42 citations as recorded by crossref.

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5. 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. https://doi.org/10.5194/acp-21-4403-2021
6. An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data H. Kim et al. https://doi.org/10.3390/rs14020389
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8. Improvement of the Aerosol Forecast and Analysis Over East Asia With Joint Assimilation of Two Geostationary Satellite Observations Y. Cheng et al. https://doi.org/10.1029/2022GL099908
9. Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method X. Zhang et al. https://doi.org/10.1029/2020JD033599
10. Development and Evaluation of a Novel 3D Variational Assimilation Framework for Regional Chemistry–Weather Forecasting Z. Zhuang et al. https://doi.org/10.1007/s13351-026-5233-3
11. The Aerosol Module in the Community Radiative Transfer Model (v2.2 and v2.3): accounting for aerosol transmittance effects on the radiance observation operator C. Lu et al. https://doi.org/10.5194/gmd-15-1317-2022
12. A long-term high-resolution air quality reanalysis with a public-facing air quality dashboard over the Contiguous United States (CONUS) R. Kumar et al. https://doi.org/10.5194/essd-17-1807-2025
13. 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. https://doi.org/10.1029/2018JD029009
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15. JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP B. Huang et al. https://doi.org/10.1029/2022MS003232
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18. Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation S. Liu et al. https://doi.org/10.3390/rs11202338
19. Unveiling 3-D evolution and mechanisms of ozone pollution in Changzhou, China: Insights from lidar observations and modelling Z. Liu et al. https://doi.org/10.1016/j.envpol.2024.124556
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21. Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia S. Lee et al. https://doi.org/10.1016/j.atmosenv.2021.118921
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23. The impact of multi-species surface chemical observation assimilation on air quality forecasts in China Z. Peng et al. https://doi.org/10.5194/acp-18-17387-2018
24. Information constraints in variational data assimilation M. Kahnert https://doi.org/10.1002/qj.3347
25. Using Lidar technology to assess regional air pollution and improve estimates of PM2.5 transport in the North China Plain Y. Xiang et al. https://doi.org/10.1088/1748-9326/ab9cfd
26. PM2.5 assimilation within JEDI for NOAA's regional Air Quality Model (AQMv7): application to the September 2020 Western US wildfires H. Wang et al. https://doi.org/10.5194/gmd-19-2479-2026
27. Limitations of ozone data assimilation with adjustment of NOx emissions: mixed effects on NO2 forecasts over Beijing and surrounding areas X. Tang et al. https://doi.org/10.5194/acp-16-6395-2016
28. Hourly PM2.5 Estimates across California from 2018 to 2023 S. White et al. https://doi.org/10.1021/acsestair.5c00372
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30. Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions R. Žabkar et al. https://doi.org/10.5194/gmd-8-2119-2015
31. Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. https://doi.org/10.1029/2018MS001475
32. Estimation of aerosol properties over the Chinese desert region with MODIS AOD assimilation in a global model X. Yin et al. https://doi.org/10.1016/j.accre.2016.04.001
33. A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods Y. Tang et al. https://doi.org/10.5194/gmd-10-4743-2017
34. Assimilating FY-4B satellite aerosol data to improve PM₂.₅ and surface shortwave radiation prediction F. Hu et al. https://doi.org/10.1016/j.atmosres.2026.108764
35. Impact of Assimilating GEMS Aerosol Optical Depth on Asian Dust Storm Prediction: Comparative Assessment with MODIS Observation E. Lee et al. https://doi.org/10.1007/s13143-025-00407-6
36. Shipborne lidar measurements of ozone over the southeastern coastal regions of China in winter L. Lv et al. https://doi.org/10.1016/j.envres.2025.121165
37. Assimilating a blended dataset of satellite-based estimations and in situ observations to improve WRF-Chem PM2.5 prediction X. Ma et al. https://doi.org/10.1016/j.atmosenv.2023.120284
38. The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation S. Wei et al. https://doi.org/10.5194/gmd-17-795-2024
39. Source, transport and impact of biomass burning emissions in spring 2024 in Yunnan, China L. Lv et al. https://doi.org/10.1016/j.jes.2025.10.040
40. Satellite imagery and products of the 16–17 February 2020 Saharan Air Layer dust event over the eastern Atlantic: impacts of water vapor on dust detection and morphology L. Grasso et al. https://doi.org/10.5194/amt-14-1615-2021
41. Constructing the 3D spatial distribution of PM2.5 concentrations during the 2022 Beijing Winter Olympics using LiDAR vertical observation networks and machine learning models Z. Wang et al. https://doi.org/10.1016/j.envint.2025.109875
42. Vertical structure and transport characteristic of aerosol and O3 during the emergency control period in Wuhan, China, using vehicle-lidar observations Y. Pan et al. https://doi.org/10.1016/j.atmosenv.2024.120762