Articles | Volume 25, issue 22
https://doi.org/10.5194/acp-25-16729-2025
© Author(s) 2025. 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-25-16729-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2025
Research article |
| 25 Nov 2025
| 25 Nov 2025
Quantification and parameterization of snowflake fall speeds in the atmospheric surface-layer
Spencer Donovan
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA
Timothy J. Garrett
Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USA
Eric R. Pardyjak
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA
Related authors
Dhiraj K. Singh, Spencer Donovan, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 6973–6990, https://doi.org/10.5194/amt-14-6973-2021, https://doi.org/10.5194/amt-14-6973-2021, 2021
Short summary
Short summary
This paper describes a new instrument for quantifying the physical characteristics of hydrometeors such as snow and rain. The device can measure the mass, size, density and type of individual hydrometeors as well as their bulk properties. The instrument is called the Differential Emissivity Imaging Disdrometer (DEID) and is composed of a thermal camera and hotplate. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm.
Thomas D. DeWitt, Timothy J. Garrett, and Karlie N. Rees
EGUsphere, https://doi.org/10.5194/egusphere-2025-3486, https://doi.org/10.5194/egusphere-2025-3486, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Clouds appear chaotic, but they in fact follow fractal mathematical patterns similar to coastlines. We measured their fractal properties using satellite images and found two key numbers that describe cloud shapes: one for how rough individual cloud edges are, and another for how clouds of different sizes organize together. We recommend methodology that provides objective ways to verify whether climate models accurately simulate real clouds.
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, and Jérôme C. Riedi
Nonlin. Processes Geophys., 31, 497–513, https://doi.org/10.5194/npg-31-497-2024, https://doi.org/10.5194/npg-31-497-2024, 2024
Short summary
Short summary
The shapes of clouds viewed from space reflect vertical and horizontal motions in the atmosphere. We theorize that, globally, cloud perimeter complexity is related to the dimension of turbulence also governed by horizontal and vertical motions. We find agreement between theory and observations from various satellites and a numerical model and, remarkably, that the theory applies globally using only basic planetary physical parameters from the smallest scales of turbulence to the planetary scale.
Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 17, 4581–4598, https://doi.org/10.5194/amt-17-4581-2024, https://doi.org/10.5194/amt-17-4581-2024, 2024
Short summary
Short summary
Accurate measurements of the properties of snowflakes are challenging to make. We present a new technique for the real-time measurement of the density of freshly fallen individual snowflakes. A new thermal-imaging instrument, the Differential Emissivity Imaging Disdrometer (DEID), is shown to be capable of providing accurate estimates of individual snowflake and bulk snow hydrometeor density. The method exploits the rate of heat transfer during the melting of a snowflake on a hotplate.
Thomas D. DeWitt and Timothy J. Garrett
Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, https://doi.org/10.5194/acp-24-8457-2024, 2024
Short summary
Short summary
There is considerable disagreement on mathematical parameters that describe the number of clouds of different sizes as well as the size of the largest clouds. Both are key defining characteristics of Earth's atmosphere. A previous study provided an incorrect explanation for the disagreement. Instead, the disagreement may be explained by prior studies not properly accounting for the size of their measurement domain. We offer recommendations for how the domain size can be accounted for.
Thomas D. DeWitt, Timothy J. Garrett, Karlie N. Rees, Corey Bois, Steven K. Krueger, and Nicolas Ferlay
Atmos. Chem. Phys., 24, 109–122, https://doi.org/10.5194/acp-24-109-2024, https://doi.org/10.5194/acp-24-109-2024, 2024
Short summary
Short summary
Viewed from space, a defining feature of Earth's atmosphere is the wide spectrum of cloud sizes. A recent study predicted the distribution of cloud sizes, and this paper compares the prediction to observations. Although there is nuance in viewing perspective, we find robust agreement with theory across different climatological conditions, including land–ocean contrasts, time of year, or latitude, suggesting a minor role for Coriolis forces, aerosol loading, or surface temperature.
Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, and Rob Stoll
Geosci. Model Dev., 16, 5729–5754, https://doi.org/10.5194/gmd-16-5729-2023, https://doi.org/10.5194/gmd-16-5729-2023, 2023
Short summary
Short summary
The Quick Environmental Simulation (QES) tool is a low-computational-cost fast-response framework. It provides high-resolution wind and concentration information to study complex problems, such as spore or smoke transport, urban pollution, and air quality. This paper presents the particle dispersion model and its validation against analytical solutions and wind-tunnel data for a mock-urban setting. In all cases, the model provides accurate results with competitive computational performance.
Timothy J. Garrett, Matheus R. Grasselli, and Stephen Keen
Earth Syst. Dynam., 13, 1021–1028, https://doi.org/10.5194/esd-13-1021-2022, https://doi.org/10.5194/esd-13-1021-2022, 2022
Short summary
Short summary
Current world economic production is rising relative to energy consumption. This increase in
production efficiencysuggests that carbon dioxide emissions can be decoupled from economic activity through technological change. We show instead a nearly fixed relationship between energy consumption and a new economic quantity, historically cumulative economic production. The strong link to the past implies inertia may play a more dominant role in societal evolution than is generally assumed.
Karlie N. Rees and Timothy J. Garrett
Atmos. Meas. Tech., 14, 7681–7691, https://doi.org/10.5194/amt-14-7681-2021, https://doi.org/10.5194/amt-14-7681-2021, 2021
Short summary
Short summary
Monte Carlo simulations are used to establish baseline precipitation measurement uncertainties according to World Meteorological Organization standards. Measurement accuracy depends on instrument sampling area, time interval, and precipitation rate. Simulations are compared with field measurements taken by an emerging hotplate precipitation sensor. We find that the current collection area is sufficient for light rain, but a larger collection area is required to detect moderate to heavy rain.
Dhiraj K. Singh, Spencer Donovan, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 6973–6990, https://doi.org/10.5194/amt-14-6973-2021, https://doi.org/10.5194/amt-14-6973-2021, 2021
Short summary
Short summary
This paper describes a new instrument for quantifying the physical characteristics of hydrometeors such as snow and rain. The device can measure the mass, size, density and type of individual hydrometeors as well as their bulk properties. The instrument is called the Differential Emissivity Imaging Disdrometer (DEID) and is composed of a thermal camera and hotplate. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm.
Karlie N. Rees, Dhiraj K. Singh, Eric R. Pardyjak, and Timothy J. Garrett
Atmos. Chem. Phys., 21, 14235–14250, https://doi.org/10.5194/acp-21-14235-2021, https://doi.org/10.5194/acp-21-14235-2021, 2021
Short summary
Short summary
Accurate predictions of weather and climate require descriptions of the mass and density of snowflakes as a function of their size. Few measurements have been obtained to date because snowflakes are so small and fragile. This article describes results from a new instrument that automatically measures individual snowflake size, mass, and density. Key findings are that small snowflakes have much lower densities than is often assumed and that snowflake density increases with temperature.
Kyle E. Fitch, Chaoxun Hang, Ahmad Talaei, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 1127–1142, https://doi.org/10.5194/amt-14-1127-2021, https://doi.org/10.5194/amt-14-1127-2021, 2021
Short summary
Short summary
Snow measurements are very sensitive to wind. Here, we compare airflow and snowfall simulations to Arctic observations for a Multi-Angle Snowflake Camera to show that measurements of fall speed, orientation, and size are accurate only with a double wind fence and winds below 5 m s−1. In this case, snowflakes tend to fall with a nearly horizontal orientation; the largest flakes are as much as 5 times more likely to be observed. Adjustments are needed for snow falling in naturally turbulent air.
