Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-907-2020
https://doi.org/10.5194/amt-13-907-2020
Research article
 | 
26 Feb 2020
Research article |  | 26 Feb 2020

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

Related authors

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
Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras
Christine Aebi, Julian Gröbner, Niklaus Kämpfer, and Laurent Vuilleumier
Atmos. Meas. Tech., 10, 4587–4600, https://doi.org/10.5194/amt-10-4587-2017,https://doi.org/10.5194/amt-10-4587-2017, 2017
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025,https://doi.org/10.5194/amt-18-73-2025, 2025
Short summary
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024,https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024,https://doi.org/10.5194/amt-17-7027-2024, 2024
Short summary
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024,https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024,https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary

Cited articles

Ackerman, T. P., Flynn, D. M., and Marchand, R. T.: Quantifying the magnitude of anomalous solar absorption, J. Geophys. Res.-Atmos., 108, 4273, https://doi.org/10.1029/2002JD002674, 2003. a
Aebi, C., Gröbner, J., Kämpfer, N., and Vuilleumier, L.: Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras, Atmos. Meas. Tech., 10, 4587–4600, https://doi.org/10.5194/amt-10-4587-2017, 2017. a, b, c, d
Aebi, C., Gröbner, J., and Kämpfer, N.: Cloud fraction determined by thermal infrared and visible all-sky cameras, Atmos. Meas. Tech., 11, 5549–5563, https://doi.org/10.5194/amt-11-5549-2018, 2018. a
Amiridis, V., Marinou, E., Tsekeri, A., Wandinger, U., Schwarz, A., Giannakaki, E., Mamouri, R., Kokkalis, P., Binietoglou, I., Solomos, S., Herekakis, T., Kazadzis, S., Gerasopoulos, E., Proestakis, E., Kottas, M., Balis, D., Papayannis, A., Kontoes, C., Kourtidis, K., Papagiannopoulos, N., Mona, L., Pappalardo, G., Le Rille, O., and Ansmann, A.: LIVAS: a 3-D multi-wavelength aerosol/cloud database based on CALIPSO and EARLINET, Atmos. Chem. Phys., 15, 7127–7153, https://doi.org/10.5194/acp-15-7127-2015, 2015. a
Anderson, G. P.: AFGL atmospheric constituent profiles (0–120km), Hanscom AFB, MA: Optical Physics Division, Air Force Geophysics Laboratory, AFGL-TR; 86-0110, U.S. Air Force Geophysics Laboratory, Optical Physics Division, 1986. a
Download
Short summary
Clouds are one of the largest sources of uncertainties in climate models. The current study estimates the cloud optical thickness (COT), the effective droplet radius and the single scattering albedo of stratus–altostratus and cirrus–cirrostratus clouds in Payerne, Switzerland, by combining ground- and satellite-based measurements and radiative transfer models. The estimated values are thereafter compared with data retrieved from other methods. The mean COT is distinct for different seasons.