Articles | Volume 10, issue 12
Atmos. Meas. Tech., 10, 4587–4600, 2017
https://doi.org/10.5194/amt-10-4587-2017
Atmos. Meas. Tech., 10, 4587–4600, 2017
https://doi.org/10.5194/amt-10-4587-2017

Research article 30 Nov 2017

Research article | 30 Nov 2017

Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras

Christine Aebi et al.

Related authors

Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne
Christine Aebi, Julian Gröbner, Stelios Kazadzis, Laurent Vuilleumier, Antonis Gkikas, and Niklaus Kämpfer
Atmos. Meas. Tech., 13, 907–923, https://doi.org/10.5194/amt-13-907-2020,https://doi.org/10.5194/amt-13-907-2020, 2020
Short summary
Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period
Stephan Nyeki, Stefan Wacker, Christine Aebi, Julian Gröbner, Giovanni Martucci, and Laurent Vuilleumier
Atmos. Chem. Phys., 19, 13227–13241, https://doi.org/10.5194/acp-19-13227-2019,https://doi.org/10.5194/acp-19-13227-2019, 2019
Short summary
Cloud fraction determined by thermal infrared and visible all-sky cameras
Christine Aebi, Julian Gröbner, and Niklaus Kämpfer
Atmos. Meas. Tech., 11, 5549–5563, https://doi.org/10.5194/amt-11-5549-2018,https://doi.org/10.5194/amt-11-5549-2018, 2018
Short summary

Related subject area

Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Application of cloud particle sensor sondes for estimating the number concentration of cloud water droplets and liquid water content: case studies in the Arctic region
Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
Atmos. Meas. Tech., 14, 4971–4987, https://doi.org/10.5194/amt-14-4971-2021,https://doi.org/10.5194/amt-14-4971-2021, 2021
Short summary
24-hour cloud cover calculation using ground-based imager with machine learning
Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-179,https://doi.org/10.5194/amt-2021-179, 2021
Revised manuscript accepted for AMT
Short summary
The University of Washington Ice-Liquid Discriminator (UWILD) improves single particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning
Rachel Atlas, Johannes Mohrmann, Joseph Finlon, Jeremy Lu, Ian Hsiao, Robert Wood, and Minghui Diao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-123,https://doi.org/10.5194/amt-2021-123, 2021
Revised manuscript accepted for AMT
Short summary
Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements
Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
Atmos. Meas. Tech., 13, 5595–5619, https://doi.org/10.5194/amt-13-5595-2020,https://doi.org/10.5194/amt-13-5595-2020, 2020
Short summary
A convolutional neural network for classifying cloud particles recorded by imaging probes
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020,https://doi.org/10.5194/amt-13-2219-2020, 2020
Short summary

Cited articles

Allan, R. P.: Combining satellite data and models to estimate cloud radiative effect at the surface and in the atmosphere, Meterol. Appl., 18, 324–333, https://doi.org/10.1002/met.285, 2011.
Allan, R. P., Slingo, A., Milton, S. F., and Brooks, M. E.: Evaluation of the met office global forecast model using geostationary earth radiation budget (gerb) data, Q. J. Roy. Meteor. Soc., 133, 1993–2010, https://doi.org/10.1002/qj.166, 2007.
Alonso, J., Batlles, F. J., López, G., and Ternero, A.: Sky camera imagery processing based on a sky classification using radiometric data, Energy, 68, 599–608, https://doi.org/10.1016/j.energy.2014.02.035, 2014.
Berk, A., Anderson, G. P., Acharya, P. K., Bernstein, L. S., Muratov, L., Lee, J., Fox, M. J., Adler-Golden, S. M., Chetwynd, J. H., Hoke, M. L., Lockwood, R. B., Cooley, T. W., and Gardner, J. A.: Modtran5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options, SPIE processing, https://doi.org/10.1117/12.578758, 2005.
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware, R. H.: GPS meteorology: Remote sensing of atmospheric water vapour using the global positioning system, J. Geophys. Res., 97, 15787–15801, https://doi.org/10.1029/92JD01517, 1992.
Download
Short summary
The current study analyses the cloud radiative effect during the daytime depending on cloud fraction and cloud type at two stations in Switzerland over a time period of 3–5 years. Information about fractional cloud coverage and cloud type is retrieved from images taken by visible all-sky cameras. Cloud cover, cloud type and other atmospheric parameters have an influence on the magnitude of the longwave cloud effect as well as on the shortwave.