Articles | Volume 19, issue 23
https://doi.org/10.5194/acp-19-14917-2019
© Author(s) 2019. 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-19-14917-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Dec 2019
Research article |
| 10 Dec 2019
| 10 Dec 2019
The impact of fluctuations and correlations in droplet growth by collision–coalescence revisited – Part 2: Observational evidence of gel formation in warm clouds
Lester Alfonso
CORRESPONDING AUTHOR
Universidad Autónoma de la Ciudad de México, Mexico City,
09790, Mexico
Graciela B. Raga
Centro de Ciencias de la Atmósfera, UNAM, Mexico City, 04510,
Mexico
Darrel Baumgardner
Droplet Measurement Technologies, Boulder, CO, USA
Related authors
Camilo Fernando Rodríguez Genó and Léster Alfonso
Geosci. Model Dev., 15, 493–507, https://doi.org/10.5194/gmd-15-493-2022, https://doi.org/10.5194/gmd-15-493-2022, 2022
Short summary
Short summary
The representation of the collision–coalescence process in models of different scales has been a great source of uncertainty for many years. The aim of this paper is to show that machine learning techniques can be a useful tool in order to incorporate this process by emulating the explicit treatment of microphysics. Our results show that the machine learning parameterization mimics the evolution of actual droplet size distributions very well.
Almuth Neuberger, Rahul Ranjan, Hao Ding, Fredrik Mattsson, Lea Haberstock, Darrel Baumgardner, Stefano Decesari, Annica M. L. Ekman, Dagen D. Hughes, Claudia Mohr, Marco Paglione, Ilona Riipinen, Matteo Rinaldi, and Paul Zieger
Atmos. Chem. Phys., 26, 11991–12017, https://doi.org/10.5194/acp-26-11991-2026, https://doi.org/10.5194/acp-26-11991-2026, 2026
Short summary
Short summary
We studied how particles from air pollution that absorb water but do not yet form fog droplets influence fog and visibility in northern Italy’s Po Valley. Using detailed field observations and computer modeling, we found that these “hydrated” particles can explain many low-visibility events. Recognizing their role helps improve forecasts of fog, air quality, and climate effects in polluted regions.
Evelyn Freney, Karine Sellegri, Therese Barthelmeß, Anja Engel, Darrel Baumgardner, and Dagen Hughes
EGUsphere, https://doi.org/10.5194/egusphere-2026-87, https://doi.org/10.5194/egusphere-2026-87, 2026
Short summary
Short summary
The exchange of material between the ocean and atmosphere plays an important role in regulating Earth’s climate. Through wave action, the ocean releases tiny airborne particles that influence atmospheric processes. This study examines how biological and chemical processes in seawater affect the properties of particles emitted from the ocean, highlighting the complex links between ocean biology and marine aerosols.
M. Fernanda Córdoba, Rachel Chang, Harry Alvarez-Ospina, Aramis Olivos-Ortiz, Graciela B. Raga, Daniel Rosas-Ramírez, Guadalupe Campos, Isabel Márquez, Telma Castro, and Luis A. Ladino
Atmos. Meas. Tech., 18, 2463–2479, https://doi.org/10.5194/amt-18-2463-2025, https://doi.org/10.5194/amt-18-2463-2025, 2025
Short summary
Short summary
The present study shows the development of the UNAM-MARine Aerosol Tank (UNAM-MARAT), a device that simulates wave breaking to generate marine aerosol particles. The portable and automatic tank is able to generate particle concentrations as high as 2000 cm-3, covering a wide range of sizes, similar to those found in the ambient marine boundary layer. The sea spray aerosol generated from three natural seawater samples was found to act as ice-nucleating particles (INPs) via immersion freezing.
Bighnaraj Sarangi, Darrel Baumgardner, Ana Isabel Calvo, Benjamin Bolaños-Rosero, Roberto Fraile, Alberto Rodríguez-Fernández, Delia Fernández-González, Carlos Blanco-Alegre, Cátia Gonçalves, Estela D. Vicente, and Olga L. Mayol-Bracero
Atmos. Chem. Phys., 25, 843–865, https://doi.org/10.5194/acp-25-843-2025, https://doi.org/10.5194/acp-25-843-2025, 2025
Short summary
Short summary
Measurements of fluorescing aerosol particle properties have been made during two major African dust events, one over the island of Puerto Rico and the other over the city of León, Spain. The measurements were made with two wideband integrated bioaerosol spectrometers. A significant change in the background aerosol properties, at both locations, is observed when the dust is in the respective regions.
