Articles | Volume 8, issue 1
https://doi.org/10.5194/gmd-8-1-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-1-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
06 Jan 2015
Development and technical paper |
| 06 Jan 2015
Parameterizing deep convection using the assumed probability density function method
R. L. Storer
CORRESPONDING AUTHOR
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
B. M. Griffin
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
J. Höft
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
J. K. Weber
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
E. Raut
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
V. E. Larson
University of Wisconsin – Milwaukee, Department of Mathematical Sciences, Milwaukee, WI, USA
Pacific Northwest National Laboratory, Richland, WA, USA
P. J. Rasch
Pacific Northwest National Laboratory, Richland, WA, USA
Viewed
Total article views: 5,350 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Jun 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,252 | 1,714 | 384 | 5,350 | 165 | 166 |
- HTML: 3,252
- PDF: 1,714
- XML: 384
- Total: 5,350
- BibTeX: 165
- EndNote: 166
Total article views: 4,550 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Jan 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,814 | 1,382 | 354 | 4,550 | 149 | 159 |
- HTML: 2,814
- PDF: 1,382
- XML: 354
- Total: 4,550
- BibTeX: 149
- EndNote: 159
Total article views: 800 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Jun 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 438 | 332 | 30 | 800 | 16 | 7 |
- HTML: 438
- PDF: 332
- XML: 30
- Total: 800
- BibTeX: 16
- EndNote: 7
Cited
23 citations as recorded by crossref.
- Methodology to determine the coupling of continental clouds with surface and boundary layer height under cloudy conditions from lidar and meteorological data T. Su et al. 10.5194/acp-22-1453-2022
- Parameterizing microphysical effects on variances and covariances of moisture and heat content using a multivariate probability density function: a study with CLUBB (tag MVCS) B. Griffin & V. Larson 10.5194/gmd-9-4273-2016
- A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection M. Wong & M. Ovchinnikov 10.1175/JAS-D-16-0146.1
- A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model K. Thayer-Calder et al. 10.5194/gmd-8-3801-2015
- Effects of spatially distributed sectoral water management on the redistribution of water resources in an integrated water model N. Voisin et al. 10.1002/2016WR019767
- Climatology of mesopause region nocturnal temperature, zonal wind and sodium density observed by sodium lidar over Hefei, China (32° N, 117° E) T. Li et al. 10.5194/acp-18-11683-2018
- Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2) L. Fowler et al. 10.5194/gmd-13-2851-2020
- Vertical overlap of probability density functions of cloud and precipitation hydrometeors M. Ovchinnikov et al. 10.1002/2016JD025158
- A new subgrid-scale representation of hydrometeor fields using a multivariate PDF B. Griffin & V. Larson 10.5194/gmd-9-2031-2016
- Evaluating the relationship between topography and groundwater using outputs from a continental‐scale integrated hydrology model L. Condon & R. Maxwell 10.1002/2014WR016774
- A cloudy planetary boundary layer oscillation arising from the coupling of turbulence with precipitation in climate simulations X. Zheng et al. 10.1002/2017MS000993
- Using large eddy simulations to reveal the size, strength, and phase of updraft and downdraft cores of an Arctic mixed‐phase stratocumulus cloud E. Roesler et al. 10.1002/2016JD026055
- Linking Deep and Shallow Convective Mass Fluxes via an Assumed Entrainment Distribution in CAM5‐CLUBB: Parameterization and Simulated Precipitation Variability B. Yang et al. 10.1029/2020MS002357
- A Budget Analysis of the Variances of Temperature and Moisture in Precipitating Shallow Cumulus Convection V. Schemann & A. Seifert 10.1007/s10546-016-0230-1
- A Framework for Convection and Boundary Layer Parameterization Derived from Conditional Filtering J. Thuburn et al. 10.1175/JAS-D-17-0130.1
- A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds S. Hagos et al. 10.1029/2019MS001798
- A low-dimensional subsurface model for saturated and unsaturated flow processes: ability to address heterogeneity S. Weill et al. 10.1007/s10596-017-9613-8
- Ongoing Breakthroughs in Convective Parameterization C. Rio et al. 10.1007/s40641-019-00127-w
- GTS v1.0: a macrophysics scheme for climate models based on a probability density function C. Shiu et al. 10.5194/gmd-14-177-2021
- Impact of a shallow groundwater table on the global water cycle in the IPSL land–atmosphere coupled model F. Wang et al. 10.1007/s00382-017-3820-9
- Evaluation of Subgrid-Scale Hydrometeor Transport Schemes Using a High-Resolution Cloud-Resolving Model M. Wong et al. 10.1175/JAS-D-15-0060.1
- A flexible importance sampling method for integrating subgrid processes E. Raut & V. Larson 10.5194/gmd-9-413-2016
- Aerosol Impacts on Mesoscale Convective Systems Forming Under Different Vertical Wind Shear Conditions Q. Chen et al. 10.1029/2018JD030027
23 citations as recorded by crossref.
