53 citations as recorded by crossref.

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2. 25-Year analysis of tropical cirrus clouds: insights from ground and space-based LIDAR observations P. Mol et al. https://doi.org/10.1016/j.atmosres.2025.108158
3. Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm D. Miller et al. https://doi.org/10.5194/amt-13-3447-2020
4. 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
5. Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts B. Gasparini et al. https://doi.org/10.5194/acp-23-15413-2023
6. A Multi-Stage Deep Learning Framework for Multi-Source Cloud Top Height Retrieval from FY-4A/AGRI Data Y. Cheng et al. https://doi.org/10.3390/atmos16111288
7. Particle reflectivity and movements in cirrus clouds over Beijing from four years of Ka radar measurements J. Huo et al. https://doi.org/10.1016/j.atmosres.2020.105211
8. Seasonal distribution of cirrus cloud characteristics and their rapid descent from polarization lidar measurements at the west coast of India M. Aswini et al. https://doi.org/10.1007/s00704-023-04518-w
9. Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks J. Strandgren et al. https://doi.org/10.5194/amt-10-3547-2017
10. An ensemble of state-of-the-art ash dispersion models: towards probabilistic forecasts to increase the resilience of air traffic against volcanic eruptions M. Plu et al. https://doi.org/10.5194/nhess-21-2973-2021
11. Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network S. Sun-Mack et al. https://doi.org/10.5194/amt-17-3323-2024
12. Cirrus Clouds A. Heymsfield et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0010.1
13. GVCCS: a dataset for contrail identification and tracking on visible whole sky camera sequences G. Jarry et al. https://doi.org/10.5194/essd-18-1037-2026
14. Development of neural network retrievals of liquid cloud properties from multi-angle polarimetric observations M. Segal-Rozenhaimer et al. https://doi.org/10.1016/j.jqsrt.2018.08.030
15. A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1 S. Larosa et al. https://doi.org/10.3390/rs15071798
16. Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method P. Minnis et al. https://doi.org/10.1002/2015JD024456
17. Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks C. White et al. https://doi.org/10.5194/amt-14-3371-2021
18. How Well Can Persistent Contrails Be Predicted? K. Gierens et al. https://doi.org/10.3390/aerospace7120169
19. Observing long-lived longwave contrail forcing A. Sonabend-W et al. https://doi.org/10.5194/amt-19-1951-2026
20. Algorithm for Interpreting Light Backscattering Matrices of Cirrus Clouds for the Retrieval of Their Microphysical Parameters V. Shishko et al. https://doi.org/10.1134/S1024856019040134
21. How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties J. Mayer et al. https://doi.org/10.5194/amt-17-5161-2024
22. Constraining Ice Water Content of Thin Antarctic Cirrus Clouds Using Ground-Based Lidar and Satellite Data S. Alexander & A. Klekociuk https://doi.org/10.1175/JAS-D-20-0251.1
23. Properties of ice cloud over Beijing from surface Ka-band radar observations during 2014–2017 J. Huo et al. https://doi.org/10.5194/acp-20-14377-2020
24. Retrieval of the base heights and geometric thicknesses of clouds based on the PARASOL measurement T. Ji et al. https://doi.org/10.1016/j.rse.2026.115477
25. A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations C. Wang et al. https://doi.org/10.5194/amt-13-2257-2020
26. Retrieval of Vertical Profile of Cirrus Cloud Effective Particle Size Using Reflected Line Spectra in 1.38 μm Band Y. Gu et al. https://doi.org/10.1029/2020EA001119
27. Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants T. Sirch et al. https://doi.org/10.5194/amt-10-409-2017
28. Bayesian cloud-top phase determination for Meteosat Second Generation J. Mayer et al. https://doi.org/10.5194/amt-17-4015-2024
29. Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking A. Taravat et al. https://doi.org/10.3390/rs70201529
30. CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System G. Stephens et al. https://doi.org/10.1175/BAMS-D-16-0324.1
31. Neural network cloud top pressure and height for MODIS N. Håkansson et al. https://doi.org/10.5194/amt-11-3177-2018
32. 基于神经网络反演中国南海海域透明卷云参数 陆. Lu Wenqiang et al. https://doi.org/10.3788/AOS230605
33. Theoretical assessment of measurement conditions for a near-infrared polarimetric instrument adapted for stratospheric aerosols observation Y. Yukhymchuk et al. https://doi.org/10.1016/j.jqsrt.2025.109700
34. A global record of single-layered ice cloud properties and associated radiative heating rate profiles from an A-Train perspective E. Dolinar et al. https://doi.org/10.1007/s00382-019-04682-8
35. Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. https://doi.org/10.3390/rs15051247
36. The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. https://doi.org/10.3390/rs13163128
37. Contrail altitude estimation using GOES-16 ABI data and deep learning V. Meijer et al. https://doi.org/10.5194/amt-17-6145-2024
38. Cloud parameter retrieval based on satellite data: A review of methods, advances, and challenges Z. Li et al. https://doi.org/10.1016/j.atmosres.2026.109130
39. An extended lidar-based cirrus cloud retrieval scheme: first application over an Arctic site K. Nakoudi et al. https://doi.org/10.1364/OE.414770
40. Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data F. Xu et al. https://doi.org/10.3390/rs16122136
41. Ice water path retrievals from Meteosat-9 using quantile regression neural networks A. Amell et al. https://doi.org/10.5194/amt-15-5701-2022
42. Atmospheric Correction Above Clouds for FY-4A AGRI Based on RTTOV Y. Wang et al. https://doi.org/10.1109/TGRS.2024.3494743
43. Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization Y. Cheng et al. https://doi.org/10.3390/s24020541
44. 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
45. Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network X. Wang et al. https://doi.org/10.1016/j.rse.2022.113026
46. ML-CIRRUS: The Airborne Experiment on Natural Cirrus and Contrail Cirrus with the High-Altitude Long-Range Research Aircraft HALO C. Voigt et al. https://doi.org/10.1175/BAMS-D-15-00213.1
47. Retrieval of Cloud Optical Thickness During Nighttime from FY-4B AGRI Using a Convolutional Neural Network D. Xia et al. https://doi.org/10.3390/rs17050737
48. CERES MODIS Cloud Product Retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO C. Yost et al. https://doi.org/10.1109/TGRS.2020.3015155
49. Neural network radiative transfer for imaging spectroscopy B. Bue et al. https://doi.org/10.5194/amt-12-2567-2019
50. Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI J. Strandgren et al. https://doi.org/10.5194/amt-10-4317-2017
51. Cirrus cloud shape detection by tomographic extinction retrievals from infrared limb emission sounder measurements J. Ungermann et al. https://doi.org/10.5194/amt-13-7025-2020
52. Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers C. White et al. https://doi.org/10.1175/AIES-D-21-0001.1
53. Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne C. Aebi et al. https://doi.org/10.5194/amt-13-907-2020