Mahen Konwar, Benjamin Werden, Edward C. Fortner, Sudarsan Bera, Mercy Varghese, Subharthi Chowdhuri, Kurt Hibert, Philip Croteau, John Jayne, Manjula Canagaratna, Neelam Malap, Sandeep Jayakumar, Shivsai A. Dixit, Palani Murugavel, Duncan Axisa, Darrel Baumgardner, Peter F. DeCarlo, Doug R. Worsnop, and Thara Prabhakaran
Atmos. Meas. Tech., 17, 2387–2400, https://doi.org/10.5194/amt-17-2387-2024, https://doi.org/10.5194/amt-17-2387-2024, 2024
Short summary
Short summary
In a warm cloud seeding experiment hygroscopic particles are released to alter cloud processes to induce early raindrops. During the Cloud–Aerosol Interaction and Precipitation Enhancement Experiment, airborne mini aerosol mass spectrometers analyse the particles on which clouds form. The seeded clouds showed higher concentrations of chlorine and potassium, the oxidizing agents of flares. Small cloud droplet concentrations increased, and seeding particles were detected in deep cloud depths.
Bighnaraj Sarangi, Darrel Baumgardner, Benjamin Bolaños-Rosero, and Olga L. Mayol-Bracero
Atmos. Chem. Phys., 22, 9647–9661, https://doi.org/10.5194/acp-22-9647-2022, https://doi.org/10.5194/acp-22-9647-2022, 2022
Short summary
Short summary
Here, the fluorescent characteristics and cloud-forming efficiency of aerosols at an urban site in Puerto Rico are discussed. The results from this pilot study highlight the capabilities of ultraviolet-induced fluorescence (UV-IF) measurements for characterizing the properties of fluorescing aerosol particles, as they relate to the daily evolution of primary biological aerosol particles. This work has established a database of measurements on which future, longer-term studies will be initiated.
Diana L. Pereira, Irma Gavilán, Consuelo Letechipía, Graciela B. Raga, Teresa Pi Puig, Violeta Mugica-Álvarez, Harry Alvarez-Ospina, Irma Rosas, Leticia Martinez, Eva Salinas, Erika T. Quintana, Daniel Rosas, and Luis A. Ladino
Atmos. Chem. Phys., 22, 6435–6447, https://doi.org/10.5194/acp-22-6435-2022, https://doi.org/10.5194/acp-22-6435-2022, 2022
Short summary
Short summary
Airborne particles were i) collected in an agricultural fields and ii) generated in the laboratory from agricultural soil samples to analyze their ice nucleating abilities. It was found that the size and chemical composition of the Mexican agricultural dust particles influence their ice nucleating behavior, where the organic components are likely responsible for their efficiency as INPs. The INP concentrations from the present study are comparable to those from higher latitudes.
Graciela B. Raga, Darrel Baumgardner, Blanca Rios, Yanet Díaz-Esteban, Alejandro Jaramillo, Martin Gallagher, Bastien Sauvage, Pawel Wolff, and Gary Lloyd
Atmos. Chem. Phys., 22, 2269–2292, https://doi.org/10.5194/acp-22-2269-2022, https://doi.org/10.5194/acp-22-2269-2022, 2022
Short summary
Short summary
The In-Service Aircraft for a Global Observing System (IAGOS) is a small fleet of commercial aircraft that carry a suite of meteorological, gas, aerosol, and cloud sensors and have been measuring worldwide for almost 9 years, since late 2011. Extreme ice events (EIEs) have been identified from the IAGOS cloud measurements and linked to surface emissions for biomass and fossil fuel consumption. The results reported here are highly relevant for climate change and flight operations forecasting.
Camilo Fernando Rodríguez Genó and Léster Alfonso
Geosci. Model Dev., 15, 493–507, https://doi.org/10.5194/gmd-15-493-2022, https://doi.org/10.5194/gmd-15-493-2022, 2022
Short summary
Short summary
The representation of the collision–coalescence process in models of different scales has been a great source of uncertainty for many years. The aim of this paper is to show that machine learning techniques can be a useful tool in order to incorporate this process by emulating the explicit treatment of microphysics. Our results show that the machine learning parameterization mimics the evolution of actual droplet size distributions very well.
Elvis Torres-Delgado, Darrel Baumgardner, and Olga L. Mayol-Bracero
Atmos. Chem. Phys., 21, 18011–18027, https://doi.org/10.5194/acp-21-18011-2021, https://doi.org/10.5194/acp-21-18011-2021, 2021
Short summary
Short summary
African dust aerosols can travel thousands of kilometers and reach the Caribbean and other places, where they can serve as ice and cloud condensation nuclei and alter precipitation patterns. Cloud microphysical properties (droplet number and size) were measured in a Caribbean tropical montane cloud forest along with models and satellite products. The results of the study suggest that meteorology and air mass history are more important for cloud processes than aerosols transported from Africa.
