69 citations as recorded by crossref.

1. Comprehensive thematic T-matrix reference database: a 2017–2019 update M. Mishchenko https://doi.org/10.1016/j.jqsrt.2019.106692
2. Morphological and radiative characteristics of soot aggregates: Experimental and numerical research E. Sutcu et al. https://doi.org/10.1038/s41598-019-57045-y
3. Characterization of brown carbon absorption in different European environments through source contribution analysis H. Navarro-Barboza et al. https://doi.org/10.5194/acp-25-2667-2025
4. Potential of AOD Retrieval Using Atmospheric Emitted Radiance Interferometer (AERI) J. Seo et al. https://doi.org/10.3390/rs14020407
5. Improved Land AOD Retrieval of GK-2A/AMI via Background Surface Reflectance Based on sRTLS-BRDF Inversion D. Jung et al. https://doi.org/10.3390/rs18071018
6. Assessing Multi‐Stream Radiative Transfer Schemes for the Calculation of Aerosol Radiative Forcing in the Martian Atmosphere H. Chen‐Chen et al. https://doi.org/10.1029/2021JE006889
7. Retrieval and validation of cloud condensation nuclei from satellite and airborne measurements over the Indian Monsoon region A. Aravindhavel et al. https://doi.org/10.1016/j.atmosres.2023.106802
8. Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions D. Piontek et al. https://doi.org/10.1016/j.jvolgeores.2021.107174
9. MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models P. Kumar et al. https://doi.org/10.1038/s41612-024-00652-y
10. Effect of Spectral Variability of Aerosol Optical Properties on Direct Aerosol Radiative Effect S. Kato et al. https://doi.org/10.3389/frsen.2022.904505
11. Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0) J. Vicent et al. https://doi.org/10.5194/gmd-13-1945-2020
12. Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – estimation of 15 min aerosol optical depth from MSG/SEVIRI and evaluation with reference data X. Ceamanos et al. https://doi.org/10.5194/amt-16-2575-2023
13. Extended POLIPHON dust conversion factor dataset for lidar-derived cloud condensation nuclei and ice-nucleating particle concentration profiles Y. He et al. https://doi.org/10.5194/amt-18-5669-2025
14. Aerosol particle depolarization ratio at 1565 nm measured with a Halo Doppler lidar V. Vakkari et al. https://doi.org/10.5194/acp-21-5807-2021
15. Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street R. Kahn et al. https://doi.org/10.1029/2022RG000796
16. Quantifying particulate matter optical properties and flow rate in industrial stack plumes from the PRISMA hyperspectral imager G. Calassou et al. https://doi.org/10.5194/amt-17-57-2024
17. Retrieval of aerosol composition from spectral aerosol optical depth and optical properties using a machine learning approach D. Ji et al. https://doi.org/10.5194/amt-19-421-2026
18. Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing X. Cheng et al. https://doi.org/10.1016/j.scitotenv.2019.05.186
19. Long-range transport of coarse mineral dust: an evaluation of the Met Office Unified Model against aircraft observations N. Ratcliffe et al. https://doi.org/10.5194/acp-24-12161-2024
20. Measuring particle size and concentration of non-spherical particles by combined light extinction and scattering method M. Jiang et al. https://doi.org/10.1016/j.measurement.2021.109911
21. A Regional Aerosol Model for the Middle Urals Based on CALIPSO Measurements E. Nagovitsyna et al. https://doi.org/10.3390/atmos15010048
22. Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index C. Fajardo-Zambrano et al. https://doi.org/10.3390/rs14061333
23. A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements G. Choudhury & M. Tesche https://doi.org/10.5194/essd-15-3747-2023
24. Near-Field Single-Scattering Calculations of Aerosols: Sensitivity Studies N. Arreyndip et al. https://doi.org/10.3390/opt4020028
25. A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations F. Alexandri et al. https://doi.org/10.5194/amt-17-1739-2024
26. On the use of light polarization to investigate the size, shape, and refractive index dependence of backscattering Ångström exponents A. Miffre et al. https://doi.org/10.1364/OL.385107
27. Long-term characterisation of the vertical structure of the Saharan Air Layer over the Canary Islands using lidar and radiosonde profiles: implications for radiative and cloud processes over the subtropical Atlantic Ocean Á. Barreto et al. https://doi.org/10.5194/acp-22-739-2022
28. Apparent surface-to-sky radiance ratio of natural waters including polarization and aerosol effects: implications for above-water radiometry T. Harmel https://doi.org/10.3389/frsen.2023.1307976
29. Closing the gap: an algorithmic approach to reconciling in-situ and remotely sensed aerosol properties J. Schlosser et al. https://doi.org/10.5194/amt-18-7187-2025
30. Assessing the Potential of Geostationary Satellites for Aerosol Remote Sensing Based on Critical Surface Albedo X. Ceamanos et al. https://doi.org/10.3390/rs11242958
31. Retrieval of aerosol properties from in situ, multi-angle light scattering measurements using invertible neural networks R. Boiger et al. https://doi.org/10.1016/j.jaerosci.2022.105977
32. Volume-to-extinction ratio: an important property of dust A. Papetta et al. https://doi.org/10.5194/acp-26-2055-2026
33. Pristine oceans are a significant source of uncertainty in quantifying global cloud condensation nuclei G. Choudhury et al. https://doi.org/10.5194/acp-25-3841-2025
34. Detection Performance Analysis of Marine Wind by Lidar and Radar under All-Weather Conditions Y. Peng et al. https://doi.org/10.3390/rs16122212
35. VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model L. Bugliaro et al. https://doi.org/10.5194/nhess-22-1029-2022
