Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-1119-2023
https://doi.org/10.5194/gmd-16-1119-2023
Development and technical paper
 | 
15 Feb 2023
Development and technical paper |  | 15 Feb 2023

AerSett v1.0: a simple and straightforward model for the settling speed of big spherical atmospheric aerosols

Sylvain Mailler, Laurent Menut, Arineh Cholakian, and Romain Pennel

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Cited articles

Adebiyi, A. A. and Kok, J. F.: Climate models miss most of the coarse dust in the atmosphere, Sci. Adv., https://doi.org/10.1126/sciadv.aaz9507, 2020. a
Betzer, P. R., Carder, K. L., Duce, R. A., Merrill, J. T., Tindale, R. W., Uematsu, M., Costello, D. K., Young, R. W., Feely, R. A., Breland, J. A., Bernstein, R. E., and Greco, A. M.: Long-range transport of giant mineral aerosol particles, Nature, 336, 568–571, https://doi.org/10.1038/336568a0, 1988. a
Bell, C. and Contributors: Thermo: Chemical properties component of Chemical Engineering Design Library (ChEDL), GitHub [code], https://github.com/CalebBell/thermo (last access: 10 February 2023), 2016–2021. a
Cheng, N.-S.: Comparison of formulas for drag coefficient and settling velocity of spherical particles, Powder Technol., 189, 395–398, https://doi.org/10.1016/j.powtec.2008.07.006, 2009. a, b, c, d, e
Clift, R. and Gauvin, W. H.: Motion of entrained particles in gas streams, Can. J. Chem. Eng., 49, 439–448, https://doi.org/10.1002/cjce.5450490403, 1971. a, b, c, d, e, f, g, h, i, j, k, l
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Short summary
Large or even giant particles of mineral dust exist in the atmosphere but, so far, solving an non-linear equation was needed to calculate the speed at which they fall in the atmosphere. The model we present, AerSett v1.0 (AERosol SETTling version 1.0), provides a new and simple way of calculating their free-fall velocity in the atmosphere, which will be useful to anyone trying to understand and represent adequately the transport of giant dust particles by the wind.