Cited articles
Aldous, D. J.: Deterministic and stochastic models for coalescence (aggregation,
coagulation): A review of the mean-field theory for probabilistic, Bernoulli, 5, 3–48, 1999.
Alfonso, L.: An algorithm for the numerical solution of the multivariate master equation for stochastic coalescence, Atmos. Chem. Phys., 15, 12315–12326, https://doi.org/10.5194/acp-15-12315-2015, 2015.
Alfonso, L. and Raga, G. B.: The impact of fluctuations and correlations in droplet growth by collision–coalescence revisited – Part 1: Numerical calculation of post-gel droplet size distribution, Atmos. Chem. Phys., 17, 6895–6905, https://doi.org/10.5194/acp-17-6895-2017, 2017.
Alfonso, L., Raga, G. B., and Baumgardner, D.: The validity of the kinetic collection equation revisited, Atmos. Chem. Phys., 8, 969–982, https://doi.org/10.5194/acp-8-969-2008, 2008.
Alfonso, L., Raga, G. B., and Baumgardner, D.: The validity of the kinetic collection equation revisited – Part 2: Simulations for the hydrodynamic kernel, Atmos. Chem. Phys., 10, 7189–7195, https://doi.org/10.5194/acp-10-7189-2010, 2010.
Alfonso, L., Raga, G. B., and Baumgardner, D.: The validity of the kinetic collection equation revisited – Part 3: Sol–gel transition under turbulent conditions, Atmos. Chem. Phys., 13, 521–529, https://doi.org/10.5194/acp-13-521-2013, 2013.
Baker, M., Corbin, R. G., and Latham, J.: The influence of entrainment on
the evolution of cloud drop spectra: I. A model of inhomogeneous mixing, Q.
J. Roy. Meteor. Soc., 106, 581–598, 1980.
Bayewitz, M. H., Yerushalmi, J., Katz, S., and Shinnar, R.: The extent of
correlations in a stochastic coalescence process, J. Atmos. Sci., 31, 1604–1614, 1974.
Bazant, M. Z.: Largest cluster in subcritical percolation, Phys. Rev. E, 62, 1660, https://doi.org/10.1103/PhysRevE.62.1660,
2000.
Beard, K. V. and Ochs, H. T.: Warm-rain initiation – An overview of
microphysical mechanisms, J. Appl. Meteorol., 32, 608–625, 1993.
Bhattacharjee, S. M.: Critical Phenomena: An Introduction from a modern
perspective, in: Field Theories in Condensed Matter Physics, 69–117, Hindustan Book Agency, Gurgaon, 2001.
Botet, R.: Where are correlations hidden in the distribution of the largest fragment?, PoS, 007, WPCF2011, 2011.
Botet, R. and Płoszajczak, M.: Exact order-parameter distribution for
critical mean-field percolation and critical aggregation, Phys. Rev. Lett., 95, 185702, https://doi.org/10.1103/PhysRevLett.95.185702, 2005.
Botet, R., Płoszajczak, M., Chbihi, A., Borderie, B., Durand, D., and
Frankland, J.: Universal fluctuations in heavy-ion collisions in the Fermi
energy domain, Phys. Rev. Lett., 86, 3514, https://doi.org/10.1103/PhysRevLett.86.3514, 2001.
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, 2018.
Clusel, M. and Bertin, E.: Global fluctuations in physical systems: a subtle
interplay between sum and extreme value statistics, Int. J. Mod. Phys. B, 22, 3311–3368, 2008.
Cooper, W. A.: Effects of variable droplet growth histories on droplet size
distributions. Part I. Theory, J. Atmos. Sci., 46, 1301–1311, 1989.
Drake, R. L.: The scalar transport equation of coalescence theory: Moments
and kernels, J. Atmos. Sci., 29, 537–547, 1972.
Drake, R. L. and Wright, T. J.: The scalar transport equation of coalescence
theory: New families of exact solutions, J. Atmos. Sci., 29, 548–556, 1972.
Dziekan, P. and Pawlowska, H.: Stochastic coalescence in Lagrangian cloud microphysics, Atmos. Chem. Phys., 17, 13509–13520, https://doi.org/10.5194/acp-17-13509-2017, 2017.
