Articles | Volume 23, issue 9
https://doi.org/10.5194/acp-23-5217-2023
© Author(s) 2023. 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-23-5217-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 May 2023
Research article |
| 09 May 2023
| 09 May 2023
Mixed-phase direct numerical simulation: ice growth in cloud-top generating cells
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Lulin Xue
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Sarah Tessendorf
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Kyoko Ikeda
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Courtney Weeks
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Roy Rasmussen
National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Melvin Kunkel
Idaho Power Company, Boise, ID, USA
Derek Blestrud
Idaho Power Company, Boise, ID, USA
Shaun Parkinson
Idaho Power Company, Boise, ID, USA
Melinda Meadows
Idaho Power Company, Boise, ID, USA
Nick Dawson
Idaho Power Company, Boise, ID, USA
Related authors
Sisi Chen, Lulin Xue, Sarah A. Tessendorf, Thomas Chubb, Andrew Peace, Suzanne Kenyon, Johanna Speirs, Jamie Wolff, and Bill Petzke
Atmos. Chem. Phys., 25, 6703–6724, https://doi.org/10.5194/acp-25-6703-2025, https://doi.org/10.5194/acp-25-6703-2025, 2025
Short summary
Short summary
This study aims to investigate how cloud seeding affects snowfall in Australia's Snowy Mountains. By running simulations with different setups, we found that seeding impact varies greatly with weather conditions. Seeding increased snow in stable weather but sometimes reduced it in stormy weather. This helps us to better understand when seeding works best to boost water supplies.
Aaron Wang, Steve Krueger, Sisi Chen, Mikhail Ovchinnikov, Will Cantrell, and Raymond A. Shaw
Atmos. Chem. Phys., 24, 10245–10260, https://doi.org/10.5194/acp-24-10245-2024, https://doi.org/10.5194/acp-24-10245-2024, 2024
Short summary
Short summary
We employ two methods to examine a laboratory experiment on clouds with both ice and liquid phases. The first assumes well-mixed properties; the second resolves the spatial distribution of turbulence and cloud particles. Results show that while the trends in mean properties generally align, when turbulence is resolved, liquid droplets are not fully depleted by ice due to incomplete mixing. This underscores the threshold of ice mass fraction in distinguishing mixed-phase clouds from ice clouds.
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.
Sisi Chen, Lulin Xue, and Man-Kong Yau
Atmos. Chem. Phys., 20, 10111–10124, https://doi.org/10.5194/acp-20-10111-2020, https://doi.org/10.5194/acp-20-10111-2020, 2020
Short summary
Short summary
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.
Yufei Chu, Guo Lin, Min Deng, Lulin Xue, Weiwei Li, Hyeyum Hailey Shin, Jun A. Zhang, Hanqing Guo, and Zhien Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2490, https://doi.org/10.5194/egusphere-2025-2490, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We developed a new machine learning approach to estimate the height of the mixing layer in the lower atmosphere, which is important for predicting weather and air quality. By using daily temperature and heat patterns, the model learns how the atmosphere changes throughout the day. It gives accurate results across different locations and seasons, helping improve future climate and weather forecasts through better understanding of surface–atmosphere interactions.
Meilian Chen, Xiaoqin Jing, Jiaojiao Li, Jing Yang, Xiaobo Dong, Bart Geerts, Yan Yin, Baojun Chen, Lulin Xue, Mengyu Huang, Ping Tian, and Shaofeng Hua
Atmos. Chem. Phys., 25, 7581–7596, https://doi.org/10.5194/acp-25-7581-2025, https://doi.org/10.5194/acp-25-7581-2025, 2025
Short summary
Short summary
Several recent studies have reported complete cloud glaciation induced by airborne-based glaciogenic cloud seeding over plains. Since turbulence is an important factor to maintain clouds in a mixed phase, it is hypothesized that turbulence may have an impact on the seeding effect. This hypothesis is evident in the present study, which shows that turbulence can accelerate the impact of airborne glaciogenic seeding of stratiform clouds.
