acp-21-2637-2021