Editorial Type:
Article
Article Type:
Research Article

Improving Parameterization of Entrainment Rate for Shallow Convection with Aircraft Measurements and Large-Eddy Simulation

Chunsong Lu * Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Chinese Academy of Sciences, Beijing, China
Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York

Search for other papers by Chunsong Lu in
Current site
Google Scholar
PubMed Close
,
Yangang Liu Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York

Search for other papers by Yangang Liu in
Current site
Google Scholar
PubMed Close
,
Guang J. Zhang Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
** Center for Earth System Science, Tsinghua University, Beijing, China

Search for other papers by Guang J. Zhang in
Current site
Google Scholar
PubMed Close
,
Xianghua Wu School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, China

Search for other papers by Xianghua Wu in
Current site
Google Scholar
PubMed Close
,
Satoshi Endo Biological, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York

Search for other papers by Satoshi Endo in
Current site
Google Scholar
PubMed Close
,
Le Cao Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China

Search for other papers by Le Cao in
Current site
Google Scholar
PubMed Close
,
Yueqing Li Institute of Plateau Meteorology, China Meteorological Administration, Chengdu, China

Search for other papers by Yueqing Li in
Current site
Google Scholar
PubMed Close
, and
Xiaohao Guo * Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing, China
Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

Search for other papers by Xiaohao Guo in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2016
DOI:
https://doi.org/10.1175/JAS-D-15-0050.1
Page(s):
761–773
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

This work examines the relationships of entrainment rate to vertical velocity, buoyancy, and turbulent dissipation rate by applying stepwise principal component regression to observational data from shallow cumulus clouds collected during the Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign over the ARM Southern Great Plains (SGP) site near Lamont, Oklahoma. The cumulus clouds during the RACORO campaign simulated using a large-eddy simulation (LES) model are also examined with the same approach. The analysis shows that a combination of multiple variables can better represent entrainment rate in both the observations and LES than any single-variable fitting. Three commonly used parameterizations are also tested on the individual cloud scale. A new parameterization is thus presented that relates entrainment rate to vertical velocity, buoyancy, and dissipation rate; the effects of treating clouds as ensembles and humid shells surrounding cumulus clouds on the new parameterization are discussed. Physical mechanisms underlying the relationships of entrainment rate to vertical velocity, buoyancy, and dissipation rate are also explored.

Corresponding author address: Chunsong Lu, NUIST, Room 1010, Qixiang Bldg., No. 219, Ningliu Road, Nanjing, Jiangsu 210044, China. E-mail: luchunsong110@hotmail.com

Abstract

This work examines the relationships of entrainment rate to vertical velocity, buoyancy, and turbulent dissipation rate by applying stepwise principal component regression to observational data from shallow cumulus clouds collected during the Routine Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign over the ARM Southern Great Plains (SGP) site near Lamont, Oklahoma. The cumulus clouds during the RACORO campaign simulated using a large-eddy simulation (LES) model are also examined with the same approach. The analysis shows that a combination of multiple variables can better represent entrainment rate in both the observations and LES than any single-variable fitting. Three commonly used parameterizations are also tested on the individual cloud scale. A new parameterization is thus presented that relates entrainment rate to vertical velocity, buoyancy, and dissipation rate; the effects of treating clouds as ensembles and humid shells surrounding cumulus clouds on the new parameterization are discussed. Physical mechanisms underlying the relationships of entrainment rate to vertical velocity, buoyancy, and dissipation rate are also explored.

Corresponding author address: Chunsong Lu, NUIST, Room 1010, Qixiang Bldg., No. 219, Ningliu Road, Nanjing, Jiangsu 210044, China. E-mail: luchunsong110@hotmail.com
Save
Cover Journal of the Atmospheric Sciences
Journal of the Atmospheric Sciences

  • Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, part I. J. Atmos. Sci., 31, 674–701, doi:10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bajpai, N., 2009: Business Statistics. Pearson, 794 pp.

  • Betts, A. K., 1975: Parametric interpretation of trade-wind cumulus budget studies. J. Atmos. Sci., 32, 1934–1945, doi:10.1175/1520-0469(1975)032<1934:PIOTWC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Blossey, P. N., and Coauthors, 2013: Marine low cloud sensitivity to an idealized climate change: The CGILS LES intercomparison. J. Adv. Model. Earth Syst., 5, 234–258, doi:10.1002/jame.20025.