Cited articles
Alcott, T. I. and Steenburgh, W. J.: Snow-to-liquid ratio variability and prediction at a high-elevation site in Utah's Wasatch Mountains, Weather Forecast., 25, 323–337, https://doi.org/10.1175/2009WAF2222311.1, 2010. a
Bao, J. and Windmiller, J. M.: Impact of microphysics on tropical precipitation extremes in a global storm-resolving model, Geophys. Res. Lett., 48, e2021GL094206, https://doi.org/10.1029/2021GL094206, 2021. a
Cohen, J. and Rind, D.: The effect of snow cover on the climate, J. Climate, 4, 689–706, 1991. a
Colle, B. A., Garvert, M. F., Wolfe, J. B., Mass, C. F., and Woods, C. P.: The 13–14 December 2001 IMPROVE-2 event. Part III: Simulated microphysical budgets and sensitivity studies, J. Atmos. Sci., 62, 3535–3558, https://doi.org/10.1175/JAS3552.1, 2005. a, b
Dimotakis, P. E. Debussy, F. D. and Koochesfahani, M. M.: Particle streak velocity field measurements in a two-dimensional mixing layer, Phys. Fluid., 24, 995–999, https://doi.org/10.1063/1.863481, 1981. a
Eidhammer, T., Gettelman, A., Thayer-Calder, K., Watson-Parris, D., Elsaesser, G., Morrison, H., van Lier-Walqui, M., Song, C., and McCoy, D.: An Extensible Perturbed Parameter Ensemble (PPE) for the Community Atmosphere Model Version 6, Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, 2024. a
Fassnacht, S., Innes, J., Kouwen, N., and Soulis, E.: The specific surface area of fresh dendritic snow crystals, Hydrol. Process., 13, 2945–2962, 1999. a
Fornari, W., Picano, F., Sardina, G., and Brandt, L.: Reduced particle settling speed in turbulence, J. Fluid Mech., 808, 153–167, 2016. a
Fovell, R. G. and Su, H.: Impact of cloud microphysics on hurricane track forecasts, Geophys. Res. Lett., 34, L24810, https://doi.org/10.1029/2007GL031723, 2007. a
Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012. a
Garrett, T. J., Singh, D. K., and Pardyjak, E. R.: Settling and rotation of frozen hydrometeors in turbulent air, Geophys. Res. Lett., 52, e2025GL114780, https://doi.org/10.1029/2025GL114780, 2025. a, b, c
Garvert, M. F., Woods, C. P., Colle, B. A., Mass, C. F., Hobbs, P. V., Stoelinga, M. T., and Wolfe, J. B.: The 13–14 December 2001 IMPROVE-2 event. Part II: Comparisons of MM5 model simulations of clouds and precipitation with observations, J. Atmos. Sci., 62, 3520–3534, https://doi.org/10.1175/JAS3551.1, 2005. a
Good, G., Ireland, P., Bewley, G., Bodenschatz, E., Collins, L., and Warhaft, Z.: Settling regimes of inertial particles in isotropic turbulence, J. Fluid Mech., 759, R3–R13, https://doi.org/10.1017/jfm.2014.602, 2014. a, b
Grabowski, W. W. and Wang, L.-P.: Growth of cloud droplets in a turbulent environment, Annu. Rev. Fluid Mech., 45, 293–324, 2013. a
Grzegorczyk, P., Yadav, S., Zanger, F., Theis, A., Mitra, S. K., Borrmann, S., and Szakáll, M.: Fragmentation of ice particles: laboratory experiments on graupel–graupel and graupel–snowflake collisions, Atmos. Chem. Phys., 23, 13505–13521, https://doi.org/10.5194/acp-23-13505-2023, 2023. a
Gunn, K. L. S. and Marshall, J. S.: The distribution with size of aggregate snowflakes, J. Meteorol., 15, 452–461, https://doi.org/10.1175/1520-0469(1958)015<0452:TDWSOA>2.0.CO;2, 1958. a
Hagos, S., Ruby Leung, L., Zhao, C., Feng, Z., and Sakaguchi, K.: How do microphysical processes influence large-scale precipitation variability and extremes?, Geophys. Res. Lett., 2017GL076375, https://doi.org/10.1002/2017GL076375, 2018. a
Ireland, P. J., Bragg, A. D., and Collins, L. R.: The effect of Reynolds number on inertial particle dynamics in isotropic turbulence. Part 2. Simulations with gravitational effects, J. Fluid. Mech., 796, 659–711, 2016. a
