67 citations as recorded by crossref.

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3. Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire X. Ye et al. 10.5194/acp-21-14427-2021
4. Assimilation of Aerosol Optical Depth Into the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke) T. Jones et al. 10.1029/2022JD037454
5. Evaluation of MERRA-2 data for aerosols patterns over the Kingdom of Saudi Arabia A. Labban & M. Butt 10.1016/j.heliyon.2023.e17047
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8. Online coupled regional meteorology chemistry models in Europe: current status and prospects A. Baklanov et al. 10.5194/acp-14-317-2014
9. A top-down assessment using OMI NO<sub>2</sub> suggests an underestimate in the NO<sub><i>x</i></sub> emissions inventory in Seoul, South Korea, during KORUS-AQ D. Goldberg et al. 10.5194/acp-19-1801-2019
10. RETRACTED ARTICLE: Rainfall trend based on computer image system and English translation of tourist attractions in coastal cities X. Zou 10.1007/s12517-021-07357-z
11. Improving the sectional Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosols of the Weather Research and Forecasting-Chemistry (WRF-Chem) model with the revised Gridpoint Statistical Interpolation system and multi-wavelength aerosol optical measurements: the dust aerosol observation campaign at Kashi, near the Taklimakan Desert, northwestern China W. Chang et al. 10.5194/acp-21-4403-2021
12. Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system J. Guerrette & D. Henze 10.5194/gmd-8-1857-2015
13. Long‐Term Trends of High Aerosol Pollution Events and Their Climatic Impacts in North America Using Multiple Satellite Retrievals and Modern‐Era Retrospective Analysis for Research and Applications version 2 Q. Jin & S. Pryor 10.1029/2019JD031137
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15. 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
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17. 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
18. 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
19. RETRACTED ARTICLE: Evaluation of agricultural climate and regional agricultural economic efficiency based on remote sensing analysis X. Lu 10.1007/s12517-021-07153-9
20. 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
21. Background error statistics for aerosol variables from WRF/Chem predictions in Southern California Z. Zang et al. 10.1007/s13143-015-0063-8
22. Sea salt emission, transport and influence on size-segregated nitrate simulation: a case study in northwestern Europe by WRF-Chem Y. Chen et al. 10.5194/acp-16-12081-2016
23. How much information do extinction and backscattering measurements contain about the chemical composition of atmospheric aerosol? M. Kahnert & E. Andersson 10.5194/acp-17-3423-2017
24. Lidar vertical observation network and data assimilation reveal key processes driving the 3-D dynamic evolution of PM<sub>2.5</sub> concentrations over the North China Plain Y. Xiang et al. 10.5194/acp-21-7023-2021
25. Optimizing the Numerical Simulation of the Dust Event of March 2021: Integrating Aerosol Observations through Multi-Scale 3D Variational Assimilation in the WRF-Chem Model S. Mei et al. 10.3390/rs16111852
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27. Central American biomass burning smoke can increase tornado severity in the U.S. P. Saide et al. 10.1002/2014GL062826
28. Implementation of aerosol assimilation in Gridpoint Statistical Interpolation (v. 3.2) and WRF-Chem (v. 3.4.1) M. Pagowski et al. 10.5194/gmd-7-1621-2014
29. JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP B. Huang et al. 10.1029/2022MS003232
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32. Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia S. Lee et al. 10.1016/j.atmosenv.2021.118921
33. Bayesian Inference Approach to Quantify Primary and Secondary Organic Carbon in Fine Particulate Matter Using Major Species Measurements K. Liao et al. 10.1021/acs.est.2c09412
34. Estimating adult mortality attributable to PM2.5 exposure in China with assimilated PM2.5 concentrations based on a ground monitoring network J. Liu et al. 10.1016/j.scitotenv.2016.05.165
35. A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations D. Wang et al. 10.5194/gmd-15-1821-2022
36. Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic K. Han et al. 10.3390/app11041959
