Articles | Volume 13, issue 2
Atmos. Meas. Tech., 13, 907–923, 2020
Atmos. Meas. Tech., 13, 907–923, 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 et al.

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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,, 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,, 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,, 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,, 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
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.