    • Search Google Scholar
    • Export Citation

  • Blyth, A. M., 1993: Entrainment in cumulus clouds. J. Appl. Meteor., 32, 626–641, doi:10.1175/1520-0450(1993)032<0626:EICC>2.0.CO;2.

  • Cai, Q., G. J. Zhang, and T. Zhou, 2013: Impacts of shallow convection on MJO simulation: A moist static energy and moisture budget analysis. J. Climate, 26, 2417–2431, doi:10.1175/JCLI-D-12-00127.1.

    • Search Google Scholar
    • Export Citation

  • Chan, K. R., J. Dean-Day, S. W. Bowen, and T. P. Bui, 1998: Turbulence measurements by the DC-8 Meteorological Measurement System. Geophys. Res. Lett., 25, 1355–1358, doi:10.1029/97GL03590.

    • Search Google Scholar
    • Export Citation

  • Dawe, J. T., and P. H. Austin, 2013: Direct entrainment and detrainment rate distributions of individual shallow cumulus clouds in an LES. Atmos. Chem. Phys., 13, 7795–7811, doi:10.5194/acp-13-7795-2013.

    • Search Google Scholar
    • Export Citation

  • Deardorff, J., 1980: Stratocumulus-capped mixed layers derived from a three-dimensional model. Bound.-Layer Meteor., 18, 495–527, doi:10.1007/BF00119502.

    • Search Google Scholar
    • Export Citation

  • Del Genio, A. D., and J. Wu, 2010: The role of entrainment in the diurnal cycle of continental convection. J. Climate, 23, 2722–2738, doi:10.1175/2009JCLI3340.1.

    • Search Google Scholar
    • Export Citation

  • Deng, Z., C. Zhao, Q. Zhang, M. Huang, and X. Ma, 2009: Statistical analysis of microphysical properties and the parameterization of effective radius of warm clouds in Beijing area. Atmos. Res., 93, 888–896, doi:10.1016/j.atmosres.2009.04.011.

    • Search Google Scholar
    • Export Citation

  • de Rooy, W. C., and A. P. Siebesma, 2010: Analytical expressions for entrainment and detrainment in cumulus convection. Quart. J. Roy. Meteor. Soc., 136, 1216–1227.

    • Search Google Scholar
    • Export Citation

  • Diskin, G. S., J. R. Podolske, G. W. Sachse, and T. A. Slate, 2002: Open-path airborne tunable diode laser hygrometer. Diode Lasers and Applications in Atmospheric Sensing, A. Fried, Ed., International Society for Optical Engineering (SPIE Proceedings, Vol. 4817), 196–204, doi:10.1117/12.453736.

  • Endo, S., and Coauthors, 2015: RACORO continental boundary layer cloud investigations: 2. Large-eddy simulations of cumulus clouds and evaluation with in situ and ground-based observations. J. Geophys. Res. Atmos., 120, 5993–6014, doi:10.1002/2014JD022525.

    • Search Google Scholar
    • Export Citation

  • Gerber, H. E., G. M. Frick, J. B. Jensen, and J. G. Hudson, 2008: Entrainment, mixing, and microphysics in trade-wind cumulus. J. Meteor. Soc. Japan, 86A, 87–106, doi:10.2151/jmsj.86A.87.

    • Search Google Scholar
    • Export Citation

  • Grant, A. L. M., and A. R. Brown, 1999: A similarity hypothesis for shallow cumulus transports. Quart. J. Roy. Meteor. Soc., 125, 1913–1936, doi:10.1002/qj.49712555802.

    • Search Google Scholar
    • Export Citation

  • Gregory, D., 2001: Estimation of entrainment rate in simple models of convective clouds. Quart. J. Roy. Meteor. Soc., 127, 53–72, doi:10.1002/qj.49712757104.

    • Search Google Scholar
    • Export Citation

  • Heus, T., and H. J. J. Jonker, 2008: Subsiding shells around shallow cumulus clouds. J. Atmos. Sci., 65, 1003–1018, doi:10.1175/2007JAS2322.1.

    • Search Google Scholar
    • Export Citation

  • Jolliffe, I. T., 1982: A note on the use of principal components in regression. J. Roy. Stat. Soc., 31C, 300–303, doi:10.2307/2348005.

