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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 18
Atmos. Chem. Phys., 15, 10453–10470, 2015
https://doi.org/10.5194/acp-15-10453-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 15, 10453–10470, 2015
https://doi.org/10.5194/acp-15-10453-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 24 Sep 2015

Research article | 24 Sep 2015

Ice phase in altocumulus clouds over Leipzig: remote sensing observations and detailed modeling

M. Simmel, J. Bühl, A. Ansmann, and I. Tegen M. Simmel et al.
  • TROPOS, Leibniz Institute for Tropospheric Research, Permoser Str. 15, 04318 Leipzig, Germany

Abstract. The present work combines remote sensing observations and detailed cloud modeling to investigate two altocumulus cloud cases observed over Leipzig, Germany. A suite of remote sensing instruments was able to detect primary ice at rather high temperatures of −6 °C. For comparison, a second mixed phase case at about −25 °C is introduced. To further look into the details of cloud microphysical processes, a simple dynamics model of the Asai-Kasahara (AK) type is combined with detailed spectral microphysics (SPECS) forming the model system AK-SPECS. Vertical velocities are prescribed to force the dynamics, as well as main cloud features, to be close to the observations. Subsequently, sensitivity studies with respect to ice microphysical parameters are carried out with the aim to quantify the most important sensitivities for the cases investigated.

For the cases selected, the liquid phase is mainly determined by the model dynamics (location and strength of vertical velocity), whereas the ice phase is much more sensitive to the microphysical parameters (ice nucleating particle (INP) number, ice particle shape). The choice of ice particle shape may induce large uncertainties that are on the same order as those for the temperature-dependent INP number distribution.

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The paper combines remote sensing observations and detailed cloud modeling. It was shown that the main features of the observations could be captured which allows one to perform sensitivity studies. Those show that the liquid phase is mainly determined by the dynamical parameters of the model, whereas the ice phase is dominated by microphysical parameters such as ice nuclei number and ice particle shape.
The paper combines remote sensing observations and detailed cloud modeling. It was shown that...
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