Articles | Volume 10, issue 9
Atmos. Meas. Tech., 10, 3547–3573, 2017
Atmos. Meas. Tech., 10, 3547–3573, 2017
Research article
29 Sep 2017
Research article | 29 Sep 2017

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Johan Strandgren et al.

Related authors

The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON
Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
Atmos. Chem. Phys., 21, 4285–4318,,, 2021
Short summary
Towards spaceborne monitoring of localized CO2 emissions: an instrument concept and first performance assessment
Johan Strandgren, David Krutz, Jonas Wilzewski, Carsten Paproth, Ilse Sebastian, Kevin R. Gurney, Jianming Liang, Anke Roiger, and André Butz
Atmos. Meas. Tech., 13, 2887–2904,,, 2020
Short summary
Spectral sizing of a coarse-spectral-resolution satellite sensor for XCO2
Jonas Simon Wilzewski, Anke Roiger, Johan Strandgren, Jochen Landgraf, Dietrich G. Feist, Voltaire A. Velazco, Nicholas M. Deutscher, Isamu Morino, Hirofumi Ohyama, Yao Té, Rigel Kivi, Thorsten Warneke, Justus Notholt, Manvendra Dubey, Ralf Sussmann, Markus Rettinger, Frank Hase, Kei Shiomi, and André Butz
Atmos. Meas. Tech., 13, 731–745,,, 2020
Short summary
Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI
Johan Strandgren, Jennifer Fricker, and Luca Bugliaro
Atmos. Meas. Tech., 10, 4317–4339,,, 2017
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A kriging-based analysis of cloud liquid water content using CloudSat data
Jean-Marie Lalande, Guillaume Bourmaud, Pierre Minvielle, and Jean-François Giovannelli
Atmos. Meas. Tech., 15, 4411–4429,,, 2022
Short summary
High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds
Julia Fuchs, Hendrik Andersen, Jan Cermak, Eva Pauli, and Rob Roebeling
Atmos. Meas. Tech., 15, 4257–4270,,, 2022
Short summary
Retrievals of ice microphysical properties using dual-wavelength polarimetric radar observations during stratiform precipitation events
Eleni Tetoni, Florian Ewald, Martin Hagen, Gregor Köcher, Tobias Zinner, and Silke Groß
Atmos. Meas. Tech., 15, 3969–3999,,, 2022
Short summary
The surface longwave cloud radiative effect derived from space lidar observations
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923,,, 2022
Short summary
Cloud phase and macrophysical properties over the Southern Ocean during the MARCUS field campaign
Baike Xi, Xiquan Dong, Xiaojian Zheng, and Peng Wu
Atmos. Meas. Tech., 15, 3761–3777,,, 2022
Short summary

Cited articles

Ackerman, S. A., Smith, W. L., Revercomb, H. E., and Spinhirne, J. D.: The 27–28 October 1986 FIRE IFO Cirrus Case Study: Spectral Properties of Cirrus Clouds in the 8–12 µm Window, Mon. Weather Rev., 118, 2377–2388,<2377:TOFICC>2.0.CO;2, 1990.
Ackerman, S., Holz, R., Frey, R., Eloranta, E., Maddux, B., and McGill, M.: Cloud detection with MODI S. Part II: validation, J. Atmos. Ocean. Tech., 25, 1073–1086, 2008.
Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C., and Gumley, L. E.: Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103, 32141–32157, 1998.
Avery, M., Winker, D., Heymsfield, A., Vaughan, M., Young, S., Hu, Y., and Trepte, C.: Cloud ice water content retrieved from the CALIOP space-based lidar, Geophys. Res. Lett., 39, L05808,, 2012.
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281–305, 2012.
Short summary
The new algorithm CiPS is presented and validated. CiPS detects cirrus clouds, identifies opaque pixels and retrieves the corresponding optical thickness, cloud top height and ice water path from the geostationary imager MSG/SEVIRI. CiPS utilises a set of four artificial neural networks trained with space-borne lidar data, thermal MSG/SEVIRI observations, model data and auxiliary data. To demonstrate the capabilities of CiPS, the life cycle of a thin cirrus cloud is analysed.