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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- 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
- Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts B. Gasparini et al. 10.5194/acp-23-15413-2023
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- 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. 10.3788/AOS230605
- Seasonal distribution of cirrus cloud characteristics and their rapid descent from polarization lidar measurements at the west coast of India M. Aswini et al. 10.1007/s00704-023-04518-w
- 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
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- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
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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
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation D. Piontek et al. 10.3390/rs13163128
- A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1 S. Larosa et al. 10.3390/rs15071798
- An extended lidar-based cirrus cloud retrieval scheme: first application over an Arctic site K. Nakoudi et al. 10.1364/OE.414770
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- 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
- Remote Sensing Retrieval of Cloud Top Height Using Neural Networks and Data from Cloud-Aerosol Lidar with Orthogonal Polarization Y. Cheng et al. 10.3390/s24020541
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