Articles | Volume 7, issue 24
https://doi.org/10.5194/acp-7-6145-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
https://doi.org/10.5194/acp-7-6145-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
18 Dec 2007
Technical note: A new day- and night-time Meteosat Second Generation Cirrus Detection Algorithm MeCiDA
W. Krebs
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
H. Mannstein
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
L. Bugliaro
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
B. Mayer
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
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Cited
26 citations as recorded by crossref.
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- Factors controlling contrail cirrus optical depth B. Kärcher et al. 10.5194/acp-9-6229-2009
- A new machine-learning based cloud mask using harmonized data of two Meteosat generations shows a general decrease in cloudiness over Europe in recent decades S. Gaurav et al. 10.1016/j.rse.2025.114599
- Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks J. Strandgren et al. 10.5194/amt-10-3547-2017
- Diurnal evolution of cloud base heights in convective cloud fields from MSG/SEVIRI data R. Meerkötter & L. Bugliaro 10.5194/acp-9-1767-2009
- 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
- Ground-based observations for the validation of contrails and cirrus detection in satellite imagery H. Mannstein et al. 10.5194/amt-3-655-2010
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- An improved cirrus detection algorithm MeCiDA2 for SEVIRI and its evaluation with MODIS F. Ewald et al. 10.5194/amt-6-309-2013
- CHARACTERIZING CLOUD COVER AND SATELLITE REVISIT WITH CLOUD MASKS IN NORTH WEST ENGLAND E. Ogunbadewa 10.3846/20296991.2012.679803
- Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI L. Bugliaro et al. 10.5194/acp-11-5603-2011
- Summertime Convective Initiation Nowcasting over Southeastern China Based on Advanced Himawari Imager Observations X. ZHUGE & X. ZOU 10.2151/jmsj.2018-041
- Determination of circumsolar radiation from Meteosat Second Generation B. Reinhardt et al. 10.5194/amt-7-823-2014
- Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data F. Xu et al. 10.3390/rs16122136
- An automatic self-learning cloud-filtering algorithm for Meteosat Second Generation–Spinning Enhanced Visible and Infrared Imager P. Salvador et al. 10.1080/2150704X.2012.714086
- The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals F. Romano et al. 10.3390/rs16142578
- Retrieval of Cloud Optical Thickness During Nighttime from FY-4B AGRI Using a Convolutional Neural Network D. Xia et al. 10.3390/rs17050737
- A fast method for the retrieval of integrated longwave and shortwave top-of-atmosphere upwelling irradiances from MSG/SEVIRI (RRUMS) M. Vázquez-Navarro et al. 10.5194/amt-6-2627-2013
- Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images M. Tebbi & B. Haddad 10.1016/j.atmosres.2016.04.013
- Rapidly Evolving Cirrus Clouds Modulated by Convectively Generated Gravity Waves A. Prasad et al. 10.1029/2019JD030538
- Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing S. Kox et al. 10.5194/amt-7-3233-2014
- 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
- A Parametric Radiative Forcing Model for Contrail Cirrus U. Schumann et al. 10.1175/JAMC-D-11-0242.1
- Estimation of Urban Air Temperature From a Rural Station Using Remotely Sensed Thermal Infrared Data F. Schuch et al. 10.1016/j.egypro.2017.12.720
- Bayesian cloud-top phase determination for Meteosat Second Generation J. Mayer et al. 10.5194/amt-17-4015-2024
- Simulation of visible light at night from infrared measurements using deep learning technique J. Yan et al. 10.1080/10106049.2023.2227610
26 citations as recorded by crossref.
- A Lagrangian analysis of pockets of open cells over the southeastern Pacific K. Smalley et al. 10.5194/acp-22-8197-2022
- Factors controlling contrail cirrus optical depth B. Kärcher et al. 10.5194/acp-9-6229-2009
- A new machine-learning based cloud mask using harmonized data of two Meteosat generations shows a general decrease in cloudiness over Europe in recent decades S. Gaurav et al. 10.1016/j.rse.2025.114599
- Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks J. Strandgren et al. 10.5194/amt-10-3547-2017
- Diurnal evolution of cloud base heights in convective cloud fields from MSG/SEVIRI data R. Meerkötter & L. Bugliaro 10.5194/acp-9-1767-2009
- 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
- Ground-based observations for the validation of contrails and cirrus detection in satellite imagery H. Mannstein et al. 10.5194/amt-3-655-2010
- The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development D. Piontek et al. 10.3390/rs13163112
- An improved cirrus detection algorithm MeCiDA2 for SEVIRI and its evaluation with MODIS F. Ewald et al. 10.5194/amt-6-309-2013
- CHARACTERIZING CLOUD COVER AND SATELLITE REVISIT WITH CLOUD MASKS IN NORTH WEST ENGLAND E. Ogunbadewa 10.3846/20296991.2012.679803
- Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI L. Bugliaro et al. 10.5194/acp-11-5603-2011
- Summertime Convective Initiation Nowcasting over Southeastern China Based on Advanced Himawari Imager Observations X. ZHUGE & X. ZOU 10.2151/jmsj.2018-041
- Determination of circumsolar radiation from Meteosat Second Generation B. Reinhardt et al. 10.5194/amt-7-823-2014
- Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data F. Xu et al. 10.3390/rs16122136
- An automatic self-learning cloud-filtering algorithm for Meteosat Second Generation–Spinning Enhanced Visible and Infrared Imager P. Salvador et al. 10.1080/2150704X.2012.714086
- The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals F. Romano et al. 10.3390/rs16142578
- Retrieval of Cloud Optical Thickness During Nighttime from FY-4B AGRI Using a Convolutional Neural Network D. Xia et al. 10.3390/rs17050737
- A fast method for the retrieval of integrated longwave and shortwave top-of-atmosphere upwelling irradiances from MSG/SEVIRI (RRUMS) M. Vázquez-Navarro et al. 10.5194/amt-6-2627-2013
- Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images M. Tebbi & B. Haddad 10.1016/j.atmosres.2016.04.013
- Rapidly Evolving Cirrus Clouds Modulated by Convectively Generated Gravity Waves A. Prasad et al. 10.1029/2019JD030538
- Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing S. Kox et al. 10.5194/amt-7-3233-2014
- 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
- A Parametric Radiative Forcing Model for Contrail Cirrus U. Schumann et al. 10.1175/JAMC-D-11-0242.1
- Estimation of Urban Air Temperature From a Rural Station Using Remotely Sensed Thermal Infrared Data F. Schuch et al. 10.1016/j.egypro.2017.12.720
- Bayesian cloud-top phase determination for Meteosat Second Generation J. Mayer et al. 10.5194/amt-17-4015-2024
- Simulation of visible light at night from infrared measurements using deep learning technique J. Yan et al. 10.1080/10106049.2023.2227610
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