64 citations as recorded by crossref.

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4. Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe M. Chrit & M. Majdi 10.3390/atmos13050763
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8. Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders I. De Feis et al. 10.3390/s20082352
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16. Optimal Interpolation of Aeronet Radiometric Network Observations for the Evaluation of the Aerosol Optical Thickness Distribution in the Eastern European Region N. Miatselskaya et al. 10.1007/s10812-022-01357-x
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23. 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
24. Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign Y. Wang et al. 10.5194/acp-14-3511-2014
25. Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth B. Sič et al. 10.5194/amt-9-5535-2016
26. Mapping particulate matter in alpine regions with satellite and ground-based measurements: An exploratory study for data assimilation E. Emili et al. 10.1016/j.atmosenv.2011.05.051
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34. A comparison of correlation-length estimation methods for the objective analysis of surface pollutants at Environment and Climate Change Canada R. Ménard et al. 10.1080/10962247.2016.1177620
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40. 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
41. RETRACTED ARTICLE: Rainfall trend based on computer image system and English translation of tourist attractions in coastal cities X. Zou 10.1007/s12517-021-07357-z
42. Impact of model resolution and its representativeness consistency with observations on operational prediction of PM2.5 with 3D-VAR data assimilation Y. Wei et al. 10.1016/j.apr.2024.102141
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45. Remote sensing of two exceptional winter aerosol pollution events and representativeness of ground-based measurements A. Baron et al. 10.5194/acp-20-6749-2020
46. Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al. 10.5194/acp-14-12031-2014
47. Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. 10.3402/tellusb.v68.29812
48. 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
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50. Improving the PM10 estimates of the air quality model AURORA by using Optimal Interpolation**This work was supported by: • Research Council KUL: CoE PFV/10/002 (OPTEC), PhD/Postdoc grants • Flemish Government: iMinds Medical Information Technologies SBO 2015 • Belgian Federal Science Policy Office: IUAP P7/19 (DYSCO, Dynamical systems, control and optimization, 2012-2017). O. Mauricio Agudelo et al. 10.1016/j.ifacol.2015.12.287
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52. Comparing mesoscale chemistry-transport model and remote-sensed Aerosol Optical Depth C. Carnevale et al. 10.1016/j.atmosenv.2010.10.029
53. RETRACTED ARTICLE: Green urban vegetation planning and economic efficiency based on remote sensing images and grid geographic space Y. Lu & L. Lei 10.1007/s12517-021-07146-8
54. Surface data assimilation of chemical compounds over North America and its impact on air quality and Air Quality Health Index (AQHI) forecasts A. Robichaud 10.1007/s11869-017-0485-9
55. Multi-year objective analyses of warm season ground-level ozone and PM2.5 over North America using real-time observations and Canadian operational air quality models A. Robichaud & R. Ménard 10.5194/acp-14-1769-2014
56. Real-time air quality forecasting, part II: State of the science, current research needs, and future prospects Y. Zhang et al. 10.1016/j.atmosenv.2012.02.041
57. Improving PM2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter Z. Peng et al. 10.5194/acp-17-4837-2017
58. Assimilation of INSAT-3D imager retrieved aerosol optical depth for enhanced dust storm prediction using WRF-Chem model A. Khan et al. 10.1088/2515-7620/ad9712
59. A Study on Influence of Meteorological Patterns on Data Assimilation Effect using the Air Quality Prediction Model T. Kim et al. 10.5572/KOSAE.2019.35.1.049
60. The impact of data assimilation into the meteorological WRF model on birch pollen modelling M. Werner et al. 10.1016/j.scitotenv.2021.151028
61. JRAero: the Japanese Reanalysis for Aerosol v1.0 K. Yumimoto et al. 10.5194/gmd-10-3225-2017
62. Next Generation Air Quality Models: Dynamical Mesh, New Insights into Mechanism, Datasets and Applications J. Li et al. 10.1007/s40726-025-00355-9
63. 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
64. Data assimilation methods for urban air quality at the local scale C. Nguyen & L. Soulhac 10.1016/j.atmosenv.2021.118366