Articles | Volume 21, issue 5
https://doi.org/10.5194/acp-21-4059-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-21-4059-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Cloud droplet diffusional growth in homogeneous isotropic turbulence: bin microphysics versus Lagrangian super-droplet simulations
Wojciech W. Grabowski
CORRESPONDING AUTHOR
Mesoscale and Microscale Meteorology Laboratory, National Center for
Atmospheric Research,
Boulder, CO 80307, USA
Lois Thomas
HPCS, Indian Institute of Tropical Meteorology, Ministry of Earth
Sciences, Pune 411008, India
Department of Atmospheric and Space Sciences, Savitribai Phule Pune
University, Pune 411007, India
Related authors
Wojciech W. Grabowski and Hanna Pawlowska
Atmos. Chem. Phys., 25, 5273–5285, https://doi.org/10.5194/acp-25-5273-2025, https://doi.org/10.5194/acp-25-5273-2025, 2025
Short summary
Short summary
A simple diagram to depict cloud droplets' formation via the activation of cloud condensation nuclei (CCN) as well as their subsequent growth and evaporation is presented.
Damian K. Wójcik, Michał Z. Ziemiański, and Wojciech W. Grabowski
EGUsphere, https://doi.org/10.5194/egusphere-2025-1017, https://doi.org/10.5194/egusphere-2025-1017, 2025
Short summary
Short summary
Representation of severe convection is a challenge for the numerical weather prediction models. We show that an explicit stochastic convection initiation scheme allows numerical representation of the isolated bow echo of severe social impact, showing its cold-pool-driven dynamics, formation of the rear inflow jet and strong surface winds. In moist convection context, we polemize with the idea of horizontal sizes of model perturbations being no less than the effective model’s resolution.
Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, https://doi.org/10.5194/acp-23-13791-2023, 2023
Short summary
Short summary
As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Istvan Geresdi, Lulin Xue, Sisi Chen, Youssef Wehbe, Roelof Bruintjes, Jared A. Lee, Roy M. Rasmussen, Wojciech W. Grabowski, Noemi Sarkadi, and Sarah A. Tessendorf
Atmos. Chem. Phys., 21, 16143–16159, https://doi.org/10.5194/acp-21-16143-2021, https://doi.org/10.5194/acp-21-16143-2021, 2021
Short summary
Short summary
By releasing soluble aerosols into the convective clouds, cloud seeding potentially enhances rainfall. The seeding impacts are hard to quantify with observations only. Numerical models that represent the detailed physics of aerosols, cloud and rain formation are used to investigate the seeding impacts on rain enhancement under different natural aerosol backgrounds and using different seeding materials. Our results indicate that seeding may enhance rainfall under certain conditions.
Wojciech W. Grabowski and Hugh Morrison
Atmos. Chem. Phys., 21, 13997–14018, https://doi.org/10.5194/acp-21-13997-2021, https://doi.org/10.5194/acp-21-13997-2021, 2021
Short summary
Short summary
The paper provides a discussion of key elements of moist convective dynamics: cloud buoyancy, latent heating, precipitation, and entrainment. The motivation comes from recent discussions concerning differences in convective dynamics in polluted and pristine environments.
Wojciech W. Grabowski and Hanna Pawlowska
Atmos. Chem. Phys., 25, 5273–5285, https://doi.org/10.5194/acp-25-5273-2025, https://doi.org/10.5194/acp-25-5273-2025, 2025
Short summary
Short summary
A simple diagram to depict cloud droplets' formation via the activation of cloud condensation nuclei (CCN) as well as their subsequent growth and evaporation is presented.
Damian K. Wójcik, Michał Z. Ziemiański, and Wojciech W. Grabowski
EGUsphere, https://doi.org/10.5194/egusphere-2025-1017, https://doi.org/10.5194/egusphere-2025-1017, 2025
Short summary
Short summary
Representation of severe convection is a challenge for the numerical weather prediction models. We show that an explicit stochastic convection initiation scheme allows numerical representation of the isolated bow echo of severe social impact, showing its cold-pool-driven dynamics, formation of the rear inflow jet and strong surface winds. In moist convection context, we polemize with the idea of horizontal sizes of model perturbations being no less than the effective model’s resolution.
Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, https://doi.org/10.5194/acp-23-13791-2023, 2023
Short summary
Short summary
As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Istvan Geresdi, Lulin Xue, Sisi Chen, Youssef Wehbe, Roelof Bruintjes, Jared A. Lee, Roy M. Rasmussen, Wojciech W. Grabowski, Noemi Sarkadi, and Sarah A. Tessendorf
Atmos. Chem. Phys., 21, 16143–16159, https://doi.org/10.5194/acp-21-16143-2021, https://doi.org/10.5194/acp-21-16143-2021, 2021
Short summary
Short summary
By releasing soluble aerosols into the convective clouds, cloud seeding potentially enhances rainfall. The seeding impacts are hard to quantify with observations only. Numerical models that represent the detailed physics of aerosols, cloud and rain formation are used to investigate the seeding impacts on rain enhancement under different natural aerosol backgrounds and using different seeding materials. Our results indicate that seeding may enhance rainfall under certain conditions.
Wojciech W. Grabowski and Hugh Morrison
Atmos. Chem. Phys., 21, 13997–14018, https://doi.org/10.5194/acp-21-13997-2021, https://doi.org/10.5194/acp-21-13997-2021, 2021
Short summary
Short summary
The paper provides a discussion of key elements of moist convective dynamics: cloud buoyancy, latent heating, precipitation, and entrainment. The motivation comes from recent discussions concerning differences in convective dynamics in polluted and pristine environments.
Cited articles
Andrejczuk, M., Grabowski, W. W., Malinowski, S. P., and Smolarkiewicz P.
K.: Numerical simulation of cloud – clear air interfacial mixing, J. Atmos.
Sci., 61, 1726–1739, 2004.
Andrejczuk, M., Reisner, J. M., Henson, B., Dubey, M. K., and Jeffery, C.
A.: The potential impacts of pollution on a nondrizzling stratus deck: Does
aerosol number matter more than type?, J. Geophys. Res., 113, D19204,
https://doi.org/10.1029/2007JD009445, 2008.
Arabas, S. and Shima, S.-I.: Large-eddy simulations of trade wind cumuli
using particle-based microphysics with Monte Carlo coalescence, J. Atmos.
Sci., 70, 2768–2777, https:// doi.org/10.1175/JAS-D-12-0295.1, 2013.
Arabas, S., Jaruga, A., Pawlowska, H., and Grabowski, W. W.: libcloudph++ 1.0: a single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++, Geosci. Model Dev., 8, 1677–1707, https://doi.org/10.5194/gmd-8-1677-2015, 2015.
Benmoshe, N., Pinsky, M., Pokrovsky, A., and Khain, A.: Turbulent effects on
the microphysics and initiation of warm rain in deep convective clouds: 2-D
simulations by a spectral mixed-phase microphysics cloud model, J. Geophys.
Res., 117, D06220, https://doi.org/10.1029/2011JD016603, 2012.
Clark, T. L.: Numerical modeling of the dynamics and microphysics of warm
cumulus convection, J. Atmos. Sci., 30, 857–878, 1973.
Dziekan, P., Waruszewski, M., and Pawlowska, H.: University of Warsaw Lagrangian Cloud Model (UWLCM) 1.0: a modern large-eddy simulation tool for warm cloud modeling with Lagrangian microphysics, Geosci. Model Dev., 12, 2587–2606, https://doi.org/10.5194/gmd-12-2587-2019, 2019.
Grabowski, W. W.: Separating physical impacts from natural variability using piggybacking technique, Adv. Geosci., 49, 105–111, https://doi.org/10.5194/adgeo-49-105-2019, 2019.
Grabowski, W. W.: Comparison of Eulerian bin and Lagrangian
particle-based schemes in simulations of Pi Chamber dynamics and
microphysics, J. Atmos. Sci., 77, 1151–1165, 2020a.
Grabowski, W. W.: Comparison of Eulerian bin and Lagrangian
particle-based microphysics in simulations of nonprecipitating cumulus, J.
Atmos. Sci., 77, 3951–3970, 2020b.
Grabowski, W. W.: Lagrangian and Eulerian droplets in DNS. Version 1.0. UCAR/NCAR – DASH Repository, https://doi.org/10.5065/2ybq-nd09, 2020c.
Grabowski, W. W. and Abade, G. C.: Broadening of cloud droplet spectra
through eddy hopping: Turbulent adiabatic parcel simulations, J. Atmos.
