Articles | Volume 16, issue 18
https://doi.org/10.5194/acp-16-12059-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/acp-16-12059-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Sep 2016
Research article |
| 27 Sep 2016
Parameterizing cloud condensation nuclei concentrations during HOPE
Luke B. Hande
CORRESPONDING AUTHOR
Karlsruhe Institute of Technology, Karlsruhe, Germany
Christa Engler
Leibniz-Institute for Tropospheric Research, Leipzig, Germany
Corinna Hoose
Karlsruhe Institute of Technology, Karlsruhe, Germany
Ina Tegen
Leibniz-Institute for Tropospheric Research, Leipzig, Germany
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- Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model M. Costa-Surós et al. 10.5194/acp-20-5657-2020
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- The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON H. Rybka et al. 10.5194/acp-21-4285-2021
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- The HD(CP)<sup>2</sup> Observational Prototype Experiment (HOPE) – an overview A. Macke et al. 10.5194/acp-17-4887-2017
- Contrail formation within cirrus: ICON-LEM simulations of the impact of cirrus cloud properties on contrail formation P. Verma & U. Burkhardt 10.5194/acp-22-8819-2022
- Cloud Condensation Nuclei Closure Study Using Airborne Measurements Over the Southern Great Plains G. Kulkarni et al. 10.1029/2022JD037964
- Large‐eddy simulations over Germany using ICON: a comprehensive evaluation R. Heinze et al. 10.1002/qj.2947
- Cloud Characteristics and Their Effects on Solar Irradiance According to the ICON Model, CLOUDNET and BSRN Observations J. Shuvalova et al. 10.3390/atmos14121769
- Flow‐ and scale‐dependent spatial predictability of convective precipitation combining different model uncertainty representations T. Matsunobu et al. 10.1002/qj.4713
- Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic J. Kretzschmar et al. 10.5194/acp-20-13145-2020
- Sensitivity of cloud-phase distribution to cloud microphysics and thermodynamics in simulated deep convective clouds and SEVIRI retrievals C. Han et al. 10.5194/acp-23-14077-2023
- Estimation of cloud condensation nuclei number concentrations and comparison to in situ and lidar observations during the HOPE experiments C. Genz et al. 10.5194/acp-20-8787-2020
- Importance of aerosols and shape of the cloud droplet size distribution for convective clouds and precipitation C. Barthlott et al. 10.5194/acp-22-2153-2022
- Representing Microphysical Uncertainty in Convective‐Scale Data Assimilation Using Additive Noise Y. Feng et al. 10.1029/2021MS002606
- The impact of microphysical uncertainty conditional on initial and boundary condition uncertainty under varying synoptic control T. Matsunobu et al. 10.5194/wcd-3-1273-2022
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- The sub-adiabatic model as a concept for evaluating the representation and radiative effects of low-level clouds in a high-resolution atmospheric model V. Barlakas et al. 10.5194/acp-20-303-2020
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Latest update: 17 Jul 2024
Short summary
An aerosol model was used to simulate the concentration of natural and anthropogenic aerosols over Germany. Using a detailed parameterization of CCN activation, which includes information of aerosol chemical and physical properties, CCN concentrations were calculated. Using these results, a series of best fit functions were used to define a new parameterization, which is a simple function of vertical velocity and pressure. The new parameterization is easy to implement in models.
An aerosol model was used to simulate the concentration of natural and anthropogenic aerosols...
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