37. Information constraints in variational data assimilation M. Kahnert 10.1002/qj.3347
38. Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum J. Zhang et al. 10.5194/gmd-14-27-2021
39. Using Lidar technology to assess regional air pollution and improve estimates of PM2.5 transport in the North China Plain Y. Xiang et al. 10.1088/1748-9326/ab9cfd
40. 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
41. Satellite retrieval of aerosol combined with assimilated forecast M. Yoshida et al. 10.5194/acp-21-1797-2021
42. Toward Improving Short‐Term Predictions of Fine Particulate Matter Over the United States Via Assimilation of Satellite Aerosol Optical Depth Retrievals R. Kumar et al. 10.1029/2018JD029009
43. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation C. Randles et al. 10.1175/JCLI-D-16-0609.1
44. 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
45. Aerosol data assimilation using data from Himawari‐8, a next‐generation geostationary meteorological satellite K. Yumimoto et al. 10.1002/2016GL069298
46. A multi-model assessment for the 2006 and 2010 simulations under the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part II. Evaluation of column variable predictions using satellite data K. Wang et al. 10.1016/j.atmosenv.2014.07.044
47. Estimation of Satellite-Based SO42− and NH4+ Composition of Ambient Fine Particulate Matter over China Using Chemical Transport Model Y. Si et al. 10.3390/rs9080817
48. Measurement report: Assessing the impacts of emission uncertainty on aerosol optical properties and radiative forcing from biomass burning in peninsular Southeast Asia Y. Jin et al. 10.5194/acp-24-367-2024
49. Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing T. Eck et al. 10.1029/2018JD028313
50. Probing into the wintertime meteorology and particulate matter (PM2.5 and PM10) forecast over Delhi A. Sengupta et al. 10.1016/j.apr.2022.101426
51. Background error covariance with balance constraints for aerosol species and applications in variational data assimilation Z. Zang et al. 10.5194/gmd-9-2623-2016
52. Global Chemical Composition of Ambient Fine Particulate Matter for Exposure Assessment S. Philip et al. 10.1021/es502965b
53. Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. 10.1016/j.jes.2022.04.012
54. GIST-PM-Asia v1: development of a numerical system to improve particulate matter forecasts in South Korea using geostationary satellite-retrieved aerosol optical data over Northeast Asia S. Lee et al. 10.5194/gmd-9-17-2016
55. 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
56. 3D assimilation and radiative impact assessment of aerosol black carbon over the Indian region using aircraft, balloon, ground-based, and multi-satellite observations N. Kala et al. 10.5194/acp-23-12801-2023
57. Retrieval of Aerosol Fine-Mode Fraction from Intensity and Polarization Measurements by PARASOL over East Asia Y. Zhang et al. 10.3390/rs8050417
58. 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
59. Retrieval of the Fine-Mode Aerosol Optical Depth over East China Using a Grouped Residual Error Sorting (GRES) Method from Multi-Angle and Polarized Satellite Data Y. Zhang et al. 10.3390/rs10111838
60. Enhancing Accuracy of Air Quality and Temperature Forecasts During Paddy Crop Residue Burning Season in Delhi Via Chemical Data Assimilation R. Kumar et al. 10.1029/2020JD033019
61. Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM2.5 episodes over Eastern China J. Pang et al. 10.1016/j.atmosenv.2018.02.011
62. Coupling aerosol optics to the MATCH (v5.5.0) chemical transport model and the SALSA (v1) aerosol microphysics module E. Andersson & M. Kahnert 10.5194/gmd-9-1803-2016
63. Assessment of biomass burning smoke influence on environmental conditions for multiyear tornado outbreaks by combining aerosol‐aware microphysics and fire emission constraints P. Saide et al. 10.1002/2016JD025056
64. Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART C. Ma et al. 10.1029/2019JD031465
65. Aerosol optical depth assimilation for a modal aerosol model: Implementation and application in AOD forecasts over East Asia J. Pang et al. 10.1016/j.scitotenv.2020.137430
66. Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ P. Saide et al. 10.5194/acp-20-6455-2020
67. Assimilating aerosol observations with a “hybrid” variational‐ensemble data assimilation system C. Schwartz et al. 10.1002/2013JD020937