62 citations as recorded by crossref.

1. Inverse Model Parameter Estimation Based on Sensitivity Analysis for Improvement of PM10Forecasting S. Yu et al. 10.9717/kmms.2015.18.7.886
2. Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model N. Schutgens et al. 10.5194/acp-10-2561-2010
3. Monitoring aerosols over Europe: an assessment of the potential benefit of assimilating the VIS04 measurements from the future MTG/FCI geostationary imager M. Descheemaecker et al. 10.5194/amt-12-1251-2019
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
5. Precipitation Data Assimilation System Based on a Neural Network and Case-Based Reasoning System J. Lu et al. 10.3390/info9050106
6. Probing into the impact of 3DVAR assimilation of surface PM10observations over China using process analysis Z. Jiang et al. 10.1002/jgrd.50495
7. 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. 10.1007/s11430-019-9601-4
8. Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions S. Li et al. 10.5194/gmd-16-4171-2023
9. Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders I. De Feis et al. 10.3390/s20082352
10. Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China H. Ye et al. 10.1016/j.atmosres.2022.106069
11. On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations J. Vira & M. Sofiev 10.1016/j.atmosenv.2011.09.066
12. Application of a chemical transport model and optimized data assimilation methods to improve air quality assessment C. Silibello et al. 10.1007/s11869-014-0235-1
13. 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
14. Assimilation of PM2.5 ground base observations to two chemical schemes in WRF-Chem – The results for the winter and summer period M. Werner et al. 10.1016/j.atmosenv.2018.12.016
15. Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth N. Miatselskaya et al. 10.3390/atmos14010032
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
17. Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: lidar observations L. El Amraoui et al. 10.5194/amt-13-4645-2020
18. Estimation of aerosol particle number distributions with Kalman Filtering – Part 1: Theory, general aspects and statistical validity T. Viskari et al. 10.5194/acp-12-11767-2012
19. Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing X. Cheng et al. 10.1016/j.scitotenv.2019.05.186
20. Data assimilation of surface air pollutants (O3 and NO2) in the regional-scale air quality model AURORA U. Kumar et al. 10.1016/j.atmosenv.2012.06.005
21. Impact of CALIPSO profile data assimilation on 3-D aerosol improvement in a size-resolved aerosol model H. Ye et al. 10.1016/j.atmosres.2021.105877
22. 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
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. Assimilating aerosol observations with a “hybrid” variational-ensemble data assimilation system C. Schwartz et al. 10.1002/2013JD020937
25. Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign Y. Wang et al. 10.5194/acp-14-3511-2014
26. 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
27. 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
28. Assimilation of surface NO<sub>2</sub> and O<sub>3</sub> observations into the SILAM chemistry transport model J. Vira & M. Sofiev 10.5194/gmd-8-191-2015
29. Sensitivity tests for an ensemble Kalman filter for aerosol assimilation N. Schutgens et al. 10.5194/acp-10-6583-2010
30. OPTIMAL INTERPOLATION OF AERONET RADIOMETRIC NETWORK OBSERVATIONS FOR THE EVALUATION OF THE AEROSOL OPTICAL DEPTH DISTRIBUTION IN THE EASTERN EUROPEAN REGION N. Miatselskaya et al. 10.47612/0514-7506-2022-89-2-246-253
31. 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
32. A new approach for optimizing air pollutant emissions using Newtonian relaxation and the coupled WRF-CAMx model: a case study in Xuzhou city, China Y. Li et al. 10.1007/s12517-020-06002-5
33. EARLINET: potential operationality of a research network M. Sicard et al. 10.5194/amt-8-4587-2015
34. A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM<sub>2.5</sub> prediction Z. Li et al. 10.5194/acp-13-4265-2013
35. 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
36. 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
37. Assimilation of ground versus lidar observations for PM<sub>10</sub> forecasting Y. Wang et al. 10.5194/acp-13-269-2013
38. Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM<sub>2.5</sub> forecasts across China Y. Liang et al. 10.5194/gmd-13-6285-2020
39. Wet scavenging process of particulate matter (PM10): A multivariate complex network approach T. Plocoste et al. 10.1016/j.apr.2021.101095
40. Improvement of PM2.5 forecast over China by the joint adjustment of initial conditions and emissions with the NLS-4DVar method S. Zhang et al. 10.1016/j.atmosenv.2021.118896
41. 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
42. 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
43. Detecting local climate zone change and its effects on PM10 distribution using fuzzy machine learning in Tehran, Iran M. Maleki et al. 10.1016/j.uclim.2023.101506
44. An Assessment of Optimality of Observations in High-resolution Weather Forecasting P. Goswami & V. Rakesh 10.1007/s00024-015-1155-1
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
49. 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
50. Kalman filter-based air quality forecast adjustment K. De Ridder et al. 10.1016/j.atmosenv.2012.01.032
51. Comparing mesoscale chemistry-transport model and remote-sensed Aerosol Optical Depth C. Carnevale et al. 10.1016/j.atmosenv.2010.10.029
52. 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
53. 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
54. Multi-year objective analyses of warm season ground-level ozone and PM<sub>2.5</sub> over North America using real-time observations and Canadian operational air quality models A. Robichaud & R. Ménard 10.5194/acp-14-1769-2014
55. 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
56. Improving PM<sub>2. 5</sub> 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
57. Simultaneous three-dimensional variational assimilation of surface fine particulate matter and MODIS aerosol optical depth C. Schwartz et al. 10.1029/2011JD017383
58. 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
59. The impact of data assimilation into the meteorological WRF model on birch pollen modelling M. Werner et al. 10.1016/j.scitotenv.2021.151028
60. JRAero: the Japanese Reanalysis for Aerosol v1.0 K. Yumimoto et al. 10.5194/gmd-10-3225-2017
61. 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
62. Data assimilation methods for urban air quality at the local scale C. Nguyen & L. Soulhac 10.1016/j.atmosenv.2021.118366