36. Deep learning-driven Mie scattering prediction method for radially varying spherical particles G. Wang et al. https://doi.org/10.1016/j.optlastec.2024.111170
37. Assimilating spaceborne lidar dust extinction can improve dust forecasts J. Escribano et al. https://doi.org/10.5194/acp-22-535-2022
38. Towards improved retrieval of aerosol properties from the geostationary orbit with the new Meteosat Third Generation-Imager satellite A. Georgeot et al. https://doi.org/10.5194/amt-18-4665-2025
39. Optimization of the sulfate aerosol hygroscopicity parameter in WRF-Chem A. Kim et al. https://doi.org/10.5194/gmd-14-259-2021
40. Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements G. Choudhury & M. Tesche https://doi.org/10.3390/rs14143342
41. Classification of Atmospheric Aerosols based on Photometric Measurements and Empirical Regional Model MUrA E. Nagovitsyna et al. https://doi.org/10.1134/S1024856025700046
42. Aerosol backscatter profiles from ceilometers: validation of water vapor correction in the framework of CeiLinEx2015 M. Wiegner et al. https://doi.org/10.5194/amt-12-471-2019
43. ACIX-III Aqua: evaluation of atmospheric correction for hyperspectral PRISMA imagery over inland and coastal waters C. Giardino et al. https://doi.org/10.1080/01431161.2025.2574517
44. ALiDAn: Spatiotemporal and Multiwavelength Atmospheric Lidar Data Augmentation A. Vainiger et al. https://doi.org/10.1109/TGRS.2022.3201436
45. An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity S. Rémy et al. https://doi.org/10.5194/gmd-17-7539-2024
46. Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements G. Choudhury et al. https://doi.org/10.5194/acp-22-7143-2022
47. Opposite Effects of Mineral Dust Nonsphericity and Size on Dust‐Induced Snow Albedo Reduction T. Shi et al. https://doi.org/10.1029/2022GL099031
48. Modeling Optical Properties of Non‐Cubical Sea‐Salt Particles F. Kanngießer & M. Kahnert https://doi.org/10.1029/2020JD033674
49. Phases in fine volcanic ash A. Hornby et al. https://doi.org/10.1038/s41598-023-41412-x
50. Fair Evaluation of Orientation‐Averaging Techniques in Light‐Scattering Simulations: Comment on “Evaluation of Higher‐Order Quadrature Schemes in Improving Computational Efficiency for Orientation‐Averaged Single‐Scattering Properties of Nonspherical Ice Particles” by Fenni et al M. Yurkin https://doi.org/10.1029/2021JD036088
51. Dust particle size, shape and optical depth during the 2018/MY34 martian global dust storm retrieved by MSL Curiosity rover Navigation Cameras H. Chen-Chen et al. https://doi.org/10.1016/j.icarus.2020.114021
52. Spectral Aerosol Optical Depth Retrievals by Ground-Based Fourier Transform Infrared Spectrometry Á. Barreto et al. https://doi.org/10.3390/rs12193148
53. The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. https://doi.org/10.3390/rs13163112
54. A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements A. Kauppi et al. https://doi.org/10.3390/rs16111945
55. Study on solar radiation transfer model of double skin façade with spray aerosol Y. Wang et al. https://doi.org/10.1016/j.ijthermalsci.2025.109875
56. From fine to giant: multi-instrument assessment of the dust particle size distribution at an emission source during the J-WADI field campaign H. Meyer et al. https://doi.org/10.5194/amt-19-21-2026
57. Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements G. Choudhury & M. Tesche https://doi.org/10.5194/amt-15-639-2022
58. Eradiate: an accurate and flexible radiative transfer model for earth observation and atmospheric science V. Leroy et al. https://doi.org/10.5194/gmd-19-4289-2026
59. The Cloud Indicator: A novel algorithm for automatic detection and classification of clouds using airborne in situ observations M. Dollner et al. https://doi.org/10.1016/j.atmosres.2024.107504
60. An application of the boundary element method (BEM) to the calculation of the single-scattering properties of very complex ice crystals in the microwave and sub-millimetre regions of the electromagnetic spectrum A. Kleanthous et al. https://doi.org/10.1016/j.jqsrt.2023.108793
61. Insights into microphysical and optical properties of typical mineral dust within urban snowpack via wet and dry deposition in Changchun, northeastern China T. Shi et al. https://doi.org/10.5194/tc-19-2821-2025
62. What caused a record high PM10 episode in northern Europe in October 2020? C. Groot Zwaaftink et al. https://doi.org/10.5194/acp-22-3789-2022
63. Are Backscattering Ångström Exponents merely indicative of particles size? Advancing beyond by using light polarization A. Miffre & P. Rairoux https://doi.org/10.1051/epjconf/202636202029
64. Influence of particle properties on measuring a low-particulate-mass concentration by light extinction method M. Jiang et al. https://doi.org/10.1016/j.fuel.2020.119460
65. A sensitivity study on radiative effects due to the parameterization of dust optical properties in models I. Fountoulakis et al. https://doi.org/10.5194/acp-24-4915-2024
66. Optical properties of ensemble of complex externally mixed aerosol particles under different relative humidity conditions M. WANG et al. https://doi.org/10.7498/aps.74.20241140
67. Impact of particle size, refractive index, and shape on the determination of the particle scattering coefficient – an optical closure study evaluating different nephelometer angular truncation and illumination corrections M. Teri et al. https://doi.org/10.5194/amt-15-3161-2022
68. Investigation of Dust-Induced Surface Erosion During Martian Planetary Entries M. Kroells et al. https://doi.org/10.2514/1.A36250
69. Inversion Model of Nonspherical Aerosol Particle Size Distribution Based on Tikhonov’s Regularization With the PSO-GA Algorithm Z. Qiao et al. https://doi.org/10.1109/TGRS.2023.3317845