Articles | Volume 7, issue 10
Atmos. Meas. Tech., 7, 3233–3246, 2014
https://doi.org/10.5194/amt-7-3233-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 7, 3233–3246, 2014
https://doi.org/10.5194/amt-7-3233-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
01 Oct 2014
Research article
| 01 Oct 2014
Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing
S. Kox et al.
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- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. 10.3390/rs13163128
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- ML-CIRRUS: The Airborne Experiment on Natural Cirrus and Contrail Cirrus with the High-Altitude Long-Range Research Aircraft HALO C. Voigt et al. 10.1175/BAMS-D-15-00213.1
- Properties of ice cloud over Beijing from surface Ka-band radar observations during 2014–2017 J. Huo et al. 10.5194/acp-20-14377-2020
- CERES MODIS Cloud Product Retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO C. Yost et al. 10.1109/TGRS.2020.3015155
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- A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations C. Wang et al. 10.5194/amt-13-2257-2020
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- Retrieval of Vertical Profile of Cirrus Cloud Effective Particle Size Using Reflected Line Spectra in 1.38 μm Band Y. Gu et al. 10.1029/2020EA001119
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- Mitigating the Climate Impact from Aviation: Achievements and Results of the DLR WeCare Project V. Grewe et al. 10.3390/aerospace4030034
27 citations as recorded by crossref.
- Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking A. Taravat et al. 10.3390/rs70201529
- CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System G. Stephens et al. 10.1175/BAMS-D-16-0324.1
- Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm D. Miller et al. 10.5194/amt-13-3447-2020
- Neural network cloud top pressure and height for MODIS N. Håkansson et al. 10.5194/amt-11-3177-2018
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- Particle reflectivity and movements in cirrus clouds over Beijing from four years of Ka radar measurements J. Huo et al. 10.1016/j.atmosres.2020.105211
- A global record of single-layered ice cloud properties and associated radiative heating rate profiles from an A-Train perspective E. Dolinar et al. 10.1007/s00382-019-04682-8
- Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks J. Strandgren et al. 10.5194/amt-10-3547-2017
- 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. 10.5194/nhess-21-2973-2021
- Cirrus Clouds A. Heymsfield et al. 10.1175/AMSMONOGRAPHS-D-16-0010.1
- Development of neural network retrievals of liquid cloud properties from multi-angle polarimetric observations M. Segal-Rozenhaimer et al. 10.1016/j.jqsrt.2018.08.030
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. 10.3390/rs13163128
- An extended lidar-based cirrus cloud retrieval scheme: first application over an Arctic site K. Nakoudi et al. 10.1364/OE.414770
- Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method P. Minnis et al. 10.1002/2015JD024456
- Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks C. White et al. 10.5194/amt-14-3371-2021
- How Well Can Persistent Contrails Be Predicted? K. Gierens et al. 10.3390/aerospace7120169
- Algorithm for Interpreting Light Backscattering Matrices of Cirrus Clouds for the Retrieval of Their Microphysical Parameters V. Shishko et al. 10.1134/S1024856019040134
- ML-CIRRUS: The Airborne Experiment on Natural Cirrus and Contrail Cirrus with the High-Altitude Long-Range Research Aircraft HALO C. Voigt et al. 10.1175/BAMS-D-15-00213.1
- Properties of ice cloud over Beijing from surface Ka-band radar observations during 2014–2017 J. Huo et al. 10.5194/acp-20-14377-2020
- CERES MODIS Cloud Product Retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO C. Yost et al. 10.1109/TGRS.2020.3015155
- Neural network radiative transfer for imaging spectroscopy B. Bue et al. 10.5194/amt-12-2567-2019
- A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations C. Wang et al. 10.5194/amt-13-2257-2020
- Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI J. Strandgren et al. 10.5194/amt-10-4317-2017
- Retrieval of Vertical Profile of Cirrus Cloud Effective Particle Size Using Reflected Line Spectra in 1.38 μm Band Y. Gu et al. 10.1029/2020EA001119
- Cirrus cloud shape detection by tomographic extinction retrievals from infrared limb emission sounder measurements J. Ungermann et al. 10.5194/amt-13-7025-2020
- Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants T. Sirch et al. 10.5194/amt-10-409-2017
- Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne C. Aebi et al. 10.5194/amt-13-907-2020
1 citations as recorded by crossref.
Saved (final revised paper)
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