Articles | Volume 17, issue 6
https://doi.org/10.5194/amt-17-1739-2024
https://doi.org/10.5194/amt-17-1739-2024
Research article
 | 
26 Mar 2024
Research article |  | 26 Mar 2024

A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations

Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche

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Cited articles

Ackerman, A., Kirkpatrick, M., Stevens, D., and Toon, O.: The impact of humidity above stratiform clouds on indirect aerosol climate forcing, Nature, 432, 1014–1017, https://doi.org/10.1038/nature03174, 2004. a
Adrian, R. J. and Westerweel, J.: Particle image velocimetry, Cambridge University Press, ISBN 9780521440080, 586 pp., 2010. a
Albrecht, B.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and Holmén, K.: Mesoscale Variations of Tropospheric Aerosols, J. Atmos. Sci., 60, 119–136, https://doi.org/10.1175/1520-0469(2003)060<0119:MVOTA>2.0.CO;2, 2003. a
Ansmann, A., Mamouri, R.-E., Hofer, J., Baars, H., Althausen, D., and Abdullaev, S. F.: Dust mass, cloud condensation nuclei, and ice-nucleating particle profiling with polarization lidar: updated POLIPHON conversion factors from global AERONET analysis, Atmos. Meas. Tech., 12, 4849–4865, https://doi.org/10.5194/amt-12-4849-2019, 2019. a
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Short summary
We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.