On numerical broadening of particle size spectra: a condensational growth study using PyMPDATA
Michael Olesik, Jakub Bana\'skiewicz, Piotr Bartman, Manuel, Baumgartner, Simon Unterstrasser, Sylwester Arabas

TL;DR
This study evaluates the numerical diffusion in modeling particle size spectra growth due to condensation, demonstrating how MPDATA schemes can reduce spectral broadening in atmospheric cloud simulations.
Contribution
It introduces and assesses MPDATA variants for Eulerian modeling of particle growth, reducing numerical diffusion in size spectrum simulations.
Findings
Proper MPDATA variant choice reduces spectral broadening tenfold.
Single corrective iteration halves the droplet spectrum dispersion.
Numerical diffusion affects both spatial and spectral advection in models.
Abstract
This work discusses the numerical aspects of representing the diffusional (condensational) growth in particulate systems such as atmospheric clouds. It focuses on the Eulerian modeling approach, in which the evolution of the particle size spectrum is carried out using a fixed-bin discretization associated with inherent numerical diffusion. Focus is on the applications of MPDATA numerical schemes (variants explored include: infinite-gauge, non-oscillatory, third-order-terms and recursive antidiffusive correction). Methodology for handling coordinate transformations associated with both particle size distribution variable choice and numerical grid layout are expounded. Analysis of the performance of the scheme is performed using: (i) an analytically solvable box-model test case, and (ii) the single-column "KiD" test case in which the size-spectral advection due to condensation is solved…
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Taxonomy
TopicsAtmospheric aerosols and clouds · Atmospheric chemistry and aerosols · Wind and Air Flow Studies