Sisi Chen, Lulin Xue, Sarah A. Tessendorf, Thomas Chubb, Andrew Peace, Suzanne Kenyon, Johanna Speirs, Jamie Wolff, and Bill Petzke
Atmos. Chem. Phys., 25, 6703–6724, https://doi.org/10.5194/acp-25-6703-2025, https://doi.org/10.5194/acp-25-6703-2025, 2025
Short summary
Short summary
This study aims to investigate how cloud seeding affects snowfall in Australia's Snowy Mountains. By running simulations with different setups, we found that seeding impact varies greatly with weather conditions. Seeding increased snow in stable weather but sometimes reduced it in stormy weather. This helps us to better understand when seeding works best to boost water supplies.
Aaron Wang, Steve Krueger, Sisi Chen, Mikhail Ovchinnikov, Will Cantrell, and Raymond A. Shaw
Atmos. Chem. Phys., 24, 10245–10260, https://doi.org/10.5194/acp-24-10245-2024, https://doi.org/10.5194/acp-24-10245-2024, 2024
Short summary
Short summary
We employ two methods to examine a laboratory experiment on clouds with both ice and liquid phases. The first assumes well-mixed properties; the second resolves the spatial distribution of turbulence and cloud particles. Results show that while the trends in mean properties generally align, when turbulence is resolved, liquid droplets are not fully depleted by ice due to incomplete mixing. This underscores the threshold of ice mass fraction in distinguishing mixed-phase clouds from ice clouds.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
Short summary
Short summary
We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Yabin Gou, Haonan Chen, Hong Zhu, and Lulin Xue
Atmos. Chem. Phys., 23, 2439–2463, https://doi.org/10.5194/acp-23-2439-2023, https://doi.org/10.5194/acp-23-2439-2023, 2023
Short summary
Short summary
This article investigates the complex precipitation microphysics associated with super typhoon Lekima using a host of in situ and remote sensing observations, including rain gauge and disdrometer data, as well as polarimetric radar observations. The impacts of precipitation microphysics on multi-source data consistency and radar precipitation estimation are quantified. It is concluded that the dynamical precipitation microphysical processes must be considered in radar precipitation estimation.
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.
Youssef Wehbe, Sarah A. Tessendorf, Courtney Weeks, Roelof Bruintjes, Lulin Xue, Roy Rasmussen, Paul Lawson, Sarah Woods, and Marouane Temimi
Atmos. Chem. Phys., 21, 12543–12560, https://doi.org/10.5194/acp-21-12543-2021, https://doi.org/10.5194/acp-21-12543-2021, 2021
Short summary
Short summary
The role of dust aerosols as ice-nucleating particles is well established in the literature, whereas their role as cloud condensation nuclei is less understood, particularly in polluted desert environments. We analyze coincident aerosol size distributions and cloud particle imagery collected over the UAE with a research aircraft. Despite the presence of ultra-giant aerosol sizes associated with dust, an active collision–coalescence process is not observed within the limited depths of warm cloud.
Trude Eidhammer, Adam Booth, Sven Decker, Lu Li, Michael Barlage, David Gochis, Roy Rasmussen, Kjetil Melvold, Atle Nesje, and Stefan Sobolowski
Hydrol. Earth Syst. Sci., 25, 4275–4297, https://doi.org/10.5194/hess-25-4275-2021, https://doi.org/10.5194/hess-25-4275-2021, 2021
Short summary
Short summary
We coupled a detailed snow–ice model (Crocus) to represent glaciers in the Weather Research and Forecasting (WRF)-Hydro model and tested it on a well-studied glacier. Several observational systems were used to evaluate the system, i.e., satellites, ground-penetrating radar (used over the glacier for snow depth) and stake observations for glacier mass balance and discharge measurements in rivers from the glacier. Results showed improvements in the streamflow projections when including the model.
