42 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.
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.
3. Inverse modeling of black carbon emissions over China using ensemble data assimilation P. Wang et al.
4. Ensemble filter based estimation of spatially distributed parameters in a mesoscale dust model: experiments with simulated and real data V. Khade et al.
5. Sensitive areas for target observation associated with meteorological forecasts for dust storm events in the Beijing–Tianjin–Hebei region L. Yang & W. Duan
6. Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al.
7. Machine learning for observation bias correction with application to dust storm data assimilation J. Jin et al.
8. 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.
9. The value of satellite observations in the analysis and short-range prediction of Asian dust A. Benedetti et al.
10. Inverse modeling of the 2021 spring super dust storms in East Asia J. Jin et al.
11. Estimation of aerosol particle number distributions with Kalman Filtering – Part 1: Theory, general aspects and statistical validity T. Viskari et al.
12. Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing X. Cheng et al.
13. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation J. Huang et al.
14. Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0 J. Jin et al.
15. 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.
16. Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition? M. Kacenelenbogen et al.
17. Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM2.5 Predictions M. Gao et al.
18. PM10 data assimilation over south Korea to Asian dust forecasting model with the optimal interpolation method E. Lee et al.
19. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models M. Bocquet et al.
20. 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.
21. Model bias correction for dust storm forecast using ensemble Kalman filter C. Lin et al.
22. 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.
23. Data assimilation of CALIPSO aerosol observations T. Sekiyama et al.
24. Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model N. Schutgens et al.
25. Sensitivity tests for an ensemble Kalman filter for aerosol assimilation N. Schutgens et al.
26. Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar J. Jin et al.
27. 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.
28. Assimilation of remote sensing into crop growth models: Current status and perspectives J. Huang et al.
29. Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation W. Zhuo et al.
30. Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5 S. Park et al.
31. Development of a coupled aerosol lidar data quality assurance and control scheme with Monte Carlo analysis and bilateral filtering H. Wang et al.
32. How aerosol size matters in aerosol optical depth (AOD) assimilation and the optimization using the Ångström exponent J. Jin et al.
33. 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.
34. Long‐term inverse modeling of Asian dust: Interannual variations of its emission, transport, deposition, and radiative forcing K. Yumimoto & T. Takemura
35. 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.
36. Dust Emission Inversion Using Himawari‐8 AODs Over East Asia: An Extreme Dust Event in May 2017 J. Jin et al.
37. 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.
38. Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions S. Li et al.
39. Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al.
40. Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime S. Feng et al.
41. Dust storm ensemble forecast experiments in East Asia J. Zhu et al.
42. Assimilating aerosol optical properties related to size and absorption from POLDER/PARASOL with an ensemble data assimilation system A. Tsikerdekis et al.