    • Search Google Scholar
    • Export Citation

  • Kumar, B., J. Schumacher, and R. Shaw, 2013: Cloud microphysical effects of turbulent mixing and entrainment. Theor. Comput. Fluid Dyn., 27, 361–376, doi:10.1007/s00162-012-0272-z.

  • Kumar, B., J. Schumacher, and R. Shaw, 2014: Lagrangian mixing dynamics at the cloudy–clear air interface. J. Atmos. Sci., 71, 2564–2580, doi:10.1175/JAS-D-13-0294.1.

    • Search Google Scholar
    • Export Citation

  • Lawson, R. P., and W. A. Cooper, 1990: Performance of some airborne thermometers in clouds. J. Atmos. Oceanic Technol., 7, 480–494, doi:10.1175/1520-0426(1990)007<0480:POSATI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lehmann, K., H. Siebert, and R. A. Shaw, 2009: Homogeneous and inhomogeneous mixing in cumulus clouds: Dependence on local turbulence structure. J. Atmos. Sci., 66, 3641–3659, doi:10.1175/2009JAS3012.1.

    • Search Google Scholar
    • Export Citation

  • Lin, C., 1999: Some bulk properties of cumulus ensembles simulated by a cloud-resolving model. Part II: Entrainment profiles. J. Atmos. Sci., 56, 3736–3748, doi:10.1175/1520-0469(1999)056<3736:SBPOCE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lin, W., and Coauthors, 2015: RACORO continental boundary layer cloud investigations: 3. Separation of parameterization biases in single-column model CAM5 simulations of shallow cumulus. J. Geophys. Res. Atmos., 120, 6015–6033, doi:10.1002/2014JD022524.

    • Search Google Scholar
    • Export Citation

  • Lu, C., Y. Liu, S. Niu, and A. M. Vogelmann, 2012a: Observed impacts of vertical velocity on cloud microphysics and implications for aerosol indirect effects. Geophys. Res. Lett., 39, L21808, doi:10.1029/2012GL053599.

    • Search Google Scholar
    • Export Citation

  • Lu, C., Y. Liu, S. Niu, and A. M. Vogelmann, 2012b: Lateral entrainment rate in shallow cumuli: Dependence on dry air sources and probability density functions. Geophys. Res. Lett., 39, L20812, doi:10.1029/2012GL053646.

    • Search Google Scholar
    • Export Citation

  • Lu, C., Y. Liu, S. S. Yum, S. Niu, and S. Endo, 2012c: A new approach for estimating entrainment rate in cumulus clouds. Geophys. Res. Lett., 39, L04802, doi:10.1029/2011GL050546.

    • Search Google Scholar
    • Export Citation

  • Lu, C., S. Niu, Y. Liu, and S. Niu, 2013: Empirical relationship between entrainment rate and microphysics in cumulus clouds. Geophys. Res. Lett., 40, 2333–2338, doi:10.1002/grl.50445.

    • Search Google Scholar
    • Export Citation

  • Lu, C., Y. Liu, and S. Niu, 2014a: Entrainment-mixing parameterization in shallow cumuli and effects of secondary mixing events. Chin. Sci. Bull., 59, 896–903, doi:10.1007/s11434-013-0097-1.

    • Search Google Scholar
    • Export Citation

  • Lu, C., Y. Liu, S. Niu, and S. Endo, 2014b: Scale dependence of entrainment-mixing mechanisms in cumulus clouds. J. Geophys. Res. Atmos., 119, 13 877–13 890, doi:10.1002/2014JD022265.

    • Search Google Scholar
    • Export Citation

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 663–16 682, doi:10.1029/97JD00237.

    • Search Google Scholar
    • Export Citation

  • Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665–1677, doi:10.1175/JAS3446.1.

    • Search Google Scholar
    • Export Citation

  • Neggers, R. A. J., A. P. Siebesma, and H. J. J. Jonker, 2002: A multiparcel model for shallow cumulus convection. J. Atmos. Sci., 59, 1655–1668, doi:10.1175/1520-0469(2002)059<1655:AMMFSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Podolske, J. R., G. W. Sachse, and G. S. Diskin, 2003: Calibration and data retrieval algorithms for the NASA Langley/Ames Diode Laser Hygrometer for the NASA Transport and Chemical Evolution Over the Pacific (TRACE-P) mission. J. Geophys. Res., 108, 8792, doi:10.1029/2002JD003156.