Jakob, C.: Ice clouds in numerical weather prediction models: progress, problems, and prospects, in: Cirrus, Oxford University Press, https://doi.org/10.1093/oso/9780195130720.003.0020, 2002. a
Legagneux, L., Cabanes, A., and Dominé, F.: Measurement of the specific surface area of 176 snow samples using methane adsorption at 77 K, J. Geophys. Res.-Atmos., 107, ACH 5-1–ACH 5-15, https://doi.org/10.1029/2001JD001016, 2002. a
Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T. A., Stähli, M., and Zappa, M.: ALPINE3D: a detailed model of mountain surface processes and its application to snow hydrology, Hydrol. Process., 20, 2111–2128, 2006. a
Lehning, M., Löwe, H., Ryser, M., and Raderschall, N.: Inhomogeneous precipitation distribution and snow transport in steep terrain, Water Resour. Res., 44, W07404, https://doi.org/10.1029/2007WR006545, 2008. a, b
Li, C., Lim, K., Berk, T., Abraham, A., Heisel, M., Guala, M., Coletti, F., and Hong, J.: Settling and clustering of snow particles in atmospheric turbulence, J. Fluid Mech., 912, A49, https://doi.org/10.1017/jfm.2020.1153, 2021a. a
Li, J., Abraham, A., Guala, M., and Hong, J.: Evidence of preferential sweeping during snow settling in atmospheric turbulence, J. Fluid. Mech., 928, https://doi.org/10.1017/jfm.2021.816, 2021b. a
Li, J., Abraham, A., Guala, M., and Hong, J.: Evidence of preferential sweeping during snow settling in atmospheric turbulence, J. Fluid Mech., 928, A8, https://doi.org/10.1017/jfm.2021.816, 2021c. a
Li, J., Guala, M., and Hong, J.: Field investigation of 3-D snow settling dynamics under weak atmospheric turbulence, J. Fluid Mech., 997, A33, https://doi.org/10.1017/jfm.2024.601, 2024a. a
Li, J., Guala, M., and Hong, J.: Field investigation of 3D snow settling dynamics under weak atmospheric turbulence, arXiv [preprint], https://doi.org/10.48550/arXiv.2406.08737, 13 June 2024b. a, b
Li, S., Bragg, A. D., and Katul, G.: Reduced sediment settling in turbulent flows due to basset history and virtual mass effects, Geophys. Res. Lett., 50, e2023GL105810, https://doi.org/10.1029/2023GL105810, 2023. a
Maxey, M.: The gravitational settling of aerosol particles in homogeneous turbulence and random flow fields, J. Fluid Mech., 174, 441–465, 1987. a
Maxey, M. t. and Corrsin, S.: Gravitational settling of aerosol particles in randomly oriented cellular flow fields, J. Atmos. Sci., 43, 1112–1134, 1986. a
Mercado, J. M., Prakash, V. N., Tagawa, Y., Sun, C., Lohse, D., and for Turbulence Research), I. C.: Lagrangian statistics of light particles in turbulence, Phys. Fluids, 24, 055106, https://doi.org/10.1063/1.4719148, 2012. a
Milbrandt, J. A. and Morrison, H.: Prediction of graupel density in a bulk microphysics scheme, J. Atmos. Sci., 70, 410–429, 2013. a
Milbrandt, J. A., Yau, M. K., Mailhot, J., Bélair, S., and McTaggart-Cowan, R.: Simulation of an orographic precipitation event during IMPROVE-2. Part II: Sensitivity to the number of moments in the bulk microphysics scheme, Mon. Wea. Rev., 138, 625–642, https://doi.org/10.1175/2009MWR3121.1, 2010. a
Mitchell, D. L., Rasch, P., Ivanova, D., McFarquhar, G., and Nousiainen, T.: Impact of small ice crystal assumptions on ice sedimentation rates in cirrus clouds and GCM simulations, Geophys. Res. Lett., 35, 9806–+, https://doi.org/10.1029/2008GL033552, 2008. a
Morrison, H. and Milbrandt, J. A.: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests, J. Atmos. Sci., 72, 287–311, https://doi.org/10.1175/JAS-D-14-0065.1, 2015. a