Falkovich, G. and Pumir, A.: Sling effect in collisions of water droplets in
turbulent clouds, J. Atmos. Sci., 64, 4497–4505, 2007.
Feingold, G. and Chuang, P. Y.: Analysis of the influence of film-forming
compounds on droplet growth: Implications for cloud microphysical processes
and climate, J. Atmos. Sci., 59, 2006–2018, 2002.
Feingold, G., Cotton, W. R., Kreidenweis, S. M., and Davis, J. T.: The
impact of giant cloud condensation nuclei on drizzle formation in
stratocumulus: Implications for cloud radiative properties, J. Atmos. Sci., 56,
4100–4117, 1999.
Gillespie, D. T.: An Exact Method for Numerically Simulating the Stochastic
Coalescence Process in a Cloud, J. Atmos. Sci., 32, 1977–1989, 1975.
Gnedenko, B. V.: Theory of probability, Chelsea Publishing Co., N.Y., 4th Edn., 2017.
Göke, S., Ochs, H. T., and Rauber, R. M.: Radar analysis of
precipitation initiation in maritime versus continental clouds near the
Florida coast: Inferences concerning the role of CCN and giant nuclei, J.
Atmos. Sci., 64, 3695–3707, https://doi.org/10.1175/JAS3961.1, 2007.
Golovin, A. M.: The Solution of the Coagulation Equation for Raindrops.
Taking Condensation into Account, Soviet Physics Doklady, Vol. 8, 191–193, 1963.
Grabowski, W. W. and Wang, L.-P.: Growth of cloud droplets in a turbulent
environment, Annu. Rev. Fluid Mech., 45, 293–324, 2013.
Gruyer, D., Frankland, J. D., Botet, R., Płoszajczak, M., Bonnet, E.,
Chbihi, A., … and Guinet, D.: Nuclear multifragmentation time scale and
fluctuations of the largest fragment size, Phys. Rev. Lett., 110, 172701, https://doi.org/10.1103/PhysRevLett.110.172701, 2013.
Gumbel, E. J.: Statistics of extremes, Columbia University Press, New York, 1958.
Hall, W. D.: A detailed microphysical model within a two-dimensional dynamic
framework: Model description and preliminary results, J. Atmos. Sci., 37, 2486–2507,
1980.
Inaba, S., Tanaka, H., Ohtsuki, K., and Nakazawa, K.: High accuracy
statistical simulation of planetary accretion: I. Test of the accuracy by
comparison with the solution to the stochastic coagulation equation, Earth
Planets Space, 51, 205–217, 1999.
Johnson, D. B.: The role of giant and ultragiant aerosol particles in warm
rain initiation, J. Atmos. Sci., 39, 448–460, 1982.
Kolb, M. and Axelos, A. V.: Gelation Transition versus Percolation
Theory, in: Correlations and Connectivity, Springer, Dordrecht, 255–261, 1990.
Kostinski, A. B. and Shaw, R. A.: Fluctuations and luck in droplet growth by
coalescence, B. Am. Meteorol. Soc., 86, 235–244, 2005.
Laurenzi, I. J., Bartels, J. D., and Diamond, S. L.: A general algorithm for
exact simulation of multicomponent aggregation processes, J. Comput. Phys., 177, 418–449,
2002.
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., Brandenburg, A., Svensson, G., Haugen, N. E. L., Mehlig, B., and
Rogachevskii, I.: Effect of Turbulence on Collisional Growth of Cloud
Droplets, J. Atmos. Sci., 75,
3469–3487, https://doi.org/10.1175/JAS-D-18-0081.1, 2018.
Long, A.: Solutions to the droplet collection equation for polynomial
kernels, J. Atmos. Sci., 31, 1040–1052, 1974.
Lushnikov, A. A.: Coagulation in finite systems, J. Colloid Interf. Sci., 65, 276–285, 1978.
Lushnikov, A. A.: From sol to gel exactly, Phys. Rev. Lett., 93, 198302, https://doi.org/10.1103/PhysRevLett.93.198302,
2004.
Lushnikov, A. A., Negin, A. E., and Pakhomov, A. V.: Experimental observation
of the aerosol-aerogel transition, Chem. Phys. Lett., 175, 138–142, 1990.
Marcus, A. H.: Stochastic coalescence, Technometrics, 10, 133–143, 1968.
Matsoukas, T.: Statistical Thermodynamics of Irreversible Aggregation: The
Sol-Gel Transition, Sci. Rep.-UK, 5, 8855, https://doi.org/10.1038/srep08855, 2015.
