Articles | Volume 11, issue 6
https://doi.org/10.5194/amt-11-3373-2018
https://doi.org/10.5194/amt-11-3373-2018
Research article
 | 
13 Jun 2018
Research article |  | 13 Jun 2018

The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors

Oliver Sus, Martin Stengel, Stefan Stapelberg, Gregory McGarragh, Caroline Poulsen, Adam C. Povey, Cornelia Schlundt, Gareth Thomas, Matthew Christensen, Simon Proud, Matthias Jerg, Roy Grainger, and Rainer Hollmann

Related authors

GIRAFE v1: a global climate data record for precipitation accompanied by a daily sampling uncertainty
Hannes Konrad, Rémy Roca, Anja Niedorf, Stephan Finkensieper, Marc Schröder, Sophie Cloché, Giulia Panegrossi, Paolo Sanò, Christopher Kidd, Rômulo Augusto Jucá Oliveira, Karsten Fennig, Thomas Sikorski, Madeleine Lemoine, and Rainer Hollmann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-568,https://doi.org/10.5194/essd-2024-568, 2025
Preprint under review for ESSD
Short summary
Can general circulation models (GCMs) represent cloud liquid water path adjustments to aerosol–cloud interactions?
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024,https://doi.org/10.5194/acp-24-13633-2024, 2024
Short summary
SARAH-3 – satellite-based climate data records of surface solar radiation
Uwe Pfeifroth, Jaqueline Drücke, Steffen Kothe, Jörg Trentmann, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 16, 5243–5265, https://doi.org/10.5194/essd-16-5243-2024,https://doi.org/10.5194/essd-16-5243-2024, 2024
Short summary
Analysis of ship emission effects on clouds over the southeastern Atlantic using geostationary satellite observations
Nikos Benas, Jan Fokke Meirink, Rob Roebeling, and Martin Stengel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3135,https://doi.org/10.5194/egusphere-2024-3135, 2024
Short summary
Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data, 16, 3001–3016, https://doi.org/10.5194/essd-16-3001-2024,https://doi.org/10.5194/essd-16-3001-2024, 2024
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025,https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025,https://doi.org/10.5194/amt-18-1659-2025, 2025
Short summary
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025,https://doi.org/10.5194/amt-18-1621-2025, 2025
Short summary
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025,https://doi.org/10.5194/amt-18-1325-2025, 2025
Short summary
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025,https://doi.org/10.5194/amt-18-1209-2025, 2025
Short summary

Cited articles

Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman, S. A., Heidinger, A. K., and Yang, P.: MODIS Cloud-Top Property Refinements for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012. a
Berrisford, P., Dee, D., Poli, P., Brugge, R., Fielding, K., Fuentes, M., Kållberg, P., Kobayashi, S., Uppala, S., and Simmons, A.: The ERA-Interim archive Version 2.0, Shinfield Park, Reading, 2011. a, b
CDO: Climate Data Operators, available at: http://www.mpimet.mpg.de/cdo, last access: 1 July 2015. a
Ceccaldi, M., Delanoë, J., Hogan, R. J., Pounder, N. L., Protat, A., and Pelon, J.: From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations, J. Geophys. Res.-Atmos., 118, 7962–7981, https://doi.org/10.1002/jgrd.50579, 2013. a
Christensen, M. W., Stephens, G. L., and Lebsock, M. D.: Exposing biases in retrieved low cloud properties from CloudSat: A guide for evaluating observations and climate data, J. Geophys. Res.-Atmos., 118, 12120–12131, https://doi.org/10.1002/2013JD020224, 2013. a
Short summary
This paper presents a new cloud detection and classification framework, CC4CL. It applies a sophisticated optimal estimation method to derive cloud variables from satellite data of various polar-orbiting platforms and sensors (AVHRR, MODIS, AATSR). CC4CL provides explicit uncertainty quantification and long-term consistency for decadal timeseries at various spatial resolutions. We analysed 5 case studies to show that cloud height estimates are very realistic unless optically thin clouds overlap.
Share