Sisi Chen, Lulin Xue, and Man-Kong Yau
Atmos. Chem. Phys., 20, 10111–10124, https://doi.org/10.5194/acp-20-10111-2020, https://doi.org/10.5194/acp-20-10111-2020, 2020
Short summary
Short summary
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
Avramov, A. and Harrington, J. Y.: Influence of parameterized ice habit on
simulated mixed phase Arctic clouds, J. Geophys. Res.-Atmos., 115,
D03205, https://doi.org/10.1029/2009JD012108, 2010.
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.
Barrett, A. I., Hogan, R. J., and Forbes, R. M.: Why are mixed-phase
altocumulus clouds poorly predicted by large-scale models? Part 1. Physical
processes, J. Geophys. Res.-Atmos., 122, 9903–9926, https://doi.org/10.1002/2016JD026321, 2017.
Baumgardner, D, Abel, S. J., Axisa, D., Cotton, R., Crosier, J., Field, P., Gurganus, C., Heymsfield, A., Korolev, A., Kraemer, M., and Lawson, P.: Cloud Ice Properties: In Situ Measurement
Challenges, Meteor. Mon., 58, 9.1–9.23,
https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0011.1, 2017.
Chen, J.-P., and Lamb, D.: The Theoretical Basis for the Parameterization of
Ice Crystal Habits: Growth by Vapor Deposition, J. Atmospheric Sci., 51,
1206–1222, https://doi.org/10.1175/1520-0469(1994)051<1206:TTBFTP>2.0.CO;2, 1994.
Chen, S.: Replication Data for “Mixed-phase Direct Numerical Simulation: Ice
Growth in Cloud-Top Generating Cells”, V1, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/BKYGWW, 2022a.
Chen, S.: NCAR mixed-phase direct numerical simulation (DNS) (1.0), Zenodo [code],
https://doi.org/10.5281/zenodo.7226573, 2022b.
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
Chen, S., Yau, M. K., and Bartello, P.: Turbulence Effects of Collision
Efficiency and Broadening of Droplet Size Distribution in Cumulus Clouds, J.
Atmos. Sci., 75, 203–217, https://doi.org/10.1175/JAS-D-17-0123.1, 2018a.
Chen, S., Yau, M.-K., Bartello, P., and Xue, L.: Bridging the condensation–collision size gap: a direct numerical simulation of continuous droplet growth in turbulent clouds, Atmos. Chem. Phys., 18, 7251–7262, https://doi.org/10.5194/acp-18-7251-2018, 2018b.
Chen, S., Xue, L., and Yau, M.-K.: Impact of aerosols and turbulence on cloud droplet growth: an in-cloud seeding case study using a parcel–DNS (direct numerical simulation) approach, Atmos. Chem. Phys., 20, 10111–10124, https://doi.org/10.5194/acp-20-10111-2020, 2020.
Chen, S., Yau, M. K., and Yau, M. K.: Hygroscopic Seeding Effects of Giant
Aerosol Particles Simulated by the Lagrangian-Particle-Based Direct
Numerical Simulation, Geophys. Res. Lett., 48, e2021GL094621, https://doi.org/10.1029/2021GL094621, 2021.
Desai, N., Chandrakar, K. K., Kinney, G., Cantrell, W., and Shaw, R. A.:
Aerosol-Mediated Glaciation of Mixed-Phase Clouds: Steady-State Laboratory
Measurements, Geophys. Res. Lett., 46, 9154–9162, https://doi.org/10.1029/2019GL083503, 2019.
Field, P. R., Lawson, R. P., Brown, P. R., Lloyd, G., Westbrook, C., Moisseev,
D., Miltenberger, A., Nenes, A., Blyth, A., Choularton, T., and Connolly,
P.: Secondary ice production: Current state of the science and
recommendations for the future, Meteor. Mon., 58, 7–1, 2017.
