Articles | Volume 15, issue 9
Geosci. Model Dev., 15, 3879–3899, 2022
Geosci. Model Dev., 15, 3879–3899, 2022
Development and technical paper
13 May 2022
Development and technical paper | 13 May 2022

On numerical broadening of particle-size spectra: a condensational growth study using PyMPDATA 1.0

Michael A. Olesik et al.

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Cited articles

Abade, G., Grabowski, W. W., and Pawlowska, H.: Broadening of Cloud Droplet Spectra through Eddy Hopping: Turbulent Entraining Parcel Simulations, J. Atmos. Sci., 75, 3365–3379,, 2018. a
Arabas, S. and Pawlowska, H.: Adaptive method of lines for multi-component aerosol condensational growth and CCN activation, Geosci. Model Dev., 4, 15–31,, 2011. a
Arabas, S. and Shima, S.-I.: Large-Eddy Simulations of Trade Wind Cumuli Using Particle-Based Microphysics with Monte Carlo Coalescence, J. Atmos. Sci., 70, 2768–2777,, 2013. a
Arabas, S. and Shima, S.: On the CCN (de)activation nonlinearities, Nonlin. Processes Geophys., 24, 535–542,, 2017. a
Arabas, S., Pawlowska, H., and Grabowski, W.: Effective radius and droplet spectral width from in-situ aircraft observations in trade-wind cumuli during RICO, Geophys. Res. Lett., 36, L11803,, 2009. a
Short summary
In systems such as atmospheric clouds, droplets undergo growth through condensation of vapor. The broadness of the resultant size spectrum of droplets influences precipitation likelihood and the radiative properties of clouds. One of the inherent limitations of simulations of the problem is the so-called numerical diffusion causing overestimation of the spectrum width, hence the term numerical broadening. In the paper, we take a closer look at one of the algorithms used in this context: MPDATA.