Articles | Volume 7, issue 10
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.
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
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
now at: European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany
L. Bugliaro
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
A. Ostler
Karlsruher Institut für Technologie, Institut für Meteorologie und Klimaforschung (IMK-IFU), Garmisch-Partenkirchen, Germany
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- 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
- A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1 S. Larosa et al. 10.3390/rs15071798
- 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
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- 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. 10.5194/amt-17-5161-2024
- Constraining Ice Water Content of Thin Antarctic Cirrus Clouds Using Ground-Based Lidar and Satellite Data S. Alexander & A. Klekociuk 10.1175/JAS-D-20-0251.1
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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
- 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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- 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
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