Fridlind, A. M., van Diedenhoven, B., Ackerman, A. S., Avramov, A., Mrowiec,
A., Morrison, H., Zuidema, P., and Shupe, M. D.: A FIRE-ACE/SHEBA Case Study
of Mixed-Phase Arctic Boundary Layer Clouds: Entrainment Rate Limitations on
Rapid Primary Ice Nucleation Processes, J. Atmos. Sci., 69, 365–389,
https://doi.org/10.1175/JAS-D-11-052.1, 2012.
Fukuta, N.: Experimental Studies on the Growth of Small Ice Crystals, J.
Atmospheric Sci., 26, 522–531, https://doi.org/10.1175/1520-0469(1969)026<0522:ESOTGO>2.0.CO;2, 1969.
Gotoh, T., Suehiro, T., and Saito, I.: Continuous growth of cloud droplets
in cumulus cloud, New J. Phys., 18, 043042, https://doi.org/10.1088/1367-2630/18/4/043042, 2016.
Gotoh, T., Saito, I., and Watanabe, T.: Spectra of supersaturation and
liquid water content in cloud turbulence, Phys. Rev. Fluids, 6, 110512,
https://doi.org/10.1103/PhysRevFluids.6.110512, 2021.
Grabowski, W. W. and Wang, L.-P.: Growth of Cloud Droplets in a Turbulent
Environment, Annu. Rev. Fluid Mech., 45, 293–324, https://doi.org/10.1146/annurev-fluid-011212-140750, 2013.
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,
https://doi.org/10.1016/j.atmosres.2010.10.020, 2011.
Heymsfield, A. J., Krämer, M., Luebke, A., Brown, P., Cziczo, D. J., Franklin, C., Lawson, P., Lohmann, U., McFarquhar, G., Ulanowski, Z., and Van Tricht, K.: Cirrus Clouds, Meteor. Mon., 58,
2.1–2.26, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0010.1, 2017.
Hoffmann, F.: Effects of Entrainment and Mixing on the
Wegener–Bergeron–Findeisen Process, J. Atmos. Sci., 77, 2279–2296,
https://doi.org/10.1175/JAS-D-19-0289.1, 2020.
Jensen, A. A. and Harrington, J. Y.: Modeling Ice Crystal Aspect Ratio
Evolution during Riming: A Single-Particle Growth Model, J. Atmos.
Sci., 72, 2569–2590, https://doi.org/10.1175/JAS-D-14-0297.1, 2015.
Jensen, A. A., Harrington, J. Y., Morrison, H., and Milbrandt, J. A.:
Predicting Ice Shape Evolution in a Bulk Microphysics Model, J. Atmos.
Sci., 74, 2081–2104, https://doi.org/10.1175/JAS-D-16-0350.1, 2017.
Keeler, J. M., Jewett, B. F., Rauber, R. M., McFarquhar, G. M., Rasmussen, R. M.,
Xue, L., Liu, C., and Thompson, G.: Dynamics of Cloud-Top Generating
Cells in Winter Cyclones. Part I: Idealized Simulations in the Context of
Field Observations, J. Atmos. Sci., 73, 1507–1527, https://doi.org/10.1175/JAS-D-15-0126.1, 2016.
Khain, A., Pinsky, M., and Korolev, A.: Combined Effect of the
Wegener-Bergeron-Findeisen Mechanism and Large Eddies on Microphysics of
Mixed-Phase Stratiform Clouds, J. Atmos. Sci., 79, 383–407,
https://doi.org/10.1175/JAS-D-20-0269.1, 2022.
Koop, T. and Mahowald, N.: The seeds of ice in clouds, Nature, 498, 302–303, https://doi.org/10.1038/nature12256, 2013.
Korolev, A.: Limitations of the Wegener–Bergeron–Findeisen Mechanism in
the Evolution of Mixed-Phase Clouds, J. Atmos. Sci., 64, 3372–3375,
https://doi.org/10.1175/JAS4035.1, 2007.
Korolev, A.: Rates of phase transformations in mixed-phase clouds, Q. J. Roy.
