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
Viewed
Total article views: 2,656 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 May 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,708 | 842 | 106 | 2,656 | 249 | 105 | 92 |
- HTML: 1,708
- PDF: 842
- XML: 106
- Total: 2,656
- Supplement: 249
- BibTeX: 105
- EndNote: 92
Total article views: 2,236 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 Sep 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,495 | 651 | 90 | 2,236 | 156 | 86 | 76 |
- HTML: 1,495
- PDF: 651
- XML: 90
- Total: 2,236
- Supplement: 156
- BibTeX: 86
- EndNote: 76
Total article views: 420 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 May 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 213 | 191 | 16 | 420 | 93 | 19 | 16 |
- HTML: 213
- PDF: 191
- XML: 16
- Total: 420
- Supplement: 93
- BibTeX: 19
- EndNote: 16
Cited
31 citations as recorded by crossref.
- Simulation of mixed-phase clouds with the ICON large-eddy model in the complex Arctic environment around Ny-Ålesund V. Schemann & K. Ebell 10.5194/acp-20-475-2020
- PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere M. Mech et al. 10.5194/gmd-13-4229-2020
- Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index C. Fajardo-Zambrano et al. 10.3390/rs14061333
- Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1) C. Neuhauser et al. 10.5194/gmd-16-4617-2023
- Impacts of combined microphysical and land-surface uncertainties on convective clouds and precipitation in different weather regimes C. Barthlott et al. 10.5194/acp-22-10841-2022
- 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
- Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations M. Haghighatnasab et al. 10.5194/acp-22-8457-2022
- Temperature and cloud condensation nuclei (CCN) sensitivity of orographic precipitation enhanced by a mixed-phase seeder–feeder mechanism: a case study for the 2015 Cumbria flood J. Thomas et al. 10.5194/acp-23-1987-2023
- Interaction of microphysics and dynamics in a warm conveyor belt simulated with the ICOsahedral Nonhydrostatic (ICON) model A. Oertel et al. 10.5194/acp-23-8553-2023
- 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
- Relative impact of aerosol, soil moisture, and orography perturbations on deep convection L. Schneider et al. 10.5194/acp-19-12343-2019
- 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
- Aerosol‐Cloud‐Precipitation Interactions in the Context of Convective Self‐Aggregation H. Beydoun & C. Hoose 10.1029/2018MS001523
- 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
- Sensitivity of the 2014 Pentecost storms over Germany to different model grids and microphysics schemes C. Barthlott et al. 10.1002/qj.3019
- Potential of polarization/Raman lidar to separate fine dust, coarse dust, maritime, and anthropogenic aerosol profiles R. Mamouri & A. Ansmann 10.5194/amt-10-3403-2017
- Secondary Ice Formation in Idealised Deep Convection—Source of Primary Ice and Impact on Glaciation A. Miltenberger et al. 10.3390/atmos11050542
- The impact of aerosols and model grid spacing on a supercell storm from Swabian MOSES 2021 C. Barthlott et al. 10.1002/qj.4687
- Profiling of Saharan dust from the Caribbean to western Africa – Part 2: Shipborne lidar measurements versus forecasts A. Ansmann et al. 10.5194/acp-17-14987-2017
- Aerosol Effects on Clouds and Precipitation over Central Europe in Different Weather Regimes C. Barthlott & C. Hoose 10.1175/JAS-D-18-0110.1
31 citations as recorded by crossref.
- Simulation of mixed-phase clouds with the ICON large-eddy model in the complex Arctic environment around Ny-Ålesund V. Schemann & K. Ebell 10.5194/acp-20-475-2020
- PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere M. Mech et al. 10.5194/gmd-13-4229-2020
- Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index C. Fajardo-Zambrano et al. 10.3390/rs14061333
- Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1) C. Neuhauser et al. 10.5194/gmd-16-4617-2023
- Impacts of combined microphysical and land-surface uncertainties on convective clouds and precipitation in different weather regimes C. Barthlott et al. 10.5194/acp-22-10841-2022
- 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
- Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations M. Haghighatnasab et al. 10.5194/acp-22-8457-2022
- Temperature and cloud condensation nuclei (CCN) sensitivity of orographic precipitation enhanced by a mixed-phase seeder–feeder mechanism: a case study for the 2015 Cumbria flood J. Thomas et al. 10.5194/acp-23-1987-2023
- Interaction of microphysics and dynamics in a warm conveyor belt simulated with the ICOsahedral Nonhydrostatic (ICON) model A. Oertel et al. 10.5194/acp-23-8553-2023
- 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
- Relative impact of aerosol, soil moisture, and orography perturbations on deep convection L. Schneider et al. 10.5194/acp-19-12343-2019
- 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
- Aerosol‐Cloud‐Precipitation Interactions in the Context of Convective Self‐Aggregation H. Beydoun & C. Hoose 10.1029/2018MS001523
- 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
- Sensitivity of the 2014 Pentecost storms over Germany to different model grids and microphysics schemes C. Barthlott et al. 10.1002/qj.3019
- Potential of polarization/Raman lidar to separate fine dust, coarse dust, maritime, and anthropogenic aerosol profiles R. Mamouri & A. Ansmann 10.5194/amt-10-3403-2017
- Secondary Ice Formation in Idealised Deep Convection—Source of Primary Ice and Impact on Glaciation A. Miltenberger et al. 10.3390/atmos11050542
- The impact of aerosols and model grid spacing on a supercell storm from Swabian MOSES 2021 C. Barthlott et al. 10.1002/qj.4687
- Profiling of Saharan dust from the Caribbean to western Africa – Part 2: Shipborne lidar measurements versus forecasts A. Ansmann et al. 10.5194/acp-17-14987-2017
- Aerosol Effects on Clouds and Precipitation over Central Europe in Different Weather Regimes C. Barthlott & C. Hoose 10.1175/JAS-D-18-0110.1
Latest update: 06 Jan 2025
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...
Special issue
Altmetrics
Final-revised paper
Preprint