Articles | Volume 21, issue 16
https://doi.org/10.5194/acp-21-12317-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-12317-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of high- and low-vorticity turbulence on cloud–environment mixing and cloud microphysics processes
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pashan, Pune 411008, India
Rahul Ranjan
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pashan, Pune 411008, India
Department of Atmospheric and Space Science, Savitribai Phule Pune University, Pune 411007, India
Man-Kong Yau
Department of Atmospheric and Ocean Science, McGill University, Montréal, Quebec H3A 0B9, Canada
Sudarsan Bera
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pashan, Pune 411008, India
Suryachandra A. Rao
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pashan, Pune 411008, India
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This study employs a parcel–DNS (direct numerical simulation) modeling framework to accurately resolve the aerosol–droplet–turbulence interactions in an ascending air parcel. The effect of turbulence, aerosol hygroscopicity, and aerosol mass loading on droplet growth and rain formation is investigated through a series of in-cloud seeding experiments in which hygroscopic particles were seeded near the cloud base.
Cited articles
Ayala, O., Rosa, B., Wang L. P., and Grabowski, W. W.: Effects of turbulence on the geometric collision rate of sedimenting droplets, Part 1: Results from direct numerical simulation, New J. Phys., 10, 075015, https://doi.org/10.1088/1367-2630/10/7/075015, 2008. a
Baker, M. B. and Latham, J.: The Evolution of Droplet Spectra and the Rate of Production of Embryonic Raindrops in Small Cumulus Clouds, J. Atmos. Sci., 36, 1612–1615, 1979. a
Bengtsson, L.: The global atmospheric water cycle, IOP Publishing Ltd, Environ. Res. Lett., 5, 025202, https://doi.org/10.1088/1748-9326/5/2/025202, 2010. a
Bera, S.: Droplet spectral dispersion by lateral mixing process in continental deep cumulus clouds, J. Atmos. Sol.-Terr. Phys., 214, 105550, https://doi.org/10.1016/j.jastp.2021.105550, 2021. a
Bera, S., Prabha, T. V., and Grabowski, W. W.: Observations of monsoon convective cloud microphysics over India and role of entrainment-mixing, J. Geophys. Res.-Atmos., 121, 9767–9788, https://doi.org/10.1002/2016JD025133, 2016. a, b
Bock, H. H.: Clustering Methods: A History of K-Means Algorithms, in: Selected Contributions in Data Analysis and Classification, edited by: Brito, P., Cucumel, G., Bertand, P., and de Carvalho, F., Berlin, Heidelberg, Springer Berlin Heidelberg, 161–172, https://doi.org/10.1007/978-3-540-73560-1_15, 2007. a
Brenguier, J. and Chaumat, L.: Droplet Spectra Broadening in Cumulus Clouds, Part I: Broadening in Adiabatic Cores, J. Atmos. Sci., 58, 628–641, 2001.
Chen, S., Bartello, P., Yau, M. K., Vaillancourt, P. A., and Zwijsen, K.: Cloud Droplet Collisions in Turbulent Environment: Collision Statistics and Parameterization, J. Atmos. Sci., 73, 621–636, https://doi.org/10.1175/JAS-D-15-0203.1, 2016. a
Clift, R., Grace, J. R., and Weber, M. E.: Bubbles, Drops, and Particles, Dover Publications, Incorporated, 1978. a
Cooper, W. A.: Effects of Variable Droplet Growth Histories on Droplet Size Distributions, Part I: Theory, J. Atmos. Sci., 46, 1301–1311, https://doi.org/10.1175/1520-0469(1989)046<1301:EOVDGH>2.0.CO;2, 1989. a
Cooper, W. A., Baumgardner, D., and Dye, J. E.: Evolution of the droplet spectra in Hawaiian orographic clouds, in: Preprints AMS Conf. Cloud Phys., Snowmass, 52–55, 1986. a
Devenish, B. J., Bartello, P. Brenguier, J. L., Collins, L. R., Grabowski, W. W., IJzermans, R. H. A., Malinowski, S. P., Reeks, M. W., Vassilicos, J. C., Wang, L. P., and Warhaft, Z.: Droplet growth in warm turbulent clouds, Q. J. Roy. Meteor. Soc., 138, 1401–1429, https://doi.org/10.1002/qj.1897, 2012. a