- Methodology to determine the coupling of continental clouds with surface and boundary layer height under cloudy conditions from lidar and meteorological data T. Su et al. 10.5194/acp-22-1453-2022
- Parameterizing microphysical effects on variances and covariances of moisture and heat content using a multivariate probability density function: a study with CLUBB (tag MVCS) B. Griffin & V. Larson 10.5194/gmd-9-4273-2016
- A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection M. Wong & M. Ovchinnikov 10.1175/JAS-D-16-0146.1
- A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model K. Thayer-Calder et al. 10.5194/gmd-8-3801-2015
- Effects of spatially distributed sectoral water management on the redistribution of water resources in an integrated water model N. Voisin et al. 10.1002/2016WR019767
- Climatology of mesopause region nocturnal temperature, zonal wind and sodium density observed by sodium lidar over Hefei, China (32° N, 117° E) T. Li et al. 10.5194/acp-18-11683-2018
- Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2) L. Fowler et al. 10.5194/gmd-13-2851-2020
- Vertical overlap of probability density functions of cloud and precipitation hydrometeors M. Ovchinnikov et al. 10.1002/2016JD025158
- A new subgrid-scale representation of hydrometeor fields using a multivariate PDF B. Griffin & V. Larson 10.5194/gmd-9-2031-2016
- Evaluating the relationship between topography and groundwater using outputs from a continental‐scale integrated hydrology model L. Condon & R. Maxwell 10.1002/2014WR016774
- A cloudy planetary boundary layer oscillation arising from the coupling of turbulence with precipitation in climate simulations X. Zheng et al. 10.1002/2017MS000993
- Using large eddy simulations to reveal the size, strength, and phase of updraft and downdraft cores of an Arctic mixed‐phase stratocumulus cloud E. Roesler et al. 10.1002/2016JD026055
- Linking Deep and Shallow Convective Mass Fluxes via an Assumed Entrainment Distribution in CAM5‐CLUBB: Parameterization and Simulated Precipitation Variability B. Yang et al. 10.1029/2020MS002357
- A Budget Analysis of the Variances of Temperature and Moisture in Precipitating Shallow Cumulus Convection V. Schemann & A. Seifert 10.1007/s10546-016-0230-1
- A Framework for Convection and Boundary Layer Parameterization Derived from Conditional Filtering J. Thuburn et al. 10.1175/JAS-D-17-0130.1
- A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds S. Hagos et al. 10.1029/2019MS001798
- A low-dimensional subsurface model for saturated and unsaturated flow processes: ability to address heterogeneity S. Weill et al. 10.1007/s10596-017-9613-8
- Ongoing Breakthroughs in Convective Parameterization C. Rio et al. 10.1007/s40641-019-00127-w
- GTS v1.0: a macrophysics scheme for climate models based on a probability density function C. Shiu et al. 10.5194/gmd-14-177-2021
- Impact of a shallow groundwater table on the global water cycle in the IPSL land–atmosphere coupled model F. Wang et al. 10.1007/s00382-017-3820-9
- Evaluation of Subgrid-Scale Hydrometeor Transport Schemes Using a High-Resolution Cloud-Resolving Model M. Wong et al. 10.1175/JAS-D-15-0060.1
- A flexible importance sampling method for integrating subgrid processes E. Raut & V. Larson 10.5194/gmd-9-413-2016
- Aerosol Impacts on Mesoscale Convective Systems Forming Under Different Vertical Wind Shear Conditions Q. Chen et al. 10.1029/2018JD030027
Saved (final revised paper)
Saved (preprint)
Latest update: 18 Apr 2024
Short summary
Representing clouds in climate models is a challenging problem. It is particularly difficult to represent deep convective clouds and, historically, deep convective parameterization is separate from the representation of other cloud types. Here we use a single-column cloud model to simulate three deep convective cases, and two shallow cloud cases. The results look reasonable, demonstrating that it may be possible to use one parameterization within a climate model for all cloud types.
Representing clouds in climate models is a challenging problem. It is particularly difficult to...
