41 citations as recorded by crossref.

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2. Evaluation of MODIS and two reanalysis aerosol optical depth products over AERONET sites H. Shi et al. https://doi.org/10.1016/j.atmosres.2019.01.009
3. 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. https://doi.org/10.1016/j.rse.2017.02.013
4. Generalized background error covariance matrix model (GEN_BE v2.0) G. Descombes et al. https://doi.org/10.5194/gmd-8-669-2015
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
7. Assimilating Fengyun-4A observations to improve WRF-Chem PM2.5 predictions in China J. Hong et al. https://doi.org/10.1016/j.atmosres.2021.105878
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. 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
11. 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
12. 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
13. Improving WRF-Chem PM2.5 predictions by combining data assimilation and deep-learning-based bias correction X. Ma et al. https://doi.org/10.1016/j.envint.2024.109199
14. 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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17. 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
18. 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
19. Impact of Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth and AirNow PM2.5 assimilation on Community Multi‐scale Air Quality aerosol predictions over the contiguous United States T. Chai et al. https://doi.org/10.1002/2016JD026295
20. 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
21. Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM 2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing J. McHenry et al. https://doi.org/10.1080/10962247.2015.1096862
22. 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
23. Information constraints in variational data assimilation M. Kahnert https://doi.org/10.1002/qj.3347
24. 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
25. 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
26. 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
27. Hourly PM2.5 Estimates across California from 2018 to 2023 S. White et al. https://doi.org/10.1021/acsestair.5c00372
28. Lidar vertical observation network and data assimilation reveal key processes driving the 3-D dynamic evolution of PM2.5 concentrations over the North China Plain Y. Xiang et al. https://doi.org/10.5194/acp-21-7023-2021
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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