    • Search Google Scholar
    • Export Citation

  • Romps, D. M., 2010: A direct measure of entrainment. J. Atmos. Sci., 67, 1908–1927, doi:10.1175/2010JAS3371.1.

  • Schmid, B., and Coauthors, 2014: The DOE ARM Aerial Facility. Bull. Amer. Meteor. Soc., 95, 723–742, doi:10.1175/BAMS-D-13-00040.1.

  • Simpson, J., 1971: On cumulus entrainment and one-dimensional models. J. Atmos. Sci., 28, 449–455, doi:10.1175/1520-0469(1971)028<0449:OCEAOD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Squires, P., and J. S. Turner, 1962: An entraining jet model for cumulo-nimbus updraughts. Tellus, 14A, 422–434, doi:10.1111/j.2153-3490.1962.tb01355.x.

    • Search Google Scholar
    • Export Citation

  • Turner, J. S., 1962: The “starting plume” in neutral surroundings. J. Fluid Mech., 13, 356–368, doi:10.1017/S0022112062000762.

  • Vogelmann, A. M., and Coauthors, 2012: RACORO extended-term aircraft observations of boundary layer clouds. Bull. Amer. Meteor. Soc., 93, 861–878, doi:10.1175/BAMS-D-11-00189.1.

    • Search Google Scholar
    • Export Citation

  • Vogelmann, A. M., and Coauthors, 2015: RACORO continental boundary layer cloud investigations: 1. Case study development and ensemble large-scale forcings. J. Geophys. Res. Atmos., 120, 5962–5992, doi:10.1002/2014JD022713.

    • Search Google Scholar
    • Export Citation

  • von Salzen, K., and N. A. McFarlane, 2002: Parameterization of the bulk effects of lateral and cloud-top entrainment in transient shallow cumulus clouds. J. Atmos. Sci., 59, 1405–1430, doi:10.1175/1520-0469(2002)059<1405:POTBEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wang, X., and M. Zhang, 2014: Vertical velocity in shallow convection for different plume types. J. Adv. Model. Earth Syst., 6, 478–489, doi:10.1002/2014MS000318.

    • Search Google Scholar
    • Export Citation

  • Wang, X., Q. Bao, K. Liu, G. Wu, and Y. Liu, 2011: Features of rainfall and latent heating structure simulated by two convective parameterization schemes. Sci. China Earth Sci., 54, 1779–1788, doi:10.1007/s11430-011-4282-2.

    • Search Google Scholar
    • Export Citation

  • Wang, Y., L. Zhou, and K. Hamilton, 2007: Effect of convective entrainment/detrainment on the simulation of the tropical precipitation diurnal cycle. Mon. Wea. Rev., 135, 567–585, doi:10.1175/MWR3308.1.

    • Search Google Scholar
    • Export Citation

  • Wu, X., L. Deng, X. Song, G. Vettoretti, W. R. Peltier, and G. J. Zhang, 2007: Impact of a modified convective scheme on the Madden-Julian Oscillation and El Niño–Southern Oscillation in a coupled climate model. Geophys. Res. Lett., 34, L16823, doi:10.1029/2007GL030637.

    • Search Google Scholar
    • Export Citation

  • Xie, S., R. T. Cederwall, and M. Zhang, 2004: Developing long-term single-column model/cloud system–resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations. J. Geophys. Res., 109, D01104, doi:10.1029/2003JD004045.

    • Search Google Scholar
    • Export Citation

  • Zhang, G. J., and X. Song, 2009: Interaction of deep and shallow convection is key to Madden-Julian Oscillation simulation. Geophys. Res. Lett., 36, L09708, doi:10.1029/2009GL037340.

    • Search Google Scholar
    • Export Citation

  • Zhang, M. H., J. L. Lin, R. T. Cederwall, J. J. Yio, and S. C. Xie, 2001: Objective analysis of ARM IOP data: Method and sensitivity. Mon. Wea. Rev., 129, 295–311, doi:10.1175/1520-0493(2001)129<0295:OAOAID>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1650 803 264
PDF Downloads 527 124 5