Franklin, C. N., Vaillancourt, P. A., Yau, M. K., and Bartello, P.: Collision Rates of Cloud Droplets in Turbulent Flow, J. Atmos. Sci., 62, 2451–2466, https://doi.org/10.1175/JAS3493.1, 2005. a
Gerber, H. E., Frick, G. M., Jensen, J. G., and Hudson, J. G.: Entrainment, Mixing, and Microphysics in Trade-Wind Cumulus, J. Meteorol. Soc. Jpn. Ser. II, 86, 87–106, 2008. a
Grabowski, W. W. and Petch, J. C.: Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation, Struengmann Forum Report, in: DEEP CONVECTIVE CLOUDS. NCAR, USA, Opensky, available at: https://opensky.ucar.edu/islandora/object/books:211 (last access: 11 August 2021), 2009. a
Grabowski, W. W. and Vaillancourt, P.: Comments on Preferential concentration of cloud droplets by turbulence: Effects on the early evolution of cumulus cloud droplet spectra, J. Atmos. Sci., 56, 1433–1436, 1999. a
Grabowski, W. W. and Wang, L. P.: Growth of Cloud Droplets in a Turbulent Environment, Ann. Rev. Fluid Mech., 45, 293–324, https://doi.org/10.1146/annurev-fluid-011212-140750, 2013. a, b, c
Harrison, E. F., Minnis P., Barkstrom, B. R., Ramanathan V., Cess, R. D., and Gibson, G. G.: Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment, J. Geophys. Res.-Atmos., 95, 18687–18703, https://doi.org/10.1029/JD095iD11p18687, 1990. a
Holton, J. R. and Hakim, G. J.: An Introduction to Dynamic Meteorology (Fifth Edition), Academic Press, ISBN 9780123848666, https://doi.org/10.1016/B978-0-12-384866-6.00039-8, 2013. a
Jimenez, J., Wray, A. A., Saffman, P. G., and Rogallo, R. S.: The structure of intense vorticity in isotropic turbulence, J. Fluid Mech., 255, 65–90, 1993. a
Jonas, P.: Growth of droplets in cloud edge downdraughts, Q. J. Roy. Meteor. Soc., 117, 243–255, https://doi.org/10.1002/qj.49711749711, 1991. a
Jonas, P. R.: Turbulence and Cloud Microphysics, Elsevier, 40, 283–306, https://doi.org/10.1016/0169-8095(95)00035-6, 1996. a
Karpińska, K., Bodenschatz, J. F. E., Malinowski, S. P., Nowak, J. L., Risius, S., Schmeissner, T., Shaw, R. A., Siebert, H., Xi, H., Xu, H., and Bodenschatz, E.: Turbulence-induced cloud voids: observation and interpretation, Atmos. Chem. Phys., 19, 4991–5003, https://doi.org/10.5194/acp-19-4991-2019, 2019. a
Khain, A. M., Pinsky, T., Elperin, N., Kleeorin, I., Rogachevskii, A., and Kostinski, A.: Critical comments to results of investigations of drop collisions in turbulent clouds, Atmos. Res., 86, 1–20, https://doi.org/10.1016/j.atmosres.2007.05.003, 2007. a
Kumar, B., Schumacher, J., and Shaw, R. A.: Lagrangian Mixing Dynamics at the Cloudy-Clear Air Interface, J. Atmos. Sci., 71, 2564–2580, https://doi.org/10.1175/JAS-D-13-0294.1, 2014. a, b
Kumar, B., Janetzko, F., Schumacher, J., and Shaw, R. A.: Extreme responses of a coupled scalar–particle system during turbulent mixing, New J. Phys., 14, 115020, https://doi.org/10.1088/1367-2630/14/11/115020, 2012. a
Kumar, B., Bera, S., Prabha, T. V., and Grabowski, W. W.: Cloud-edge mixing: Direct numerical simulation and observations in Indian Monsoon clouds, J. Adv. Model. Earth Sy., 9, 332–353, https://doi.org/10.1002/2016MS000731, 2017. a, b
Kumar, B., Götzfried, P., Suresh, N., Schumacher, J., and Shaw R. A.: Scale Dependence of Cloud Microphysical Response to Turbulent Entrainment and Mixing, J. Adv. Model. Earth Sy., 10, 2777–2785, https://doi.org/10.1029/2018MS001487, 2018. a
Latham, J. and Reed, R. L.: Laboratory studies of the effects of mixing on the evolution of cloud droplet spectra, Q. J. Roy. Meteor. Soc., 103, 297–306, https://doi.org/10.1002/qj.49710343607, 1997. a
Lehmann, K., Siebert, H., and Shaw, R. A.: Homogeneous and Inhomogeneous Mixing in Cumulus Clouds: Dependence on Local Turbulence Structure, J. Atmos. Sci., 66, 3641–3659, https://doi.org/10.1175/2009JAS3012.1, 2009. a