Onishi, R. and Seifert, A.: Reynolds-number dependence of turbulence enhancement on collision growth, Atmos. Chem. Phys., 16, 12441–12455, https://doi.org/10.5194/acp-16-12441-2016, 2016.
Pinsky, M. B. and Khain, A. P.: Collisions of small drops in a turbulent
flow. Part II: Effects of flow accelerations, J. Atmos. Sci., 61, 1926–1939, 2004.
Pinsky, M. B., Khain, A. P., and Shapiro, M.: Collisions of cloud droplets in
a turbulent flow. Part IV: Droplet hydrodynamic interaction, J. Atmos. Sci., 64,
2462–2482, 2007.
Pinsky, M., Khain, A., and Krugliak, H.: Collisions of cloud droplets in a
turbulent flow. Part V: Application of detailed tables of turbulent
collision rate enhancement to simulation of droplet spectra
evolution, J. Atmos. Sci., 65, 357–374, 2008.
Pruppacher, H. R. and Klett, J. D.: Microphysics of clouds and precipitation,
Kluwer Academic Publishers, Dordrecht, the Netherlands, 1997.
Roach, W. T.: On the effect of radiative exchange on the growth by
condensation of a cloud or fog droplet, Q. J. Roy. Meteor. Soc., 102, 361–372, 1976.
Scott, W. T.: Analytic studies of cloud droplet coalescence, J. Atmos. Sci.,
25, 54–65, 1968.
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, 1998.
Shima, S.-I., Kusano, K., Kawano, A., Sugiyama, T., and Kawahara, S.: The
super-droplet 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,
2009.
Sidin, R. S., Hagmeijer, R., and Sachs, U.: Evaluation of master
equations for the droplet size distribution in condensing flow, Phys. Fluids, 21,
073303,
https://doi.org/10.1063/1.3180863, 2009.
Spiegel, J. K., Zieger, P., Bukowiecki, N., Hammer, E., Weingartner, E., and Eugster, W.: Evaluating the capabilities and uncertainties of droplet measurements for the fog droplet spectrometer (FM-100), Atmos. Meas. Tech., 5, 2237–2260, https://doi.org/10.5194/amt-5-2237-2012, 2012.
Tanaka, H. and Nakazawa, K.: Stochastic coagulation equation and the validity of
the statistical coagulation equation, J. Geomagn. Geoelectr., 45, 361–381, 1993.
Tanaka, H. and Nakazawa, K.: Validity of the statistical coagulation
equation and runaway growth of protoplanets, Icarus, 107, 404–412,
1994.
Telford, J.: A new aspect of coalescence theory, J. Meteorol., 12, 436–444,
1955.
Touchette, H.: The large deviation approach to statistical mechanics,
Phys. Rep., 478, 1–69, 2009.
Van den Heever, S. C. and Cotton, W. R.: Urban aerosol impacts on downwind
convective storms, J. Appl. Meteorol. Clim., 46, 828–850, 2007.
Wang, L.-P., Ayala, O., Rosa, B., and Grabowski, W. W.: Turbulent collision
efficiency of heavy particles relevant to cloud droplets, New J. Phys., 10, 075013,
https://doi.org/10.1088/1367-2630/10/7/075013, 2008.
Warner, J.: The microstructure of cumulus cloud. Part I. General features of
the droplet spectrum, J. Atmos. Sci., 26, 1049–1059, 1969.
Wetherill, G. W.: Comparison of analytical and physical modeling of
planetesimal Accumulation, Icarus, 88, 336–354, 1990.
Wilkinson, M.: Large deviation analysis of rapid onset of rain
showers, Phys. Rev. Lett., 116, 018501, https://doi.org/10.1103/PhysRevLett.116.018501, 2016.
Yin, Y., Levin, Z., Reisin, T. G., and Tzivion, S.: The effect of giant
cloud condensation nuclei on
the development of precipitation in convective clouds – A numerical study,
Atmos. Res., 53, 91–116, 2000.
Zhang, H., Wang, X., You, M., and Liu, C.: Water-yield relations and
water-use efficiency of winter wheat in the North China Plain, Irrigation Sci., 19, 37–45,
1999.
Short summary
The aim of this paper is to find some observational evidence of gel formation in clouds, by analyzing the distribution of the largest droplet at an early stage of cloud formation, and to show that the mass of the gel (
lucky droplet) is a mixture of Gaussian and Gumbel distributions. The results obtained may help advance the understanding of precipitation formation and are a novel application of the theory of critical phenomena in cloud physics.
The aim of this paper is to find some observational evidence of gel formation in clouds, by...
Altmetrics
Final-revised paper
Preprint