Meteor. Soc., 134, 595–608, https://doi.org/10.1002/qj.230,
2008.
Korolev, A. and Field, P. R.: The Effect of Dynamics on Mixed-Phase Clouds:
Theoretical Considerations, J. Atmos. Sci., 65, 66–86, https://doi.org/10.1175/2007JAS2355.1, 2008.
Korolev, A. and Isaac, G.: Roundness and Aspect Ratio of Particles in Ice
Clouds. J. Atmos. Sci., 60, 1795–1808, https://doi.org/10.1175/1520-0469(2003)060<1795:RAAROP>2.0.CO;2, 2003.
Korolev, A. and Milbrandt, J.: How are mixed-phase clouds mixed?, Geophys.
Res. Lett., 49, e2022GL099578, https://doi.org/10.1029/2022GL099578, 2022.
Korolev, A., Isaac, G. A., and Hallett, J.: Ice particle habits in Arctic
clouds, Geophys. Res. Lett., 26, 1299–1302, https://doi.org/10.1029/1999GL900232, 1999.
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z.,
Williams, E., Abel, S. J., Axisa, D., Borrmann, S., and Crosier, J.: Mixed-Phase
Clouds: Progress and Challenges, Meteor. Mon., 58, 5.1–5.50, https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017.
Kumjian, M. R., Rutledge, S. A., Rasmussen, R. M., Kennedy, P. C., and
Dixon, M.: High-Resolution Polarimetric Radar Observations of Snow-Generating
Cells, J. Appl. Meteorol. Clim., 53, 1636–1658, https://doi.org/10.1175/JAMC-D-13-0312.1, 2014.
Lee, S. S., Ha, K.-J., Manoj, M. G., Kamruzzaman, M., Kim, H., Utsumi, N., Zheng, Y., Kim, B.-G., Jung, C. H., Um, J., Guo, J., Choi, K. O., and Kim, G.-U.: Midlatitude mixed-phase stratocumulus clouds and their interactions with aerosols: how ice processes affect microphysical, dynamic, and thermodynamic development in those clouds and interactions?, Atmos. Chem. Phys., 21, 16843–16868, https://doi.org/10.5194/acp-21-16843-2021, 2021.
Li, X.-Y., Brandenburg, A., Svensson, G., Haugen, N. E. L., Mehlig, B., and
Rogachevskii, I.: Condensational and Collisional Growth of Cloud Droplets in a
Turbulent Environment, J. Atmos. Sci., 77, 337–353, https://doi.org/10.1175/JAS-D-19-0107.1, 2020.
Morrison, H. and Pinto, J. O. : Intercomparison of Bulk Cloud Microphysics
Schemes in Mesoscale Simulations of Springtime Arctic Mixed-Phase Stratiform
Clouds, Mon. Weather Rev., 134, 1880–1900, https://doi.org/10.1175/MWR3154.1, 2006.
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and
Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci.,
5, 11–17, https://doi.org/10.1038/ngeo1332, 2012.
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., and Posselt, D. J.: Confronting the Challenge of Modeling Cloud
and Precipitation Microphysics, J. Adv. Model. Earth Sy., 12, 8, https://doi.org/10.1029/2019MS001689, 2020.
Ovchinnikov, M., Ackerman, A. S., Avramov, A., Cheng, A., Fan, J., Fridlind, A. M., Ghan, S., Harrington, J., Hoose, C., Korolev, A., and McFarquhar, G. M.: Intercomparison of large-eddy simulations of
Arctic mixed-phase clouds: Importance of ice size distribution assumptions,
J. Adv. Model. Earth Sy., 6, 223–248, https://doi.org/10.1002/2013MS000282, 2014.
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007.
Rauber, R. M., Plummer, D. M., Macomber, M. K., Rosenow, A. A., McFarquhar, G. M., Jewett, B. F., Leon, D., Owens, N., and Keeler, J. M.: The Role of Cloud-Top Generating Cells and Boundary Layer Circulations in the Finescale Radar Structure of a Winter Cyclone over the Great Lakes, Mon. Weather Rev., 143, 2291–2318, https://doi.org/10.1175/MWR-D-14-00350.1, 2015.