Lian, H., Charalampous, G., and Hardalupas, Y.: Preferential concentration of poly-dispersed droplets in stationary isotropic turbulence, Exper. Fluids, 54, 1525, https://doi.org/10.1007/s00348-013-1525-3, 2013. a
Lu, C., Liu, Y., Niu, S., Krueger, S., and Wagner, T.: Exploring parameterization for turbulent entrainment‐mixing processes in clouds, J. Geophys. Res.-Atmos., 118, 185–194, https://doi.org/10.1029/2012JD018464, 2013. a, b
Luo, S., Lu, C., Liu, Y., Bian, J., Gao, W., Li, J., Xu, X., Gao, S., Yang, S., and Guo, X.: Parameterizations of entrainment‐mixing mechanisms and their effects on cloud droplet spectral width based on numerical simulations, J. Geophys. Res.-Atmos., 125, e2020JD032972, https://doi.org/10.1029/2020JD032972, 2020. a
Pedregosa, F.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res.h, 12, 2825–2830, 2011. a
Pinsky, M., Khain, A., and Shapiro, M.,: Stochastic effects of cloud droplet hydrodynamic interaction in a turbulent flow, Atmos. Res., 53, 131–169, https://doi.org/10.1016/S0169-8095(99)00048-4, 2000. a
Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and Precipitation, Springer, 1997. a
Randall, D. A. and Tjemkes, S. : Clouds, the earth’s radiation budget, and the hydrologic cycle, Glob. Planet. Change, 4, 3–9, https://doi.org/10.1016/0921-8181(91)90063-3, 1991. a
Riemer, N. and Wexler, A. S.: Droplets to Drops by Turbulent Coagulation, J. Atmos. Sci., 62, 1962–1975, https://doi.org/10.1175/JAS3431.1, 2005. a
Shaw, R. A.: Particle-turbulence interactions in atmospheric clouds, Annu. Rev. Fluid Mech., 35, 183–227, https://doi.org/10.1146/annurev.fluid.35.101101.161125, 2003.
a, b
Shaw, R. A., Reade, W. C., Collins, L. R., and Verlinde, J.: Preferential Concentration of Cloud Droplets by Turbulence: Effects on the Early Evolution of Cumulus Cloud Droplet Spectra, J. Atmos. Sci., 55, 1965–1976, https://doi.org/10.1175/1520-0469(1998)055<1965:PCOCDB>2.0.CO;2, 1998. a, b, c, d, e, f, g, h, i, j
SIONLib: Scalable library for parallel access to task-local files. Germany: Forschungszentrum Jülich, available at: https://www.fz-juelich.de/ias/jsc/EN/Expertise/Support/Software/SIONlib/_node.html (last access: 11 August 2021), 2020. a
Squires, K. D. and Eaton, J. K.: Measurements of particle dispersion obtained from direct numerical simulations of isotropic turbulence, J. Fluid Mech., 226, 1–35, 1991. a
Vaillancourt, P. A. and Yau, M. K.: Review of Particle-Turbulence Interactions and Consequences for Cloud Physic, B. Am. Meteorol. Soc., 81, 285–298, https://doi.org/10.1175/1520-0477(2000)081<0285:ROPIAC>2.3.CO;2, 2000. a, b, c, d
Vaillancourt, P. A., Yau, M. K., and Grabowski, W. W.: Upshear and Downshear Evolution of Cloud Structure and Spectral Properties, J. Atmos. Sci., 54, 1203–1217, https://doi.org/10.1175/1520-0469(1997)054<1203:UADEOC>2.0.CO;2, 1997. a, b
Vaillancourt, P. A., Yau, M. K., Bartello, P., and Grabowski, W. W.: Microscopic Approach to Cloud Droplet Growth by Condensation, Part II: Turbulence, Clustering, and Condensational Growth, J. Atmos. Sci., 59, 3421–3435, https://doi.org/10.1175/1520-0469(2002)059<3421:MATCDG>2.0.CO;2, 2002.
Short summary
The characteristics of turbulent clouds are affected by the entrainment of ambient dry air and its subsequent mixing. A turbulent flow generates vorticities of different intensities, and regions with high vorticity (HV) and low vorticity (LV) exist. This study provides a detailed analysis of different properties of turbulent flows and cloud droplets in the HV and LV regions in order to understand the impact of vorticity production on cloud microphysical and mixing processes.
The characteristics of turbulent clouds are affected by the entrainment of ambient dry air and...
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