Sci., 74, 1485–1493, 2017.
Grabowski, W. W., Andrejczuk, M., and Wang, L.-P.: Droplet growth in a bin
warm-rain scheme with Twomey CCN activation, Atmos. Res., 99, 290–301,
2011.
Grabowski, W. W., Dziekan, P., and Pawlowska, H.: Lagrangian condensation microphysics with Twomey CCN activation, Geosci. Model Dev., 11, 103–120, https://doi.org/10.5194/gmd-11-103-2018, 2018.
Grabowski, W. W., Morrison, H., Shima, S.-I., Abade, G. C., Dziekan, P., and
Pawlowska, H.: Modeling of cloud microphysics: Can we do better?, B. Am.
Meteorol. Soc., 100, 655–672, https://doi.org/10.1175/BAMS-D-18-0005.1, 2019.
Grinstein, F. F., Margolin, L. G., and Rider, W. J.: Implicit Large Eddy
Simulation: Computing Turbulent Fluid Dynamics, Cambridge University Press,
2007.
Hoffmann, F., Yamaguchi, T., and Feingold, G.: Inhomogeneous mixing in
Lagrangian cloud models: Effects on the production of precipitation embryos,
J. Atmos. Sci., 76, 113–133, https://doi.org/10.1175/JAS-D-18-0087.1,
2019.
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O., Levin, Z., Pinsky,
M., Phillips, V., Prabhakaran, T., Teller, A., van den Heever, S. C., and Yano, J.-I.: Representation of microphysical processes in
cloud-resolving models: Spectral (bin) microphysics versus bulk
parameterization, Rev. Geophys., 53, 247–322,
https://doi.org/10.1002/2014RG000468, 2015.
Kogan, Y. L.: The simulation of a convective cloud in a 3-D model with
explicit microphysics. Part I: Model description and sensitivity
experiments, J. Atmos. Sci., 48, 1160–1189,
https://doi.org/10.1175/1520-0469(1991)048<1160:TSOACC>2.0.CO;2, 1991.
Lanotte, A. S., Seminara, A., and Toschi, F.: Cloud droplet growth by
condensation in homogeneous isotropic turbulence, J. Atmos. Sci., 66,
1685–1697, 2009.
Lasher-Trapp, S. G., Cooper, W. A., and Blyth, A. M.: Broadening of droplet
size distributions from entrainment and mixing in a cumulus cloud, Q. J.
Roy. Meteor. Soc., 131, 195–220, https://doi.org/10.1256/qj.03.199, 2005.
Li, X.-Y., Brandenburg, A., Haugen, N. E. L., and Svensson, G.: Eulerian and
Lagrangian approaches to multidimensional condensation and collection, J.
Adv. Model. Earth Sy., 9, 1116–1137, https://doi.org/10.1002/2017MS000930, 2017.
Li, X.-Y., Svensson, G., Brandenburg, A., and Haugen, N. E. L.: Cloud-droplet growth due to supersaturation fluctuations in stratiform clouds, Atmos. Chem. Phys., 19, 639–648, https://doi.org/10.5194/acp-19-639-2019, 2019.
Margolin, L. G. and Rider, W. J.: A rationale for implicit turbulence
modelling, Int. J. Numer. Meth. Fl., 39, 821–841, 2002.
Margolin, L. G., Rider, W. J., and Grinstein, F. F.: Modeling turbulent flow
with implicit les, J. Turbul., 7, N15, https://doi.org/10.1080/14685240500331595, 2006.
Mordy, W.: Computations of the growth by condensation of a population of
cloud droplets, Tellus, 11, 16–44,
https://doi.org/10.1111/j.2153-3490.1959.tb00003.x, 1959.
Morrison, H., Witte, M., Bryan, G. H., Harrington, J. Y., and Lebo, Z. J.:
Broadening of modeled cloud droplet spectra using bin microphysics in an
Eulerian spatial domain. J. Atmos. Sci., 75, 4005–4030,
https://doi.org/10.1175/JAS-D-18-0055.1, 2018.
Onishi, R., Baba, Y., and Takahashi, K.: Large-scale forcing with less
communication in finite-difference simulations of stationary isotropic
turbulence, J. Comp. Phys., 230, 4088–4099, 2011.