Rogers, R. R. and Yau, M. K.: A short course in cloud physics, 3rd ed.,
Butterworth-Heinemann, ISBN 0-7506-3215-1, 1989.
Saito, I., Gotoh, T., and Watanabe, T.: Broadening of Cloud Droplet Size
Distributions by Condensation in Turbulence, J. Meteorol. Soc. Jpn.,
97, 867–891, https://doi.org/10.2151/jmsj.2019-049, 2019.
Shaw, R. A., Cantrell, W., Chen, S., Chuang, P., Donahue, N., Feingold, G., Kollias, P., Korolev, A., Kreidenweis, S., Krueger, S., and Mellado, J. P.: Cloud–Aerosol–Turbulence Interactions: Science
Priorities and Concepts for a Large-Scale Laboratory Facility, B. Am.
Meteorol. Soc., 101, E1026–E1035, https://doi.org/10.1175/BAMS-D-20-0009.1, 2020.
Storelvmo, T. and Tan, I.: The Wegener-Bergeron-Findeisen process – Its
discovery and vital importance for weather and climate, Meteorol. Z., 24,
455–461, https://doi.org/10.1127/metz/2015/0626, 2015.
Tessendorf, S. A., French, J. R., Friedrich, K., Geerts, B., Rauber, R. M.,
Rasmussen, R. M., Xue, L., Ikeda, K., Blestrud, D. R., Kunkel, M. L., and
Parkinson, S.: A Transformational Approach to Winter Orographic Weather
Modification Research: The SNOWIE Project, B. Am. Meteorol. Soc., 100,
71–92, https://doi.org/10.1175/BAMS-D-17-0152.1, 2019.
Vaillancourt, P. A., 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, https://doi.org/10.1175/1520-0469(2001)058<1945:MATCDG>2.0.CO;2, 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, https://doi.org/10.1175/1520-0469(2002)059<3421:MATCDG>2.0.CO;2, 2002.
Vowinckel, B., Biegert, E., Luzzatto-Fegiz, P., and Meiburg, E.:
Consolidation of freshly deposited cohesive and noncohesive sediment:
Particle-resolved simulations, Phys. Rev. Fluids, 4, 074305, https://doi.org/10.1103/PhysRevFluids.4.074305, 2019a.
Vowinckel, B., Withers, J., Luzzatto-Fegiz, P., and Meiburg, E.: Settling of
cohesive sediment: particle-resolved simulations, J. Fluid Mech., 858,
5–44, https://doi.org/10.1017/jfm.2018.757, 2019b.
Wang, Y., McFarquhar, G. M., Rauber, R. M., Zhao, C., Wu, W., Finlon, J. A., Stechman, D. M., Stith, J., Jensen, J. B., Schnaiter, M., and Järvinen, E.: Microphysical Properties of Generating Cells Over the
Southern Ocean: Results From SOCRATES, J. Geophys. Res.-Atmos., 125, e2019JD032237,
https://doi.org/10.1029/2019JD032237, 2020.
Zhang, C. and Harrington, J. Y.: Including surface kinetic effects in
simple models of ice vapor diffusion, J. Atmos. Sci.,
71, 372–390, https://doi.org/10.1175/JAS-D-13-0153.1, 2014.
Short summary
The possible mechanism of effective ice growth in the cloud-top generating cells in winter orographic clouds is explored using a newly developed ultra-high-resolution cloud microphysics model. Simulations demonstrate that a high availability of moisture and liquid water is critical for producing large ice particles. Fluctuations in temperature and moisture down to millimeter scales due to cloud turbulence can substantially affect the growth history of the individual cloud particles.
The possible mechanism of effective ice growth in the cloud-top generating cells in winter...
Altmetrics
Final-revised paper
Preprint