Preprints
https://doi.org/10.5194/egusphere-2025-3060
https://doi.org/10.5194/egusphere-2025-3060
08 Jul 2025
 | 08 Jul 2025
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Quantification and parameterization of snowflake fall speeds in the atmospheric surface-layer

Spencer Donovan, Dhiraj K. Singh, Timothy J. Garrett, and Eric R. Pardyjak

Abstract. The modeled settling speed of frozen hydrometeors has important implications for the prediction of weather and climate. However, it is usually assumed, erroneously, that they fall in still air. Here, we present novel field measurements of individual snowflake microphysical properties and their settling velocities in atmospheric surface-layer turbulence. Individual snowflake motions are tracked in a laser light sheet using particle streak velocimetry (PSV). A hotplate device, the Differential Emissivity Imaging Disdrometer (DEID), is used to obtain precise estimates of snowflake mass, density, and size. Relative to calculated terminal velocities in still air, we present enhancements and reductions of snowflake settling speeds in turbulent air for a broad range of Reynolds and Stokes numbers. Functional forms describing actual snowflake fall speeds are presented and explored. In particular, a promising non-dimensional functional form for the ratio of actual particle fall speed to terminal velocity is presented in terms of turbulence intensity and a new variable called the shape density index or SDI, which is related to an individual hydrometeor's microphysical structure.

Competing interests: Conflict of interest: The DEID technology is protected through patent US20210172855A1 co-authored with D.K.S., E.R.P., and T.J.G. and is commercially available through Particle Flux Analytics, Inc. T.J.G. is a co-owner of Particle Flux Analytics, Inc., which has a license from the University of Utah to commercialize the DEID. Some authors are members of the editorial board of Atmospheric Chemistry and Physics.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Spencer Donovan, Dhiraj K. Singh, Timothy J. Garrett, and Eric R. Pardyjak

Status: open (until 29 Sep 2025)

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  • RC1: 'Comment on egusphere-2025-3060', Anonymous Referee #1, 16 Jul 2025 reply
Spencer Donovan, Dhiraj K. Singh, Timothy J. Garrett, and Eric R. Pardyjak
Spencer Donovan, Dhiraj K. Singh, Timothy J. Garrett, and Eric R. Pardyjak

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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.
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