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
Viewed
Total article views: 5,877 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Apr 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,319 | 3,432 | 126 | 5,877 | 146 | 120 |
- HTML: 2,319
- PDF: 3,432
- XML: 126
- Total: 5,877
- BibTeX: 146
- EndNote: 120
Total article views: 4,300 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Oct 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,951 | 2,263 | 86 | 4,300 | 127 | 110 |
- HTML: 1,951
- PDF: 2,263
- XML: 86
- Total: 4,300
- BibTeX: 127
- EndNote: 110
Total article views: 1,577 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Apr 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 368 | 1,169 | 40 | 1,577 | 19 | 10 |
- HTML: 368
- PDF: 1,169
- XML: 40
- Total: 1,577
- BibTeX: 19
- EndNote: 10
Cited
43 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
- VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO C. Yost et al. 10.3390/rs15051349
- 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
- Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts B. Gasparini et al. 10.5194/acp-23-15413-2023
- 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
- 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
- Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network S. Sun-Mack et al. 10.5194/amt-17-3323-2024
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- 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
- 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
- Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data F. Xu et al. 10.3390/rs16122136
- Ice water path retrievals from Meteosat-9 using quantile regression neural networks A. Amell et al. 10.5194/amt-15-5701-2022
- 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
- How Well Can Persistent Contrails Be Predicted? K. Gierens et al. 10.3390/aerospace7120169
- 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. 10.5194/nhess-22-1029-2022
- Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network X. Wang et al. 10.1016/j.rse.2022.113026
- 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
- 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
- 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
- Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers C. White et al. 10.1175/AIES-D-21-0001.1
- 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
- Bayesian cloud-top phase determination for Meteosat Second Generation J. Mayer et al. 10.5194/amt-17-4015-2024
- Mitigating the Climate Impact from Aviation: Achievements and Results of the DLR WeCare Project V. Grewe et al. 10.3390/aerospace4030034
42 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
- VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO C. Yost et al. 10.3390/rs15051349
- 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
- Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts B. Gasparini et al. 10.5194/acp-23-15413-2023
- 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
- 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
- Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network S. Sun-Mack et al. 10.5194/amt-17-3323-2024
- Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers D. Piontek et al. 10.3390/rs15051247
- 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
- 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
- Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data F. Xu et al. 10.3390/rs16122136
- Ice water path retrievals from Meteosat-9 using quantile regression neural networks A. Amell et al. 10.5194/amt-15-5701-2022
- 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
- How Well Can Persistent Contrails Be Predicted? K. Gierens et al. 10.3390/aerospace7120169
- 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. 10.5194/nhess-22-1029-2022
- Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network X. Wang et al. 10.1016/j.rse.2022.113026
- 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
- 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
- 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
- Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers C. White et al. 10.1175/AIES-D-21-0001.1
- 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
- Bayesian cloud-top phase determination for Meteosat Second Generation J. Mayer et al. 10.5194/amt-17-4015-2024
1 citations as recorded by crossref.
Saved (final revised paper)
Latest update: 26 Jul 2024