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., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van Diedenhoven, B., and Xue, L.: Confronting the challenge of modeling cloud and precipitation microphysics, J. Adv. Model. Earth Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a
Morrison, T. J., Meisenheimer, T., Garrett, T., Singh, D., Donovan, S., and Pardyjak, E.: Relating storm-snow avalanche instabilities to data collected from the Differential Emissivity Imaging Disdrometer (DEID), Cold Reg. Sci. Technol., 210, 103839, https://doi.org/10.1016/j.coldregions.2023.103839, 2023. a, b
Mott, R. and Lehning, M.: Meteorological modeling of very high-resolution wind fields and snow deposition for mountains, J. Hydrol., 11, 934–949, 2010. a
Nielsen, P.: Mean and variance of the velocity of solid particles in turbulence, in: Particle-Laden Flow, Springer, 385–391, https://doi.org/10.1007/978-1-4020-6218-6_30, 2007. a
Parodi, A. and Emanuel, K.: A theory for buoyancy and velocity scales in deep moist convection, J. Atmos. Sci., 66, 3449–3463, https://doi.org/10.1175/2009JAS3103.1, 2009. a
Posselt, D. J., He, F., Bukowski, J., and Reid, J. S.: On the relative sensitivity of a tropical deep convective storm to changes in environment and cloud microphysical parameters, J. Atmos. Sci., 76, 1163–1185, https://doi.org/10.1175/JAS-D-18-0181.1, 2019. a
Rees, K. N., Singh, D. K., Pardyjak, E. R., and Garrett, T. J.: Mass and density of individual frozen hydrometeors, Atmos. Chem. Phys., 21, 14235–14250, https://doi.org/10.5194/acp-21-14235-2021, 2021. a, b
Shaw, R. A.: Particle-turbulence interactions in atmospheric clouds, Annu. Rev. Fluid Mech., 35, 183–227, 2003. a
Singh, D. K., Pardyjak, E. R., and Garrett, T. J.: Time-resolved measurements of the densities of individual frozen hydrometeors and fresh snowfall, Atmos. Meas. Tech., 17, 4581–4598, https://doi.org/10.5194/amt-17-4581-2024, 2024. a, b, c
Singh, M. S. and O'Gorman, P. A.: Influence of microphysics on the scaling of precipitation extremes with temperature, Geophys. Res. Lett., 41, https://doi.org/10.1002/2014GL061222, 2014. a
Stull, R. B.: An introduction to boundary layer meteorology, Vol. 13, Springer Science and Business Media, https://doi.org/10.1007/978-94-009-3027-8, 1988. a, b
Sundaram, S. and Collins, L. R.: Collision statistics in an isotropic turbulent suspension. Part 1. Direct numerical simulations, J. Fluid Mech., 335, 75–109, https://doi.org/10.1017/S0022112096004454, 1997. a
Vincent, O. R., and Folorunso, O.: A descriptive algorithm for sobel image edge detection, in: Proceedings of informing science and IT education conference (InSITE), Vol. 40, 97–107, 2009. a
Wilczak, J. M., Oncley, S. P., and Stage, S. A.: Sonic anemometer tilt correction algorithms, Bound.-Lay. Meteorol., 99, 127–150, 2001. a
Yamaguchi, S., Ishizaka, M., Motoyoshi, H., Nakai, S., Vionnet, V., Aoki, T., Yamashita, K., Hashimoto, A., and Hachikubo, A.: Measurement of specific surface area of fresh solid precipitation particles in heavy snowfall regions of Japan, The Cryosphere, 13, 2713–2732, https://doi.org/10.5194/tc-13-2713-2019, 2019. a
Short summary
Accurate snowfall prediction requires quantifying how snowflakes interact with atmospheric turbulence. Using field-based imaging techniques, we directly measured the mass, size, density, and fall speed of snowflakes in surface-layer turbulence. We found that turbulence and microstructure jointly modulate fall speed, often deviating from the terminal velocity in still air. These results inform new parameterizations for numerical weather and climate models.
Accurate snowfall prediction requires quantifying how snowflakes interact with atmospheric...
Altmetrics
Final-revised paper
Preprint