Riechelmann, T., Noh, Y., and Raasch, S.: A new method for large-eddy
simulations of clouds with Lagrangian droplets including the effects of
turbulent collision, New J. Phys., 14, 065008, https://doi.org/10.1088/1367-2630/14/6/065008, 2012.
Rosales, C. and Meneveau, C.: Linear forcing in numerical simulations of
isotropic turbulence: Physical space implementations and convergence
properties, Phys. Fluids., 17, 095106, https://doi.org/10.1063/1.2047568, 2005.
Sardina, G., Picano, F., Brandt, L., and Caballero, R.: Continuous growth of
droplet size variance due to condensation in turbulent clouds, Phys. Rev.
Lett., 115, 184501, https://doi.org/10.1103/PhysRevLett.115.184501, 2015.
Shima, S.-I., Kusano, K., Kawano, A., Sugiyama, T., and Kawahara, S.: The
superdroplet method for the numerical simulation of clouds and
precipitation: A particle-based and probabilistic microphysics model coupled
with a non-hydrostatic model, Q. J. Roy. Meteor. Soc., 135, 1307–1320,
https://doi.org/10.1002/qj.441, 2009.
Siebert, H., Lehmann, K., and Wendisch, M.: Observations of small-Scale
turbulence and energy dissipation rates in the cloudy boundary layer, J.
Atmos. Sci., 63, 1451–1466, 2006.
Siebesma, A. P., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J., Duynkerke, P. G.,
Jiang, H., Khairoutdinov, M., Lewellen, D., Moeng, C. H., Sanchez, E., Bjorn
Stevens, B., and Stevens, D. E.: A large eddy simulation intercomparison
study of shallow cumulus convection, J. Atmos. Sci., 60, 1201–1219,
https://doi.org/10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2, 2003.
Stevens, B., Moeng, C. H., Ackerman, A. S., Bretherton, C. S., Chlond, A., de Roode, S.,
Edwards, J., Golaz, J. C., Jiang, H., Khairoutdinov, M., Kirkpatrick, M. P., Lewellen, D. C.,
Lock, A., Müller, F., Stevens, D. E., Whelan, E., and Zhu, P.: Evaluation of large-eddy simulations via
observations of nocturnal marine stratocumulus, Mon. Weather Rev., 133,
1443–1462, 2005.
Thomas, L., Grabowski, W. W., and Kumar, B.: Diffusional growth of cloud droplets in homogeneous isotropic turbulence: DNS, scaled-up DNS, and stochastic model, Atmos. Chem. Phys., 20, 9087–9100, https://doi.org/10.5194/acp-20-9087-2020, 2020.
Unterstrasser, S., Hoffmann, F., and Lerch, M.: Collection/aggregation algorithms in Lagrangian cloud microphysical models: rigorous evaluation in box model simulations, Geosci. Model Dev., 10, 1521–1548, https://doi.org/10.5194/gmd-10-1521-2017, 2017.
Vaillancourt, P., Yau, M. K., and Grabowski, W. W.: Microscopic approach to
cloud droplet growth by condensation. Part I: Model description and results
without turbulence, J. Atmos. Sci., 58, 1945–1964, 2001.
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, 2002.
vanZanten, M. C., Stevens, B., Nuijens, L., Siebesma, A. P., Ackerman, A. S., Burnet, F.,
Cheng, A., Couvreux, F., Jiang, H., Khairoutdinov, M., Kogan, Y., Lewellen, D. C., Mechem, D.,
Nakamura, K., Noda, A., Shipway, B. J., Slawinska, J., Wang, S., and Wyszogrodzki, A.: Controls on precipitation and cloudiness in
simulations of trade-wind cumulus as observed during RICO, J. Adv.
Model. Earth Sy., 3, M06001, https://doi.org/10.1029/2011MS000056, 2011.
Short summary
This paper presents a modeling study that investigates the impact of cloud turbulence on the diffusional growth of cloud droplets and compares modeling results to analytic solutions published in the past. The focus is on comparing the two microphysics modeling methodologies – the Eulerian bin microphysics and Lagrangian particle-based microphysics – and exposing their limitations.
This paper presents a modeling study that investigates the impact of cloud turbulence on the...
Altmetrics
Final-revised paper
Preprint