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

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 simultaneously with the spatial advection in the vertical physical coordinate, and in which the supersaturation evolution is coupled with the droplet growth through water mass budget. The single-column problem involves numerical solution of a two-dimensional advection problem (spectral and spatial dimensions). The box-model simulations demonstrate that, for the problem considered, even a tenfold decrease of the spurious numerical spectral broadening can be obtained by a proper choice of the MPDATA variant (maintaining the same spatial and temporal resolution), yet at an increased computational cost. Analyses using the single-column test case reveal that the width of the droplet size spectrum is affected by numerical diffusion pertinent to both spatial and spectral advection. Application of even a single corrective iteration of MPDATA robustly decreases the relative dispersion of the droplet spectrum, roughly by a factor of two at the levels of maximal liquid water content.